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

Gandarilla-Pérez, Carlos A.; Mergny, Pierre; Weigt, Martin; Bitbol, Anne-Florence

doi:10.1103/physreve.101.032413

Cited by 8 publications

(24 citation statements)

References 47 publications

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“…Conversely, when µ 70, Fig 2 shows that the performance of partner inference in our data set including both ingredients approaches the one obtained with only structural contacts (see Methods and Ref. [47]). Indeed, when µ becomes large enough, similarities due to phylogeny vanish even between closest relatives, and all sequences become effectively independent.…”

Section: Correlations From Structural Contacts and From Phylogeny Both Contribute To The Performance Of Partner Inferencementioning

confidence: 68%

“…Each proposed mutation is accepted with a probability p given by the Metropolis criterion at sampling temperature T : where Δ H is the difference between the value of H after the mutation and before it. This models natural selection for maintaining structure [47]. Indeed, all mutations that decrease H are accepted ( p = 1), while those that increase H can be rejected ( p < 1), and will generally be rejected if T is small.…”

Section: Model and Methodsmentioning

confidence: 99%

“…The equilibration time is determined by the convergence of the Hamiltonian value, see Fig. S2 (note that the convergence of the absolute magnetization correlation function gives similar results [47]). Second, to consider the limiting case that only involves phylogeny, synthetic sequences are evolved along a phylogenetic tree, and all proposed mutations are accepted, simulating neutral evolution, where mutations have no fitness effect.…”

Section: Model and Methodsmentioning

confidence: 99%

“…Inferring the couplings and the fields that appropriately reproduce the empirical covariances is a difficult problem [71]. Within the mean-field approximation (mfDCA), which we employ for partner inference as in [24, 47, 48], inferred coupling strengths can be simply approximated by in the reference-state gauge [6, 7, 72]. One then makes a gauge change to the zero-sum (or Ising) gauge [24, 48, 54], which attributes the smallest possible fraction of the energy to the couplings, and the largest possible fraction to the fields [5, 54].…”

Section: Model and Methodsmentioning

confidence: 99%

“…3). Each sequence is generated starting from a different initial random sequence [47]. The equilibration time is determined by the convergence of the Hamiltonian value, see Fig.…”

Section: Generating Synthetic Data With Controlled Amounts Of Structural Constraints and Phylogenymentioning

confidence: 99%

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Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Gerardos

Dietler

Bitbol

2021

Preprint

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Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families. Does successful inference mainly rely on correlations from structural contacts or from phylogeny, or both? Do these two types of signal combine constructively or hinder each other? To address these questions, we generate and analyze synthetic data produced using a minimal model that allows us to control the amounts of structural constraints and phylogeny. We show that correlations from these two sources combine constructively to increase the performance of partner inference by DCA or MI. Furthermore, signal from phylogeny can rescue partner inference when signal from contacts becomes less informative, including in the realistic case where inter-protein contacts are restricted to a small subset of sites. We also demonstrate that DCA-inferred couplings between non-contact pairs of sites improve partner inference in the presence of strong phylogeny, while deteriorating it otherwise. Moreover, restricting to non-contact pairs of sites preserves inference performance in the presence of strong phylogeny. In a natural dataset, as well as in realistic synthetic data based on it, we find that non-contact pairs of sites contribute positively to partner inference performance, and that restricting to them preserves performance, evidencing an important role of phylogeny.Author summaryIn protein sequence data, the amino acid usages at different sites of a protein or of two interacting proteins can be correlated because of functional constraints. For instance, the need to maintain physico-chemical complementarity among two sites that are in contact in the three-dimensional structure of a protein complex causes such correlations. However, correlations can also arise due to shared evolutionary history, even in the absence of any functional constraint. While these phylogenetic correlations are known to obscure the inference of structural contacts, we show, using controlled synthetic data, that correlations from structure and phylogeny combine constructively to allow the inference of protein partners from sequences. We also show that pairs of amino acids that are not in contact in the structure have a major impact on partner inference in a natural dataset and in realistic synthetic ones. These findings explain the success of methods based on pairwise maximum-entropy models or on information theory at predicting protein partners from sequences.

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Section: Correlations From Structural Contacts and From Phylogeny Both Contribute To The Performance Of Partner Inferencementioning

confidence: 68%

Section: Model and Methodsmentioning

confidence: 99%

Section: Model and Methodsmentioning

confidence: 99%

Section: Model and Methodsmentioning

confidence: 99%

“…3). Each sequence is generated starting from a different initial random sequence [47]. The equilibration time is determined by the convergence of the Hamiltonian value, see Fig.…”

Section: Generating Synthetic Data With Controlled Amounts Of Structural Constraints and Phylogenymentioning

confidence: 99%

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Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Gerardos

Dietler

Bitbol

2021

Preprint

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Evolutionary dynamics of a lattice dimer: a toy model for stability vs. affinity trade-offs in proteins

Loffredo,

Vesconi,

Razban

et al. 2023

J. Phys. A: Math. Theor.

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Understanding how a stressor applied on a biological system shapes its evolution is key to achieving targeted evolutionary control. Here we present a toy model of two interacting lattice proteins to quantify the response to the selective pressure defined by the binding energy. We generate sequence data of proteins and study how the sequence and structural properties of dimers are affected by the applied selective pressure, both during the evolutionary process and in the stationary regime. In particular we show that internal contacts of native structures lose strength, while inter-structure contacts are strengthened due to the folding-binding competition. We discuss how dimerization is achieved through enhanced mutability on the interacting faces, and how the designability of each native structure changes upon introduction of the stressor.

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Impact of phylogeny on structural contact inference from protein sequence data

Dietler

Lupo

Bitbol

2023

J. R. Soc. Interface.

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Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ancestry, their sequences also feature phylogenetic correlations, which can impair contact inference. We investigate this effect by generating controlled synthetic data from a minimal model where the importance of contacts and of phylogeny can be tuned. We demonstrate that global inference methods, specifically Potts models, are more resilient to phylogenetic correlations than local methods, based on covariance or mutual information. This holds whether or not phylogenetic corrections are used, and may explain the success of global methods. We analyse the roles of selection strength and of phylogenetic relatedness. We show that sites that mutate early in the phylogeny yield false positive contacts. We consider natural data and realistic synthetic data, and our findings generalize to these cases. Our results highlight the impact of phylogeny on contact prediction from protein sequences and illustrate the interplay between the rich structure of biological data and inference.

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

Cited by 8 publications

References 47 publications

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Evolutionary dynamics of a lattice dimer: a toy model for stability vs. affinity trade-offs in proteins

Impact of phylogeny on structural contact inference from protein sequence data

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