Analysis of the residue–residue coevolution network and the functionally important residues in proteins

Lee, Byung‐Chul; Park, Keunwan; Kim, Dongsup

doi:10.1002/prot.21972

Cited by 51 publications

(38 citation statements)

References 42 publications

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“…Coevolving residues tend to be spatially coupled and correspond to functionally important sites exhibiting correlated and compensatory mutations in homologous proteins [113]. These residues could also form networks with connections corresponding to coevolutionary interaction strengths between nodes, where the underlying small-world topology of such networks is similar to the structure-based residue interaction networks [114–116]. Furthermore, coevolving residues can assemble into structurally stable and quasi-independent modules of physically interacting residues termed ‘protein sectors’ [103,104].…”

Section: Introductionmentioning

confidence: 99%

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

Tse¹,

Verkhivker²

2015

PLoS ONE

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Quantifying binding specificity and drug resistance of protein kinase inhibitors is of fundamental importance and remains highly challenging due to complex interplay of structural and thermodynamic factors. In this work, molecular simulations and computational alanine scanning are combined with the network-based approaches to characterize molecular determinants underlying binding specificities of the ABL kinase inhibitors. The proposed theoretical framework unveiled a relationship between ligand binding and inhibitor-mediated changes in the residue interaction networks. By using topological parameters, we have described the organization of the residue interaction networks and networks of coevolving residues in the ABL kinase structures. This analysis has shown that functionally critical regulatory residues can simultaneously embody strong coevolutionary signal and high network centrality with a propensity to be energetic hot spots for drug binding. We have found that selective (Nilotinib) and promiscuous (Bosutinib, Dasatinib) kinase inhibitors can use their energetic hot spots to differentially modulate stability of the residue interaction networks, thus inhibiting or promoting conformational equilibrium between inactive and active states. According to our results, Nilotinib binding may induce a significant network-bridging effect and enhance centrality of the hot spot residues that stabilize structural environment favored by the specific kinase form. In contrast, Bosutinib and Dasatinib can incur modest changes in the residue interaction network in which ligand binding is primarily coupled only with the identity of the gate-keeper residue. These factors may promote structural adaptability of the active kinase states in binding with these promiscuous inhibitors. Our results have related ligand-induced changes in the residue interaction networks with drug resistance effects, showing that network robustness may be compromised by targeted mutations of key mediating residues. This study has outlined mechanisms by which inhibitor binding could modulate resilience and efficiency of allosteric interactions in the kinase structures, while preserving structural topology required for catalytic activity and regulation.

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Section: Introductionmentioning

confidence: 99%

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

Tse¹,

Verkhivker²

2015

PLoS ONE

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“…Combining the outcome of several prediction methods into a consensus prediction (termed HelixCorr), this fraction could be further increased to more than 55%. Recent publications analyzing co-evolving residues already highlighted that residue co-evolution may have other than structural reasons (Gloor et al 2005;Lee et al 2008). Th e results obtained with HelixCorr additionally indicated that coevolution not only occurs to maintain specifi c amino acids required for a structural contact but also infl uences the correct formation of a helix-helix contact by aff ecting its sequence context.…”

Section: Co-evolving Residues In Membrane Proteinsmentioning

confidence: 77%

Predicting residue and helix contacts in membrane proteins

Fuchs

Kirschner

Frishman

2010

Structural Bioinformatics of Membrane Proteins

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Helix-helix contacts are an important feature of alpha-helical membrane proteins as they defi ne their characteristic helix bundle structure. No bioinformatics approaches for the prediction of pairwise residue contacts in membrane proteins have existed until recently. In this chapter we describe novel contact prediction methods based on residue coevolution and machine learning techniques specifi cally geared towards membrane proteins. While contact prediction accuracies are limited to ~10% using co-evolving residues alone, machine learning methods are able to improve these accuracies signifi cantly to more than 25% by using available membrane protein structures as a training dataset and incorporating membrane protein specifi c sequence features into the prediction process. Importantly, predicted residue contacts allow for identifi cation of interacting transmembrane helices with high accuracy. As diff erent membrane protein structures can be distinguished by their specifi c patt ern of helix interactions, predicted residue contacts may not only serve as structural constraints in modeling experiments, but also constitute valuable information for structural classifi cation of membrane proteins with unknown structure.

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“…This fact has been exploited to infer the structural factor such as interresidue contacts [13] and to evaluate the correctness of de novo model [28]. Recently, it has been also shown that key residues can be identified by analyzing residue-residue coevolution network [29].…”

Section: Discussionmentioning

confidence: 99%

Linear predictive coding representation of correlated mutation for protein sequence alignment

Jeong

Kim

2009

Proceedings of the Third International Workshop on Data and Text Mining in Bioinformatics

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Although both conservation and correlated mutation (CM) are important information reflecting the different sorts of context in multiple sequence alignment, most of alignment methods use sequence profiles that only represent conservation. There is no general way to represent correlated mutation and incorporate it with sequence alignment yet. We develop a novel method, CM profile, to represent correlated mutation as the spectral feature derived by using linear predictive coding where correlated mutations among different positions are represented by a fixed number of values. We combine CM profile with conventional sequence profile to improve alignment quality. For distantly related protein pairs, using CM profile improves the profile-profile alignment with or without predicted secondary structure. Especially, at superfamily level, combining CM profile with sequence profile improves profileprofile alignment by 9.5% while predicted secondary structure does by 6.0%. More significantly, using both of them improves profileprofile alignment by 13.9%. We also exemplify the effectiveness of CM profile by demonstrating that the resulting alignment preserves share coevolution and contacts. Because of the generality of CM profile, it can be used for other bioinformatics applications in the same way of using sequence profile.

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Analysis of the residue–residue coevolution network and the functionally important residues in proteins

Cited by 51 publications

References 42 publications

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

Molecular Determinants Underlying Binding Specificities of the ABL Kinase Inhibitors: Combining Alanine Scanning of Binding Hot Spots with Network Analysis of Residue Interactions and Coevolution

Predicting residue and helix contacts in membrane proteins

Linear predictive coding representation of correlated mutation for protein sequence alignment

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