INTREPID—INformation-theoretic TREe traversal for Protein functional site IDentification

Motivation: Identification of functionally important residues in proteins plays a significant role in biological discovery. Here, we present INTREPID—an information–theoretic approach for functional site identification that exploits the information in large diverse multiple sequence alignments (MSAs). INTREPID uses a traversal of the phylogeny in combination with a positional conservation score, based on Jensen–Shannon divergence, to rank positions in an MSA. While knowledge of protein 3D structure can significantly improve the accuracy of functional site identification, since structural information is not available for a majority of proteins, INTREPID relies solely on sequence information. We evaluated INTREPID on two tasks: predicting catalytic residues and predicting specificity determinants. Results: In catalytic residue prediction, INTREPID provides significant improvements over Evolutionary Trace, ConSurf as well as over a baseline global conservation method on a set of 100 manually curated enzymes from the Catalytic Site Atlas. In particular, INTREPID is able to better predict catalytic positions that are not globally conserved and hence, attains improved sensitivity at high values of specificity. We also investigated the performance of INTREPID as a function of the evolutionary divergence of the protein family. We found that INTREPID is better able to exploit the diversity in such families and that accuracy improves when homologs with very low sequence identity are included in an alignment. In specificity determinant prediction, when subtype information is known, INTREPID-SPEC, a variant of INTREPID, attains accuracies that are competitive with other approaches for this task. Availability: INTREPID is available for 16919 families in the PhyloFacts resource (http://phylogenomics.berkeley.edu/phylofacts). Contact: sriram_s@cs.berkeley.edu Supplementary information: Relevant online supplementary material is available at http://phylogenomics.berkeley.edu/INTREPID.


S-2.1 Greater evolutionary divergence improves the accuracies of INTREPID and Global-JS
We have shown in Section 3.1.3 of the main paper that greater evolutionary divergence improves the accuracy of INTREPID. Global-JS also improves with the inclusion of additional homologs but appears to be somewhat less robust to sequence divergence ( Figure S-4). For instance, at 90% specificity, the sensitivity of Global-JS prediction is 42% on the most restricted alignment (removing sequences with less than 25% identity to the seed) but increases to about 79% on the unrestricted alignment.
Our results are in agreement with previous studies (Panchenko et al., 2004;Aloy et al., 2001;Landgraf et al., 2001). In Landgraf et al. (2001), the recall of their scoring functions improved when the E-value cutoff for homolog inclusion was reduced from 10 −50 to 10 −20 while (Aloy et al., 2001) observed a considerable improvement in accuracy of their method when their alignments had sequence identity less than 30%. Similar results were also reported by Panchenko et al. (2004), where the performance doubled on alignments with average sequence identity of 20% relative to those with average identity of around 45%. An important difference is that our alignments are highly divergent. In the experiments reported by Panchenko et al. (2004), the least divergent dataset had a minimum percent identity of 25% to the seed. For  catalytic residue prediction, we observe that it is beneficial to use highly divergent alignments with minimum percent identities extending as low as 10%.

S-2.2 The accuracy of INTREPID decreases with distance from the seed
To test the effectiveness of INTREPID prediction for proteins not used as seeds for selecting and aligning homologs, we took three families of enzymes from CSA containing at least two members each in the core (manually curated) dataset. One of these sequences for each family was used as a seed for clustering homologs, and we used INTREPID to predict critical residues for all members. We ensured that sequence identities with the seed were not so high as to make the experiment uninformative (i.e., homologous enzymes from CSA were selected with less than 50% identity). Table S-4 compares the ranks of catalytic residues in a sequence that was not used as seed to the ranks when the same sequence was a seed. As sequence identity to the seed decreases, the accuracy of INTREPID also decreases. In the context of predicting catalytic positions in a single protein as opposed to the entire family, these results would apply to other sequence-based methods as well. Based on these limited results, we would recommend ensuring that the sequence of interest has sequence identity > 50% to the seed. Effect of alignment diversity on the accuracy of Global-JS on the CSA-100 dataset: ROC curve for Global-JS on alignments with varying degrees of evolutionary divergence, indicated by the minimum percent identity to the seed. The original alignment with no sequences removed is labelled "Unrestricted". Global-JS performs significantly better with increasing evolutionary divergence -from 42% sensitivity at 90% specificity and 25% identity trimming to 79% sensitivity when no sequences are removed. varying degrees of evolutionary divergence, indicated by the minimum percent identity to the seed. The original alignment with no sequences removed is labelled "Unrestricted". INTREPID-LO performs significantly better with increasing evolutionary divergence -from 42% sensitivity at 90% specificity and 25% identity trimming to 76% sensitivity when no sequences are removed. (Right) Global-LO also benefits from increased sequence diversity though at 90% specificity, it appears to have optimal performance if sequences with identity less than 10% to the seed are excluded.

S-3 EXAMPLES OF INTREPID PREDICTIONS
In this section, we analyze INTREPID predictions on families found in the PhyloFacts resource (http://phylogenomics.berkeley. edu/phylofacts). For the families found in PhyloFacts, homologs were gathered from UniProt (Apweiler et al., 2004) using Flowerpower (Krishnamurthy et al., 2007) (with global-local settings and number of SHMM iterations set to 3) and re-aligned using MUSCLE (Edgar, 2004). For this analysis, we built neighbor-joining trees using the PHYLIP software though other tree construction algorithms may be used in practice. PhyloFacts displays the top 5% of the INTREPID predictions though the cutoff may be varied by the user (we handle tied scores by selecting all residues at a given score).

S-3.1 Dihydroneopterin aldolase
Dihydroneopterin aldolase catalyzes the conversion of 7,8-dihydroneopterin (DHNP) to 6-hydroxymethyl-7,8-dihydropterin (HP) playing an essential role in the folate biosynthesis pathway. Mammals, unlike bacteria, plants, and yeast, lack a complete folate biosynthesis pathway and obtain folate from their diet (Lawrence et al., 2005). Hence, dihydroneopterin aldolase, along with other enzymes in the folate biosynthesis pathway, has served as a target for antimicrobial and antibacterial agents (Lawrence et al., 2005).  Fig. S-6. Effect of alignment diversity on catalytic residue prediction on the CSA-100 dataset: (Left) ROC curve for INTREPID-RE on alignments with varying degrees of evolutionary divergence, indicated by the minimum percent identity to the seed. The original alignment with no sequences removed is labelled "Unrestricted". INTREPID-RE performs significantly better with increasing evolutionary divergence -from 42% sensitivity at 90% specificity and 25% identity trimming to 85% sensitivity when no sequences are removed. (Right) Global-RE also benefits from increased sequence diversity. PDB id Percent id Rank as non-seed Rank as seed 1hti 44.4 57,72,2,29,30 54,67,2,11,29 1pxv 32.9 124,124,4,4 9,32,7,17 1azw 10.8 201,120,64 8,2,1 Table S-4. INTREPID accuracy decreases with distance from the seed: The table shows the ranks (Rank as non-seed) assigned to the CSA catalytic residues by INTREPID on a sequence which was not the seed. These ranks are compared to the ranks (Rank as seed) when the same sequence was used as the seed. As the sequence identity to the seed decreases, the accuracy decreases as seen from the numerically higher "Rank as non-seed" column.
INTREPID predictions on dihydroneopterin aldolase from Staphylococcus aureus (PDB Id:1dhn) are shown in Figure S-12. INTREPID correctly predicts the residues E22 and K100, which are listed as catalytic in the CSA. INTREPID also predicts residues Q27 which forms a hydrogen bond to the substrate in the crystal structure of neopterin(NP), an analog of 7,8-dihydroneopterin (DHNP) ; Y54 which is known to coordinate catalysis with E22; K74 which influences the affinity of the enzyme for the substrate Hennig et al., 1998); and G17 and H16.

S-3.2 Src Homology 2 (SH2) domain
SH2 domains are found in multi-domain intracellular signaling proteins and play key roles in assembling signaling complexes by binding to phosphotyrosine moieties in target proteins. Key residues in SH2 binding pockets determine the specificity of interaction, and thereby the  Fig. S-8. INTREPID more effectively identifies catalytic residues that are not conserved across the family: Scatter plot comparing the ranks assigned by INTREPID and Global-JS on catalytic residues that are not conserved across the family. Lower ranks correspond to residues that are easily identified as catalytic. The diagonal denotes residues on which both methods do equally well, residues above the diagonal are those for which INTREPID gives better ranks, and residues below the diagonal are those for which Global-JS gives better ranks. Here, INTREPID gives better ranks to 34 residues and Global-JS to 15. pathways in which these proteins participate. The protein used in this analysis, Src SH2 domain from Rous sarcoma virus (PDB accession 1SPS chain C), has been crystallized with an 11-residue polypeptide Waksman et al. (1993). Three distinct ligand-binding pockets have been identified; a phosphotyrosine binding pocket, glutamate binding pocket and hydrophobic binding pocket. The key residues in these ligand-binding pockets are R12, R32, S34, E35, T37, C42, K57, H58, Y59, K60, I71, T72, Y87, D92, G93 and L94 Waksman et al. (1993) ;  Fig. S-10. Alignment of sequences in the subtree containing enoyl-[acyl-carrier-protein] reductase from Escherichia Coli (PDB id: 1mfp) made non-redundant at 70% sequence identity. Positions marked in red correspond to catalytic residues (K163, Y156). This subtree contains 199 sequences out of the original 833 sequences. Notice that Y156 is conserved within this subtree while it has a frequency of only 25% in the entire family. See Section 3.1.4 of the main paper for details. Songyang et al. (1993). Amongst the top 6 residue predicted by INTREPID are R32, H58 with the other predictions being W5, G27, F29 and F77. INTREPID predictions for dihydroneopterin aldolase from Staphylococcus aureus (PDB Id:2dhn, BPG accession:bpg020587). INTREPID correctly predicts the catalytic residues E22, K100, Q27, K74, and Y54. Of these, only E22 and K100 are listed in the CSA. The non-CSA functional residues refer to INTREPID predictions that are not listed in the CSA but have experimental evidence of being catalytic (Q27, K75, and Y54) (see text).