- Methodology article
- Open Access
Functional annotation by identification of local surface similarities: a novel tool for structural genomics
BMC Bioinformatics volume 6, Article number: 194 (2005)
Protein function is often dependent on subsets of solvent-exposed residues that may exist in a similar three-dimensional configuration in non homologous proteins thus having different order and/or spacing in the sequence. Hence, functional annotation by means of sequence or fold similarity is not adequate for such cases.
We describe a method for the function-related annotation of protein structures by means of the detection of local structural similarity with a library of annotated functional sites. An automatic procedure was used to annotate the function of local surface regions. Next, we employed a sequence-independent algorithm to compare exhaustively these functional patches with a larger collection of protein surface cavities. After tuning and validating the algorithm on a dataset of well annotated structures, we applied it to a list of protein structures that are classified as being of unknown function in the Protein Data Bank. By this strategy, we were able to provide functional clues to proteins that do not show any significant sequence or global structural similarity with proteins in the current databases.
This method is able to spot structural similarities associated to function-related similarities, independently on sequence or fold resemblance, therefore is a valuable tool for the functional analysis of uncharacterized proteins. Results are available at http://cbm.bio.uniroma2.it/surface/structuralGenomics.html
Detection of sequence or fold similarity is often used to infer the function of uncharacterized proteins. By this approach one can tentatively assign a function to approximately 45–80% of the proteins identified by the genomic projects [1, 2]. However, function is mostly determined by the physical, chemical and geometric properties of the protein surfaces [3, 4], and cases have been described where the same local spatial distribution of residues important for function is achieved with apparently unrelated structures and/or sequences . One of the best known examples is represented by the SHD catalytic triad of serine proteinases [6–8]. Furthermore, surface similarities have been detected in unrelated ATP/GTP binding proteins [9, 10] and in the guanine binding sites of p21Ras family GTPases or in the RNA binding site of bacterial ribonucleases . By local structural comparison Hwang et al.  were able to infer correctly the nucleotide binding ability of an uncharacterized Methanococcus jannaschii protein.
On the other hand, similar folds can have different functions if their active sites have diverged [12–15]. As a consequence, methods purely relying on sequence and global structure comparison may lead to inaccurate function-related annotations in cases in which few residues are responsible for the specificity of substrate interaction.
The vast majority of well-studied functions (enzymatic activities, binding abilities etc.) are encoded by a relatively small set of residues, often not contiguous in the protein sequence but organized in a conserved geometry on the protein surface that may be used as a marker for reliable functional annotation. Although exposed to the solvent, these function-related residues are often located in surface clefts or cavities . Such residues define functional modules conserved in some proteins sharing a molecular function even if differing in sequence and structure. Several tools for discovering conserved three-dimensional patterns in protein structures have already been proposed [17–20]. Schmitt et al.  developed a clique-based method to detect functional relationships among proteins. This approach does not rely on detection of sequence or fold homology and highlights a number of non-obvious similarities among protein cavities. The algorithm, however, is computationally intensive and cannot be applied to an all-against-all analysis of protein surface regions. Binkowski and co-workers  recently described an approach for detecting sequence and spatial patterns of protein surfaces: the underlying algorithm is fast, but cannot identify similarities that are independent of the residue order in the compared proteins. Two related papers [23, 24] describe a method for local structural similarity detection, which is of great relevance since it is able to evaluate the statistical significance of each match. This method (PINTS) has been then used to analyze protein structures from structural genomics projects . Other recent papers present algorithms able to find structural motifs possibly related to a function and to use them to scan protein structure libraries [26–31].
In a previous work  we described the construction of a non redundant library of surface annotated functional sites and a fast comparison algorithm able to find structural similarities independently on the residue sequence order. We report here the analysis of the results of the first all-versus-all comparison of the protein functional sites, the validation of the comparison procedure in a test dataset and its application for annotating a dataset composed of proteins solved in structural genomics projects. The results are available for experimental test at the address http://cbm.bio.uniroma2.it/surface/structuralGenomics.html.
Results and discussion
Functional sites comparison
We used the compendium of protein surface regions associated to molecular functional sites stored in the SURFACE database . This is a collection of 1521 annotated functional regions obtained following the procedure described in Figure 1 and in the Methods section. Each patch has at least a function-related annotation, that may be the ability to bind a certain ligand, or a match with a PROSITE or ELM pattern [33, 34]. Ligand-binding abilities are included among gene ontology (GO) molecular functions , as well as many PROSITE patterns and ELM motifs. Some other PROSITE patterns correspond to short motifs that are conserved in all members of certain protein families, which not necessarily are associated to known function-related residues. We chose to include this class of patterns in our annotation system, since they offer a quick way to verify the reliability of a match, and in many cases these motifs do contain functional residues. Hence, our annotations can be classified either as molecular functions or protein signatures. It is worth noticing that the annotation is extended to the whole patch but is also assigned to a subset of specific annotated functional residues.
In  the structural matches obtained from the comparison of he SURFACE library against the entire collection of surface clefts (both annotated and not annotated) were evaluated by means of the Z-score of each match length against the distribution of the match lengths for any given annotated patch. Here we perform an exhaustive analysis in order to find conditions for which a structural similarity also suggests a function-related similarity. First, only those matches which include annotated functional residues are considered, therefore each structural similarity match is likely to hold a functional meaning. This step is crucial since many matches may be obtained because of general fold similarity, without an underlying functional relationship. Finding a functional match induces an annotation of at least some of the residues, and suggests reasonable hypotheses as to function (we are currently investigating how to use our approach to find novel function-related structural motifs, i.e. recurrent structural matches between proteins that can not be explained only by fold similarity and that may imply a previously undetected functional similarity).
From the comparison of the SURFACE library against the entire collection of surface clefts, we collected a grand total of 65910 stringent matches among patch pairs, about 4.5% of which involve 6 or more residues and 4.5% involve 10 or more residues. A not negligible amount of these matches involve residue pairs whose relative distance is not conserved in the corresponding protein sequences. More interestingly, some of the matches involve residues whose sequence order and/or sequence spacing is different in the two proteins: some of these cases, that may be examples of convergent evolution, are currently under investigation. As an example, metals can interact with proteins by means of similar arrangements of residues that can be found across different folds [36–38]. Scanning our dataset with zinc-binding patches leads to the finding of significant matches to proteins belonging to 42 different folds and 6 different classes as defined by SCOP . Different metal-binding patches lead to similar findings, even though less dramatic. Further analysis would suggest how many of these cases are associated with functional similarities as well.
The fraction of matches validated (as described in the Methods section) sensibly increases with the Z-score (Table 1). At lower Z-scores, the GO terms and SWISS-PROT keywords validation methods are more represented, while, for more significant matches, ability to bind the same ligands, fold similarity and co-presence of PROSITE motifs become more relevant.
The matches that cannot be structurally or functionally justified by these methods and that are characterized by a high Z-score are relatively few (see Table 1). 171 matches out of 2173 (7.9%) having a Z-score higher than 7 are not validated following the above mentioned criteria (Table 1). Of these 171 matches, 130 can be considered as true positive matches, confirmed by literature and information derived from different sources and databases. The remaining 41 matches (1.9%) are not confirmed and should be tested experimentally. About 2% of the highly significant matches can be considered as possible false positive hits or new annotations. Some of these cases are shown and discussed in Figure 2(a,b).
From this validation procedure the emerging result is that, using stringent parameters in the comparison step and using the Z-score as a threshold, our algorithm is reliable and able to spot local structural similarities related to functional relationships with only few non confirmed hits, which can be considered as false positives or as testable hypotheses.
An estimation of false negative matches (defining false negative match as the missing detection of structural similarity between two proteins sharing the same function) is not immediate, for the reason that the same or similar molecular function may be achieved in different ways using a different three-dimensional residue arrangement. We estimated the occurrence of false negatives for PROSITE annotated patches, using the list of known true positives (for which the function encoded by the pattern is experimentally verified) for each pattern that is provided by PROSITE. The procedure is done as follows: for all the patches annotated with a given PROSITE pattern, we collect all matches obtained scanning with these patches the entire patches dataset, selecting only those matches having Z-score higher than a fixed threshold. The fraction of known true positives that are not found using the pattern-annotated patches as queries (i.e. the false negatives), when retrieving only those matches having Z-score higher than 5, is 0.3 (meaning that we are able to correctly retrieve the 70% of the occurrences of PROSITE patterns in the dataset), and it raises to 0.35 setting the Z-score threshold to 7.
To further test the ability of the procedure in finding known cases of functional similarities among proteins for which sequence and/or structure similarity is not significant, a number of benchmark cases were investigated (Figure 3):
The S. cerevisiae and the E. coli chorismate mutase (PDB codes: 1ecm and 4csm, respectively), despite the very low sequence identity, share a similar fold and a similar main functional site [18, 21]. The 1ecm largest patch is annotated for the oxy-bridged prephenic acid binding ability. Using this patch as a query, the highest Z-score match is found with the 4csm largest patch (Figure 3a).
The Glyoxalase/Bleomycin resistance protein/Dihydroxybiphenyl dioxygenase fold is common to several unrelated metal ion binding proteins sharing similar catalytic mechanisms, including the bleomycin resistance protein, glyoxalase I, and a family of extradiol dioxygenases . We detected a significant similarity among P. fluorescens 4-hydroxyphenylpyruvate dioxygenase (PDB code 1cjx), B. cepacia 2,3-dihydroxybiphenyl 1,2-dioxygenase (1han), P. putida catechol 2,3-dioxygenase (1mpy) and P. shermanii methylmalonyl-Coa epimerase (1jc5). The comparison algorithm correctly identifies the residues involved in Fe binding (Figure 3b). 1han second largest patch is annotated for the iron binding ability. Structural matches with 1mpy, 1cjx and 1jc5 functional sites are found at high Z-score (7.19).
Metal ions can be coordinated by histidine clusters. We identified a similarity between the human tumor necrosis factor-alpha-converting enzyme (PDB code: 1bkc) Zn binding site and the E. coli peptide deformylase (PDB code: 1icj) Ni binding site, despite their sequence and fold diversity (Figure 3c). The zinc-binding patch of 1bkc shares eight residues in the same structural conformation with the nickel-binding patch of 1icj, with a Z-score of 10.66.
Nucleotide binding abilities can be associated with several unrelated proteins; we detected a high-scoring match between the GTP-binding annotated patch of the human p21 ras protein (5p21) and the L. casei Hpr kinase (1jb1) that aligns eight residues with a Z-score of 9.01 (Figure 3d). These two proteins do not share any significant sequence or fold similarities.
As a further test, we analyzed the flavin-adenine dinucleotide (FAD) binding pockets, known to share structural similarities with other adenine-containing nucleotide binding pockets, despite sequence and fold differences [41, 42]. FAD consists of an adenosine monophosphate (AMP) linked to a flavin mononucleotide (FMN) through a pyrophosphate bond and is involved as a cofactor in many biological processes. Using the FAD-binding patch of the Zea mays polyamine oxidase (1b37) as a bait, we selected 9 prey patches with Z-score higher than 12: 8 preys are annotated as being able to bind a FAD molecule and belongs to the same SCOP fold (FAD/NAD(P)-binding domain). The remaining trapped patch is the biggest patch of the trimethylamine dehydrogenase from Methylophilus methylotrophus (1djn), an iron-sulfur flavoprotein, and it is annotated as ADP-binding. 1djn is co-crystallized also with a FMN, which is very similar to FAD, but this ligand is associated to the second largest patch of the 1djn structure. The residues, which were associated by the alignment program, are shown in Figure 3e. These proteins share a very low sequence similarity, which cannot be revealed using BLAST2 . The ADP binding patch of the 1djn structure is nicely superposed to the other patches in the binding pocket (Figure 3e), but shares no evident fold similarity with the other ones, and belongs to a different SCOP fold (the nucleotide-binding domain). When the selected structures in Figure 3f are physically superposed (finding the least-square fitting of the matching residues), also the ligands bound to these structures turn out to be nicely superposed. The procedure could therefore highlight the ability to bind a subset of the FAD molecule, namely an ADP molecule in the 1djn major patch, even with very low levels of sequence and structure similarity. Using each FAD binding patch to scan the dataset, we selected only proteins for which known functional properties are consistent with the FAD or nucleotide binding ability.
Structural genomics proteins analysis
With the stringent parameters described above, we were able to detect only matches linked to function-related similarities, even in cases of non-homologous proteins. For that reason, once proved to be reliable, the procedure can be applied as a predictive tool to obtain clues concerning the function(s) of uncharacterized proteins.
We selected 257 protein structures from the PDB, corresponding to 513 chains that are marked as being of unknown function, or for being a hypothetical protein or for having been solved within a structural genomics project. We analyzed these structures by looking for reliable similarities to our functional sites library and were able to suggest one or more molecular functions to 191 of these chains, for a total of 534 similarity matches. For each match, we checked if the previously described criteria hold (i.e. common GO term, SwissProt keyword, EC number or SURFACE annotation). If not, a literature search has been done to verify the functional relationship. By means of this analysis of the likelihood of each single match, we found that 322 (the 60.3%) of these hits are validated by experimental analysis that have already characterized many of these proteins, while only 29 matches (5.4%) are not found confirmed in previous findings; 107 (20%) hits involve proteins for which the functions are still unknown; 76 hits (14.2%) involve proteins for which a hypothetical function has been assigned by means of sequence or structure global similarity. In this latter case, the function-related annotation obtained from our method can be considered as a new functional annotation that corrects or improves the actual function assignment. Hence, we were able to propose a function by similarity using the annotated patch database 184 times, to 127 different chains (matches with Z-score at least 7 are shown in Table 2). 56% of these new functional annotations are about a PROSITE pattern, the remaining 44% about a ligand binding ability; this is somewhat surprising, since the majority of the patches annotations in the SURFACE library regards binding abilities. A selection of the proposed functional regions is shown in Figure 2(c,d,e,f), while the complete list can be found at http://cbm.bio.uniroma2.it/surface/structuralGenomics.html. For each match we tested the BLAST2 pair-wise sequence similarity between the sequence of the protein to which the query patch belongs and the target protein sequence, the PsiBLAST sequence similarity matches obtained by running the target sequence versus the non-redundant SwissProt+TrEMBL sequence database, the global structural similarities of the target structure in the PDB using SSM, and the local similarity using PINTS . The match with the highest Z-score (14.29) is between the B. subtilis Yqvk protein (PDB code 1rty), and the Wolinella succinogenes fumarate reductase cytochrome B subunit major patch (1qlaC1), annotated with the heme group binding ability; the structural similarity involves 7 residues. The two proteins do not share any sequence or structural similarity, as checked using BLAST and the structural comparison algorithm SSM . A PsiBLAST run of the Yqvk sequence against the non-redundant SwissProt+TrEMBL shows a significant similarity (E-value 4e-19) with the mouse cobalamin adenosyltransferase (SwissProt entry name MMAB_MOUSE), while the SSM comparison against the whole PDB leads to only one significant similarity, with another uncharacterized protein, the conserved protein 0546 From Thermoplasma acidophilum (1nog). A PINTS comparison  of Yqvk, against pre-compiled libraries of structural patterns, retrieves as most significant matches one with the human Small Nuclear Ribonucleoprotein Sm D3 (PDB code 1d3b), aligning 3 pairs of residues with r.m.s.d 0.32 and E-value 0.00481, and another with the pig Dihydropyrimidine Dehydrogenase 1htx (3 pairs aligned with r.m.s.d. 0.337 and E-value 0.00839). The heme binding ability thus may be a new functional annotation of this poorly known protein. The second highest Z-score match (13.32, 9 residues structurally aligned) occurs between Hi1480 protein from Haemophilus influenzae (1mw5) and the bovine cytochrome Bc1 heme-binding patch (1bgyC2). No significant sequence similarity is found in the SwissProt+TrEMBL (the highest match, whose E-value is 2.1, involves the putative E. coli RNA helicase, SwissProt entry name RHLE_ECOLI), as well as no significant matches are found using SSM. PINTS matches involving three residues are found with the virus influenzae Bha/Lsta protein (1mqm) and the Candida tropicalis Enoyl Thioester Reductase 2 (1h0k), whose E-values are 0.401 and 0.451, respectively. Another high-score match (Z-score 10.05, length 7 residues) is found between the B. subtilis protein Yqeu (1vhk) and the E. coli Grea transcript cleavage factor major patch (1grj_1), which is annotated with the GREAB_1 PROSITE pattern, a signature of this class of cleavage factors. Yqeu share SSM-detected structural similarities with another unknown-function protein (namely H. influenzae 1nxz) and significant sequence similarity with a list of hypothetical and uncharacterized bacterial proteins. PINTS reports a local structural similarity with the zinc-binding site of the E. coli CTP-ligated T state aspartate transcarbamoylase (E-value 0.00894, r.m.s.d 0.544 over three pairs of residues).
In some cases we found a structural similarity between a protein with unknown function and two patches annotated with the same function, giving strength to the hypothesis of function-related similarity. The conserved hypothetical protein (Tm0667) from Thermotoga maritima (PDB code 1j6o) shows a structural similarity with surface patches of E. coli nucleotidyltransferase (1gupA2) and Desulfovibrio gigas rubredoxin:oxygen oxidoreductase (1e5dA4), both annotated with the iron binding ability. The E. coli lysozyme inhibitor (1gpq), whose function is still uncharacterized, may bind ATP given the similarity to the Rattus norvegicus 6-Phosphofructo-2-Kinase/ Fructose-2,6-Bisphosphatase major patch (1bif_1) and the mouse Aaa ATPase P97 (second patch (1e32A2)).
For each described match we propose that the detected structural similarity reveals a function-related similarity. For each match we checked whether the similarity could have been detected by means of sequence similarity, as checked using BLAST and PsiBLAST, or structural comparison, as checked by means of SSM and PINTS. Our approach, that is based on comparison of local functional surface residues, independently on their sequence order, may overcome the limitations of current methods possibly due to our incomplete knowledge of the sequence/structure/function relationship or to convergent evolution. Even using PINTS, which is a tool similar in philosophy to our approach, the findings are different, suggesting that different tools may be complementary in the difficult task of protein functional annotation; on the other hand, this may also highlight the difficulty in evaluating the significance of local similarities that in many cases are restricted to a very small number of residues.
The expected burst in the number of protein structures that are not associated to a biological function, stimulated by the structure genomics programs, has emphasized the need for tools to reveal structural regularities even in proteins that do not share sequence or fold similarity [1, 45]. Protein structures selected in structural genomics projects usually share very little sequence similarity with the dataset of already characterized proteins . Sequence analysis tools are therefore unsuitable for inferring their functions. Moreover, cases are known where active site residues are not conserved in proteins sharing a common structural fold; therefore, "traditional" structure comparison tools are also not always able to help in function-related annotation.
Using a fully automated procedure, we obtained a reliable library of protein annotated functional sites. A fast structural comparison algorithm allows the rapid scanning of one or more protein structures with the library looking for local structural similarities. This method is designed to help in functional annotation in difficult cases. Our annotated surface patches determination and comparison method offers a new and powerful resource for detecting related function among unrelated proteins, for proteins solved in structural genomics projects or for identifying new function-related sites on the surface of already characterized proteins. We have been able to provide one or more functional clues to a large set of novel proteins, and, where functional evidences are already known, our findings confirm them. Moreover, just as proteins with different sequence and fold can share a similar functional site, proteins with similar sequence and/or fold can have small local differences leading to a completely different function [1, 21]. Our method, which is focused on a detailed analysis of functional sites, is able to successfully predict protein functions in these difficult cases. Therefore, it can be used in analyzing the complex evolutionary relationships among protein sequence, structure and function [47–49]. The complete list of the functional predictions that we obtained is accessible at URL http://cbm.bio.uniroma2.it/surface/structuralGenomics.html; the structurally similar residues are shown for each match, and the structural superposition can be viewed through the browser plug-in Chime or RasMol. A novel publicly available web server, PdbFun , has been developed to allow the on-line structural comparison of user-defined subsets of residues of protein chains, and pre-defined subsets, like the SURFACE library of annotated functional sites, will be provided.
Functional site library extraction and annotation
The SURFACE database  stores a library of 1521 annotated function-related surface regions obtained using the following procedure (described in Figure 1): first, the SURFNET algorithm  is applied to a non-redundant, representative list of around 2000 protein chains from the PDB database  (downloadable at http://www.ncbi.nlm.nih.gov/Structure/VAST/nrpdb.html) in order to find all the surface clefts with a volume higher than an arbitrary threshold (200 Å3); then for each cleft, a surface patch is identified as a collection of solvent-exposed residues using the MASK algorithm (that is part of the SURFNET package); finally, we infer the function of such surface patches using two kinds of annotations: ability to bind (associated to surface patch residues that are contacting a bound ligand), and match with PROSITE or ELM [33, 34] functional motifs. The ability to bind annotation is carried out selecting those residues within 3.5 Å distance from any of the atoms of a ligand found in the crystal structure. Whenever a single patch contains more than 75% of the ligand-contacting residues (62% of the cases), we assign the ligand binding ability to this surface cleft. Considering only large organic molecules and metal ions, the ratio of the ligands that can be unequivocally associated to a single patch raises to 78%. PROSITE annotations are achieved scanning the sequences of monomers in our dataset using the ScanProsite algorithm , finding 928 matches. 12 matches were found with the ELM  experimentally verified instances. We did not consider those patterns marked by PROSITE as unspecific. Moreover, we annotated only those residues that correspond to non-X positions in the regular expression and that are exposed to the solvent according to the NACCESS procedure [54, 55]. Once the dataset chains have been annotated, we map the annotated residues on the structure and in the surface patches. Whenever a single patch contains more than 75% of the pattern exposed residues, we assign the function encoded by this pattern to the patch (43% of the cases).
A sequence/fold-independent algorithm was used for local surface comparison . The algorithm starts from a seed match (a pair of residues in the query that can be found in the target, at the same distance and with similar physical and chemical characteristics). The structural superposition, obtained by the quaternions method  and assessed at each step by residue similarity and root mean square deviation (r.m.s.d.) of the matching residues, is extended adding neighboring residues to the seed match until r.m.s.d and residue similarity are under user-defined thresholds (we used a similarity at least equal to 0.3 for each added pair of residues, and an average similarity at least equal to 1.2, using the Dayhoff substitution matrix  and 0.8Å as maximum r.m.s.d.). We consider only structural matches that include at least a fixed fraction (50%) of functional annotated residues, to increase the likelihood that the structural match is a function-related match as well. The algorithm is very fast and explores all the combinations of similar/identical residues in a sequence-independent way. The score of the match is the number of residues that can be superposed within the defined similarity thresholds. The significance of the score is evaluated by calculating the Z-score over the score distribution of the query patch comparison with the whole dataset: for each match, the Z-score is computed as the difference between the score of the match and the average score of all the matches for the query patch, divided by the standard deviation.
In order to obtain an estimate of the number of true positive matches, defining a true positive match as a structural similarity that implies also a functional similarity, we checked if the two matching proteins share also: (i) a common Gene Ontology (GO) term; (ii) a common SwissProt keyword; (iii) the same Enzyme Classification (EC) number; (iv) the same functional annotation (i.e. the binding of the same ligand or a match with the same PROSITE or ELM pattern). Gene Ontology terms search is limited to molecular function or biological process annotations linked to PDB structures from the GOA project . SwissProt  keywords were extracted from the SwissProt entries corresponding to the DBREF field in the PDB  files header. If this was not available, we extracted the sequence from the order of residues in the structure, then we looked for a close homolog (sequence similarity higher than 95% using BLAST) in the SwissProt database. Some keywords were excluded because not referring to protein functions (i.e. Structural protein, Polymorphism, Alternative promoter usage, etc.). Furthermore, we checked whether the two matching proteins share more than 40% of sequence similarity or the same fold using the SCOP structural classification  at the superfamily level. Our database is composed of patches extracted from a non-redundant list of structures, therefore these cases are infrequent.
Shapiro L, Harris T: Finding function through structural genomics. Curr Opin Biotechnol 2000, 11(1):31–35. 10.1016/S0958-1669(99)00064-6
Whisstock JC, Lesk AM: Prediction of protein function from protein sequence and structure. Q Rev Biophys 2003, 36(3):307–340. 10.1017/S0033583503003901
Fischer D, Norel R, Wolfson H, Nussinov R: Surface motifs by a computer vision technique: searches, detection, and implications for protein-ligand recognition. Proteins 1993, 16(3):278–292. 10.1002/prot.340160306
Norel R, Fischer D, Wolfson HJ, Nussinov R: Molecular surface recognition by a computer vision-based technique. Protein Eng 1994, 7(1):39–46.
Kauvar LM, Villar HO: Deciphering cryptic similarities in protein binding sites. Curr Opin Biotechnol 1998, 9(4):390–394. 10.1016/S0958-1669(98)80013-X
Lesk AM, Fordham WD: Conservation and variability in the structures of serine proteinases of the chymotrypsin family. J Mol Biol 1996, 258(3):501–537. 10.1006/jmbi.1996.0264
Fischer D, Wolfson H, Lin SL, Nussinov R: Three-dimensional, sequence order-independent structural comparison of a serine protease against the crystallographic database reveals active site similarities: potential implications to evolution and to protein folding. Protein Sci 1994, 3(5):769–778.
Contreras JA, Karlsson M, Osterlund T, Laurell H, Svensson A, Holm C: Hormone-sensitive lipase is structurally related to acetylcholinesterase, bile salt-stimulated lipase, and several fungal lipases. Building of a three-dimensional model for the catalytic domain of hormone-sensitive lipase. J Biol Chem 1996, 271(49):31426–31430. 10.1074/jbc.271.49.31426
Kobayashi N, Go N: ATP binding proteins with different folds share a common ATP-binding structural motif. Nat Struct Biol 1997, 4(1):6–7. 10.1038/nsb0197-6
Via A, Ferre F, Brannetti B, Valencia A, Helmer-Citterich M: Three-dimensional view of the surface motif associated with the P-loop structure: cis and trans cases of convergent evolution. J Mol Biol 2000, 303(4):455–465. 10.1006/jmbi.2000.4151
Hwang KY, Chung JH, Kim SH, Han YS, Cho Y: Structure-based identification of a novel NTPase from Methanococcus jannaschii. Nat Struct Biol 1999, 6(7):691–696. 10.1038/10745
Wistow G, Piatigorsky J: Recruitment of enzymes as lens structural proteins. Science 1987, 236(4808):1554–1556.
Holm L, Sander C: An evolutionary treasure: unification of a broad set of amidohydrolases related to urease. Proteins 1997, 28(1):72–82. 10.1002/(SICI)1097-0134(199705)28:1<72::AID-PROT7>3.0.CO;2-L
Ganfornina MD, Sanchez D: Generation of evolutionary novelty by functional shift. Bioessays 1999, 21(5):432–439. 10.1002/(SICI)1521-1878(199905)21:5<432::AID-BIES10>3.0.CO;2-T
Todd AE, Orengo CA, Thornton JM: Plasticity of enzyme active sites. Trends Biochem Sci 2002, 27(8):419–426. 10.1016/S0968-0004(02)02158-8
Laskowski RA, Luscombe NM, Swindells MB, Thornton JM: Protein clefts in molecular recognition and function. Protein Sci 1996, 5(12):2438–2452.
Kleywegt GJ: Recognition of spatial motifs in protein structures. J Mol Biol 1999, 285(4):1887–1897. 10.1006/jmbi.1998.2393
Rosen M, Lin SL, Wolfson H, Nussinov R: Molecular shape comparisons in searches for active sites and functional similarity. Protein Eng 1998, 11(4):263–277. 10.1093/protein/11.4.263
Preissner R, Goede A, Rother K, Osterkamp F, Koert U, Froemmel C: Matching organic libraries with protein-substructures. J Comput Aided Mol Des 2001, 15(9):811–817. 10.1023/A:1013158818807
Kinoshita K, Nakamura H: Identification of protein biochemical functions by similarity search using the molecular surface database eF-site. Protein Sci 2003, 12(8):1589–1595. 10.1110/ps.0368703
Schmitt S, Kuhn D, Klebe G: A new method to detect related function among proteins independent of sequence and fold homology. J Mol Biol 2002, 323(2):387–406. 10.1016/S0022-2836(02)00811-2
Binkowski TA, Adamian L, Liang J: Inferring functional relationships of proteins from local sequence and spatial surface patterns. J Mol Biol 2003, 332(2):505–526. 10.1016/S0022-2836(03)00882-9
Stark A, Sunyaev S, Russell RB: A model for statistical significance of local similarities in structure. J Mol Biol 2003, 326(5):1307–1316. 10.1016/S0022-2836(03)00045-7
Stark A, Russell RB: Annotation in three dimensions. PINTS: Patterns in Non-homologous Tertiary Structures. Nucleic Acids Res 2003, 31(13):3341–3344. 10.1093/nar/gkg506
Stark A, Shkumatov A, Russell RB: Finding functional sites in structural genomics proteins. Structure (Camb) 2004, 12(8):1405–1412. 10.1016/j.str.2004.05.012
Singh R, Saha M: Identifying structural motifs in proteins. Pac Symp Biocomput 2003, 228–239.
Chen BY, Fofanov VY, Kristensen DM, Kimmel M, Lichtarge O, Kavraki LE: Algorithms for structural comparison and statistical analysis of 3D protein motifs. Pac Symp Biocomput 2005, 334–345.
Schmollinger M, Fischer I, Nerz C, Pinkenburg S, Gotz F, Kaufmann M, Lange KJ, Reuter R, Rosenstiel W, Zell A: ParSeq: searching motifs with structural and biochemical properties. Bioinformatics 2004, 20(9):1459–1461. 10.1093/bioinformatics/bth083
Torrance JW, Bartlett GJ, Porter CT, Thornton JM: Using a library of structural templates to recognise catalytic sites and explore their evolution in homologous families. J Mol Biol 2005, 347(3):565–581. 10.1016/j.jmb.2005.01.044
Wangikar PP, Tendulkar AV, Ramya S, Mali DN, Sarawagi S: Functional sites in protein families uncovered via an objective and automated graph theoretic approach. J Mol Biol 2003, 326(3):955–978. 10.1016/S0022-2836(02)01384-0
Pal D, Eisenberg D: Inference of protein function from protein structure. Structure (Camb) 2005, 13(1):121–130. 10.1016/j.str.2004.10.015
Ferre F, Ausiello G, Zanzoni A, Helmer-Citterich M: SURFACE: a database of protein surface regions for functional annotation. Nucleic Acids Res 2004, 32(Database):D240–244. 10.1093/nar/gkh054
Falquet L, Pagni M, Bucher P, Hulo N, Sigrist CJ, Hofmann K, Bairoch A: The PROSITE database, its status in 2002. Nucleic Acids Res 2002, 30(1):235–238. 10.1093/nar/30.1.235
Puntervoll P, Linding R, Gemund C, Chabanis-Davidson S, Mattingsdal M, Cameron S, Martin DM, Ausiello G, Brannetti B, Costantini A, et al.: ELM server: A new resource for investigating short functional sites in modular eukaryotic proteins. Nucleic Acids Res 2003, 31(13):3625–3630. 10.1093/nar/gkg545
Camon E, Magrane M, Barrell D, Lee V, Dimmer E, Maslen J, Binns D, Harte N, Lopez R, Apweiler R: The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res 2004, 32(Database):D262–266. 10.1093/nar/gkh021
Alberts IL, Nadassy K, Wodak SJ: Analysis of zinc binding sites in protein crystal structures. Protein Sci 1998, 7(8):1700–1716.
Tainer JA, Roberts VA, Getzoff ED: Protein metal-binding sites. Curr Opin Biotechnol 1992, 3(4):378–387. 10.1016/0958-1669(92)90166-G
Barondeau DP, Getzoff ED: Structural insights into protein-metal ion partnerships. Curr Opin Struct Biol 2004, 14(6):765–774. 10.1016/j.sbi.2004.10.012
Andreeva A, Howorth D, Brenner SE, Hubbard TJ, Chothia C, Murzin AG: SCOP database in 2004: refinements integrate structure and sequence family data. Nucleic Acids Res 2004, 32(Database):D226–229. 10.1093/nar/gkh039
McCarthy AA, Baker HM, Shewry SC, Patchett ML, Baker EN: Crystal structure of methylmalonyl-coenzyme A epimerase from P. shermanii: a novel enzymatic function on an ancient metal binding scaffold. Structure (Camb) 2001, 9(7):637–646. 10.1016/S0969-2126(01)00622-0
Fraaije MW, Mattevi A: Flavoenzymes: diverse catalysts with recurrent features. Trends Biochem Sci 2000, 25(3):126–132. 10.1016/S0968-0004(99)01533-9
Dym O, Eisenberg D: Sequence-structure analysis of FAD-containing proteins. Protein Sci 2001, 10(9):1712–1728. 10.1110/ps.12801
Tatusova TA, Madden TL: BLAST 2 Sequences, a new tool for comparing protein and nucleotide sequences. FEMS Microbiol Lett 1999, 174(2):247–250. 10.1016/S0378-1097(99)00149-4
Krissinel E, Henrick K: Protein structure comparison in 3D based on secondary structure matching (SSM) followed by Ca alignment, scored by a new structural similarity function. Proceedings of the 5th International Conference on Molecular Structural Biology, Vienna, September 3–7 2003 2003., 88:
Teichmann SA, Murzin AG, Chothia C: Determination of protein function, evolution and interactions by structural genomics. Curr Opin Struct Biol 2001, 11(3):354–363. 10.1016/S0959-440X(00)00215-3
Chance MR, Bresnick AR, Burley SK, Jiang JS, Lima CD, Sali A, Almo SC, Bonanno JB, Buglino JA, Boulton S, et al.: Structural genomics: a pipeline for providing structures for the biologist. Protein Sci 2002, 11(4):723–738. 10.1110/ps.4570102
Todd AE, Orengo CA, Thornton JM: Evolution of protein function, from a structural perspective. Curr Opin Chem Biol 1999, 3(5):548–556. 10.1016/S1367-5931(99)00007-1
Thornton JM, Todd AE, Milburn D, Borkakoti N, Orengo CA: From structure to function: approaches and limitations. Nat Struct Biol 2000, 7(Suppl):991–994. 10.1038/80784
Irving JA, Whisstock JC, Lesk AM: Protein structural alignments and functional genomics. Proteins 2001, 42(3):378–382. 10.1002/1097-0134(20010215)42:3<378::AID-PROT70>3.0.CO;2-3
Ausiello G, Zanzoni A, Peluso D, Via A, Helmer-Citterich M: pdbFun: mass selection and fast comparison of annotated PDB residues. Nucleic Acids Research 2005, 33(Web server issue):W133–7. 10.1093/nar/gki499
Laskowski RA: SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph 1995, 13(5):323–330. 307–328 10.1016/0263-7855(95)00073-9
Berman HM, Battistuz T, Bhat TN, Bluhm WF, Bourne PE, Burkhardt K, Feng Z, Gilliland GL, Iype L, Jain S, et al.: The Protein Data Bank. Acta Crystallogr D Biol Crystallogr 2002, 58(Pt 6 No 1):899–907. 10.1107/S0907444902003451
Gattiker A, Bienvenut WV, Bairoch A, Gasteiger E: FindPept, a tool to identify unmatched masses in peptide mass fingerprinting protein identification. Proteomics 2002, 2(10):1435–1444. 10.1002/1615-9861(200210)2:10<1435::AID-PROT1435>3.0.CO;2-9
Hubbard S, Thornton JM: NACCESS, Computer Program. In Department of Biochemistry and Molecular Biology. University College London; 1993.
Hubbard SJ, Campbell SF, Thornton JM: Molecular recognition. Conformational analysis of limited proteolytic sites and serine proteinase protein inhibitors. J Mol Biol 1991, 220(2):507–530. 10.1016/0022-2836(91)90027-4
Coutsias EA, Seok C, Dill KA: Using quaternions to calculate RMSD. J Comput Chem 2004, 25(15):1849–1857. 10.1002/jcc.20110
Schwartz R, Dayhoff M: Matrices for detecting distant relationships. Foundation NBR. Washington DC; 1979.
Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, et al.: The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 2003, 31(1):365–370. 10.1093/nar/gkg095
Rice P, Longden I, Bleasby A: EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet 2000, 16(6):276–277. 10.1016/S0168-9525(00)02024-2
The authors thank Gianni Cesareni and Arthur Lesk for helpful support and discussion. We gratefully acknowledge the support of Telethon GGP04273, GENEFUN, a PNR 2001–2003 (FIRB art.8) and a PNR 2003–2007 (FIRB art.8).
FF carried out the patches definition, extraction and annotation, and the structural genomics protein functional prediction strategy, and drafted the manuscript. GA is the author of the structural comparison algorithm and participated in the design of the project. AZ participated in the procedure for the validation of structural matches, and in the creation of a relational structure to store and spread the project results. MHC participated in the project design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
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Ferrè, F., Ausiello, G., Zanzoni, A. et al. Functional annotation by identification of local surface similarities: a novel tool for structural genomics. BMC Bioinformatics 6, 194 (2005). https://doi.org/10.1186/1471-2105-6-194
- Surface Cleft
- Fold Similarity
- PROSITE Pattern
- Structural Genomic Project
- Local Structural Similarity