Mapping pathways of allosteric communication in GroEL by analysis of correlated mutations

Kass, Itamar; Horovitz, Amnon

doi:10.1002/prot.10180

Cited by 176 publications

(183 citation statements)

References 36 publications

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“…Positions that are invariant do not contain sufficient information to assess correlated variations, and positions that display random differences can generate spurious correlations. Therefore, we evaluated 3 algorithms to identify covarying positions (24,29,45). In accord with the results of Fodor and Aldrich (46), we found that the OMES method (29) performed well on this data set.…”

Section: Methodssupporting

confidence: 70%

“…Therefore, we evaluated 3 algorithms to identify covarying positions (24,29,45). In accord with the results of Fodor and Aldrich (46), we found that the OMES method (29) performed well on this data set. The other methods gave spurious correlations as a result of the relatively high degree of conservation in the HCV sequences or because their clustering methods did not perform well in this context.…”

Section: Methodssupporting

confidence: 70%

“…To identify covarying amino acid positions in the HCV ORF, we first created 10 multiple sequence alignments of the Virahep-C sequences: 1a All, 1a Marked, 1a Poor, 1a SVR, 1a Non-SVR, 1b All, 1b Marked, 1b Poor, 1b SVR, and 1b Non-SVR. A covariance score for every possible pair of the 2,955 positions in each alignment was calculated by squaring the difference between the number of observed and expected amino acid pairs and normalizing this difference by the number of entries (excluding gaps) in each column, the observed minus expected squared (OMES) method (29). The null model in this method is the expected number of covarying pairs, based on the independent count of the amino acids at each of the 2 positions.…”

Section: Virahep-c Cohort and Hcv Sequences The Consensus Pretreatmentmentioning

confidence: 99%

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Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans

Aurora¹,

Donlin²,

Cannon³

et al. 2008

J. Clin. Invest.

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Hepatitis C virus (HCV) is a common RNA virus that causes hepatitis and liver cancer. Infection is treated with IFN-α and ribavirin, but this expensive and physically demanding therapy fails in half of patients. The genomic sequences of independent HCV isolates differ by approximately 10%, but the effects of this variation on the response to therapy are unknown. To address this question, we analyzed amino acid covariance within the full viral coding region of pretherapy HCV sequences from 94 participants in the Viral Resistance to Antiviral Therapy of Chronic Hepatitis C (Virahep-C) clinical study. Covarying positions were common and linked together into networks that differed by response to therapy. There were 3-fold more hydrophobic amino acid pairs in HCV from nonresponding patients, and these hydrophobic interactions were predicted to contribute to failure of therapy by stabilizing viral protein complexes. Using our analysis to detect patterns within the networks, we could predict the outcome of therapy with greater than 95% coverage and 100% accuracy, raising the possibility of a prognostic test to reduce therapeutic failures. Furthermore, the hub positions in the networks are attractive antiviral targets because of their genetic linkage with many other positions that we predict would suppress evolution of resistant variants. Finally, covariance network analysis could be applicable to any virus with sufficient genetic variation, including most human RNA viruses.

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Section: Methodssupporting

confidence: 70%

Section: Methodssupporting

confidence: 70%

Section: Virahep-c Cohort and Hcv Sequences The Consensus Pretreatmentmentioning

confidence: 99%

See 1 more Smart Citation

Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans

Aurora¹,

Donlin²,

Cannon³

et al. 2008

J. Clin. Invest.

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show abstract

“…Many early residue contact prediction methods were derived from statistics and information theory, like OMES [8], MI [9], MIp [10] and SCA [11]. However, these methods ignore the transitive correlation between residues and thus generate many false positive results.…”

Section: Author Summarymentioning

confidence: 99%

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Mao

Wang

Zhang

et al. 2017

Preprint

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Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β -β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we introduce a novel ridge-detection-based β -β contact predictor, RDb 2 C, to identify residue pairing in β strands from any predicted residue contact map. The algorithm adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb 2 C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~62% and ~76%at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb 2 C achieves impressively higher performance, with F1-scores reaching ~76% and ~86% at the residue level and strand level, respectively. According to our tests on 61 mainly β proteins, improvement in the β -β contact prediction can further ameliorate the structural prediction. Availability:All source data and codes are available at http://166.111.152.91/Downloads.html or at the GitHub address of https://github.com/wzmao/RDb2C. Author summaryDue to the topological complexity, mainly β proteins are challenging targets in protein structure prediction. Knowledge of the pairing between β strands, especially the residue pairing pattern, can greatly facilitate the tertiary structure prediction of mainly β proteins. In this work, we developed a novel algorithm to identify the residue pairing in β strands from a predicted residue contact map.This method adopts the ridge detection technique to capture the characteristic pattern of β -β interactions from the map and then utilizes a multi-stage random forest framework to predict β -β contacts at the residue level. According to our tests, our method could effectively improve the prediction of β -β contacts even from a highly noisy contact map. Moreover, the refined β -β contact information could effectively improve the structural modeling of mainly β proteins.

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“…Such works boosted the discovery of coupled residues, which could previously have been identified only by painstaking in vitro approaches such as thermodynamic double mutant analysis [11]. The next step was to summarize information about correlated positions into pathways [15], motifs [1,20], and structural templates [20] in protein families. Today, projects undertake ambitious large-scale recombination [28] or site-directed and combinatorial mutagenesis studies [23] to identify entire building blocks of proteins important to preserve function.…”

Section: Introductionmentioning

confidence: 99%

Graphical Models of Residue Coupling in Protein Families

Thomas

Ramakrishnan

Bailey‐Kellogg

2008

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Identifying residue coupling relationships within a protein family can provide important insights into the family's evolutionary record, and has significant applications in analyzing and optimizing sequence-structure-function relationships. We present the first algorithm to infer an undirected graphical model representing residue coupling in protein families. Such a model, which we call a residue coupling network, serves as a compact description of the joint amino acid distribution, focused on the independences among residues. This stands in contrast to current methods, which manipulate dense representations of co-variation and are focused on assessing dependence, which can conflate direct and indirect relationships. Our probabilistic model provides a sound basis for predictive (will this newly designed protein be folded and functional?), diagnostic (why is this protein not stable or functional?), and abductive reasoning (what if I attempt to graft features of one protein family onto another?). Further, our algorithm can readily incorporate, as priors, hypotheses regarding possible underlying mechanistic/energetic explanations for coupling. The resulting approach constitutes a powerful and discriminatory mechanism to identify residue coupling from protein sequences and structures. Analysis results on the G-protein coupled receptor (GPCR) and PDZ domain families demonstrate the ability of our approach to effectively uncover and exploit models of residue coupling.

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Mapping pathways of allosteric communication in GroEL by analysis of correlated mutations

Cited by 176 publications

References 36 publications

Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans

Genome-wide hepatitis C virus amino acid covariance networks can predict response to antiviral therapy in humans

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Graphical Models of Residue Coupling in Protein Families

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