Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes using Coevolutionary Landscapes

Cheng, Ryan R.

doi:10.1016/j.bpj.2016.11.1525

Cited by 20 publications

(28 citation statements)

References 47 publications

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“…In practice, several traits might be selected simultaneously (see below), yielding more independent terms among the fields and couplings. More generally, such one-and two-body descriptions have been very successfully employed via Direct Coupling Analysis (DCA) to identify strongly coupled residues that are in contact within a folded protein [36][37][38], to investigate folding [39], and to predict fitness [33,[40][41][42][43][44][45] and conformational changes [46,47], as well as protein-protein interactions [48,49]. A complete model of protein covariation in nature should necessarily incorporate both the collective modes described here and the strongly coupled residue pairs which are the focus of DCA.…”

Section: /37mentioning

confidence: 99%

Revealing evolutionary constraints on proteins through sequence analysis

2019

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Statistical analysis of alignments of large numbers of protein sequences has revealed “sectors” of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector. As an illustration of a selected trait, we consider the elastic energy of an important conformational change within an elastic network model, and we show that selection acting on this energy leads to correlations among residues. For this concrete example and more generally, we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences. However, secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes. Our simple, general model leads us to propose a principled method to identify functional sectors, along with the magnitudes of mutational effects, from sequence data. We further demonstrate the robustness of these functional sectors to various forms of selection, and the robustness of our approach to the identification of multiple selected traits.

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Section: /37mentioning

confidence: 99%

Revealing evolutionary constraints on proteins through sequence analysis

2019

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“…the structure and function of protein complexes (22,23). These methods use undirected graphical models of protein sequences to find statistical dependencies between pairs of residues (24)(25)(26), successfully predicting correct protein-protein interaction pairings for families with many paralogs (27,28), informing specificity reprogramming experiments (29,30) and predicting the effect of mutations on protein function (31). Therefore, these generative models of residue dependencies may allow for characterization of residues important to specificity in the clustered Pcdh family, and prediction of interaction probability of all possible isoform pairs.…”

Section: Significancementioning

confidence: 99%

Interaction specificity of clustered protocadherins inferred from sequence covariation and structural analysis

Nicoludis

Green

Walujkar

et al. 2019

Proc. Natl. Acad. Sci. U.S.A.

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Clustered protocadherins, a large family of paralogous proteins that play important roles in neuronal development, provide an important case study of interaction specificity in a large eukaryotic protein family. A mammalian genome has more than 50 clustered protocadherin isoforms, which have remarkable homophilic specificity for interactions between cellular surfaces. A large antiparallel dimer interface formed by the first 4 extracellular cadherin (EC) domains controls this interaction. To understand how specificity is achieved between the numerous paralogs, we used a combination of structural and computational approaches. Molecular dynamics simulations revealed that individual EC interactions are weak and undergo binding and unbinding events, but together they form a stable complex through polyvalency. Strongly evolutionarily coupled residue pairs interacted more frequently in our simulations, suggesting that sequence coevolution can inform the frequency of interaction and biochemical nature of a residue interaction. With these simulations and sequence coevolution, we generated a statistical model of interaction energy for the clustered protocadherin family that measures the contributions of all amino acid pairs at the interface. Our interaction energy model assesses specificity for all possible pairs of isoforms, recapitulating known pairings and predicting the effects of experimental changes in isoform specificity that are consistent with literature results. Our results show that sequence coevolution can be used to understand specificity determinants in a protein family and prioritize interface amino acid substitutions to reprogram specific protein–protein interactions.

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“…Previous work has applied Potts models to homology search and alignment problems with specific proteins, but not to biological sequences in general [15,16,17,18,19,20,21]. Potts models have also been used to study protein-protein interactions [22,23,24,25], mutational effects [26,27,28,29,30], cellular morphogenesis [31], and collective neuron function [32]. Building upon previous methods that use pairwise sequence correlation to infer conserved base pairs in RNA structure and 3D structure in proteins [33,34], a Potts model expresses the probability that a particular sequence belongs to a family represented by an MSA as a function of all possible characters (amino acids or nucleotides) at each position and all possible pairs of characters across all positions.…”

Section: Introductionmentioning

confidence: 99%

Remote homology search with hidden Potts models

Wilburn

Eddy

2020

Preprint

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Most methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics models called Potts models have been used to infer all-by-all pairwise correlations between sites in deep multiple sequence alignments, and these pairwise couplings have improved 3D structure predictions. Here we extend the use of Potts models from structure prediction to sequence alignment and homology search by developing what we call a hidden Potts model (HPM) that merges a Potts emission process to a generative probability model of insertion and deletion. Because an HPM is incompatible with efficient dynamic programming alignment algorithms, we develop an approximate algorithm based on importance sampling, using simpler probabilistic models as proposal distributions. We test an HPM implementation on RNA structure homology search benchmarks, where we can compare directly to exact alignment methods that capture nested RNA base-pairing correlations (stochastic contextfree grammars). HPMs perform promisingly in these proof of principle experiments. Author summaryComputational homology search and alignment tools are used to infer the functions and evolutionary histories of biological sequences. Most widely used tools for sequence homology searches, such as BLAST and HMMER, rely on primary sequence conservation alone. It should be possible to make more powerful search tools by also considering higher-order covariation patterns induced by 3D structure conservation. Recent advances in 3D protein structure prediction have used a class of statistical physics models called Potts models to infer pairwise correlation structure in multiple sequence alignments. However, Potts models assume alignments are given and cannot build new alignments, limiting their use in homology search. We have extended Potts models to include a probability model of insertion and deletion so they can be applied to sequence alignment and remote homology search using a new model we call a hidden Potts model (HPM). Tests of our prototype HPM software show 1 promising results in initial benchmarking experiments, though more work will be needed to use HPMs in practical tools.

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Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes using Coevolutionary Landscapes

Cited by 20 publications

References 47 publications

Revealing evolutionary constraints on proteins through sequence analysis

Revealing evolutionary constraints on proteins through sequence analysis

Interaction specificity of clustered protocadherins inferred from sequence covariation and structural analysis

Remote homology search with hidden Potts models

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