Genome scale prediction of substrate specificity for acyl adenylate superfamily of enzymes based on active site residue profiles

Abstract

Background: Enzymes belonging to acyl:CoA synthetase (ACS) superfamily activate wide variety of substrates and play major role in increasing the structural and functional diversity of various secondary metabolites in microbes and plants. However, due to the large sequence divergence within the superfamily, it is difficult to predict their substrate preference by annotation transfer from the closest homolog. Therefore, a large number of ACS sequences present in public databases lack any functional annotation at the level of substrate specificity. Recently, several examples have been reported where the enzymes showing high sequence similarity to luciferases or coumarate:CoA ligases have been surprisingly found to activate fatty acyl substrates in experimental studies. In this work, we have investigated the relationship between the substrate specificity of ACS and their sequence/structural features, and developed a novel computational protocol for in silico assignment of substrate preference.

Results: We have used a knowledge-based approach which involves compilation of substrate specificity information for various experimentally characterized ACS and derivation of profile HMMs for each subfamily. These HMM profiles can accurately differentiate probable cognate substrates from non-cognate possibilities with high specificity (Sp) and sensitivity (Sn) (Sn = 0.91-1.0, Sp = 0.96-1.0) values. Using homologous crystal structures, we identified a limited number of contact residues crucial for substrate recognition i.e. specificity determining residues (SDRs). Patterns of SDRs from different subfamilies have been used to derive predictive rules for correlating them to substrate preference. The power of the SDR approach has been demonstrated by correct prediction of substrates for enzymes which show apparently anomalous substrate preference. Furthermore, molecular modeling of the substrates in the active site has been carried out to understand the structural basis of substrate selection. A web based prediction tool http://www.nii.res.in/pred_acs_substr.html has been developed for automated functional classification of ACS enzymes.

Conclusions: We have developed a novel computational protocol for predicting substrate preference for ACS superfamily of enzymes using a limited number of SDRs. Using this approach substrate preference can be assigned to a large number of ACS enzymes present in various genomes. It can potentially help in rational design of novel proteins with altered substrate specificities.

Figure 1

Schematic representation of the acyl-adenylate superfamily and its various subfamilies. The figure shows the chemical structure of the substrates and products for the reactions catalyzed by members of each subfamily. The various subfamilies utilize different carboxylic acid substrates and transfer acyl moiety to either CoA or enzyme bound phosphopantetheine arm. The luciferase catalyzes conversion of luciferin to oxyluciferin. All the subfamilies are known to take up a similar 3-dimensional fold (depicted in centre) which has a large N-terminal domain and a small C-terminal domain. The structure shown is the adenylation domain of gramicidin synthetase (PDB code: 1AMU). The cofactor AMP (orange) and substrate Phenylalanine (red), shown in CPK, bind in the cleft separating the two domains. The substrate binding residues are shown in cyan as ball and stick.

Figure 2

A phylogenetic tree obtained from MSA of representative members of ACS superfamily. The six subfamilies are represented in different colors. Each subfamily, namely AcCS (orange), 4CL (purple), MCS (pink), LCS (maroon), Luciferases (yellow), NRPS (green) cluster as separate groups in the dendrogarm.

Figure 3

Identification of Specificity Determining Residues (SDRs). (a)The crystal structure of adenylation domain of gramicidin synthetase (PDB code: 1AMU). The C terminal domain is colored in ochre. The A and B subdomains of the N-terminal domain are shown in green and pink respectively. The cofactor AMP (orange) and substrate phenylalanine (red) are depicted as CPK models. The 15 SDRs lining the substrate binding pocket are shown in cyan. (b) Zoomed in version of the active site. The substrate phenylalanine and AMP are shown as stick in red and orange respectively and the 15 SDRs as ball and stick. (c) The extraction of SDRs from a protein sequence. The query sequence is aligned with the structural template and the amino acids of the query corresponding to the SDRs of the template are extracted. The residues highlighted in red represent the SDRs.

Figure 4

Consensus Active Site Profile for six subfamilies. The table lists the conservation pattern of 15 positions that constitute the SDRs for various subfamilies. Number in the bracket refers to the percentage conservation of the amino acid in the alignment. The positions which have conservation >80% or <50% are shown in red and green respectively. Those positions with conservation between 50% and 80% are colored in pink. The positions which are highlighted have a subfamily specific conservation pattern and play a crucial role in controlling substrate specificity. Position number 210 (highlighted in yellow) was identified by docking studies.

Figure 5

Structural subdomains of adenylation domain. The figure depicting the A- (green) and B- (pink) subdomains of the N-terminal domain of 1AMU. The bound AMP and phenylalanine (stick representation) are also shown. The MSA of some of the representative members from each subfamily shows that most of the insertions and deletions are confined to the A-subdomain. The 14 SDRs (highlighted red) belong to the B-subdomain which is relatively conserved.

Figure 6

Docking of myristic acid onto model of long chain:CoA ligase. (a) The N-terminal domain of the homology model of long chain:CoA liagse. The figure shows that AUTODOCK sampled many conformations (shown in yellow) within the docking grid. (b) Histogram showing the various clusters obtained after docking. The cluster highlighted in red was chosen and the docked conformation was selected. (c) Comparison of the conformation of the docked ligand (green) with the conformation of the same ligand as obtained from X-ray crystallography (PDB code: 1V26) (orange).

Figure 7

Comparison of the docking based substrate bound conformations for acetyl CoA synthetase, medium chain CoA ligase and luciferase with the substrate bound structures obtained from X-ray crystallography. The structures obtained from docking are shown in green, while the corresponding crystal structures are depicted in orange. (A) acetyl CoA ligase (B) medium chain CoA ligase (C) luciferase. The RMSD values shown in the figures correspond to root mean squared deviations in ligand coordinates when the enzyme structures were optimally superposed.

Figure 8

Typical use of the query interface of pred_acs_substr for analysis of substrate specificity of ACSs. The prediction of substrate preference of the query sequence is based on two protocols, namely HMMER and PSSM-15. Each method provides the results in a tabular format, which are sorted based on the score of the query sequence against each subfamily. The two results are shown in separate pop-up windows. The last column in each table provides the link to the alignment of the query sequence with the template sequences of each subfamily.