02280nas a2200169   4500008004100000245007900041210006900120520169500189653002001884100001501904700001601919700001801935700002401953700001301977700001401990856010602004       2009                        eng d00aModLink+: Improving fold recognition by using protein-protein interactions0 aModLink Improving fold recognition by using proteinprotein inter3 a<p>MOTIVATION: Several strategies have been developed to predict the fold of a target protein sequence, most of which are based on aligning the target sequence to other sequences of known structure. Previously, we demonstrated that the consideration of protein-protein interactions significantly increases the accuracy of fold assignment compared to PSI-BLAST sequence comparisons. A drawback of our method was the low number of proteins to which a fold could be assigned. Here, we present an improved version of the method that addresses this limitation. We also compare our method to other state-of-the-art fold assignment methodologies. RESULTS: Our approach (ModLink+) has been tested on 3,716 proteins with domain folds classified in the Structural Classification Of Proteins (SCOP) as well as known interacting partners in the Database of Interacting Proteins (DIP). For this test set, the ratio of success (PPV) on fold assignment increases from 75% for PSI-BLAST, 83% for HHSearch and 81% for PRC to more than 90% for ModLink+ at the e-value cutoff of 10(-3). Under this e-value, ModLink+ can assign a fold to 30-45% of the proteins in the test set, while our previous method could cover less than 25%. When applied to 6,384 proteins with unknown fold in the yeast proteome, ModLink+ combined with PSI-BLAST assigns a fold for domains in 3,738 proteins, while PSI-BLAST alone only covers 2,122 proteins, HHSearch 2,969 and PRC 2,826 proteins, using a threshold e-value that would represent a PPV higher than 82% for each method in the test set. AVAILABILITY: The ModLink+ server is freely accessible in the World Wide Web at http://sbi.imim.es/modlink/. CONTACT: boliva@imim.es.</p>10aprotein folding1 aFornes, O.1 aAragues, R.1 aEspadaler, J.1 aMarti-Renom, M., A.1 aSali, A.1 aOliva, B.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1935710002031nas 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=1850556202608nas a2200217   4500008004100000245009500041210006900136300001200205490000600217520172500223653008401948653002102032653012902053653002102182100001602203700001302219700001402232700002402246700001402270856010602284       2007                        eng d00aCharacterization of protein hubs by inferring interacting motifs from protein interactions0 aCharacterization of protein hubs by inferring interacting motifs  a1761-710 v33 aThe characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks.10aAmino Acid Motifs Amino Acid Sequence Binding Sites Computer Simulation *Models10aChemical *Models10aMolecular Molecular Sequence Data Protein Binding Protein Interaction Mapping/*methods Proteins/*chemistry Sequence Analysis10aProtein/*methods1 aAragues, R.1 aSali, A.1 aBonet, J.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=1794170502245nas 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