@article {19357100,
	title = {ModLink+: Improving fold recognition by using protein-protein interactions},
	journal = {Bioinformatics},
	year = {2009},
	note = {<p>Journal article Bioinformatics (Oxford, England) Bioinformatics. 2009 Apr 8.</p>},
	abstract = {<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>},
	keywords = {protein folding},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=19357100},
	author = {Fornes, O. and Aragues, R. and Espadaler, J. and M. A. Marti-Renom and Sali, A. and Oliva, B.}
}
@article {18505562,
	title = {Prediction of enzyme function by combining sequence similarity and protein interactions},
	journal = {BMC Bioinformatics},
	volume = {9},
	year = {2008},
	note = {Espadaler, Jordi Eswar, Narayanan Querol, Enrique Aviles, Francesc X Sali, Andrej Marti-Renom, Marc A Oliva, Baldomero GM54762/GM/NIGMS NIH HHS/United States GM71790/GM/NIGMS NIH HHS/United States GM74929/GM/NIGMS NIH HHS/United States GM74945/GM/NIGMS NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t England BMC bioinformatics BMC Bioinformatics. 2008 May 27;9:249.},
	pages = {249},
	abstract = {BACKGROUND: 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.},
	keywords = {Amino Acid *Software Structure-Activity Relationship Substrate Specificity/genetics, Amino Acid Sequence/physiology Databases, Automated Predictive Value of Tests Protein Interaction Mapping Proteins/analysis/metabolism Sequence Alignment Sequence Analysis, Protein *Sequence Homology, Protein Enzymes/analysis/*metabolism Fuzzy Logic Pattern Recognition},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=18505562},
	author = {Espadaler, J. and Eswar, N. and Querol, E. and Aviles, F. X. and Sali, A. and M. A. Marti-Renom and Oliva, B.}
}
@article {17941705,
	title = {Characterization of protein hubs by inferring interacting motifs from protein interactions},
	journal = {PLoS Comput Biol},
	volume = {3},
	number = {9},
	year = {2007},
	note = {Aragues, Ramon Sali, Andrej Bonet, Jaume Marti-Renom, Marc A Oliva, Baldo PN2 EY016525,/EY/NEI NIH HHS/United States U54 RR022220/RR/NCRR NIH HHS/United States Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t United States PLoS computational biology PLoS Comput Biol. 2007 Sep;3(9):1761-71. Epub 2007 Jul 30.},
	pages = {1761-71},
	abstract = {The 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.},
	keywords = {Amino Acid Motifs Amino Acid Sequence Binding Sites Computer Simulation *Models, Chemical *Models, Molecular Molecular Sequence Data Protein Binding Protein Interaction Mapping/*methods Proteins/*chemistry Sequence Analysis, Protein/*methods},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=17941705},
	author = {Aragues, R. and Sali, A. and Bonet, J. and M. A. Marti-Renom and Oliva, B.}
}
@article {15883372,
	title = {Detecting remotely related proteins by their interactions and sequence similarity},
	journal = {Proc Natl Acad Sci U S A},
	volume = {102},
	number = {20},
	year = {2005},
	note = {Espadaler, Jordi Aragues, Ramon Eswar, Narayanan Marti-Renom, Marc A Querol, Enrique Aviles, Francesc X Sali, Andrej Oliva, Baldomero R01 GM54762/GM/NIGMS NIH HHS/United States Comparative Study Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov{\textquoteright}t Research Support, U.S. Gov{\textquoteright}t, P.H.S. United States Proceedings of the National Academy of Sciences of the United States of America Proc Natl Acad Sci U S A. 2005 May 17;102(20):7151-6. Epub 2005 May 9.},
	pages = {7151-6},
	abstract = {The 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.},
	keywords = {Amino Acid, Computational Biology Databases, Molecular Protein Conformation Protein Folding Proteins/*genetics/*metabolism Proteomics/*methods *Sequence Homology, Protein *Evolution},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=15883372},
	author = {Espadaler, J. and Aragues, R. and Eswar, N. and M. A. Marti-Renom and Querol, E. and Aviles, F. X. and Sali, A. and Oliva, B.}
}
@article {11394740,
	title = {Classification of protein disulphide-bridge topologies},
	journal = {J Comput Aided Mol Des},
	volume = {15},
	number = {5},
	year = {2001},
	note = {Mas, J M Aloy, P Marti-Renom, M A Oliva, B de Llorens, R Aviles, F X Querol, E Comparative Study Research Support, Non-U.S. Gov{\textquoteright}t Netherlands Journal of computer-aided molecular design J Comput Aided Mol Des. 2001 May;15(5):477-87.},
	pages = {477-87},
	abstract = {The 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.},
	keywords = {Algorithms Computer Simulation Databases as Topic Disulfides/*chemistry Models, Molecular Protein Structure, Secondary Protein Structure, Tertiary Proteins/*chemistry/*classification Software},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=11394740},
	author = {Mas, J. M. and Aloy, P. and M. A. Marti-Renom and Oliva, B. and de Llorens, R. and Aviles, F. X. and Querol, E.}
}
