Identification of biophysical interaction patterns in direct coupling analysis

Michael Schmidt and Kay Hamacher
Phys. Rev. E 103, 042418 – Published 22 April 2021

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 l2 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.

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  • Received 13 September 2020
  • Revised 7 February 2021
  • Accepted 27 March 2021

DOI:https://doi.org/10.1103/PhysRevE.103.042418

©2021 American Physical Society

Physics Subject Headings (PhySH)

Interdisciplinary PhysicsPhysics of Living SystemsStatistical Physics & Thermodynamics

Authors & Affiliations

Michael Schmidt1,* and Kay Hamacher1,2,3

  • 1Department of Physics, TU Darmstadt, Karolinenpl. 5, 64289 Darmstadt, Germany
  • 2Department of Biology, TU Darmstadt, Schnittspahnstr. 10, 64287 Darmstadt, Germany
  • 3Department of Computer Science, TU Darmstadt, Karolinenpl. 5, 64289 Darmstadt, Germany

  • *schmidt@cbs.tu-darmstadt.de

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Vol. 103, Iss. 4 — April 2021

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