@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 {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.}
}
