02031nas a2200253   4500008004100000245009200041210006900133300000800202490000600210520096700216653008801183653004501271653013401316653003101450653007301481100001801554700001401572700001501586700001901601700001301620700002401633700001401657856010601671       2008                        eng d00aPrediction of enzyme function by combining sequence similarity and protein interactions0 aPrediction of enzyme function by combining sequence similarity a  a2490 v93 aBACKGROUND: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners. RESULTS: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST. CONCLUSION: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone.10aAmino Acid *Software Structure-Activity Relationship Substrate Specificity/genetics10aAmino Acid Sequence/physiology Databases10aAutomated Predictive Value of Tests Protein Interaction Mapping Proteins/analysis/metabolism Sequence Alignment Sequence Analysis10aProtein *Sequence Homology10aProtein Enzymes/analysis/*metabolism Fuzzy Logic Pattern Recognition1 aEspadaler, J.1 aEswar, N.1 aQuerol, E.1 aAviles, F., X.1 aSali, A.1 aMarti-Renom, M., A.1 aOliva, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1850556202245nas a2200253   4500008004100000245008600041210006900127300001100196490000800207520134200215653001501557653003601572653012101608653002301729100001801752700001601770700001401786700002401800700001501824700001901839700001301858700001401871856010601885       2005                        eng d00aDetecting remotely related proteins by their interactions and sequence similarity0 aDetecting remotely related proteins by their interactions and se  a7151-60 v1023 aThe function of an uncharacterized protein is usually inferred either from its homology to, or its interactions with, characterized proteins. Here, we use both sequence similarity and protein interactions to identify relationships between remotely related protein sequences. We rely on the fact that homologous sequences share similar interactions, and, therefore, the set of interacting partners of the partners of a given protein is enriched by its homologs. The approach was bench-marked by assigning the fold and functional family to test sequences of known structure. Specifically, we relied on 1,434 proteins with known folds, as defined in the Structural Classification of Proteins (SCOP) database, and with known interacting partners, as defined in the Database of Interacting Proteins (DIP). For this subset, the specificity of fold assignment was increased from 54% for position-specific iterative BLAST to 75% for our approach, with a concomitant increase in sensitivity for a few percentage points. Similarly, the specificity of family assignment at the e-value threshold of 10(-8) was increased from 70% to 87%. The proposed method would be a useful tool for large-scale automated discovery of remote relationships between protein sequences, given its unique reliance on sequence similarity and protein-protein interactions.10aAmino Acid10aComputational Biology Databases10aMolecular Protein Conformation Protein Folding Proteins/*genetics/*metabolism Proteomics/*methods *Sequence Homology10aProtein *Evolution1 aEspadaler, J.1 aAragues, R.1 aEswar, N.1 aMarti-Renom, M., A.1 aQuerol, E.1 aAviles, F., X.1 aSali, A.1 aOliva, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1588337202134nas a2200241   4500008004100000245005900041210005800100300001100158490000700169520128500176653008301461653003201544653003201576653005801608100001601666700001301682700002401695700001401719700001901733700001901752700001501771856010601786       2001                        eng d00aClassification of protein disulphide-bridge topologies0 aClassification of protein disulphidebridge topologies  a477-870 v153 aThe preferential occurrence of certain disulphide-bridge topologies in proteins has prompted us to design a method and a program, KNOT-MATCH, for their classification. The program has been applied to a database of proteins with less than 65% homology and more than two disulphide bridges. We have investigated whether there are topological preferences that can be used to group proteins and if these can be applied to gain insight into the structural or functional relationships among them. The classification has been performed by Density Search and Hierarchical Clustering Techniques, yielding thirteen main protein classes from the superimposition and clustering process. It is noteworthy that besides the disulphide bridges, regular secondary structures and loops frequently become correctly aligned. Although the lack of significant sequence similarity among some clustered proteins precludes the easy establishment of evolutionary relationships, the program permits us to find out important structural or functional residues upon the superimposition of two protein structures apparently unrelated. The derived classification can be very useful for finding relationships among proteins which would escape detection by current sequence or topology-based analytical algorithms.10aAlgorithms Computer Simulation Databases as Topic Disulfides/*chemistry Models10aMolecular Protein Structure10aSecondary Protein Structure10aTertiary Proteins/*chemistry/*classification Software1 aMas, J., M.1 aAloy, P.1 aMarti-Renom, M., A.1 aOliva, B.1 ade Llorens, R.1 aAviles, F., X.1 aQuerol, E.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11394740