@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 {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.}
}
