Statistical physics of interacting proteins: Impact of dataset size and quality assessed in synthetic sequences

Carlos A. Gandarilla-Pérez, Pierre Mergny, Martin Weigt, and Anne-Florence Bitbol
Phys. Rev. E 101, 032413 – Published 20 March 2020

Abstract

Identifying protein-protein interactions is crucial for a systems-level understanding of the cell. Recently, algorithms based on inverse statistical physics, e.g., direct coupling analysis (DCA), have allowed to use evolutionarily related sequences to address two conceptually related inference tasks: finding pairs of interacting proteins and identifying pairs of residues which form contacts between interacting proteins. Here we address two underlying questions: How are the performances of both inference tasks related? How does performance depend on dataset size and the quality? To this end, we formalize both tasks using Ising models defined over stochastic block models, with individual blocks representing single proteins and interblock couplings protein-protein interactions; controlled synthetic sequence data are generated by Monte Carlo simulations. We show that DCA is able to address both inference tasks accurately when sufficiently large training sets of known interaction partners are available and that an iterative pairing algorithm allows to make predictions even without a training set. Noise in the training data deteriorates performance. In both tasks we find a quadratic scaling relating dataset quality and size that is consistent with noise adding in square-root fashion and signal adding linearly when increasing the dataset. This implies that it is generally good to incorporate more data even if their quality are imperfect, thereby shedding light on the empirically observed performance of DCA applied to natural protein sequences.

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  • Received 23 December 2019
  • Revised 2 March 2020
  • Accepted 4 March 2020

DOI:https://doi.org/10.1103/PhysRevE.101.032413

©2020 American Physical Society

Physics Subject Headings (PhySH)

Statistical Physics & ThermodynamicsPhysics of Living SystemsNetworks

Authors & Affiliations

Carlos A. Gandarilla-Pérez1,2, Pierre Mergny1,3, Martin Weigt1,*, and Anne-Florence Bitbol3,4,†

  • 1Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire de Biologie Computationnelle et Quantitative (LCQB, UMR 7238), F-75005 Paris, France
  • 2Facultad de Física, Universidad de la Habana, San Lázaro y L, Vedado, Habana 4, CP–10400, Cuba
  • 3Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire Jean Perrin (LJP, UMR 8237), F-75005 Paris, France
  • 4Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

  • *Corresponding author: martin.weigt@sorbonne-universite.fr
  • Corresponding author: anne-florence.bitbol@epfl.ch

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Issue

Vol. 101, Iss. 3 — March 2020

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