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 413368
Title Correlated mutations via regularized multinomial regression
Author(s) Sreekumar, J.; Braak, C.J.F. ter; Ham, R.C.H.J. van; Dijk, A.D.J. van
Source BMC Bioinformatics 12 (2011). - ISSN 1471-2105 - 13 p.
DOI https://doi.org/10.1186/1471-2105-12-444
Department(s) Biometris (PPO/PRI)
Biometris (WU MAT)
Bioinformatics
PRI BIOS Applied Bioinformatics
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) multiple sequence alignments - protein-protein interaction - mutual information - graphical models - prediction - families - identification - specificity - dimension - selection
Abstract Background In addition to sequence conservation, protein multiple sequence alignments contain evolutionary signal in the form of correlated variation among amino acid positions. This signal indicates positions in the sequence that influence each other, and can be applied for the prediction of intra- or intermolecular contacts. Although various approaches exist for the detection of such correlated mutations, in general these methods utilize only pairwise correlations. Hence, they tend to conflate direct and indirect dependencies. Results We propose RMRCM, a method for Regularized Multinomial Regression in order to obtain Correlated Mutations from protein multiple sequence alignments. Importantly, our method is not restricted to pairwise (column-column) comparisons only, but takes into account the network nature of relationships between protein residues in order to predict residue-residue contacts. The use of regularization ensures that the number of predicted links between columns in the multiple sequence alignment remains limited, preventing overprediction. Using simulated datasets we analyzed the performance of our approach in predicting residue-residue contacts, and studied how it is influenced by various types of noise. For various biological datasets, validation with protein structure data indicates a good performance of the proposed algorithm for the prediction of residue-residue contacts, in comparison to previous results. RMRCM can also be applied to predict interactions (in addition to only predicting interaction sites or contact sites), as demonstrated by predicting PDZ-peptide interactions. Conclusions A novel method is presented, which uses regularized multinomial regression in order to obtain correlated mutations from protein multiple sequence alignments.
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