Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information

Marino-Buslje, Cristina; Santos, Javier; Delfino, José M.; Nielsen, Morten

doi:10.1093/bioinformatics/btp135

Cited by 90 publications

(119 citation statements)

References 24 publications

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“…A statistically significant and diverse number of sequences (16272 sequences) in the Pfam database provided input for the MI computations. In MISTIC approach, sequence clustering is implemented to reduce sequence redundancy and sequence clusters are defined at a sequence identity threshold of 62% [148] . A lower bound of 400 sequences <62% identity is typically required in an MSA to yield statistically meaningful coevolutionary relationships.…”

Section: Methodsmentioning

confidence: 99%

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

2017

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Allosteric interactions in the Hsp70 proteins are linked with their regulatory mechanisms and cellular functions. Despite significant progress in structural and functional characterization of the Hsp70 proteins fundamental questions concerning modularity of the allosteric interaction networks and hierarchy of signaling pathways in the Hsp70 chaperones remained largely unexplored and poorly understood. In this work, we proposed an integrated computational strategy that combined atomistic and coarse-grained simulations with coevolutionary analysis and network modeling of the residue interactions. A novel aspect of this work is the incorporation of dynamic residue correlations and coevolutionary residue dependencies in the construction of allosteric interaction networks and signaling pathways. We found that functional sites involved in allosteric regulation of Hsp70 may be characterized by structural stability, proximity to global hinge centers and local structural environment that is enriched by highly coevolving flexible residues. These specific characteristics may be necessary for regulation of allosteric structural transitions and could distinguish regulatory sites from nonfunctional conserved residues. The observed confluence of dynamics correlations and coevolutionary residue couplings with global networking features may determine modular organization of allosteric interactions and dictate localization of key mediating sites. Community analysis of the residue interaction networks revealed that concerted rearrangements of local interacting modules at the inter-domain interface may be responsible for global structural changes and a population shift in the DnaK chaperone. The inter-domain communities in the Hsp70 structures harbor the majority of regulatory residues involved in allosteric signaling, suggesting that these sites could be integral to the network organization and coordination of structural changes. Using a network-based formalism of allostery, we introduced a community-hopping model of allosteric communication. Atomistic reconstruction of signaling pathways in the DnaK structures captured a direction-specific mechanism and molecular details of signal transmission that are fully consistent with the mutagenesis experiments. The results of our study reconciled structural and functional experiments from a network-centric perspective by showing that global properties of the residue interaction networks and coevolutionary signatures may be linked with specificity and diversity of allosteric regulation mechanisms.

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Section: Methodsmentioning

confidence: 99%

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

2017

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“…The MI score is calculated as a weighted sum of the log ratios between the observed and expected amino acid pair frequencies. The MI scores were translated into MI z -scores by comparing the MI values for each pair of positions with a distribution of prediction scores obtained from a large set of randomized MSAs88. The z -score is then calculated as the number of standard deviations that the observed MI value falls above the mean value obtained from the randomized MSAs.…”

Section: Calculationsmentioning

confidence: 99%

Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors

Woods

Dutta

Klein‐Seetharaman

2016

Sci Rep

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G protein-coupled receptors are a large family of membrane proteins activated by a variety of structurally diverse ligands making them highly adaptable signaling molecules. Despite recent advances in the structural biology of this protein family, the mechanism by which ligands induce allosteric changes in protein structure and dynamics for its signaling function remains a mystery. Here, we propose the use of terahertz spectroscopy combined with molecular dynamics simulation and protein evolutionary network modeling to address the mechanism of activation by directly probing the concerted fluctuations of retinal ligand and transmembrane helices in rhodopsin. This approach allows us to examine the role of conformational heterogeneity in the selection and stabilization of specific signaling pathways in the photo-activation of the receptor. We demonstrate that ligand-induced shifts in the conformational equilibrium prompt vibrational resonances in the protein structure that link the dynamics of conserved interactions with fluctuations of the active-state ligand. The connection of vibrational modes creates an allosteric association of coupled fluctuations that forms a coherent signaling pathway from the receptor ligand-binding pocket to the G-protein activation region. Our evolutionary analysis of rhodopsin-like GPCRs suggest that specific allosteric sites play a pivotal role in activating structural fluctuations that allosterically modulate functional signals.

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“…Many correction schemes for removing noise from the matrix of predicted contact scores have been examined 55 [6,22,34,37,43,56,59], and the average product correction (APC) [8] came out as a clear winner and is used in almost all recent studies. However, it is widely acknowledged in the field that our limited understanding of what noise effects APC is correcting and why it is so effectively correcting them is severely impeding progress in developing better statistical methods to predict contacting residue pairs.…”

Section: Introductionmentioning

confidence: 99%

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

Vorberg

Seemayer

Soeding

2018

Preprint

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Compensatory mutations between protein residues that are in physical contact with each other can manifest themselves as statistical couplings between the corresponding columns in a multiple sequence alignment (MSA) of the protein family. Conversely, high coupling coefficients predict residues contacts. Methods for de-novo protein structure prediction based on this approach are becoming increasingly reliable. Their main limitation is the strong systematic and statistical noise in the estimation of coupling coefficients, which has so far limited their application to very large protein families. While most research has focused on boosting contact prediction quality by adding external information, little progress has been made to improve the statistical procedure at the core. In that regard, our lack of understanding of the sources of noise poses a major obstacle. We have developed CCMgen, the first method for simulating protein evolution by providing full control over the generation of realistic synthetic MSAs with pairwise statistical couplings between residue positions. This procedure requires an exact statistical model that reliably reproduces observed alignment statistics. With CCMpredPy we also provide an implementation of persistent contrastive divergence (PCD), a precise inference technique that enables us to learn the required high-quality statistical models. We demonstrate how CCMgen can facilitate the development and testing of contact prediction methods by analysing the systematic noise contributions from phylogeny and entropy. For that purpose we propose a simple entropy correction (EC) strategy which disentangles the correction for both sources of noise. We find that entropy contributes typically roughly twice as much noise as phylogeny.

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Correction for phylogeny, small number of observations and data redundancy improves the identification of coevolving amino acid pairs using mutual information

Cited by 90 publications

References 24 publications

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

Computational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and Communication

Vibrational resonance, allostery, and activation in rhodopsin-like G protein-coupled receptors

Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction

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