Tessellate & Montage: Molecular analytics of cyclic conformations

Introduction

Cyclic molecules and systems consisting of cyclic molecules play central roles in several chemical and biological processes^1,2. RNA, DNA, proteins and carbohydrates are polymers that all contain cyclic moieties that exhibit wide ranging conformational variety. For example, the puckering of fructose can be used to illustrate the value of determining conformational statistics as recognition and binding to Fructokinase is due to pucker specificity. Similarly different sugar puckers of ribose in RNA and de-oxyribose in A-DNA and Z-DNA account for the molecular shape of the respective nucleotide helices². In DNA-protein complexes any kink in the DNA axis is accompanied by a change in deoxy-ribose pucker, demonstrating the important connection pucker plays in molecular structure and flexibility^3,4. In enzymatic transition states for glycosyl hydrolases and transferases, conformations change to provide the correct electronic state for the reaction to take place⁵.

Conformational analyses, which recognise shapes (Figure 1), are important for finding transition state leads, understanding molecular recognition events, ligand fitting in macrocycles and for molecular pattern matching.

Figure 1. Representative canonical conformers for 5,6,7,8 cycles. Twist, chair, chair, crown.

We created a standardized toolset called Tessellate & Montage for comparisons of cyclic molecule conformations (ring pucker) from structural databases and simulation trajectory data. Tessellate can readily find, calculate and quantify cyclic conformations of cycles and macrocycles in datasets. Montage forked from MultiQC⁶ is a transferable reporting package for visualizing the conformations of cycles. Montage addresses the need to compare multiple molecular data sets by incorporating results from multiple analyses into a single report. Similar tools for calculating ring conformations are shown in Table 1, however these tools do not provide methods for calculating the ring pucker of macrocycles, nor do they have the reporting capability for comparing multiple types of molecular data sets in a single report. Existing pucker models include perpendicular displacements from a mean plane^7,8, intracyclic torsions^9,10 and triangular tessellation^11–15.

A further aim was to ensure that these tools (Tessellate and Montage) function independently from informatics workflows systems, while also being readily incorporated and fully functional as part of systems such as Galaxy²¹ and the Glycome Analytics Platform²².

Methods

Tessellate is a package for quantifying ring conformation and qualifying the conformer in terms of a canonical conformer (e.g. chair, boat). Developed in Python, Tessellate uses BioPython²³ for accessing protein data bank files. Using Tessellate with Visual Molecular Dynamics (VMD) is possible through a tcl script provided. Visualising the results of this analysis is done using Montage.

Montage is a fork of the excellent MultiQC⁶ and provides reporting templates and functionality for computational chemistry and chemical glycobiology. Previously we prototyped reporting within a web platform, the aim here was to ensure that the visual reports connected easily into workflow systems such as Galaxy²¹, GAP²² and were also transferable outside of these platforms.

Use cases

Simulation – Ribose in vacuo biased molecular dynamics calculation

The conformational changes during a biased free energy simulation for ribose. This analysis can be used to identify patterns in conformational change and confirm adequate sampling of phase space (Figure 2). VMD was used to open and read coordinate and trajectory files, and write out the atomic coordinates of the ribose 5 membered ring. The atomic coordinates are read in by Tessellate, producing pucker coordinates stored as JSON. MultiQC creates an HTML report from the JSON input.

Figure 2. Scatter plot and histogram of ring pucker from timeseries data.

Comparative conformational character of DNA and RNA

The nucleotides A-DNA and Z-DNA tend to be C3’-endo (³E), but in B-DNA the tendency is to C2’-endo (²E); this accounts for the different molecular shape of the respective nucleotide helices. To explore this, PDB structures that are representative of A, B and Z DNA (1ANA, 3BSE, and 2DCG) were analysed (see Operation section and Figure 3).

Figure 3. Histogram of ring pucker in different types of DNA, the planar conformation is due to the nitrogen base.

Cyclodextrins

An analysis of alpha cyclodextrin (ACD) from the PDB shows the glucose monomer conformations are mostly ⁴C₁ (85.9%) with few deviations (Figure 4). The ring pucker of the macrocycle, as defined by treating the centre of geometry for each glucose in cyclodextrin as an atom, varies between planar (P) and half chair (H, e.g. ³H₄, ⁵H₄,²H₃) conformations.

Results from the three use cases can be found in Supplementary File 1.

Figure 4. Histograms of ring pucker in alpha cyclodextrin for the standard 6 membered cycle and for the macrocycle.

Conclusions

Tessellate enables users to compile datasets from the reservoir of PDB and simulation produced data. From this ensemble of experimentally and computationally determined ring structures, the relation between cyclic conformational preference and other variables can be discovered using Montage.

Abbreviations: ACD: Alpha Cyclodextrin; GAP: Glycome Analytics Platform; JSON: JavaScript Object Notation; PDB: Protein Data Bank; SSSR: Smallest Set of Smallest Rings

Software availability

Latest source code:

Tessellate: https://github.com/scientificomputing/Tessellate

Montage: https://github.com/scientificomputing/Montage

Archived source code as at the time of publication:

Tessellate: https://doi.org/10.5281/zenodo.1068656²⁶

Montage: https://doi.org/10.5281/zenodo.1068692²⁷

Tessellate License: Apache 2.0

Montage License: GNU GPLv3