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Identification of biophysical interaction patterns in direct coupling analysis.

Schmidt, Michael ; Hamacher, Kay (2021):
Identification of biophysical interaction patterns in direct coupling analysis.
In: Physical review. E, 103 (4), pp. 042418. ISSN 2470-0053,
DOI: 10.1103/PhysRevE.103.042418,
[Article]

Abstract

Direct-coupling analysis is a statistical learning method for protein contact prediction based on sequence information alone. The maximum entropy principle leads to an effective inverse Potts model. Predictions on contacts are based on fitted local fields and couplings from an empirical multiple sequence alignment. Typically, the l_{2} norm of the resulting two-body couplings is used for contact prediction. However, this procedure discards important information. In this paper we show that the usage of the full fields and coupling information improves prediction accuracy.

Item Type: Article
Erschienen: 2021
Creators: Schmidt, Michael ; Hamacher, Kay
Title: Identification of biophysical interaction patterns in direct coupling analysis.
Language: English
Abstract:

Direct-coupling analysis is a statistical learning method for protein contact prediction based on sequence information alone. The maximum entropy principle leads to an effective inverse Potts model. Predictions on contacts are based on fitted local fields and couplings from an empirical multiple sequence alignment. Typically, the l_{2} norm of the resulting two-body couplings is used for contact prediction. However, this procedure discards important information. In this paper we show that the usage of the full fields and coupling information improves prediction accuracy.

Journal or Publication Title: Physical review. E
Journal volume: 103
Number: 4
Divisions: 10 Department of Biology
10 Department of Biology > Computational Biology and Simulation
Date Deposited: 25 May 2021 06:14
DOI: 10.1103/PhysRevE.103.042418
Identification Number: pmid:34005861
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