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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Proteins Structure F...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Proteins Structure Function and Bioinformatics
Article . 2002 . Peer-reviewed
License: Wiley Online Library User Agreement
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MULTIPROSPECTOR: An algorithm for the prediction of protein–protein interactions by multimeric threading

Authors: Long, Lu; Hui, Lu; Jeffrey, Skolnick;

MULTIPROSPECTOR: An algorithm for the prediction of protein–protein interactions by multimeric threading

Abstract

Abstract In this postgenomic era, the ability to identify protein–protein interactions on a genomic scale is very important to assist in the assignment of physiological function. Because of the increasing number of solved structures involving protein complexes, the time is ripe to extend threading to the prediction of quaternary structure. In this spirit, a multimeric threading approach has been developed. The approach is comprised of two phases. In the first phase, traditional threading on a single chain is applied to generate a set of potential structures for the query sequences. In particular, we use our recently developed threading algorithm, PROSPECTOR. Then, for those proteins whose template structures are part of a known complex, we rethread on both partners in the complex and now include a protein–protein interfacial energy. To perform this analysis, a database of multimeric protein structures has been constructed, the necessary interfacial pairwise potentials have been derived, and a set of empirical indicators to identify true multimers based on the threading Z‐score and the magnitude of the interfacial energy have been established. The algorithm has been tested on a benchmark set comprised of 40 homodimers, 15 heterodimers, and 69 monomers that were scanned against a protein library of 2478 structures that comprise a representative set of structures in the Protein Data Bank. Of these, the method correctly recognized and assigned 36 homodimers, 15 heterodimers, and 65 monomers. This protocol was applied to identify partners and assign quaternary structures of proteins found in the yeast database of interacting proteins. Our multimeric threading algorithm correctly predicts 144 interacting proteins, compared to the 56 (26) cases assigned by PSI‐BLAST using a (less) permissive E‐value of 1 (0.01). Next, all possible pairs of yeast proteins have been examined. Predictions (n = 2865) of protein–protein interactions are made; 1138 of these 2865 interactions have counterparts in the Database of Interacting Proteins. In contrast, PSI‐BLAST made 1781 predictions, and 1215 have counterparts in DIP. An estimation of the false‐negative rate for yeast‐predicted interactions has also been provided. Thus, a promising approach to help assist in the assignment of protein–protein interactions on a genomic scale has been developed. Proteins 2002;49:350–364. © 2002 Wiley‐Liss, Inc.

Keywords

Fungal Proteins, Models, Molecular, Macromolecular Substances, Proteins, Genomics, Protein Structure, Quaternary, Dimerization, Algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
213
Top 10%
Top 1%
Top 1%
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