Prediction of protein function from structure: insights from methods for the detection of local structural similarities

Immunological evaluation of herbal extracts commonly used for treatment of mental diseases during pregnancy

Nonpsychotic mental diseases (NMDs) affect approximately 15% of pregnant women in the US. Herbal preparations are perceived a safe alternative to placenta-crossing antidepressants or benzodiazepines in the treatment of nonpsychotic mental diseases. But are these drugs really safe for mother and foetus? This question is of great relevance to physicians and patients. Therefore, this study investigates the influence of St. John's wort, valerian, hops, lavender, and California poppy and their compounds hyperforin and hypericin, protopine, valerenic acid, and valtrate, as well as linalool, on immune modulating effects in vitro. For this purpose a variety of methods was applied to assess the effects on viability and function of human primary lymphocytes. Viability was assessed via spectrometric assessment, flow cytometric detection of cell death markers and comet assay for possible genotoxicity. Functional assessment was conducted via flow cytometric assessment of proliferation, cell cycle and immunophenotyping. For California poppy, lavender, hops, and the compounds protopine and linalool, and valerenic acid, no effect was found on the viability, proliferation, and function of primary human lymphocytes. However, St. John's wort and valerian inhibited the proliferation of primary human lymphocytes. Hyperforin, hypericin, and valtrate inhibited viability, induced apoptosis, and inhibited cell division. Calculated maximum concentration of compounds in the body fluid, as well as calculated concentrations based on pharmacokinetic data from the literature, were low and supported that the observed effects in vitro would probably have no relevance on patients. In-silico analyses comparing the structure of studied substances with the structure of relevant control substances and known immunosuppressants revealed structural similarities of hyperforin and valerenic acid to the glucocorticoids. Valtrate showed structural similarities to the T cells signaling modulating drugs.

PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms

Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at https://sites.google.com/view/pfp-go/.

Cysteine Oxidation in Proteins: Structure, Biophysics, and Simulation

Cysteine side chains can exist in distinct oxidation states depending on the pH and redox potential of the environment, and cysteine oxidation plays important yet complex regulatory roles. Compared with the effects of post-translational modifications such as phosphorylation, the effects of oxidation of cysteine to sulfenic, sulfinic, and sulfonic acid on protein structure and function remain relatively poorly characterized. We present an analysis of the role of cysteine reactivity as a regulatory factor in proteins, emphasizing the interplay between electrostatics and redox potential as key determinants of the resulting oxidation state. A review of current computational approaches suggests underdeveloped areas of research for studying cysteine reactivity through molecular simulations.

Genome-Wide Identification and Analysis of NAC Transcription Factor Family in Two Diploid Wild Relatives of Cultivated Sweet Potato Uncovers Potential NAC Genes Related to Drought Tolerance

NAC (NAM, ATAF1/2, and CUC2) proteins play a pivotal role in modulating plant development and offer protection against biotic and abiotic stresses. Until now, no systematic knowledge of NAC family genes is available for the food security crop, sweet potato. Here, a comprehensive genome-wide survey of NAC domain-containing proteins identified 130 ItbNAC and 144 ItfNAC genes with full length sequences in the genomes of two diploid wild relatives of cultivated sweet potato, Ipomoea triloba and Ipomoea trifida, respectively. These genes were physically mapped onto 15 I. triloba and 16 I. trifida chromosomes, respectively. Phylogenetic analysis divided all 274 NAC proteins into 20 subgroups together with NAC transcription factors (TFs) from Arabidopsis. There were 9 and 15 tandem duplication events in the I. triloba and I. trifida genomes, respectively, indicating an important role of tandem duplication in sweet potato gene expansion and evolution. Moreover, synteny analysis suggested that most NAC genes in the two diploid sweet potato species had a similar origin and evolutionary process. Gene expression patterns based on RNA-Seq data in different tissues and in response to various hormone, biotic or abiotic treatments revealed their possible involvement in organ development and response to various biotic/abiotic stresses. The expression of 36 NAC TFs, which were upregulated in the five tissues and in response to mannitol treatment, was also determined by real-time quantitative polymerase chain reaction (RT-qPCR) in hexaploid cultivated sweet potato exposed to drought stress. Those results largely corroborated the expression profile of mannitol treatment uncovered by the RNA-Seq data. Some significantly up-regulated genes related to drought stress, such as ItbNAC110, ItbNAC114, ItfNAC15, ItfNAC28, and especially ItfNAC62, which had a conservative spatial conformation with a closely related paralogous gene, ANAC019, may be potential candidate genes for a sweet potato drought tolerance breeding program. This analysis provides comprehensive and systematic information about NAC family genes in two diploid wild relatives of cultivated sweet potato, and will provide a blueprint for their functional characterization and exploitation to improve the tolerance of sweet potato to abiotic stresses.

Genome-Wide Identification and Analysis of NAC Transcription Factor Family in Two Diploid Wild Relatives of Cultivated Sweet Potato Uncovers Potential NAC Genes Related to Drought Tolerance

NAC (NAM, ATAF1/2, and CUC2) proteins play a pivotal role in modulating plant development and offer protection against biotic and abiotic stresses. Until now, no systematic knowledge of NAC family genes is available for the food security crop, sweet potato. Here, a comprehensive genome-wide survey of NAC domain-containing proteins identified 130 ItbNAC and 144 ItfNAC genes with full length sequences in the genomes of two diploid wild relatives of cultivated sweet potato, Ipomoea triloba and Ipomoea trifida, respectively. These genes were physically mapped onto 15 I. triloba and 16 I. trifida chromosomes, respectively. Phylogenetic analysis divided all 274 NAC proteins into 20 subgroups together with NAC transcription factors (TFs) from Arabidopsis. There were 9 and 15 tandem duplication events in the I. triloba and I. trifida genomes, respectively, indicating an important role of tandem duplication in sweet potato gene expansion and evolution. Moreover, synteny analysis suggested that most NAC genes in the two diploid sweet potato species had a similar origin and evolutionary process. Gene expression patterns based on RNA-Seq data in different tissues and in response to various hormone, biotic or abiotic treatments revealed their possible involvement in organ development and response to various biotic/abiotic stresses. The expression of 36 NAC TFs, which were upregulated in the five tissues and in response to mannitol treatment, was also determined by real-time quantitative polymerase chain reaction (RT-qPCR) in hexaploid cultivated sweet potato exposed to drought stress. Those results largely corroborated the expression profile of mannitol treatment uncovered by the RNA-Seq data. Some significantly up-regulated genes related to drought stress, such as ItbNAC110, ItbNAC114, ItfNAC15, ItfNAC28, and especially ItfNAC62, which had a conservative spatial conformation with a closely related paralogous gene, ANAC019, may be potential candidate genes for a sweet potato drought tolerance breeding program. This analysis provides comprehensive and systematic information about NAC family genes in two diploid wild relatives of cultivated sweet potato, and will provide a blueprint for their functional characterization and exploitation to improve the tolerance of sweet potato to abiotic stresses.

Trends and Applications of Omics Technologies to Functional Characterisation of Enzymes and Protein Metabolites Produced by Fungi

Identifying and adopting industrial applications for proteins and enzymes derived from fungi strains have been at the focal point of several studies in recent times. To facilitate such studies, it is necessary that advancements and innovation in mycological and molecular characterisation are concomitant. This review aims to provide a detailed overview of the necessary steps employed in both qualitative and quantitative research using the omics technologies that are pertinent to fungi characterisation. This stems from the understanding that data provided from the functional characterisation of fungi and their metabolites is important towards the techno-economic feasibility of large-scale production of biological products. The review further describes how the functional gaps left by genomics, internal transcribe spacer (ITS) regions are addressed by transcriptomics and the various techniques and platforms utilised, including quantitive reverse transcription polymerase chain reaction (RT-qPCR), hybridisation techniques, and RNA-seq, and the insights such data provide on the effect of environmental changes on fungal enzyme production from an expressional standpoint. The review also offers information on the many available bioinformatics tools of analysis necessary for the analysis of the overwhelming data synonymous with the omics approach to fungal characterisation.

Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects

Background

Promiscuity in molecular interactions between small-molecules, including drugs, and proteins is widespread. Such unintended interactions can be exploited to suggest drug repurposing possibilities as well as to identify potential molecular mechanisms responsible for observed side-effects.

Methods

We perform a large-scale analysis to detect binding-site molecular interaction field similarities between the binding-sites of the primary target of 400 drugs against a dataset of 14082 cavities within 7895 different proteins representing a non-redundant dataset of all proteins with known structure. Statistically-significant cases with high levels of similarities represent potential cases where the drugs that bind the original target may in principle bind the suggested off-target. Such cases are further analysed with docking simulations to verify if indeed the drug could, in principle, bind the off-target. Diverse sources of data are integrated to associated potential cross-reactivity targets with side-effects.

Results

We observe that promiscuous binding-sites tend to display higher levels of hydrophobic and aromatic similarities. Focusing on the most statistically significant similarities (Z-score ≥ 3.0) and corroborating docking results (RMSD < 2.0 Å), we find 2923 cases involving 140 unique drugs and 1216 unique potential cross-reactivity protein targets. We highlight a few cases with a potential for drug repurposing (acetazolamide as a chorismate pyruvate lyase inhibitor, raloxifene as a bacterial quorum sensing inhibitor) as well as to explain the side-effects of zanamivir and captopril. A web-interface permits to explore the detected similarities for each of the 400 binding-sites of the primary drug targets and visualise them for the most statistically significant cases.

Conclusions

The detection of molecular interaction field similarities provide the opportunity to suggest drug repurposing opportunities as well as to identify potential molecular mechanisms responsible for side-effects. All methods utilized are freely available and can be readily applied to new query binding-sites. All data is freely available and represents an invaluable source to identify further candidates for repurposing and suggest potential mechanisms responsible for side-effects.

Electronic supplementary material

The online version of this article (doi:10.1186/s40360-017-0128-7) contains supplementary material, which is available to authorized users.

Evolutionary studies of ligand binding sites in proteins

Biological processes at their most fundamental molecular aspects are defined by molecular interactions with ligand-protein interactions in particular at the core of cellular functions such as metabolism and signalling. Divergent and convergent processes shape the evolution of ligand binding sites. The competition between similar ligands and binding sites across protein families create evolutionary pressures that affect the specificity and selectivity of interactions. This short review showcases recent studies of the evolution of ligand binding-sites and methods used to detect binding-site similarities.

Achievements and challenges in structural bioinformatics and computational biophysics

Motivation: The field of structural bioinformatics and computational biophysics has undergone a revolution in the last 10 years. Developments that are captured annually through the 3DSIG meeting, upon which this article reflects.

Results: An increase in the accessible data, computational resources and methodology has resulted in an increase in the size and resolution of studied systems and the complexity of the questions amenable to research. Concomitantly, the parameterization and efficiency of the methods have markedly improved along with their cross-validation with other computational and experimental results.

Conclusion: The field exhibits an ever-increasing integration with biochemistry, biophysics and other disciplines. In this article, we discuss recent achievements along with current challenges within the field.

Contact:Rafael.Najmanovich@USherbrooke.ca

A curated C. difficile strain 630 metabolic network: prediction of essential targets and inhibitors

BACKGROUND

Clostridium difficile is the leading cause of hospital-borne infections occurring when the natural intestinal flora is depleted following antibiotic treatment. Current treatments for Clostridium difficile infections present high relapse rates and new hyper-virulent and multi-resistant strains are emerging, making the study of this nosocomial pathogen necessary to find novel therapeutic targets.

RESULTS

We present iMLTC806cdf, an extensively curated reconstructed metabolic network for the C. difficile pathogenic strain 630. iMLTC806cdf contains 806 genes, 703 metabolites and 769 metabolic, 117 exchange and 145 transport reactions. iMLTC806cdf is the most complete and accurate metabolic reconstruction of a gram-positive anaerobic bacteria to date. We validate the model with simulated growth assays in different media and carbon sources and use it to predict essential genes. We obtain 89.2% accuracy in the prediction of gene essentiality when compared to experimental data for B. subtilis homologs (the closest organism for which such data exists). We predict the existence of 76 essential genes and 39 essential gene pairs, a number of which are unique to C. difficile and have non-existing or predicted non-essential human homologs. For 29 of these potential therapeutic targets, we find 125 inhibitors of homologous proteins including approved drugs with the potential for drug repositioning, that when validated experimentally could serve as starting points in the development of new antibiotics.

CONCLUSIONS

We created a highly curated metabolic network model of C. difficile strain 630 and used it to predict essential genes as potential new therapeutic targets in the fight against Clostridium difficile infections.

IsoCleft Finder - a web-based tool for the detection and analysis of protein binding-site geometric and chemical similarities

IsoCleft Finder is a web-based tool for the detection of local geometric and chemical similarities between potential small-molecule binding cavities and a non-redundant dataset of ligand-bound known small-molecule binding-sites. The non-redundant dataset developed as part of this study is composed of 7339 entries representing unique Pfam/PDB-ligand (hetero group code) combinations with known levels of cognate ligand similarity. The query cavity can be uploaded by the user or detected automatically by the system using existing PDB entries as well as user-provided structures in PDB format. In all cases, the user can refine the definition of the cavity interactively via a browser-based Jmol 3D molecular visualization interface. Furthermore, users can restrict the search to a subset of the dataset using a cognate-similarity threshold. Local structural similarities are detected using the IsoCleft software and ranked according to two criteria (number of atoms in common and Tanimoto score of local structural similarity) and the associated Z-score and p-value measures of statistical significance. The results, including predicted ligands, target proteins, similarity scores, number of atoms in common, etc., are shown in a powerful interactive graphical interface. This interface permits the visualization of target ligands superimposed on the query cavity and additionally provides a table of pairwise ligand topological similarities. Similarities between top scoring ligands serve as an additional tool to judge the quality of the results obtained. We present several examples where IsoCleft Finder provides useful functional information. IsoCleft Finder results are complementary to existing approaches for the prediction of protein function from structure, rational drug design and x-ray crystallography. IsoCleft Finder can be found at: http://bcb.med.usherbrooke.ca/isocleftfinder.

IsoCleft Finder - a web-based tool for the detection and analysis of protein binding-site geometric and chemical similarities

IsoCleft Finder is a web-based tool for the detection of local geometric and chemical similarities between potential small-molecule binding cavities and a non-redundant dataset of ligand-bound known small-molecule binding-sites. The non-redundant dataset developed as part of this study is composed of 7339 entries representing unique Pfam/PDB-ligand (hetero group code) combinations with known levels of cognate ligand similarity. The query cavity can be uploaded by the user or detected automatically by the system using existing PDB entries as well as user-provided structures in PDB format. In all cases, the user can refine the definition of the cavity interactively via a browser-based Jmol 3D molecular visualization interface. Furthermore, users can restrict the search to a subset of the dataset using a cognate-similarity threshold. Local structural similarities are detected using the IsoCleft software and ranked according to two criteria (number of atoms in common and Tanimoto score of local structural similarity) and the associated Z-score and p-value measures of statistical significance. The results, including predicted ligands, target proteins, similarity scores, number of atoms in common, etc., are shown in a powerful interactive graphical interface. This interface permits the visualization of target ligands superimposed on the query cavity and additionally provides a table of pairwise ligand topological similarities. Similarities between top scoring ligands serve as an additional tool to judge the quality of the results obtained. We present several examples where IsoCleft Finder provides useful functional information. IsoCleft Finder results are complementary to existing approaches for the prediction of protein function from structure, rational drug design and x-ray crystallography. IsoCleft Finder can be found at: http://bcb.med.usherbrooke.ca/isocleftfinder.

Identification of protein functional surfaces by the concept of a split pocket

The function of a protein is often fulfilled via molecular interactions on its surfaces, so identifying the functional surface(s) of a protein is helpful for understanding its function. Here, we introduce the concept of a split pocket, which is a pocket that is split by a cognate ligand. We use a geometric approach that is site-specific. Specifically, we first compute a set of all pockets in the protein with its ligand(s) and a set of all pockets with the ligand(s) removed and then compare the two sets of pockets to identify the split pocket(s) of the protein. To reduce the search space and expedite the process of surface partitioning, we design probe radii according to the physicochemical textures of molecules. Our method achieves a success rate of 96% on a benchmark test set. We conduct a large-scale computation to identify approximately 19,000 split pockets from 11,328 structures (1.16 million potential pockets); for each pocket, we obtain residue composition, solvent-accessible area, and molecular volume. With this database of split pockets, our method can be used to predict the functional surfaces of unbound structures. Indeed, the functional surface of an unbound protein may often be found from its similarity to remotely related bound forms that belong to distinct folds. Finally, we apply our method to identify glucose-binding proteins, including unbound structures. Our study demonstrates the power of geometric and evolutionary matching for studying protein functional evolution and provides a framework for classifying protein functions by local spatial patterns of functional surfaces.

Chapter 4. Predicting and characterizing protein functions through matching geometric and evolutionary patterns of binding surfaces

Predicting protein functions from structures is an important and challenging task. Although proteins are often thought to be packed as tightly as solids, closer examination based on geometric computation reveals that they contain numerous voids and pockets. Most of them are of random nature, but some are binding sites providing surfaces to interact with other molecules. A promising approach for function prediction is to infer functions through discovery of similarity in local binding pockets, as proteins binding to similar substrates/ligands and carrying out similar functions have similar physical constraints for binding and reactions. In this chapter, we describe computational methods to distinguish those surface pockets that are likely to be involved in important biological functions, and methods to identify key residues in these pockets. We further describe how to predict protein functions at large scale from structures by detecting binding surfaces similar in residue make-ups, shape, and orientation. We also describe a Bayesian Monte Carlo method that can separate selection pressure due to biological function from pressure due to protein folding. We show how this method can be used to reconstruct the evolutionary history of binding surfaces for detecting similar binding surfaces. In addition, we briefly discuss how the negative image of a binding pocket can be casted, and how such information can be used to facilitate drug discovery.

SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling

SCWRL and MolIDE are software applications for prediction of protein structures. SCWRL is designed specifically for the task of prediction of side-chain conformations given a fixed backbone usually obtained from an experimental structure determined by X-ray crystallography or NMR. SCWRL is a command-line program that typically runs in a few seconds. MolIDE provides a graphical interface for basic comparative (homology) modeling using SCWRL and other programs. MolIDE takes an input target sequence and uses PSI-BLAST to identify and align templates for comparative modeling of the target. The sequence alignment to any template can be manually modified within a graphical window of the target-template alignment and visualization of the alignment on the template structure. MolIDE builds the model of the target structure on the basis of the template backbone, predicted side-chain conformations with SCWRL and a loop-modeling program for insertion-deletion regions with user-selected sequence segments. SCWRL and MolIDE can be obtained at (http://dunbrack.fccc.edu/Software.php).

Evolution of binding sites for zinc and calcium ions playing structural roles

The geometry of metal coordination by proteins is well understood, but the evolution of metal binding sites has been less studied. Here we present a study on a small number of well-documented structural calcium and zinc binding sites, concerning how the geometry diverges between relatives, how often nonrelatives converge towards the same structure, and how often these metal binding sites are lost in the course of evolution. Both calcium and zinc binding site structure is observed to be conserved; structural differences between those atoms directly involved in metal binding in related proteins are typically less than 0.5 A root mean square deviation, even in distant relatives. Structural templates representing these conserved calcium and zinc binding sites were used to search the Protein Data Bank for cases where unrelated proteins have converged upon the same residue selection and geometry for metal binding. This allowed us to identify six "archetypal" metal binding site structures: two archetypal zinc binding sites, both of which had independently evolved on a large number of occasions, and four diverse archetypal calcium binding sites, where each had evolved independently on only a handful of occasions. We found that it was common for distant relatives of metal-binding proteins to lack metal-binding capacity. This occurred for 13 of the 18 metal binding sites we studied, even though in some of these cases the original metal had been classified as "essential for protein folding." For most of the calcium binding sites studied (seven out of eleven cases), the lack of metal binding in relatives was due to point mutation of the metal-binding residues, whilst for zinc binding sites, lack of metal binding in relatives always involved more extensive changes, with loss of secondary structural elements or loops around the binding site.

Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments

Three-dimensional (3D) protein structures now frequently lack functional annotations because of the increase in the rate at which chemical structures are solved with respect to experimental knowledge of biological activity. As a result, predicting structure-function relationships for proteins is an active research field in computational chemistry and has implications in medicinal chemistry, biochemistry and proteomics. In previous studies stochastic spectral moments were used to predict protein stability or function (González-Díaz, H. et al. Bioorg Med Chem 2005, 13, 323; Biopolymers 2005, 77, 296). Nevertheless, these moments take into consideration only electrostatic interactions and ignore other important factors such as van der Waals interactions. The present study introduces a new class of 3D structure molecular descriptors for folded proteins named the stochastic van der Waals spectral moments ((o)beta(k)). Among many possible applications, recognition of kinases was selected due to the fact that previous computational chemistry studies in this area have not been reported, despite the widespread distribution of kinases. The best linear model found was Kact = -9.44 degrees beta(0)(c) +10.94 degrees beta(5)(c) -2.40 degrees beta(0)(i) + 2.45 degrees beta(5)(m) + 0.73, where core (c), inner (i) and middle (m) refer to specific spatial protein regions. The model with a high Matthew's regression coefficient (0.79) correctly classified 206 out of 230 proteins (89.6%) including both training and predicting series. An area under the ROC curve of 0.94 differentiates our model from a random classifier. A subsequent principal components analysis of 152 heterogeneous proteins demonstrated that beta(k) codifies information different to other descriptors used in protein computational chemistry studies. Finally, the model recognizes 110 out of 125 kinases (88.0%) in a virtual screening experiment and this can be considered as an additional validation study (these proteins were not used in training or predicting series).

A Model for the Recognition of Protein Kinases Based on the Entropy of 3D van der Waals Interactions

The study and prediction of kinase function (kinomics) is of major importance for proteome research due to the widespread distribution of kinases. However, the prediction of protein function based on the similarity between a functionally annotated 3D template and a query structure may fail, for instance, if a similar protein structure cannot be identified. Alternatively, function can be assigned using 3D-structural empirical parameters. In previous studies, we introduced parameters based on electrostatic entropy (Proteins 2004, 56, 715) and molecular vibration entropy (Bioinformatics 2003, 19, 2079) but ignored other important factors such as van der Waals (vdw) interactions. In the work described here, we define 3D-vdw entropies (degrees theta(k)) and use them for the first time to derive a classifier for protein kinases. The model classifies correctly 88.0% of proteins in training and more than 85.0% of proteins in validation studies. Principal components analysis of heterogeneous proteins demonstrated that degrees theta(k) codify information that is different to that described by other bulk or folding parameters. In additional validation experiments, the model recognized 129 out of 142 kinases (90.8%) and 592 out of 677 non-kinases (87.4%) not used above. This study provides a basis for further consideration of degrees theta(k) as parameters for the empirical search for structure-function relationships.

Rapid detection of similarity in protein structure and function through contact metric distances

The characterization of biological function among newly determined protein structures is a central challenge in structural genomics. One class of computational solutions to this problem is based on the similarity of protein structure. Here, we implement a simple yet efficient measure of protein structure similarity, the contact metric. Even though its computation avoids structural alignments and is therefore nearly instantaneous, we find that small values correlate with geometrical root mean square deviations obtained from structural alignments. To test whether the contact metric detects functional similarity, as defined by Gene Ontology (GO) terms, it was compared in large-scale computational experiments to four other measures of structural similarity, including alignment algorithms as well as alignment independent approaches. The contact metric was the fastest method and its sensitivity, at any given specificity level, was a close second only to Fast Alignment and Search Tool—a structural alignment method that is slower by three orders of magnitude. Critically, nearly 40% of correct functional inferences by the contact metric were not identified by any other approach, which shows that the contact metric is complementary and computationally efficient in detecting functional relationships between proteins. A public ‘Contact Metric Internet Server’ is provided.