Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 545924
Title Improved inference of intermolecular contacts through protein–protein interaction prediction using coevolutionary analysis
Author(s) Correa Marrero, M.; Immink, G.H.; Ridder, D. de; Dijk, A.D.J. van
Source Bioinformatics 35 (2019)12. - ISSN 1367-4803 - p. 2036 - 2042.
DOI https://doi.org/10.1093/bioinformatics/bty924
Department(s) Bioinformatics
Laboratory of Molecular Biology
BIOS Plant Development Systems
Mathematical and Statistical Methods - Biometris
BIOS Applied Bioinformatics
Publication type Refereed Article in a scientific journal
Publication year 2019
Abstract Motivation: Predicting residue–residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance.
Results: We have designed Ouroboros, a novel algorithm to reduce such noise in intermolecular contact prediction. Our method iterates between weighting proteins according to how likely they are to interact based on the correlated mutations signal, and predicting correlated mutations based on the weighted sequence alignment. We show that this approach accurately discriminates between protein interaction versus non-interaction and simultaneously improves the prediction of intermolecular contact residues compared to a naive application of correlated mutation analysis. This requires no training labels concerning interactions or contacts. Furthermore, the method relaxes the assumption of one-to-one interaction of previous approaches, allowing for the study of many-to-many interactions.
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