Assembling the Tat protein translocase

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Abstract

The twin-arginine protein translocation system (Tat) transports folded proteins across the bacterial cytoplasmic membrane and the thylakoid membranes of plant chloroplasts. The Tat transporter is assembled from multiple copies of the membrane proteins TatA, TatB, and TatC. We combine sequence co-evolution analysis, molecular simulations, and experimentation to define the interactions between the Tat proteins of Escherichia coli at molecular-level resolution. In the TatBC receptor complex the transmembrane helix of each TatB molecule is sandwiched between two TatC molecules, with one of the inter-subunit interfaces incorporating a functionally important cluster of interacting polar residues. Unexpectedly, we find that TatA also associates with TatC at the polar cluster site. Our data provide a structural model for assembly of the active Tat translocase in which substrate binding triggers replacement of TatB by TatA at the polar cluster site. Our work demonstrates the power of co-evolution analysis to predict protein interfaces in multi-subunit complexes.

DOI: http://dx.doi.org/10.7554/eLife.20718.001

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Canonical sampling through velocity-rescaling

Giovanni Bussi, Michele Parrinello, Davide Donadio (2008)

We present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains constant during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. We illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liquid phases. Its performance is excellent and largely independent on the thermostat parameter also with regard to the dynamic properties.

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

David T. W. Jones, Daniel Buchan, Domenico Cozzetto … (2012)

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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Three-dimensional structures of membrane proteins from genomic sequencing.

Thomas A Hopf, Lucy Colwell, Robert Sheridan … (2012)

We show that amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families, demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization, functional sites, and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing, more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation, greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method. Copyright © 2012 Elsevier Inc. All rights reserved.

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Author and article information

Contributors

Nir Ben-Tal

Journal

Journal ID (nlm-ta): eLife

Journal ID (iso-abbrev): Elife

Journal ID (hwp): eLife

Journal ID (publisher-id): eLife

Title: eLife

Publisher: eLife Sciences Publications, Ltd

ISSN (Electronic): 2050-084X

Publication date (Electronic, pub): 03 December 2016

Publication date Collection: 2016

Volume: 5

Electronic Location Identifier: e20718

Affiliations

[1 ]deptDepartment of Biochemistry , University of Oxford , Oxford, United Kingdom

[2 ]deptDepartment of Chemistry , University of Oxford , Oxford, United Kingdom

[3 ]deptDivision of Molecular Microbiology, College of Life Sciences , University of Dundee , Dundee, United Kingdom

[4]Tel Aviv University , Israel

[5]Tel Aviv University , Israel

Author notes

phillip.stansfeld@ 123456bioch.ox.ac.uk (PJS);

ben.berks@ 123456bioch.ox.ac.uk (BCB)

[‡]

Department of Chemistry, Kings College London, London, United Kingdom.

[§]

EMBL Australia Node for Single Molecule Science, University of New South Wales, New South Wales, Australia.

[†]

These authors contributed equally to this work.

Author information

Mark I Wallace http://orcid.org/0000-0002-5692-8313

Ben C Berks http://orcid.org/0000-0001-9685-4067

Article

Publisher ID: 20718

DOI: 10.7554/eLife.20718

PMC ID: 5201420

PubMed ID: 27914200

SO-VID: 1ce0200b-99fe-47f5-86b4-857f24c1a707

License:

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

History

Date received : 22 August 2016

Date accepted : 29 November 2016

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;

Award ID: BB/L002531/1

Award Recipient : Tracy Palmer Ben C Berks

Funded by: FundRef http://dx.doi.org/10.13039/100010269, Wellcome;

Award ID: Investigator Award 107929/Z/15/Z

Award Recipient : Ben C Berks

Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;

Award ID: G1001640

Award Recipient : Tracy Palmer Ben C Berks

Funded by: FundRef http://dx.doi.org/10.13039/501100000780, European Commission;

Award ID: Marie Curie Fellowship Programme: GP7-PEOPLE-2013-IEF 626436

Award Recipient : Hajra Basit Mark I Wallace

Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;

Award ID: BB/I019855/1

Award Recipient : Phillip J Stansfeld

Funded by: FundRef http://dx.doi.org/10.13039/100010269, Wellcome;

Award ID: Investigator Award 110183/Z/15/Z

Award Recipient : Tracy Palmer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

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elife-xml-version 2.5

Author impact statement Evolutionary bioinformatics and experimentation are applied to the components of the Tat protein transport system to elucidate the structure of the membrane-bound receptor complex and to deduce a molecular description for its substrate-triggered activation.

ScienceOpen disciplines: Life sciences

Keywords: tat protein transport,sequence co-evolution,membrane protein,twin-arginine,e. coli

Data availability:

ScienceOpen disciplines: Life sciences

Keywords: tat protein transport, sequence co-evolution, membrane protein, twin-arginine, e. coli

Assembling the Tat protein translocase

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Most cited references 66

Canonical sampling through velocity-rescaling

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

Three-dimensional structures of membrane proteins from genomic sequencing.

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