Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T &amp; R458G) and NTRK3 (E176D &amp; L449F) genes using molecular dynamics simulations approach

Abeer M Al-Subaie; Balu Kamaraj; Fazil Ahmad; Khaldoon Alsamman

doi:10.12688/f1000research.131013.2

Home Browse Unraveling the molecular mechanism of novel leukemia mutations on...

ALL Metrics

Views

Downloads

Get PDF

Get XML

Export

▬

✚

Research Article

Revised

Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using molecular dynamics simulations approach

[version 2; peer review: 3 approved]

Abeer M Al-Subaie¹, Balu Kamaraj ², Fazil Ahmad³, Khaldoon Alsamman¹

PUBLISHED 18 Jun 2024

Author details Author details

¹ Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia
² Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia
³ Department of Anesthesia Technology, College of Applied Medical Sciences in Jubail, , Imam Abdulrahman Bin Faisal University, Jubail, Saudi Arabia

Abeer M Al-Subaie
Roles: Conceptualization, Data Curation, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Balu Kamaraj
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Fazil Ahmad
Roles: Data Curation, Methodology, Validation

Khaldoon Alsamman
Roles: Conceptualization, Investigation, Methodology, Validation

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Cell & Molecular Biology gateway.

This article is included in the Oncology gateway.

Abstract

Background: NTRK1, NTRK2, and NTRK3 are members of the neurotrophic receptor tyrosine kinases (NTRK) family, which encode TrkA, TrkB, and TrkC receptors, respectively. Hematologic cancers are also linked to point mutations in the NTRK gene's kinase domain. Trk fusions are the most common genetic change associated with oncogenic activity in Trk-driven liquid tumors. Thus, point mutations in NTRK genes may also play a role in tumorigenesis. The structural and functional effect of mutations in Trk-B & Trk-C proteins remains unclear.

Methods: In this research, Homology (threading-based approach) modeling and the all-atom molecular dynamics simulations approaches are applied to examine the structural and functional behavior of native and mutant Trk-B and Trk-C proteins at the molecular level.

Results: The result of this study reveals how the mutations in Trk-B (A203T & R458G) and Trk-C (E176D & L449F) proteins lost their stability and native conformations. The Trk-B mutant A203T became more flexible than the native protein, whereas the R458G mutation became more rigid than the native conformation of the Trk-B protein. Also, the Trk-C mutations (E176D & L449F) become more rigid compared to the native structure.

Conclusions: This structural transition may interrupt the function of Trk-B and Trk-C proteins. Observing the impact of NTRK-2/3 gene alterations at the atomic level could aid in discovering a viable treatment for Trk-related leukemias.

Keywords

Leukemia; Mutations; Stability; Structural loss; Molecular mechanism; Modeling; Molecular dynamic simulations.

Corresponding author: Balu Kamaraj

Competing interests: No competing interests were disclosed.

Grant information: This work was primarily supported by grants from the Deanship of Scientific Research, Imam Abdulrahman Bin Faisal University (Grant No. 2019-162-AMSJ), Dammam, Saudi Arabia.

Copyright: © 2024 M Al-Subaie A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: M Al-Subaie A, Kamaraj B, Ahmad F and Alsamman K. Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using molecular dynamics simulations approach [version 2; peer review: 3 approved]. F1000Research 2024, 12:345 (https://doi.org/10.12688/f1000research.131013.2) First published: 28 Mar 2023, 12:345 (https://doi.org/10.12688/f1000research.131013.1) Latest published: 18 Jun 2024, 12:345 (https://doi.org/10.12688/f1000research.131013.2)

Revised Amendments from Version 1

The reviewers suggested to display the model of native and mutant Trk-B and Trk-C proteins (as Figure 1). Also they suggested the show the structural changes of native and mutant Trk-B and Trk-C protein at different simulation timescales (as Figure 9a-b). The abstract is slightly modified as per the reviewer suggestion.

See the authors' detailed response to the review by Gurudeeban Selvaraj
See the authors' detailed response to the review by Rajasekaran R.
See the authors' detailed response to the review by Dicson Sheeja Malar

Introduction

NTRK1, NTRK2, and NTRK3 are members of the neurotrophic receptor tyrosine kinases (NTRKs) gene family, which code for the TrkA, TrkB, and TrkC receptors, respectively.¹ Gene fusions of NTRK genes correspond to the primary molecular changes with known carcinogenic and transformative abilities.² NTRK is detected in 1,500 to 5,000 children, teenagers, and adults with cancer yearly.¹ In-frame deletion of NTRK1 in acute myeloid leukemia has been described as a less frequent oncogenic pathway.¹^,³ A recent study indicated that 5% of persons with diverse hematologic neoplasms, including acute myeloid leukemia, lymphoblastic leukemia, and myeloproliferative disorders, have NTRK2/3 point mutations. Regardless of where the tumor is in the body, neurotrophic tyrosine receptor kinase (NTRK) gene fusions are an actionable biomarker for cancer therapy and are present in over 25 cancer types.²^,⁴ However, the frequency of NTRK gene fusions differs among various tumor types.² A wide variety of solid tumor types, including breast, cholangiocarcinoma, colorectal, gynecological, neuroendocrine, non-small cell lung, salivary gland, pancreatic, sarcoma, and thyroid cancers, have been associated with NTRK fusion-positive tumors.¹^,⁴

The NTRK2 gene, which codes for a protein called the TrkB receptor, with 822 amino acid residues, has 24 exons and is located on chromosome 9q22.17.⁵ The total length of the TrkB receptor contains the following domains as follows: N-terminal signal sequence, cysteine-rich domain; leucine-rich domain, two immunoglobulins (Ig)-like domains that make up the BDNF-binding region, transmembrane domain, Src homology two domains containing (SHC)-binding motif, T.K. domain near the C terminus and a C-terminal PLCγ-docking site.⁵^,⁶ TrkC receptor is the transcription product of the NTRK3 gene and was discovered and characterized in 1991.⁷^,⁸ The NTRK3 gene is located on chromosome 15q25.⁷^–⁹ The total length of the TrkC receptor with 839 amino acid residues represented in the human's cerebral cortex, granular cell layer, and hippocampus.¹

Nine distinct point mutations in the NTRK2 or NTRK3 genes were reported in the recent study, many of which were outside the kinase domain.¹⁰ Four of the nine distinct mutations can potentially cause cancer, and cells transformed by these mutations could be inhibited by Trk.¹⁰ The mutation NTRK2^A203T, located in the extracellular domain, was identified in a patient with primary myelofibrosis.¹⁰ In addition, the mutation NTRK2^R458G is situated in the juxtamembrane domain with two individuals, one with chronic myeloid leukemia (CML) and the other with atypical CML.¹⁰ The mutation NTRK3^E176D was detected in a patient with NPM1-mutated AML and located in the extracellular domain. The mutation NTRK3^L449F was observed in a patient with T-cell ALL and located in the transmembrane domain.¹⁰ On the other hand, Trk fusions are the most frequent genetic alteration connected to carcinogenic activity in Trk-driven liquid tumors. Thus, point mutations in the NTRK genes may also contribute to the development of tumors.

Point mutations in NTRK genes may also contribute to the tumorigenic process, even though Trk fusions correspond to the essential genetic alterations that impart oncogenic activity in Trk-driven liquid tumors. However, this has not yet been thoroughly examined. However, their functional importance is yet unknown. In this study, the all-atom molecular simulations approach uses to observe the structural and functional behavior of native and mutant Trk-B (A203T & R458G) and Trk-C (E176D & L449F) proteins at the atomic level. Our findings will help scientists better understand the molecular causes of Trk-B and Trk-C protein mutations and pave the way for developing potentially tailored treatments for Trk-related leukemia patients.

Methods

Dataset

The four novel mutations of NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes were retrieved from a recent study.¹⁰ In addition, the protein sequence of Trk-B (UNIPORT ID: Q16620) and Trk-C (UNIPROT ID: Q16288) were obtained from the UNIPROT database in FASTA format.¹¹ It helps to model the three-dimensional structure of native and mutant Trk-B and Trk-C proteins.

Native and Mutant Trk-B and Trk-C proteins modeling

The NTRK2 and NTRK3 genes encoding proteins Trk-B and Trk-C do not have the PDB structures for the entire length. Hence, we modeled the Trk-B and Trk-C proteins from the amino acid sequences using the I-TASSER¹² program, which has been deemed an accurate and effective method. It is a threading-based method for predicting structures that could construct the protein's three-dimensional configurations. It produces a high quality modeled three-dimensional (3D) structures and biological processes of proteins from their amino acid sequences. It has also predicted the five 3-D model structures for the submitted amino acid sequences and selected the best model structure with the lowest energy. We inserted the point mutations to the predicted Trk-B and Trk-C model's 3D structures to further examine the effects of mutations on Trk-B and Trk-C proteins and run the energy minimization to generate the best mutant protein structures using the SWISS PdbViewer tool. Further, the quality of the projected modeled structures of the native and mutant Trk-B and Trk-C proteins was evaluated using the PROCHECK¹³ and PROSA¹⁴ programs.

Molecular Dynamics simulations

The Molecular Dynamics simulations (MDS) were carried out using the GROMACS program.¹⁵ We applied the default parameters from our earlier studies¹⁶^–²⁰ for the MDS experiments, which are detailed below. The native and mutant Trk-B (A203T & R458G) and Trk-C (E176D & L449F) protein structures were used as MDS's starting point. The simulation used the CHARMM 36²¹ force field. The TIP3P water molecules were placed 10 Å from the box's edges to solvate the native and mutant Trk-B and Trk-C protein systems. Further, we used the genion tool to neutralize both the native and mutant Trk-B and Trk-C proteins. The energy minimization process used the steepest descent method to produce a stable protein conformation.²² The electrostatic interactions were calculated using the particle mesh Ewald method.²³ The Berendsen coupling technique was used to regulate the temperature inside the box.²⁴ Further, two different equilibration procedures for the NVT (500 ps) and NPT (500 ps) were carried out. The water and non-water molecules are controlled during the equilibration using the Parrinello-Rahman barostat pressure coupling method²⁵ and LINCS²⁶ algorithms.

Finally, based on the convergence of native and mutant Trk-B and Trk-C protein system, the simulation was performed up to 50 nanoseconds (ns). Further, to inspect the structural behavior of Trk-B and Trk-C proteins upon mutations, we examined the root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA) and the number of hydrogen bonds (H-bonds) analysis. To determine the covariance value of native and mutant Trk-B and Trk-C proteins, we used principal component analysis (PCA)²⁷^,²⁸ to assist our molecular dynamics (MD) analysis. PCA analysis is a technique that reduces data complexity and extracts concerted motions in simulations that are correlated and likely significant for biological function. During PCA analysis, a variance/covariance matrix is constructed from the trajectories after removing rotational and translational movements. This matrix is then diagonalized to identify a set of eigenvectors and eigenvalues. The eigenvalues represent the amplitude of the eigenvectors in multidimensional space, and the displacement of atoms along each eigenvector indicates the concerted motions of the protein in each direction. XMGRACE,²⁹ is a tool used to create the simulation plots.

Results

Prediction of native and mutant Trk-B and Trk-C protein's 3D structures

To accurately predict the 3D conformation protein structures without missing residues, it is crucial to observe how Trk-B and Trk-C proteins alter their conformation in response to mutations. As a result, we rebuilt the 3D structures of the Trk-B and Trk-C proteins using the I-TASSER server. The algorithm used more than ten templates individually to model the Trk-B and Trk-C proteins. The topmost template (PDB ID: 5KMK_A)³⁰ showed 74% similarity and less than 36% sequence coverage to the Trk-B protein sequence. Similarly, the PDB ID: 4FL2_A³¹ is a template showing 70% similarity and less than 41% sequence coverage to the Trk-C protein sequence. The best model structures of Trk-B and Trk-C proteins were obtained, based on the high confidence c-score, from I-TASSER. Furthermore, the mutant Trk-B (A203T & R458G) and Trk-C (E176D & L449F) structures build using the SWISS-PDB program. The accuracy of the predicted model structures of native and mutant Trk-B and Trk-C proteins was assessed by PROCHECK and PROSA programs. With a z-score value of -9.11 and a 98.8% favored and allowed region, the native Trk-B protein was examined. However, 98.1% of the favored and allowed regions were present in the mutant Trk-B (A203T & R458G) structures, and the z-score values ranged from -9.09 to -9.12. Similar results were obtained using native Trk-C protein, which had a 96.6% favored and allowed region and a z-score value of -6.54. At the same time, mutant Trk-C (E176D & L449F) structures displayed z-score values between -6.49 and -6.53 and favored and allowed regions with 95.2-95.4%, respectively. These results confirm the high degree of confidence in the predicted native and mutant modeled structures used for further study. The predicted model structures of native and mutant Trk-B and Trk-c are displayed in Figure 1a-b respectively.

Figure 1. (a) Model structures of native and mutant (A203T & R458G) Trk-B proteins. (b) Model structures of native and mutant (E176D & L449F) Trk-C proteins.

MD simulation of native and mutant Trk-B and Trk-C proteins

We used the MDS technique to examine the atom-level structural alterations in native and mutant Trk-B (A203T & R458G) and Trk-C (E176D & L449F) proteins. To evaluate the inconsistencies in structural and functional changes between the native and mutant Trk-B and Trk-C proteins, we investigated the RMSD, RMSF, Rg, SASA, H-bonds, and PCA analysis. In MD simulation, the total energy of native and mutant Trk-B and Trk-C proteins was measured from the beginning structures to examine the convergence of the protein system. As a result, the native and mutant Trk-B protein system (Figure 2a) shows a similar way of deviation from the beginning to end of the simulation time. Similarly, the native and mutant Trk-C protein systems (Figure 2b) exhibit a similar way of deviation from the beginning to the end of the simulation time.

Figure 2. (a) The native and mutant (A203T & R458G) Trk-B total energy Vs. Time for 50 ns. (b) The native and mutant (E176D & L449F) Trk-C total energy Vs. Time for the period of 50 ns.

Further, the RMSD for all Cα atoms from the initial structure was examined for the native and mutant Trk-B and Trk-C proteins (Figure 3a). From the beginning to ~6 ns in the RMSD plot, the native and mutant Trk-B structures exhibit a similar pattern of variation, but from that, the A203T mutant Trk-B structure indicates a progressive increase in RMSD value in comparison to the native Trk-B structure until the end of the simulation time (50 ns) (Figure 3a). At the same time, the R458G mutant Trk-B structures show a gradual decrease in RMSD compared to native Trk-B structures until the end of the simulation (50 ns).

Figure 3. (a) The native and mutant (A203T & R458G) Trk-B backbone RMSD for the period of 50 ns. (b) The native and mutant (E176D & L449F) Trk-C backbone RMSD for the period of 50 ns.

Table 1 lists the average RMSD values for the native and mutant (A203T & R458G) Trk-B structures. Whereas native and mutant (E176D & L449F) Trk-C structures vary from the start to 50 ns simulation time (Figure 3b). Both mutant (E176D & L449F) structures show a decrease in RMSD value compared to native Trk-C structures (Figure 3b). Table 2 lists the average RMSD values for the native and mutant (E176D & L449F) Trk-C structures. The RMSF value of native and mutant structures of both Trk-B (A203T & R458G) & Trk-C (E176D & L449F) proteins show significant changes in the structures. (Figure 4a-b). The A203T and R458G mutants showed a higher and lower degree of flexibility between the residues than the native Trk-B protein (Figure 4a). Trk-C mutant structures (E176D & L449F) showed lower flexibility between the residues than the native protein (Figure 4b). Table 1 & 2 lists the average RMSF values for native and mutant Trk-B (A203T & R458G) and Trk-C (E176D & L449F) proteins, respectively.

Table 1. Average RMSD, RMSF, Rg, SASA, H-bonds, and covariance value of native and mutant Trk-B.

Parameters	Native	A203T	R458G
RMSD	0.84 ± 0.12	0.90 ± 0.17	0.80 ± 0.13
RMSF	0.38 ± 0.19	0.41 ± 0.30	0.34 ± 0.23
R.G.	3.07 ± 0.05	3.11 ± 0.03	3.06 ± 0.02
SASA	516.43 ± 14.40	524.06 ± 13.77	511.94 ± 17.89
NH-bond(P-P)	399.22 ± 13.59	398.43 ± 12.94	399.84 ± 17.66
Co-variance	436.65	623.76	409.63

Table 2. Average RMSD, RMSF, Rg, SASA, H-bonds, and covariance value of native and mutant Trk-C.

Parameters	Native	E176D	L449F
RMSD	1.39 ± 0.28	0.88 ± 0.14	1.15 ± 0.19
RMSF	0.58 ± 0.34	0.47 ± 0.20	0.44 ± 0.18
R.G.	3.66 ± 0.11	3.61 ± 0.01	3.52 ± 0.07
SASA	545.06 ± 11.36	509.45 ± 7.63	532.99 ± 13.64
NH-bond(P-P)	384.11 ± 8.70	397.86 ± 11.59	408.49 ± 12.36
Co-variance	1124.33	649.56	562.33

Figure 4. (a) The native and mutant (A203T & R458G) Trk-B protein residues in C-α RMSF simulation data for the period of 50 ns. (b) The native and mutant (E176D & L449F) Trk-C protein residues of C-α RMSF simulation data for the period of 50 ns.

The radius of gyration and SASA analysis provide compactness in the protein system. Figure 5a–b & 6a-b displays the Rg and SASA plot for C-alpha atoms in native and mutant Trk-B (A203T & R458G) and Trk-C (E176D & L449F) proteins overtime at 300 K. Tables 1 and 2 again list the average Rg and SASA values for the native and mutant Trk-B (A203T & R458G) and Trk-C (E176D & L449F) proteins, respectively. In Figures 5a & 6a, the A203T mutant structure shows similar Rg and SASA values from the beginning to ~1.5 ns, later which increases and leads to more Rg and SASA values than native Trk-B structure up to 50 ns simulation time. The R458G mutant structure starts with the same Rg value from the beginning to ~1 ns, after which it declines and displays less Rg value from ~1 ns to ~30 ns, and then it shows a similar way of Rg value up to the end of the simulation (50 ns) (Figure 5a). Correspondingly, the R458G mutant structure offers an equal SASA value from the start of simulation to ~35 ns, later which decreases and displays less SASA value up to 50 ns simulation time (Figure 6a).

Figure 5. (a) The native and mutant (A203T & R458G) Trk-B protein compactness analysis by Rg for the period of 50 ns. (b) The native and mutant (E176D & L449F) Trk-C protein compactness analysis by Rg for the period of 50 ns.

Figure 6. (a) The SASA analysis of native and mutant (A203T & R458G) Trk-B protein structures for the period of 50 ns. (b) The SASA analysis of native and mutant (E176D & L449F) Trk-C protein structures for the period of 50 ns.

In Figure 5b, from the start to ~32 ns, the E176D mutant shows a similar Rg value compared to the native Trk-C structure, but after that, the E176D mutant shows a decrease in the Rg value than the native structure up to the 50 ns simulation time. Likewise, the SASA value of the E176D mutant displays the same deviation to the native Trk-C structure from the beginning to ~3 ns, after which it decreases and shows less SASA value up to the end of the simulation (Figure 6b). On the other hand, the L449F mutant exhibits the same Rg and SASA value as the native structure from 0 to ~14 ns after it decreases and offers less Rg value compared to the native Trk-C structure until 50 ns simulation time (Figure 5b & 6b). The h-bond is necessary for protein folding, stability, and functionality. Therefore, we counted the number of H-bonds shown in Figure 7a-b to better understand the stability of native and mutant Trk-B and Trk-C proteins. Tables 1 and 2 display the average hydrogen bond values of the native and mutant structures of the Trk-B and Trk-C proteins.

Figure 7a indicates that the native Trk-B has slightly more H-bonds than the A203T mutant. The R458G mutant exhibits slightly more H-bonds than the native Trk-B protein. E178D and L449F mutants exhibit greater h-bonds in Figure 7b than native Trk-C protein (Figure 7b).

Figure 7. (a) The H-bond simulation data for the native and mutant (A203T & R458G) Trk-B protein structures for the period of 50 ns. (b) The H-bond simulation data for the native and mutant (E176D & L449F) Trk-C protein structures for the period of 50 ns.

Further, we used the parameters based on our earlier studies³²^,³³ to perform the PCA analysis.²⁷^,²⁸ It is used to view the motion of Trk- B and Trk-C proteins upon mutations. In Figure 8a, the projection of the first two eigenvectors shows that the A203T mutant covers a more region phase space in both PC1 and PC2 plains compared to the native Trk-B protein. Whereas the R458G mutant covers a reduced region space in both PC1 and PC2 than the native Trk-B protein (Figure 8a). Similarly, the E176D and L449F mutants span a reduced region phase in the PC1 and PC2 plain than the native Trk-C protein, as shown in Figure 8b. Furthermore, we displayed the native and mutant Trk-B and Trk-C protein structures at different timescale (Figure 9a-b).

Figure 8. (a) Projection motion of the native and mutant (A203T & R458G) Trk-B protein structures for the period of 50 ns. (b) Projection motion of the native and mutant (E176D & L449F) Trk-C protein structures for the period of 50 ns.

Figure 9. (a) Snapshots of native and mutant (A203T & R458G) Trk-B protein conformation at different simulation time scale (b) Snapshots of native and mutant (E176D & L449F) Trk-C protein conformation at different simulation time scale.

Discussion

The 3-D conformation Trk-B and Trk-C proteins must be determined to investigate the structural behavior of novel mutations on the NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes. Then, the I-TASSER program is applied to model the Trk-B and Trk-C protein structures. After, the mutant Trk-B (A203T & R458G) and Trk-C (E176D & L449F) proteins are created using the SWISS PDB program. Finally, the PROSA & PROCHECK programs were applied to estimate the quality of native and mutant Trk-B and Trk-C proteins. To examine the structural significance of Trk-B and Trk-C proteins upon mutations, all the native and mutant Trk-B & Trk-C proteins were exposed to the timescale of 50 ns MD simulation. This research examined the structural changes in the Trk-B and Trk-C proteins resulting from mutations using the output trajectory from 0 to 50 ns. Between the native and mutant Trk-B and Trk-C proteins, we measured the total energy, RMSD, RMSF, Rg, SASA, and H-bonds. They are shown in Figures 2 to 7, respectively. Further, we assessed native and mutant Trk-B and Trk-C proteins using principal component analysis (PCA) (Figure 8a-b). The average values for the native and mutant Trk-B and Trk-C protein's RMSD, RMSF, Rg, SASA, NH-bond, and covariance are depicted in Tables 1 and 2, respectively.

In MD simulation, the total energy of native and mutant Trk-B and Trk-C structures was measured from the beginning structures to examine the stability of the protein system. As a result, the native and mutant Trk-B proteins converged and showed a stable conformation (Figure 2a-b) which was used for further analysis. It indicates that all the protein trajectories are stable and used for further investigations. However, in Figure 3a RMSD plot, the A203T mutant structure shows more deviation. Furthermore, the R458G mutant structure exhibited minor variation than the native Trk-B protein (Figure 3a). At the same time, the Trk-C RMSD plot, both the mutant (E176D and L449F) systems show a minor deviation from than native TrK-3 protein (Figure 3b). This result indicates that, due to mutations, both Trk-B and Trk-C proteins might undergo the structural transition, which affects the protein's function. Furthermore, the RMSF value of the native and mutant Trk-B and Trk-C proteins reveals significant structural alterations. (Figure 4a-b). The A203T and R458G mutants showed a higher and lower degree of flexibility between the residues than the native Trk-B protein (Figure 4a). It further illustrates that the mutant A203T lost their native conformation and became more flexible and the mutant R458G become rigid (Figure 4b). On the other hand, Trk-C mutant structures (E176D & L449F) showed lower flexibility between the residues than native protein (Figure 4b). It indicates that both the mutant structures of Trk-C protein lost their confirmation and became more rigid.

The radius of gyration and solvent accessibility of surface analysis provide compactness in the protein system. The Rg and SASA value of native and mutant structures of both Trk-B & Trk-C proteins are shown in Figure 5a-b & Figure 6a-b, respectively. The mutant A203T shows higher Rg and SASA values than native Trk-B (Figure 5a & Figure 6a). The mutant R458G shows a lower Rg and SASA value than native Trk-B (Figure 5a & Figure 6a). While Trk-C mutant proteins (E176D & L449F) show lower Rg and SASA values than native proteins (Figure 5b & Figure 6b). The average hydrogen bond values of native and mutant structures of both Trk-B & Trk-C proteins are shown in Figure 7a-b. In Figure 7a, the A203T mutant shows a lower H-bond, and the mutant R458G shows a higher H-bond than the native Trk-B protein. In Figure 7b, both the mutant structures (E176D & L449F) show higher H-bonds than the native Trk-C protein. These NH-bond results are well associated with RMSD, RMSF, SASA, and Rg plots. Thus, the A203T mutant enlarges in Trk-B protein, and the R458G mutant is shrunken than the native Trk-B structure. At the same time, the Trk-C protein mutations are shrunken compared to the native structure.

Further, we performed PCA analysis to observe the motion of Trk-B and Trk-C proteins upon mutations. The Trk-B mutant A203T, show more movement and leads to flexible conformation. Whereas the R458G mutant shows less motion and leads to rigid confirmation (Figure 8a). Both Trk-C mutations (E176D & L449F) show less movement and lead to rigid conformation (Figure 8b). The structural changes of native and mutant Trk-B and Trk-C proteins at different simulation timescales (0 ns, 25 ns, & 50 ns) were observed and displayed in Figure 9a-b. It clearly thus, it confirms that mutation influences the structural behavior of the Trk-B and Trk-C proteins. Further, it could affect the function of NKRT2/3 genes and is responsible for causing leukemia and other hemopoietic malignancies.

Conclusion

This research studied the structural and functional consequences of native and mutant Trk-B and Trk-C protein structures. Due to mutations, the Trk-B protein loses its stability. For example, the Trk-B mutant A203T is more flexible than the native protein, whereas the R458G mutation is more rigid than the native Trk-B conformation. In addition, compared to the native structure, the Trk-C mutations (E176D & L449F) become more rigid. As a result, the Trk-B and Trk-C protein functions may be affected due to structural change. This study will aid experimental laboratory scientists in better comprehending the molecular mechanism of novel NTRK2/3 gene mutations. Researchers may be able to use this information to establish a therapeutic target for NTRK2/3 gene-related leukemia.

Data availability

Figshare. Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using molecular dynamics simulations approach. https://doi.org/10.6084/m9.figshare.22232218.v1.³⁴

This project contains the following underlying data:

• native_Trk-B.pdb (The modeled Trk-B protein structure).
• A203T_Trk-B.pdb (The modeled mutant (A203T) Trk-B protein structure).
• R458G_Trk-C.pdb (The modeled mutant (R458G) Trk-B protein structure).
• Nat_Trk-C.pdb (The modeled Trk-C protein structure).
• E176D_Trk-C.pdb (The modeled mutant (E176D) Trk-C protein structure).
• L449F_Trk-C.pdb (The modeled mutant (L449F) Trk-C protein structure).
• Trk_B-energy.xvg (The native and mutant (A203T & R458G) Trk-B total energy Vs. Time for 50 ns)
• Trk_b-PCA.xvg (Projection motion of the native and mutant (A203T & R458G) Trk-B protein structures for the period of 50 ns).
• Trk_B-PPhbond.xvg (The H-bond simulation data for the native and mutant (A203T & R458G) Trk-B protein structures for the period of 50 ns).
• Trk_B-Rg.xvg (The native and mutant (A203T & R458G) Trk-B protein compactness analysis by Rg for the period of 50 ns.)
• Trk_B-rmsd.xvg (The native and mutant (A203T & R458G) Trk-B backbone RMSD for the period of 50 ns).
• Trk_b-rmsf.xvg (The native and mutant (A203T & R458G) Trk-B protein residues in C-α RMSF simulation data for the period of 50 ns).
• Trk_b-SASA.xvg (The SASA analysis of native and mutant (A203T & R458G) Trk-B protein structures for the period of 50 ns).
• Trk_C-2dproj_ev_1_2.xvg (Projection motion of the native and mutant (E176D & L449F) Trk-C protein structures for the period of 50 ns).
• Trk_C-energy.xvg (The native and mutant (E176D & L449F) Trk-C total energy Vs. Time for the period of 50 ns).
• Trk_C-PPhbond.xvg (The H-bond simulation data for the native and mutant (E176D & L449F) Trk-C protein structures for the period of 50 ns).
• Trk_C-Rg.xvg (The native and mutant (E176D & L449F) Trk-C protein compactness analysis by Rg for the period of 50 ns).
• Trk_C-rmsd.xvg (The native and mutant (E176D & L449F) Trk-C backbone RMSD for the period of 50 ns).
• Trk_C-rmsf.xvg (The native and mutant (E176D & L449F) Trk-C protein residues of C-α RMSF simulation data for the period of 50 ns).
• Trk_C-SASA.xvg (The SASA analysis of native and mutant (E176D & L449F) Trk-C protein structures for the period of 50 ns).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Acknowledgments

We thank the Deanship of Scientific Research (Grant No. 2019-162-AMSJ), Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia, for their support. The authors acknowledge the Bioinformatics Laboratory at the College of Applied Medical Sciences in Jubail, Imam Abdulrahman Bin Faisal University, for their computing facility to carry out this work.

References

1. Amatu A, Sartore-Bianchi A, Siena S: NTRK gene fusions as novel targets of cancer therapy across multiple tumour types. ESMO open. 2016; 1(2): e000023. PubMed Abstract | Publisher Full Text | Free Full Text
2. Vaishnavi A, Le AT, Doebele RC: TRKing down an old oncogene in a new era of targeted therapy. Cancer Discov. 2015; 5(1): 25–34. PubMed Abstract | Publisher Full Text | Free Full Text
3. Tacconelli A, Farina AR, Cappabianca L, et al.: TrkA alternative splicing: a regulated tumor-promoting switch in human neuroblastoma. Cancer cell. 2004; 6(4): 347–360. Publisher Full Text
4. Lange AM, Lo HW: Inhibiting TRK proteins in clinical cancer therapy. Cancers. 2018; 10(4): 105. PubMed Abstract | Publisher Full Text | Free Full Text
5. Yeo GS, Connie Hung CC, Rochford J, et al.: A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nat. Neurosci. 2004; 7(11): 1187–1189. PubMed Abstract | Publisher Full Text
6. Nakagawara A, Liu XG, Ikegaki N, et al.: Cloning and chromosomal localization of the human TRK-B tyrosine kinase receptor gene (NTRK2). Genomics. 1995; 25(2): 538–546. PubMed Abstract | Publisher Full Text
7. Lamballe F, Klein R, Barbacid M: trkC, a new member of the trk family of tyrosine protein kinases, is a receptor for neurotrophin-3. Cell. 1991; 66(5): 967–979. Publisher Full Text
8. Tomasson MH, Xiang Z, Walgren R, et al.: Somatic mutations and germline sequence variants in the expressed tyrosine kinase genes of patients with de novo acute myeloid leukemia. Blood, The Journal of the American Society of Hematology. 2008; 111(9): 4797–4808. Publisher Full Text
9. Valent A, Danglot G, Bernheim A: Mapping of the tyrosine kinase receptors trkA (NTRK1), trkB (NTRK2) and trkC (NTRK3) to human chromosomes 1q22, 9q22 and 15q25 by fluorescence in situ hybridization. Eur. J. Hum. Genet. 1997; 5(2): 102–104. PubMed Abstract | Publisher Full Text
10. Joshi SK, Qian K, Bisson WH, et al.: Discovery and characterization of targetable NTRK point mutations in hematologic neoplasms. Blood. 2020; 135(24): 2159–2170. PubMed Abstract | Publisher Full Text | Free Full Text
11. UniProt Consortium: UniProt: a hub for protein information. Nucleic Acids Res. 2015; 43(D1): D204–D212. Publisher Full Text
12. Zhang Y: I-TASSER server for protein 3D structure prediction. BMC bioinformatics. 2008; 9(1): 1–8. Publisher Full Text
13. Laskowski RA, Rullmann JAC, MacArthur MW, et al.: AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR. 1996; 8(4): 477–486. PubMed Abstract | Publisher Full Text
14. Wiederstein M, Sippl MJ: ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007; 35(suppl_2): W407–W410. PubMed Abstract | Publisher Full Text | Free Full Text
15. Abraham MJ, van der Spoel D , Lindahl E, et al.: GROMACS user manual version 5.0. 4. Sweden: The GROMACS Development Team at the Royal Instituta of Technology and Uppsala University; 2014.
16. Balu K, Rajendran V, Sethumadhavan R, et al.: Investigation of binding phenomenon of NSP3 and p130Cas mutants and their effect on cell signalling. Cell Biochem. Biophys. 2013; 67(2): 623–633. PubMed Abstract | Publisher Full Text
17. Kamaraj B, Purohit R: Computational screening of disease-associated mutations in OCA2 gene. Cell Biochem. Biophys. 2014; 68(1): 97–109. Publisher Full Text
18. Gopalakrishnan C, Al-Subaie AM, Yeh HY, et al.: Prioritization of SNPs in y+ LAT-1 culpable of Lysinuric protein intolerance and their mutational impacts using protein-protein docking and molecular dynamics simulation studies. J. Cell. Biochem. 2019; 120(10): 18496–18508. Publisher Full Text
19. Kamaraj B, Bogaerts A: Structure and function of p53-DNA complexes with inactivation and rescue mutations: a molecular dynamics simulation study. PLoS One. 2015; 10(8): e0134638. PubMed Abstract | Publisher Full Text | Free Full Text
20. Al-Subaie AM, Kamaraj B: The structural effect of FLT3 mutations at 835th position and their interaction with acute myeloid leukemia inhibitors: in silico approach. Int. J. Mol. Sci. 2021; 22(14): 7602. PubMed Abstract | Publisher Full Text | Free Full Text
21. Huang J, MacKerell AD Jr: CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. J. Comput. Chem. 2013; 34(25): 2135–2145. PubMed Abstract | Publisher Full Text | Free Full Text
22. Fliege J, Svaiter BF: Steepest descent methods for multicriteria optimization. Mathematical methods of operations research. 2000; 51(3): 479–494. Publisher Full Text
23. Darden T, York D, Pedersen L: Particle mesh Ewald: An N· log (N) method for Ewald sums in large systems. J. Chem. Phys. 1993; 98(12): 10089–10092. Publisher Full Text
24. Berendsen HJ, Postma JV, Van Gunsteren WF, et al.: Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984; 81(8): 3684–3690. Publisher Full Text
25. Parrinello M, Rahman A: Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 1981; 52(12): 7182–7190. Publisher Full Text
26. Hess B, Bekker H, Berendsen HJ, et al.: LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 1997; 18(12): 1463–1472. Publisher Full Text
27. Amadei A, Linssen ABM, Berendsen HJC: Essential dynamics of proteins. Proteins Struct. Funct. Genet. 1993; 17(4): 412–425. Publisher Full Text
28. Stepanova M: Dynamics of essential collective motions in proteins: theory. Phys. Rev. E. 2007; 76(5): 051918. Publisher Full Text
29. Turner PJ: XMGRACE, Version 5.1. 19. Beaverton, OR: Center for Coastal and Land-Margin Research, Oregon Graduate Institute of Science and Technology; 2005; vol. 2. .
30. Su HP, Rickert K, Burlein C, et al.: Structural characterization of nonactive site, TrkA-selective kinase inhibitors. Proc. Natl. Acad. Sci. 2017; 114(3): E297–E306. Publisher Full Text
31. Grädler U, Schwarz D, Dresing V, et al.: Structural and biophysical characterization of the Syk activation switch. J. Mol. Biol. 2013; 425(2): 309–333. PubMed Abstract | Publisher Full Text
32. Kamaraj B, Purohit R: In silico screening and molecular dynamics simulation of disease-associated nsSNP in TYRP1 gene and its structural consequences in OCA3. Biomed. Res. Int. 2013; 2013: 1–13. PubMed Abstract | Publisher Full Text | Free Full Text
33. Kamaraj B, Al-Subaie AM, Ahmad F, et al.: Effect of novel leukemia mutations (K75E & E222K) on interferon regulatory factor 1 and its interaction with DNA: insights from molecular dynamics simulations and docking studies. J. Biomol. Struct. Dyn. 2021; 39(14): 5235–5247. Publisher Full Text
34. Kamaraj B: Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using molecular dynamics simulations approach. Dataset. figshare. 2023. Publisher Full Text

Comments on this article Comments (0)

Version 2

VERSION 2 PUBLISHED 28 Mar 2023

Author details Author details

Abeer M Al-Subaie
Roles: Conceptualization, Data Curation, Investigation, Methodology, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Fazil Ahmad
Roles: Data Curation, Methodology, Validation

Khaldoon Alsamman
Roles: Conceptualization, Investigation, Methodology, Validation

Competing interests

No competing interests were disclosed.

Grant information

This work was primarily supported by grants from the Deanship of Scientific Research, Imam Abdulrahman Bin Faisal University (Grant No. 2019-162-AMSJ), Dammam, Saudi Arabia.

Article Versions (2)

version 2

Revised

Published: 18 Jun 2024, 12:345

https://doi.org/10.12688/f1000research.131013.2

version 1

Published: 28 Mar 2023, 12:345

https://doi.org/10.12688/f1000research.131013.1

© 2024 M Al-Subaie A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download

Export To

metrics

	Views	Downloads
F1000Research	-	-
PubMed Central Data from PMC are received and updated monthly.	-	-

Citations

SEE MORE DETAILS

CITE

how to cite this article

M Al-Subaie A, Kamaraj B, Ahmad F and Alsamman K. Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using molecular dynamics simulations approach [version 2; peer review: 3 approved]. F1000Research 2024, 12:345 (https://doi.org/10.12688/f1000research.131013.2)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

track

receive updates on this article

Track an article to receive email alerts on any updates to this article.

Open Peer Review

Current Reviewer Status: ?

Key to Reviewer Statuses VIEW HIDE

ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions

Version 2

VERSION 2

PUBLISHED 18 Jun 2024

Revised

Views

Reviewer Report 19 Jun 2024

Gurudeeban Selvaraj, Centre for Research in Molecular Modeling (CERMM), Department of Chemistry and Biochemistry, Concordia University, Montreal, Québec, Canada

Approved

https://doi.org/10.5256/f1000research.167790.r292453

The authors addressed all the comments raised ... Continue reading

CITE

Report a concern

Author Response 27 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

27 Jun 2024

Author Response

We’re grateful to the reviewer for taking the time and review the manuscript.
Competing Interests: No competing interests were disclosed.
We’re grateful to the reviewer for taking the time and review the manuscript.
We’re grateful to the reviewer for taking the time and review the manuscript.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 27 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

27 Jun 2024

Author Response

We’re grateful to the reviewer for taking the time and review the manuscript.
Competing Interests: No competing interests were disclosed.
We’re grateful to the reviewer for taking the time and review the manuscript.
We’re grateful to the reviewer for taking the time and review the manuscript.
Competing Interests: No competing interests were disclosed. Close
Report a concern

Views

Reviewer Report 19 Jun 2024

Rajasekaran R., Quantitative Biology Lab, Department of Integrative Biology, School of Bio Sciences and Technology, Vellore Institute of Technology (VIT, Deemed to be University), Vellore, Tamil Nadu, India

Approved

https://doi.org/10.5256/f1000research.167790.r292452

Authors addressed all the comments and now this ... Continue reading

CITE

Report a concern

Author Response 26 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

26 Jun 2024

Author Response

We’re grateful to the reviewer for taking the time and review the manuscript.
Competing Interests: No competing interests were disclosed.
We’re grateful to the reviewer for taking the time and review the manuscript.
We’re grateful to the reviewer for taking the time and review the manuscript.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 26 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

26 Jun 2024

Author Response

We’re grateful to the reviewer for taking the time and review the manuscript.
Competing Interests: No competing interests were disclosed.
We’re grateful to the reviewer for taking the time and review the manuscript.
We’re grateful to the reviewer for taking the time and review the manuscript.
Competing Interests: No competing interests were disclosed. Close
Report a concern

Version 1

VERSION 1

PUBLISHED 28 Mar 2023

Views

Reviewer Report 07 Jun 2024

Approved

https://doi.org/10.5256/f1000research.143816.r168093

Abeer et al. focused on unravelling the molecular mechanism of novel leukaemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using a molecular dynamics simulations approach. Initially, the authors have used the various bioinformatics tools to screen the mutations which causing tooth agenesis.
Further they implemented modeling and an MDS approach to elucidate the structural changes of the Trk-B and Trk-C proteins upon mutations. This study helps to understand the molecular mechanism of Trk-B and Trk-C proteins upon mutations at the atomic level. It could help the researchers to establish personalized drugs for NTRK2/3 gene-related leukaemia. Overall, the paper is well-written and explained. So, I recommend the article to be indexed.
Minor comments:
Ø Cite the reference for the force field (CHARMM 36) used in the method section of the MD simulation.
Ø Display the modeled native and mutant Trk-B and Trk-C protein structures in the article.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Computational Biology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

Report a concern

Author Response 18 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

18 Jun 2024

Author Response

We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.
The responses to the minor comments of the reviewer are addressed ... Continue reading We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.
The responses to the minor comments of the reviewer are addressed below.
Comment 1: Cite the reference for the force field (CHARMM 36) used in the method section of the MD simulation.
Response: As per your valuable suggestion we have cited and listed the reference related to CHARMM 36 in the revised manuscript version.
Comment 2: Display the modeled native and mutant Trk-B and Trk-C protein structures in the article.
Response: We agreed to your valuable suggestion. We have displayed the modeled structure of native and mutant Trk-B and Trk-C proteins in Figure 1 in the revised manuscript version.
We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.
The responses to the minor comments of the reviewer are addressed below.
Comment 1: Cite the reference for the force field (CHARMM 36) used in the method section of the MD simulation.
Response: As per your valuable suggestion we have cited and listed the reference related to CHARMM 36 in the revised manuscript version.
Comment 2: Display the modeled native and mutant Trk-B and Trk-C protein structures in the article.
Response: We agreed to your valuable suggestion. We have displayed the modeled structure of native and mutant Trk-B and Trk-C proteins in Figure 1 in the revised manuscript version.
Competing Interests: No Competing interests. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 18 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

18 Jun 2024

Author Response

We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.
The responses to the minor comments of the reviewer are addressed ... Continue reading We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.
The responses to the minor comments of the reviewer are addressed below.
Comment 1: Cite the reference for the force field (CHARMM 36) used in the method section of the MD simulation.
Response: As per your valuable suggestion we have cited and listed the reference related to CHARMM 36 in the revised manuscript version.
Comment 2: Display the modeled native and mutant Trk-B and Trk-C protein structures in the article.
Response: We agreed to your valuable suggestion. We have displayed the modeled structure of native and mutant Trk-B and Trk-C proteins in Figure 1 in the revised manuscript version.
We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.
The responses to the minor comments of the reviewer are addressed below.
Comment 1: Cite the reference for the force field (CHARMM 36) used in the method section of the MD simulation.
Response: As per your valuable suggestion we have cited and listed the reference related to CHARMM 36 in the revised manuscript version.
Comment 2: Display the modeled native and mutant Trk-B and Trk-C protein structures in the article.
Response: We agreed to your valuable suggestion. We have displayed the modeled structure of native and mutant Trk-B and Trk-C proteins in Figure 1 in the revised manuscript version.
Competing Interests: No Competing interests. Close
Report a concern

Views

Reviewer Report 06 Jun 2024

Gurudeeban Selvaraj, Centre for Research in Molecular Modeling (CERMM), Department of Chemistry and Biochemistry, Concordia University, Montreal, Québec, Canada

Approved with Reservations

https://doi.org/10.5256/f1000research.143816.r168095

The study titled "Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using molecular dynamics simulations approach" by Alsubaie et al. is a fascinating piece of research. The authors provide insights into the molecular mechanism of Trk-B and Trk-C proteins upon mutations using a molecular dynamics simulation approach. They also identify the effects of these mutations on the protein's structural conformation and its functional loss. Overall, the study presents exciting findings to the scientific audience. However, there are a few changes that need to be made for indexing purposes, as well as some areas that require further clarification:

The study's aim is missing from the abstract, and the method's tools and parameters for computational studies (modeling and MD simulation) are also absent. Additionally, modeling and structural quality assessments are not included in the results.
Please explain the reason for choosing the 5KMK_A and 4FL2_A protein structures as templates for modeling the TRK-B and Trk-C proteins. Also, provide predicted structures in the manuscript.
It would be helpful to visualize the native and mutant Trk-B protein structures at different time scales and discuss them in the manuscript.
It would also be beneficial to visualize the native and mutant Trk-C protein structures at different time scales and discuss them in the manuscript.
The authors may describe in detail the principal component analysis and XMGRACE analysis methods.
There are a number of typos (ex. higher region phase space) and grammar checks are required throughout the manuscript.
Better to use H-bonds instead of NH-bonds for the number of hydrogen bonds

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Computational Biology & Chemistry

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

Report a concern

Author Response 18 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

18 Jun 2024

Author Response

We thank the reviewer for sharing their valuable time and providing critical comments which is indeed very useful for improving the manuscript.
The responses to the questions of the reviewer ... Continue reading We thank the reviewer for sharing their valuable time and providing critical comments which is indeed very useful for improving the manuscript.
The responses to the questions of the reviewer are included below.

Comment 1: The study's aim is missing from the abstract, and the method's tools and parameters for computational studies (modeling and MD simulation) are also absent. Additionally, modeling and structural quality assessments are not included in the results.
Response: Thanks for your valuable suggestion. We have mentioned the study aims in the abstract methods section and modeling and structural quality assessment are discussed in the abstract of the revised manuscript.
Comment 2: Please explain the reason for choosing the 5KMK_A and 4FL2_A protein structures as templates for modeling the TRK-B and Trk-C proteins. Also, provide predicted structures in the manuscript.
Response: In this study, we have used a threading-based approach (I-TASSER program) to model the Trk_B and Trk-C proteins. I-TASSER searches the template for the given Trk-B and Trk-C protein sequences individually against the protein data bank. Based on the highest sequence similarity and coverage it predicts the templates individually to model the Trk-B and Trk-C proteins. As a result, PDB ID: 5KMK_A showed 74% similarity and less than 36% sequence coverage to the Trk-B protein sequence and was considered as a template for modeling the Trk-B protein. Similarly, the PDB ID: 4FL2_A shows 70% similarity and less than 41% sequence coverage to the Trk-C protein sequence and is considered a template to model the Trk_B protein. Also, we have shown the modeled native and mutant Trk-B and Trk-C proteins in the revised manuscript as Figure 1.
Comment 3: It would be helpful to visualize the native and mutant Trk-B protein structures at different time scales and discuss them in the manuscript.
Response. We agree with your valuable suggestion. We have displayed the native and mutant Trk-B protein structures at different time scales as Figure 9a and discussed in the revised manuscript.
Comment 4: It would also be beneficial to visualize the native and mutant Trk-C protein structures at different time scales and discuss them in the manuscript.
Response: We agreed to your valuable suggestion. We have displayed the native and mutant Trk-C protein structures at different time scales as Figure 9b and discussed in the revised manuscript.
Comment 5: The authors may describe in detail the principal component analysis and XMGRACE analysis methods.
Response. Thank you for the suggestion. We have discussed the PCA and XMGRACE analysis in the revised manuscript.
Comment 6: There are a number of typos (ex. higher region phase space) and grammar checks are required throughout the manuscript.
Response: We have corrected the typos in the revised version.
Comment 7: Better to use H-bonds instead of NH-bonds for the number of hydrogen bonds
Response: As per your valuable suggestion, we have used H-bonds instead of NH-bonds in the revised manuscript.
We thank the reviewer for sharing their valuable time and providing critical comments which is indeed very useful for improving the manuscript.
The responses to the questions of the reviewer are included below.

Comment 1: The study's aim is missing from the abstract, and the method's tools and parameters for computational studies (modeling and MD simulation) are also absent. Additionally, modeling and structural quality assessments are not included in the results.
Response: Thanks for your valuable suggestion. We have mentioned the study aims in the abstract methods section and modeling and structural quality assessment are discussed in the abstract of the revised manuscript.
Comment 2: Please explain the reason for choosing the 5KMK_A and 4FL2_A protein structures as templates for modeling the TRK-B and Trk-C proteins. Also, provide predicted structures in the manuscript.
Response: In this study, we have used a threading-based approach (I-TASSER program) to model the Trk_B and Trk-C proteins. I-TASSER searches the template for the given Trk-B and Trk-C protein sequences individually against the protein data bank. Based on the highest sequence similarity and coverage it predicts the templates individually to model the Trk-B and Trk-C proteins. As a result, PDB ID: 5KMK_A showed 74% similarity and less than 36% sequence coverage to the Trk-B protein sequence and was considered as a template for modeling the Trk-B protein. Similarly, the PDB ID: 4FL2_A shows 70% similarity and less than 41% sequence coverage to the Trk-C protein sequence and is considered a template to model the Trk_B protein. Also, we have shown the modeled native and mutant Trk-B and Trk-C proteins in the revised manuscript as Figure 1.
Comment 3: It would be helpful to visualize the native and mutant Trk-B protein structures at different time scales and discuss them in the manuscript.
Response. We agree with your valuable suggestion. We have displayed the native and mutant Trk-B protein structures at different time scales as Figure 9a and discussed in the revised manuscript.
Comment 4: It would also be beneficial to visualize the native and mutant Trk-C protein structures at different time scales and discuss them in the manuscript.
Response: We agreed to your valuable suggestion. We have displayed the native and mutant Trk-C protein structures at different time scales as Figure 9b and discussed in the revised manuscript.
Comment 5: The authors may describe in detail the principal component analysis and XMGRACE analysis methods.
Response. Thank you for the suggestion. We have discussed the PCA and XMGRACE analysis in the revised manuscript.
Comment 6: There are a number of typos (ex. higher region phase space) and grammar checks are required throughout the manuscript.
Response: We have corrected the typos in the revised version.
Comment 7: Better to use H-bonds instead of NH-bonds for the number of hydrogen bonds
Response: As per your valuable suggestion, we have used H-bonds instead of NH-bonds in the revised manuscript.
Competing Interests: No Competing Interests Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 18 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

18 Jun 2024

Author Response

We thank the reviewer for sharing their valuable time and providing critical comments which is indeed very useful for improving the manuscript.
The responses to the questions of the reviewer ... Continue reading We thank the reviewer for sharing their valuable time and providing critical comments which is indeed very useful for improving the manuscript.
The responses to the questions of the reviewer are included below.

Comment 1: The study's aim is missing from the abstract, and the method's tools and parameters for computational studies (modeling and MD simulation) are also absent. Additionally, modeling and structural quality assessments are not included in the results.
Response: Thanks for your valuable suggestion. We have mentioned the study aims in the abstract methods section and modeling and structural quality assessment are discussed in the abstract of the revised manuscript.
Comment 2: Please explain the reason for choosing the 5KMK_A and 4FL2_A protein structures as templates for modeling the TRK-B and Trk-C proteins. Also, provide predicted structures in the manuscript.
Response: In this study, we have used a threading-based approach (I-TASSER program) to model the Trk_B and Trk-C proteins. I-TASSER searches the template for the given Trk-B and Trk-C protein sequences individually against the protein data bank. Based on the highest sequence similarity and coverage it predicts the templates individually to model the Trk-B and Trk-C proteins. As a result, PDB ID: 5KMK_A showed 74% similarity and less than 36% sequence coverage to the Trk-B protein sequence and was considered as a template for modeling the Trk-B protein. Similarly, the PDB ID: 4FL2_A shows 70% similarity and less than 41% sequence coverage to the Trk-C protein sequence and is considered a template to model the Trk_B protein. Also, we have shown the modeled native and mutant Trk-B and Trk-C proteins in the revised manuscript as Figure 1.
Comment 3: It would be helpful to visualize the native and mutant Trk-B protein structures at different time scales and discuss them in the manuscript.
Response. We agree with your valuable suggestion. We have displayed the native and mutant Trk-B protein structures at different time scales as Figure 9a and discussed in the revised manuscript.
Comment 4: It would also be beneficial to visualize the native and mutant Trk-C protein structures at different time scales and discuss them in the manuscript.
Response: We agreed to your valuable suggestion. We have displayed the native and mutant Trk-C protein structures at different time scales as Figure 9b and discussed in the revised manuscript.
Comment 5: The authors may describe in detail the principal component analysis and XMGRACE analysis methods.
Response. Thank you for the suggestion. We have discussed the PCA and XMGRACE analysis in the revised manuscript.
Comment 6: There are a number of typos (ex. higher region phase space) and grammar checks are required throughout the manuscript.
Response: We have corrected the typos in the revised version.
Comment 7: Better to use H-bonds instead of NH-bonds for the number of hydrogen bonds
Response: As per your valuable suggestion, we have used H-bonds instead of NH-bonds in the revised manuscript.
We thank the reviewer for sharing their valuable time and providing critical comments which is indeed very useful for improving the manuscript.
The responses to the questions of the reviewer are included below.

Comment 1: The study's aim is missing from the abstract, and the method's tools and parameters for computational studies (modeling and MD simulation) are also absent. Additionally, modeling and structural quality assessments are not included in the results.
Response: Thanks for your valuable suggestion. We have mentioned the study aims in the abstract methods section and modeling and structural quality assessment are discussed in the abstract of the revised manuscript.
Comment 2: Please explain the reason for choosing the 5KMK_A and 4FL2_A protein structures as templates for modeling the TRK-B and Trk-C proteins. Also, provide predicted structures in the manuscript.
Response: In this study, we have used a threading-based approach (I-TASSER program) to model the Trk_B and Trk-C proteins. I-TASSER searches the template for the given Trk-B and Trk-C protein sequences individually against the protein data bank. Based on the highest sequence similarity and coverage it predicts the templates individually to model the Trk-B and Trk-C proteins. As a result, PDB ID: 5KMK_A showed 74% similarity and less than 36% sequence coverage to the Trk-B protein sequence and was considered as a template for modeling the Trk-B protein. Similarly, the PDB ID: 4FL2_A shows 70% similarity and less than 41% sequence coverage to the Trk-C protein sequence and is considered a template to model the Trk_B protein. Also, we have shown the modeled native and mutant Trk-B and Trk-C proteins in the revised manuscript as Figure 1.
Comment 3: It would be helpful to visualize the native and mutant Trk-B protein structures at different time scales and discuss them in the manuscript.
Response. We agree with your valuable suggestion. We have displayed the native and mutant Trk-B protein structures at different time scales as Figure 9a and discussed in the revised manuscript.
Comment 4: It would also be beneficial to visualize the native and mutant Trk-C protein structures at different time scales and discuss them in the manuscript.
Response: We agreed to your valuable suggestion. We have displayed the native and mutant Trk-C protein structures at different time scales as Figure 9b and discussed in the revised manuscript.
Comment 5: The authors may describe in detail the principal component analysis and XMGRACE analysis methods.
Response. Thank you for the suggestion. We have discussed the PCA and XMGRACE analysis in the revised manuscript.
Comment 6: There are a number of typos (ex. higher region phase space) and grammar checks are required throughout the manuscript.
Response: We have corrected the typos in the revised version.
Comment 7: Better to use H-bonds instead of NH-bonds for the number of hydrogen bonds
Response: As per your valuable suggestion, we have used H-bonds instead of NH-bonds in the revised manuscript.
Competing Interests: No Competing Interests Close
Report a concern

Views

Reviewer Report 04 Jun 2024

Dicson Sheeja Malar, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Bangkok, Thailand

Approved

https://doi.org/10.5256/f1000research.143816.r168092

The study entitled 'Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using a molecular dynamics simulations approach' was an original article that is well-organized and valuable. It observed the structural consequence of leukemia mutations on NTRK2 and NTRK3 genes.

Methods included in this study are appropriate. First, the authors used a threading-based modeling technique to predict the 3-D conformational native and mutant Trk-B and Trk-C proteins. Hence it used multiple templates to predict the protein structure from sequences. Further, the authors used GROMACS tool to perform the all-atom simulation to observe the structural changes on Trk-B and Trk-C proteins.

The results and discussion section explained well how the geometry of Trk-B and Trk-C protein structures changes due to mutations. In addition, the article gives new insights into NTRK2 and NTRK3 gene mutations related to leukemia. This information will help to develop potential drug targets in the future.

Overall, the paper is well-framed and organized. Therefore, I approve the current form of this article for indexing.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Molecular Biology, Neurodegeneative diseases and Bioinformatics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

CITE

Report a concern

Author Response 12 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

12 Jun 2024

Author Response

We’re grateful and appreciate you taking the time to share your feedback with us.
Competing Interests: No potential conflict of interest was reported by the authors.
We’re grateful and appreciate you taking the time to share your feedback with us.
We’re grateful and appreciate you taking the time to share your feedback with us.
Competing Interests: No potential conflict of interest was reported by the authors. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 12 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

12 Jun 2024

Author Response

We’re grateful and appreciate you taking the time to share your feedback with us.
Competing Interests: No potential conflict of interest was reported by the authors.
We’re grateful and appreciate you taking the time to share your feedback with us.
We’re grateful and appreciate you taking the time to share your feedback with us.
Competing Interests: No potential conflict of interest was reported by the authors. Close
Report a concern

Comments on this article Comments (0)

Version 2

VERSION 2 PUBLISHED 28 Mar 2023

Open Peer Review

Reviewer Status

Reviewer Reports

	Invited Reviewers
	1	2	3
Version 2 (revision) 18 Jun 24		read	read
Version 1 28 Mar 23	read	read	read

Dicson Sheeja Malar, Chulalongkorn University, Bangkok, Thailand
Gurudeeban Selvaraj, Concordia University, Montreal, Canada
Rajasekaran R., Vellore Institute of Technology (VIT, Deemed to be University), Vellore, India

Comments on this article

All Comments(0)

Add a comment

Browse by related subjects

Back to all reports

Reviewer Report

3 Views

19 Jun 2024 | for Version 2

Gurudeeban Selvaraj, Centre for Research in Molecular Modeling (CERMM), Department of Chemistry and Biochemistry, Concordia University, Montreal, Québec, Canada

3 Views Cite this report Responses(1)

Approved

The authors addressed all the comments raised by the reviewer. I accept the current form.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Computational Biology and Chemistry

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (1)

Back to all reports

Reviewer Report

2 Views

19 Jun 2024 | for Version 2

2 Views Cite this report Responses(1)

Approved

Authors addressed all the comments and now this manuscript can be indexed in its present form.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Computational Biology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (1)

Back to all reports

Reviewer Report

6 Views

07 Jun 2024 | for Version 1

6 Views Cite this report Responses(1)

Approved

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Computational Biology

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (1)

Author Response

18 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

We sincerely appreciate all your valuable comments and suggestions, which helped us in improving the quality of the manuscript.
The responses to the minor comments of the reviewer are addressed below.
Comment 1: Cite the reference for the force field (CHARMM 36) used in the method section of the MD simulation.
Response: As per your valuable suggestion we have cited and listed the reference related to CHARMM 36 in the revised manuscript version.
Comment 2: Display the modeled native and mutant Trk-B and Trk-C protein structures in the article.
Response: We agreed to your valuable suggestion. We have displayed the modeled structure of native and mutant Trk-B and Trk-C proteins in Figure 1 in the revised manuscript version.

View more View less

Competing Interests

No Competing interests.

Back to all reports

Reviewer Report

12 Views

06 Jun 2024 | for Version 1

Gurudeeban Selvaraj, Centre for Research in Molecular Modeling (CERMM), Department of Chemistry and Biochemistry, Concordia University, Montreal, Québec, Canada

12 Views Cite this report Responses(1)

Approved With Reservations

The study's aim is missing from the abstract, and the method's tools and parameters for computational studies (modeling and MD simulation) are also absent. Additionally, modeling and structural quality assessments are not included in the results.
Please explain the reason for choosing the 5KMK_A and 4FL2_A protein structures as templates for modeling the TRK-B and Trk-C proteins. Also, provide predicted structures in the manuscript.
It would be helpful to visualize the native and mutant Trk-B protein structures at different time scales and discuss them in the manuscript.
It would also be beneficial to visualize the native and mutant Trk-C protein structures at different time scales and discuss them in the manuscript.
The authors may describe in detail the principal component analysis and XMGRACE analysis methods.
There are a number of typos (ex. higher region phase space) and grammar checks are required throughout the manuscript.
Better to use H-bonds instead of NH-bonds for the number of hydrogen bonds

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Yes
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Computational Biology & Chemistry

Respond to this report

Responses (1)

Author Response

18 Jun 2024

Balu Kamaraj, Department of Dental Education, College of Dentistry, Imam Abdulrahman Bin Faisal University, DAMMAM, Saudi Arabia

We thank the reviewer for sharing their valuable time and providing critical comments which is indeed very useful for improving the manuscript.
The responses to the questions of the reviewer are included below.

Comment 1: The study's aim is missing from the abstract, and the method's tools and parameters for computational studies (modeling and MD simulation) are also absent. Additionally, modeling and structural quality assessments are not included in the results.
Response: Thanks for your valuable suggestion. We have mentioned the study aims in the abstract methods section and modeling and structural quality assessment are discussed in the abstract of the revised manuscript.
Comment 2: Please explain the reason for choosing the 5KMK_A and 4FL2_A protein structures as templates for modeling the TRK-B and Trk-C proteins. Also, provide predicted structures in the manuscript.
Response: In this study, we have used a threading-based approach (I-TASSER program) to model the Trk_B and Trk-C proteins. I-TASSER searches the template for the given Trk-B and Trk-C protein sequences individually against the protein data bank. Based on the highest sequence similarity and coverage it predicts the templates individually to model the Trk-B and Trk-C proteins. As a result, PDB ID: 5KMK_A showed 74% similarity and less than 36% sequence coverage to the Trk-B protein sequence and was considered as a template for modeling the Trk-B protein. Similarly, the PDB ID: 4FL2_A shows 70% similarity and less than 41% sequence coverage to the Trk-C protein sequence and is considered a template to model the Trk_B protein. Also, we have shown the modeled native and mutant Trk-B and Trk-C proteins in the revised manuscript as Figure 1.
Comment 3: It would be helpful to visualize the native and mutant Trk-B protein structures at different time scales and discuss them in the manuscript.
Response. We agree with your valuable suggestion. We have displayed the native and mutant Trk-B protein structures at different time scales as Figure 9a and discussed in the revised manuscript.
Comment 4: It would also be beneficial to visualize the native and mutant Trk-C protein structures at different time scales and discuss them in the manuscript.
Response: We agreed to your valuable suggestion. We have displayed the native and mutant Trk-C protein structures at different time scales as Figure 9b and discussed in the revised manuscript.
Comment 5: The authors may describe in detail the principal component analysis and XMGRACE analysis methods.
Response. Thank you for the suggestion. We have discussed the PCA and XMGRACE analysis in the revised manuscript.
Comment 6: There are a number of typos (ex. higher region phase space) and grammar checks are required throughout the manuscript.
Response: We have corrected the typos in the revised version.
Comment 7: Better to use H-bonds instead of NH-bonds for the number of hydrogen bonds
Response: As per your valuable suggestion, we have used H-bonds instead of NH-bonds in the revised manuscript.

View more View less

Competing Interests

No Competing Interests

Back to all reports

Reviewer Report

10 Views

04 Jun 2024 | for Version 1

Dicson Sheeja Malar, Department of Clinical Chemistry, Faculty of Allied Health Sciences, Chulalongkorn University, Bangkok, Bangkok, Thailand

10 Views Cite this report Responses(1)

Approved

Is the work clearly and accurately presented and does it cite the current literature?

Yes
Is the study design appropriate and is the work technically sound?

Yes
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Molecular Biology, Neurodegeneative diseases and Bioinformatics

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

Respond to this report

Responses (1)

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

[1] 1. Amatu A, Sartore-Bianchi A, Siena S: NTRK gene fusions as novel targets of cancer therapy across multiple tumour types. ESMO open. 2016; 1(2): e000023. PubMed Abstract | Publisher Full Text | Free Full Text

[2] 2. Vaishnavi A, Le AT, Doebele RC: TRKing down an old oncogene in a new era of targeted therapy. Cancer Discov. 2015; 5(1): 25–34. PubMed Abstract | Publisher Full Text | Free Full Text

[3] 3. Tacconelli A, Farina AR, Cappabianca L, et al.: TrkA alternative splicing: a regulated tumor-promoting switch in human neuroblastoma. Cancer cell. 2004; 6(4): 347–360. Publisher Full Text

[4] 4. Lange AM, Lo HW: Inhibiting TRK proteins in clinical cancer therapy. Cancers. 2018; 10(4): 105. PubMed Abstract | Publisher Full Text | Free Full Text

[5] 5. Yeo GS, Connie Hung CC, Rochford J, et al.: A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nat. Neurosci. 2004; 7(11): 1187–1189. PubMed Abstract | Publisher Full Text

[6] 6. Nakagawara A, Liu XG, Ikegaki N, et al.: Cloning and chromosomal localization of the human TRK-B tyrosine kinase receptor gene (NTRK2). Genomics. 1995; 25(2): 538–546. PubMed Abstract | Publisher Full Text

[7] 7. Lamballe F, Klein R, Barbacid M: trkC, a new member of the trk family of tyrosine protein kinases, is a receptor for neurotrophin-3. Cell. 1991; 66(5): 967–979. Publisher Full Text

[8] 8. Tomasson MH, Xiang Z, Walgren R, et al.: Somatic mutations and germline sequence variants in the expressed tyrosine kinase genes of patients with de novo acute myeloid leukemia. Blood, The Journal of the American Society of Hematology. 2008; 111(9): 4797–4808. Publisher Full Text

[9] 9. Valent A, Danglot G, Bernheim A: Mapping of the tyrosine kinase receptors trkA (NTRK1), trkB (NTRK2) and trkC (NTRK3) to human chromosomes 1q22, 9q22 and 15q25 by fluorescence in situ hybridization. Eur. J. Hum. Genet. 1997; 5(2): 102–104. PubMed Abstract | Publisher Full Text

[10] 10. Joshi SK, Qian K, Bisson WH, et al.: Discovery and characterization of targetable NTRK point mutations in hematologic neoplasms. Blood. 2020; 135(24): 2159–2170. PubMed Abstract | Publisher Full Text | Free Full Text

[11] 11. UniProt Consortium: UniProt: a hub for protein information. Nucleic Acids Res. 2015; 43(D1): D204–D212. Publisher Full Text

[12] 12. Zhang Y: I-TASSER server for protein 3D structure prediction. BMC bioinformatics. 2008; 9(1): 1–8. Publisher Full Text

[13] 13. Laskowski RA, Rullmann JAC, MacArthur MW, et al.: AQUA and PROCHECK-NMR: programs for checking the quality of protein structures solved by NMR. J. Biomol. NMR. 1996; 8(4): 477–486. PubMed Abstract | Publisher Full Text

[14] 14. Wiederstein M, Sippl MJ: ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007; 35(suppl_2): W407–W410. PubMed Abstract | Publisher Full Text | Free Full Text

[15] 15. Abraham MJ, van der Spoel D , Lindahl E, et al.: GROMACS user manual version 5.0. 4. Sweden: The GROMACS Development Team at the Royal Instituta of Technology and Uppsala University; 2014.

[16] 16. Balu K, Rajendran V, Sethumadhavan R, et al.: Investigation of binding phenomenon of NSP3 and p130Cas mutants and their effect on cell signalling. Cell Biochem. Biophys. 2013; 67(2): 623–633. PubMed Abstract | Publisher Full Text

[17] 17. Kamaraj B, Purohit R: Computational screening of disease-associated mutations in OCA2 gene. Cell Biochem. Biophys. 2014; 68(1): 97–109. Publisher Full Text

[18] 18. Gopalakrishnan C, Al-Subaie AM, Yeh HY, et al.: Prioritization of SNPs in y+ LAT-1 culpable of Lysinuric protein intolerance and their mutational impacts using protein-protein docking and molecular dynamics simulation studies. J. Cell. Biochem. 2019; 120(10): 18496–18508. Publisher Full Text

[19] 19. Kamaraj B, Bogaerts A: Structure and function of p53-DNA complexes with inactivation and rescue mutations: a molecular dynamics simulation study. PLoS One. 2015; 10(8): e0134638. PubMed Abstract | Publisher Full Text | Free Full Text

[20] 20. Al-Subaie AM, Kamaraj B: The structural effect of FLT3 mutations at 835th position and their interaction with acute myeloid leukemia inhibitors: in silico approach. Int. J. Mol. Sci. 2021; 22(14): 7602. PubMed Abstract | Publisher Full Text | Free Full Text

[21] 21. Huang J, MacKerell AD Jr: CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. J. Comput. Chem. 2013; 34(25): 2135–2145. PubMed Abstract | Publisher Full Text | Free Full Text

[22] 22. Fliege J, Svaiter BF: Steepest descent methods for multicriteria optimization. Mathematical methods of operations research. 2000; 51(3): 479–494. Publisher Full Text

[23] 23. Darden T, York D, Pedersen L: Particle mesh Ewald: An N· log (N) method for Ewald sums in large systems. J. Chem. Phys. 1993; 98(12): 10089–10092. Publisher Full Text

[24] 24. Berendsen HJ, Postma JV, Van Gunsteren WF, et al.: Molecular dynamics with coupling to an external bath. J. Chem. Phys. 1984; 81(8): 3684–3690. Publisher Full Text

[25] 25. Parrinello M, Rahman A: Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys. 1981; 52(12): 7182–7190. Publisher Full Text

[26] 26. Hess B, Bekker H, Berendsen HJ, et al.: LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 1997; 18(12): 1463–1472. Publisher Full Text

[27] 27. Amadei A, Linssen ABM, Berendsen HJC: Essential dynamics of proteins. Proteins Struct. Funct. Genet. 1993; 17(4): 412–425. Publisher Full Text

[28] 28. Stepanova M: Dynamics of essential collective motions in proteins: theory. Phys. Rev. E. 2007; 76(5): 051918. Publisher Full Text

[29] 29. Turner PJ: XMGRACE, Version 5.1. 19. Beaverton, OR: Center for Coastal and Land-Margin Research, Oregon Graduate Institute of Science and Technology; 2005; vol. 2. .

[30] 30. Su HP, Rickert K, Burlein C, et al.: Structural characterization of nonactive site, TrkA-selective kinase inhibitors. Proc. Natl. Acad. Sci. 2017; 114(3): E297–E306. Publisher Full Text

[31] 31. Grädler U, Schwarz D, Dresing V, et al.: Structural and biophysical characterization of the Syk activation switch. J. Mol. Biol. 2013; 425(2): 309–333. PubMed Abstract | Publisher Full Text

[32] 32. Kamaraj B, Purohit R: In silico screening and molecular dynamics simulation of disease-associated nsSNP in TYRP1 gene and its structural consequences in OCA3. Biomed. Res. Int. 2013; 2013: 1–13. PubMed Abstract | Publisher Full Text | Free Full Text

[33] 33. Kamaraj B, Al-Subaie AM, Ahmad F, et al.: Effect of novel leukemia mutations (K75E & E222K) on interferon regulatory factor 1 and its interaction with DNA: insights from molecular dynamics simulations and docking studies. J. Biomol. Struct. Dyn. 2021; 39(14): 5235–5247. Publisher Full Text

[34] 34. Kamaraj B: Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using molecular dynamics simulations approach. Dataset. figshare. 2023. Publisher Full Text

Unraveling the molecular mechanism of novel leukemia mutations on NTRK2 (A203T & R458G) and NTRK3 (E176D & L449F) genes using molecular dynamics simulations approach

Abstract

Keywords

Revised Amendments from Version 1

Introduction

Methods

Dataset

Native and Mutant Trk-B and Trk-C proteins modeling

Molecular Dynamics simulations

Results

Prediction of native and mutant Trk-B and Trk-C protein's 3D structures

Figure 1. (a) Model structures of native and mutant (A203T & R458G) Trk-B proteins. (b) Model structures of native and mutant (E176D & L449F) Trk-C proteins.

MD simulation of native and mutant Trk-B and Trk-C proteins

Figure 2. (a) The native and mutant (A203T & R458G) Trk-B total energy Vs. Time for 50 ns. (b) The native and mutant (E176D & L449F) Trk-C total energy Vs. Time for the period of 50 ns.

Figure 3. (a) The native and mutant (A203T & R458G) Trk-B backbone RMSD for the period of 50 ns. (b) The native and mutant (E176D & L449F) Trk-C backbone RMSD for the period of 50 ns.

Table 1. Average RMSD, RMSF, Rg, SASA, H-bonds, and covariance value of native and mutant Trk-B.

Table 2. Average RMSD, RMSF, Rg, SASA, H-bonds, and covariance value of native and mutant Trk-C.

Figure 4. (a) The native and mutant (A203T & R458G) Trk-B protein residues in C-α RMSF simulation data for the period of 50 ns. (b) The native and mutant (E176D & L449F) Trk-C protein residues of C-α RMSF simulation data for the period of 50 ns.

Figure 5. (a) The native and mutant (A203T & R458G) Trk-B protein compactness analysis by Rg for the period of 50 ns. (b) The native and mutant (E176D & L449F) Trk-C protein compactness analysis by Rg for the period of 50 ns.

Figure 6. (a) The SASA analysis of native and mutant (A203T & R458G) Trk-B protein structures for the period of 50 ns. (b) The SASA analysis of native and mutant (E176D & L449F) Trk-C protein structures for the period of 50 ns.

Figure 7. (a) The H-bond simulation data for the native and mutant (A203T & R458G) Trk-B protein structures for the period of 50 ns. (b) The H-bond simulation data for the native and mutant (E176D & L449F) Trk-C protein structures for the period of 50 ns.

Figure 8. (a) Projection motion of the native and mutant (A203T & R458G) Trk-B protein structures for the period of 50 ns. (b) Projection motion of the native and mutant (E176D & L449F) Trk-C protein structures for the period of 50 ns.

Figure 9. (a) Snapshots of native and mutant (A203T & R458G) Trk-B protein conformation at different simulation time scale (b) Snapshots of native and mutant (E176D & L449F) Trk-C protein conformation at different simulation time scale.

Discussion

Conclusion

Data availability

Acknowledgments

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Browse by related subjects

Competing Interests Policy

Stay Updated