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      ATPase Subdomain IA Is a Mediator of Interdomain Allostery in Hsp70 Molecular Chaperones

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          Abstract

          The versatile functions of the heat shock protein 70 (Hsp70) family of molecular chaperones rely on allosteric interactions between their nucleotide-binding and substrate-binding domains, NBD and SBD. Understanding the mechanism of interdomain allostery is essential to rational design of Hsp70 modulators. Yet, despite significant progress in recent years, how the two Hsp70 domains regulate each other's activity remains elusive. Covariance data from experiments and computations emerged in recent years as valuable sources of information towards gaining insights into the molecular events that mediate allostery. In the present study, conservation and covariance properties derived from both sequence and structural dynamics data are integrated with results from Perturbation Response Scanning and in vivo functional assays, so as to establish the dynamical basis of interdomain signal transduction in Hsp70s. Our study highlights the critical roles of SBD residues D481 and T417 in mediating the coupled motions of the two domains, as well as that of G506 in enabling the movements of the α-helical lid with respect to the β-sandwich. It also draws attention to the distinctive role of the NBD subdomains: Subdomain IA acts as a key mediator of signal transduction between the ATP- and substrate-binding sites, this function being achieved by a cascade of interactions predominantly involving conserved residues such as V139, D148, R167 and K155. Subdomain IIA, on the other hand, is distinguished by strong coevolutionary signals (with the SBD) exhibited by a series of residues (D211, E217, L219, T383) implicated in DnaJ recognition. The occurrence of coevolving residues at the DnaJ recognition region parallels the behavior recently observed at the nucleotide-exchange-factor recognition region of subdomain IIB. These findings suggest that Hsp70 tends to adapt to co-chaperone recognition and activity via coevolving residues, whereas interdomain allostery, critical to chaperoning, is robustly enabled by conserved interactions.

          Author Summary

          The Hsp70 family of molecular chaperones assists in protein folding, degradation, assembly/disassembly of some complexes, and intracellular trafficking. These activities in the cell are accomplished by coupled conformational switches/signals between their nucleotide-binding and substrate-binding domains (NBD and SBD), assisted by cognate co-chaperones. Despite significant progress in the field, the molecular basis of Hsp70 machinery and the key interactions that regulate interdomain communication are not fully understood. Using a combination of experimental and computational methods, including in vivo functional assays, sequence- and structure-based analyses and perturbation response scanning, we identified a network of conserved interactions in subdomain IA of the NBD, which plays a key ( effector) role in propagating signals between the ATP-binding and substrate-binding sites. Subdomain IIA, on the other hand, appears to adapt to J-domain co-chaperone binding by virtue of a broadly distributed cluster of co-evolving residues on the recognition surface.

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          Most cited references55

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          Direct-coupling analysis of residue coevolution captures native contacts across many protein families.

          The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.
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            The Hsp70 and Hsp60 chaperone machines.

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              PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments.

              The accurate prediction of residue-residue contacts, critical for maintaining the native fold of a protein, remains an open problem in the field of structural bioinformatics. Interest in this long-standing problem has increased recently with algorithmic improvements and the rapid growth in the sizes of sequence families. Progress could have major impacts in both structure and function prediction to name but two benefits. Sequence-based contact predictions are usually made by identifying correlated mutations within multiple sequence alignments (MSAs), most commonly through the information-theoretic approach of calculating mutual information between pairs of sites in proteins. These predictions are often inaccurate because the true covariation signal in the MSA is often masked by biases from many ancillary indirect-coupling or phylogenetic effects. Here we present a novel method, PSICOV, which introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction. Our method builds on work which had previously demonstrated corrections for phylogenetic and entropic correlation noise and allows accurate discrimination of direct from indirectly coupled mutation correlations in the MSA. PSICOV displays a mean precision substantially better than the best performing normalized mutual information approach and Bayesian networks. For 118 out of 150 targets, the L/5 (i.e. top-L/5 predictions for a protein of length L) precision for long-range contacts (sequence separation >23) was ≥ 0.5, which represents an improvement sufficient to be of significant benefit in protein structure prediction or model quality assessment. The PSICOV source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/PSICOV.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                May 2014
                15 May 2014
                : 10
                : 5
                : e1003624
                Affiliations
                [1 ]Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
                [2 ]Department of Biochemistry & Molecular Biology, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
                [3 ]Department of Pharmacology, Tsinghua University, Beijing, China
                [4 ]Department of Chemistry, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
                Chapman University, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: LMG IB. Performed the experiments: IJG YL MEB WM. Analyzed the data: IJG YL MEB WM LMG IB. Contributed reagents/materials/analysis tools: LMG IJG WM IB. Wrote the paper: IJG MEB LMG IB.

                Article
                PCOMPBIOL-D-13-01739
                10.1371/journal.pcbi.1003624
                4022485
                24831085
                784011f0-c947-476c-adc0-81e188395e29
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 3 October 2013
                : 31 March 2014
                Page count
                Pages: 17
                Funding
                This work was supported by NIH ( www.nih.gov), grants GM099738 and GM103712 to IB, and GM027616-34 to LMG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Proteins
                Chaperone Proteins
                Computational Biology
                Molecular Biology
                Molecular Biology Techniques
                Sequencing Techniques
                Sequence Analysis

                Quantitative & Systems biology
                Quantitative & Systems biology

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