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Integrating sequence variation and protein structure to identify sites under selection.

ABSTRACT: We present a novel method to identify sites under selection in protein-coding genes. Our method combines the traditional Goldman-Yang model of coding-sequence evolution with the information obtained from the 3D structure of the evolving protein, specifically the relative solvent accessibility (RSA) of individual residues. We develop a random-effects likelihood sites model in which rate classes are RSA dependent. The RSA dependence is modeled with linear functions. We demonstrate that our RSA-dependent model provides a significantly better fit to molecular sequence data than does a traditional, RSA-independent model. We further show that our model provides a natural, RSA-dependent neutral baseline for the evolutionary rate ratio ? = dN/dS Sites that deviate from this neutral baseline likely experience selection pressure for function. We apply our method to the influenza proteins hemagglutinin and neuraminidase. For hemagglutinin, our method recovers positively selected sites near the sialic acid-binding site and negatively selected sites that may be important for trimerization. For neuraminidase, our method recovers the oseltamivir resistance site and otherwise suggests that few sites deviate from the neutral baseline. Our method is broadly applicable to any protein sequences for which structural data are available or can be obtained via homology modeling or threading.

SUBMITTER: Meyer AG

PROVIDER: S-EPMC3525147 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

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Integrating sequence variation and protein structure to identify sites under selection.

Meyer Austin G AG Wilke Claus O CO

Molecular biology and evolution 20120912 1

We present a novel method to identify sites under selection in protein-coding genes. Our method combines the traditional Goldman-Yang model of coding-sequence evolution with the information obtained from the 3D structure of the evolving protein, specifically the relative solvent accessibility (RSA) of individual residues. We develop a random-effects likelihood sites model in which rate classes are RSA dependent. The RSA dependence is modeled with linear functions. We demonstrate that our RSA-dep ...[more]

PMID: 22977116

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Project description:The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing.In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy.We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues, including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7-4.8 Å C(?)-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.

Project description:BackgroundPost-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the most-studied PTMs: it occurs when a phosphate group is added to serine (Ser, S), threonine (Thr, T), or tyrosine (Tyr, Y) residue. Dysregulation of protein phosphorylation can lead to various diseases-most commonly neurological disorders, Alzheimer's disease, and Parkinson's disease-thus necessitating the prediction of S/T/Y residues that can be phosphorylated in an uncharacterized amino acid sequence. Despite a surplus of sequencing data, current experimental methods of PTM prediction are time-consuming, costly, and error-prone, so a number of computational methods have been proposed to replace them. However, phosphorylation prediction remains limited, owing to substrate specificity, performance, and the diversity of its features.MethodsIn the present study we propose machine-learning-based predictors that use the physicochemical, sequence, structural, and functional information of proteins to classify S/T/Y phosphorylation sites. Rigorous feature selection, the minimum redundancy/maximum relevance approach, and the symmetrical uncertainty method were employed to extract the most informative features to train the models.ResultsThe RF and SVM models generated using diverse feature types in the present study were highly accurate as is evident from good values for different statistical measures. Moreover, independent test sets and benchmark validations indicated that the proposed method clearly outperformed the existing methods, demonstrating its ability to accurately predict protein phosphorylation.ConclusionsThe results obtained in the present work indicate that the proposed computational methodology can be effectively used for predicting putative phosphorylation sites further facilitating discovery of various biological processes mechanisms.

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Integrating sequence variation and protein structure to identify sites under selection.

Publications

Integrating sequence variation and protein structure to identify sites under selection.

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