PREMONITION - Preprocessing motifs in protein structures for search acceleration

Introduction

The rapid development of crystallization techniques has resulted in a deluge of proteins with known structures¹. Most of the proteins are annotated using sequence alignment methods by a ‘guilt by association’ logic based on the sequence-to-structure-to-function paradigm². However, sequence alignment methods are not applicable in cases where similar functional groups are identically positioned in the active site of proteins with no sequence homology. The classic example of this phenomenon, known as convergent evolution^3,4, is the major families of serine proteases (chymotrypsin and subtilisin), where the active site is structurally and functionally identical, though there is no global sequence or structural homology⁵. According to some studies, about 42% of entries annotated as ‘unknown functions’ are true examples of proteins of unknown function⁶.

Structure-based methods have evolved to detect such convergently evolved proteins^7,8. The conservation of structural properties is the primary driving logic behind many of these identification methods, reviewed in detail previously⁹. There are essentially two categories of programs that find binding sites in proteins (binding sites are typically closely related to protein function). The first one requires a predefined set of amino acids (motifs) of a known enzymatic function to search for the same within the protein under investigation^8,10–14. The second category automatically detects similarity in the side chain patterns to classify protein functionality^7,15–18. We have demonstrated through several detailed examples, using a method (CLASP¹⁹) which falls in the first category, that such structural conservation necessitates the conservation of electrostatic properties in proteins with the same functionality^19–22.

A challenge emerging in these methods relates to the large fold space of known proteins, although the rate of increase of this space is gradually being saturated²³. Efficient parallelization has allowed the ProBiS algorithm to compare a protein query against the PDB in minutes¹⁵. To date, the identification of motifs is a task executed on the fly and applied sequentially^13,14. Thus, running multiple queries involves several invocations of the same program. Our aim is to amortize the processing times by a one-time precompilation of all possible motifs, pruned using rational distance constraints, which can be leveraged for future queries.

A simplistic approach to obtain motifs is to enumerate all possible combinations from the sequence. Motifs that span across distances rarely seen in active sites can be pruned out using structural information. In the present work, we propose a method to precompile all possible motifs comprising of a set (n=4 in this case) of predefined amino acid residues from a protein structure that occur within a specified distance (R) of each other (PREMONITION - Preprocessing motifs in protein structures for search acceleration). We have estimated R from the known active site residues of ~500 proteins annotated in the CSA database (http://www.ebi.ac.uk/thornton-srv/databases/CSA/)²⁴. PREMONITION rolls a sphere of radius R along the protein fold centered at the Cα atom of each residue, and all motifs are extracted within this sphere. The maximum number of residues that occurs within R Å of any residue in protein dataset is also computed. This sets an upper bound for the polynomial complexity of the PREMONITION algorithm, and run times for the precompilation are reasonable. After such a precompilation step, the computational time required for querying a protein structure with multiple motifs is reduced considerably.

Previously, we had proposed a computational method (PROMISE) to estimate the promiscuity of proteins with known active site residues and 3D structure using the CSA database²⁵. It took less than a minute to query one protein with all 500 motifs using PREMONITION, a process that took almost a day when done sequentially. Such speed up enables querying a much larger set of proteins using a comprehensive set of ligands, as is required in drug screening procedures.

Materials and methods

Algorithm 1 details the steps in creating the PREMONITION database for a given protein. We enumerate the steps using a concrete example for trypsin (PDBid:1A0J). Given a radius of interaction SOAS, we first compute the set residues within SOAS for each residue Residue_i. For example, let us assume we are processing residue D102. Equation 1 gives the set of residues that have at least one atom within SOAS=10 Å from the Cα of D102.

      ${\varphi }_{\mathit{\text{NearestResidues}}}^{D\text{102}}$ = [D102, W237, M90, V227, M180, G38, R62, S37, D194...]         (1)

We now take combinations of n=4 from this set, which does not necessarily include D102 (Equation 2).

      ${\varphi }_{\mathit{\text{Combinations}}}^{D\text{102}}$ = [(W237, M90, D102, V227), (W237, M90, D194, M180), (G38, R62, S37, D102)...]         (2)

After sorting the each combination based on the single letter amino acid code we obtain a set of n tuples (Equation 3).

      ${\varphi }_{\mathit{\text{SortedStrings}}}^{D\text{102}}$ = [DMVW = (102, 90, 227, 237), DDQV = (194, 90, 180, 237), DGRS = (102, 38, 62, 37)...]       (3)

Now, we add (102,90,227,237) to the global tableofmatches for the key ‘DMVW’. Thus, as we process every residue in the protein, we merge all occurrences of ‘DMVW’ (Equation 4).

      ${\varphi }_{\mathit{\text{AllMotifs}}}^{\mathit{\text{DMVW}}}$ = [(194.104.53.141), (102.90.227.237), (194.104.52.51), (194.104.53.51)...]       (4)

Extracting all motifs of ‘DMVW’ now consists of the trivial task of reading this set from the file on disk.

Adaptive Poisson-Boltzmann Solver (APBS) and PDB2PQR packages were used to calculate the potential difference between the reactive atoms of the corresponding proteins^26,27. The APBS parameters and electrostatic potential units were set as described previously in¹⁹. Protein structures were rendered by PyMol (http://www.pymol.org/). The proteins were superimposed based on the matching motifs using DECAAF²⁸.

Results and discussion

Estimating the maximum radius for computing interacting residues

First, we estimated the minimal radius of a sphere that encompasses active sites found in proteins. CSA provides catalytic residue annotation for enzymes in the PDB and is available online²⁴. The database consists of an original hand-annotated set extracted from the primary literature and a homologous set inferred by PSI-BLAST². We chose ~500 proteins from the CSA database that are annotated from the literature (SI list.doc). We computed the size of the active site (SOAS) for these known active sites by finding the minimum radius centered around one residue that encompassed the other residues. Table 1 shows the pairwise distance for the four residues that comprise the active site in trypsin (PDBid:1A0J) - Asp102, Ser195, His57 and Ala56. It can be seen that a sphere of radius 6.9 Å centered around His57 (c) would include all other residues. Other radii required to encompass all other residues of the motif and centered at a different residue is larger than this value (Asp102 = 7.8 Å, Ser195 = 9 Å and Ala56 = 9 Å). Thus, the SOAS for this protein is 6.9 Å. Figure 1a shows the frequency distribution of the SOAS for the set of 500 proteins chosen (mean = 7.3 Å, standard deviation = 1.8 Å, min = 3.5 Å and max = 13 Å). 90% of proteins have an SOAS below 10 Å.

Table 1. Pairwise distance between the active site residues in trypsin (PDBid:1A0J).

The motif consists of Asp102/OD1(a), Ser195/OG(b), His57/ND1(c) and Ala56/N(d). A sphere of radius 6.9 Å centered around His57 (c) would include all other active site residues. This is the minimal distance - the radius of any sphere needed to include all residues is more than this value.

Atom1	Atom2	Distance(Å)
a	b	7.8
a	c	5.6
a	d	2.9
b	c	3.3
b	d	9.0
c	d	6.9

Figure 1. Estimating the radius of the sphere enclosing the active site in proteins, and the number of residues in the sphere.

Data is extracted from ~500 proteins from the CSA database which are annotated from literature. (a) The size of the active site (SOAS) computed using the minimum radius centered around one residue that encompasses the other residues. (b) Number of residues enclosed by the SOAS (Red=10 Å, blue=11 Å). A residue R_j is considered to be within the SOAS of another residue R_i if any atom of R_j falls within a sphere of radius SOAS centered around the Cα of R_i.

Running CLASP using the PREMONITION modified algorithm

We queried trypsin (PDBid:1A0J) with all the 500 motifs using the modified CLASP algorithm using the PREMONITION database¹⁹. The best matches are shown in Table 2. As expected, the best matches are those with known serine catalytic triads. As an illustrative example, we chose a protein (thioesterase - PDBid:1THT) with no known relationship with trypsin. This protein has the active site motif - H241 S114 V136 W213. This corresponds to the string query ‘HSVW’ (note that the string is sorted). We extract all entries for this string (Equation 5), which are all possible occurrences of the structural motif ‘HSVW’ in the protein.

Table 2. Best matches when trypsin (PDBid:1A0J) is queried using 500 motifs from the CSA database.

As expected, the best matches are those with known catalytic triads.

PDB	Length	Description	CLASP Score
1A0J	223	Trypsin	0
1SSX	198	Alpha-lytic protease	0.2
1AZW	313	Proline iminopeptidase	0.7
1MEK	120	Protein disulfide isomerase	1
2LPR	198	Alpha-lytic protease	1.2
1SCA	274	Subtilisin carlsberg	1.2
1C4X	285	2-Hydroxy-6-oxo-6-phenylhexa-2,4-die	1.3
1A7U	277	Chloroperoxidase T	1.3
1LJL	131	Arsenate reductase	1.3
1QJ4	257	Hydroxynitrile lyase	1.3
1RGQ	200	NS3 Protease	1.3
1JKM	361	Esterase	1.4
1EH5	279	Palmitoyl protein thioesterase 1	1.4
2AAT	396	Aspartate aminotransferase	1.4
1THT	305	Thioesterase	1.5

      ${\varphi }_{\mathit{\text{Motifs}}}^{\mathit{\text{HSVW}}}$ = [(71, 26, 121, 141), (71, 26, 138, 141), (57, 214, 212, 215)...]        (5)

All entries of ‘HSVW’ are compared using CLASP. Table 3 shows the electrostatic potential difference and spatial difference in each of the motifs in Equation 5. The best scoring motif is (H57ND1,S195OG,V213N,W215NE1). However, even the best motif has a relatively large RMSD (Figure 2). Thus, this is not a significant match.

Table 3. Potential and spatial congruence of the motif (H241 S114 V136 W213) from a thioesterase (PDB:1THT) in a trypsin protein (PDB:1A0J).

This motif corresponds to the key “HSVW”. D = Pairwise distance in Å. PD = Pairwise potential difference. APBS writes out the electrostatic potential in dimensionless units of kT/e where k is Boltzmann’s constant, T is the temperature in K and e is the charge of an electron.

PDB	Active site atoms (a,b,c,d)		ab	ac	ad	bc	bd	cd
1THT	H241ND1,S114OG, VAL136N,W213NE1,	D PD	4.7 17.0	6.1 -97.0	6.3 -26.5	7.2 -114.0	9.0 -43.5	12.3 70.5
1A0J	H71ND1,S26OG, VAL121N,W141NE1,	D PD	7.1 -288.7	16.3 -334.9	6.4 -227.3	13.4 -46.2	11.9 61.4	17.5 107.6
	H71ND1,S26OG, VAL138N,W141NE1,	D PD	7.1 -288.7	13.4 -346.2	6.4 -227.3	10.1 -57.5	11.9 61.4	13.5 118.9
	H57ND1,S195OG, V212N,W215NE1,	D PD	4.8 -20.7	7.9 -130.7	7.9 -52.3	6.7 -110.0	9.6 -31.6	9.7 78.4

Figure 2. Superimposing thioesterase (PDBid:1THT, in blue) and trypsin (PDBid:1A0J, in green).

The proteins are superimposed based on the matching active site residues using DECAAF²⁸. (a) The global superimposition does not show any significant homology. (b) The detailed residue configuration. Residues from trypsin are in red, and those in the thioesterase are in blue. His57 and His241 completely overlap and are in black.