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Author

Anna G. Green

Other affiliations: University of Connecticut
Bio: Anna G. Green is an academic researcher from Harvard University. The author has contributed to research in topics: Protocadherin & Protein structure. The author has an hindex of 15, co-authored 27 publications receiving 1126 citations. Previous affiliations of Anna G. Green include University of Connecticut.

Papers
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Journal ArticleDOI
25 Sep 2014-eLife
TL;DR: Analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes, and predicts protein–protein contacts in 32 complexes of unknown structure.
Abstract: DNA is often referred to as the ‘blueprint of life’, as this molecule contains the instructions that are required to build a living organism from a single cell. But these instructions largely play out through the proteins that DNA encodes; and most proteins do not work alone. Instead they come together in different combinations, or complexes, and a single protein may participate in many complexes with different activities. Proteins are so small that it is difficult to get clear information about what they look like. Visualizing protein complexes is even harder. Most protein–protein interactions remain poorly understood, even in the best-studied organisms such as humans, yeast, and bacteria. Proteins are made from smaller molecules, called amino acids, strung together one after the other. The order in which different amino acids are arranged in a protein determines the protein’s shape and ultimately its function. Like DNA, protein sequences can change over time. Sometimes, the sequence of one protein changes in a way that prevents it binding to another protein. If these two proteins must work together for an organism to survive, the second protein will often develop a compensating change that allows the protein–protein complex to reform. Identifying pairs of changes in the sequences of pairs of proteins suggests that the two proteins interact and gives some information about how the proteins fit together. Different species can have copies of the same proteins that have slightly different sequences. Since the DNA sequences from many different organisms are already known, there are now many opportunities to find sites in pairs of proteins that have evolved together, or co-evolved, over time. To find sites that seem to have co-evolved, Hopf et al. used a computer program based on an approach from statistical physics to look at pairs of proteins that were already known to form complexes. Co-evolving sites were found in over 300 pairs of proteins; including 76 where the structure of the complex was already known. When sites that were predicted to be co-evolving were then mapped to these known complex structures, the co-evolving sites were remarkably close to the true protein–protein contacts. This indicates that the information from the co-evolved sequences is sufficient to show how two proteins fit together. Hopf et al. then turned their attention to 82 pairs of proteins that were thought to interact, but where a structure was unavailable. For 32 of these pairs, structures of the entire complex could be predicted, showing how the two proteins might interact. Furthermore, when other researchers subsequently worked out the structure of one of these complexes, the prediction was a good match to the solved complex structure. The machinery of life is largely made up of proteins, which must interact in ever-changing but precise ways. The new methods developed by Hopf et al. provide a new way to discover and investigate the details of these interactions.

497 citations

Journal ArticleDOI
TL;DR: Quinolone-resistant N. gonorrhoeae and reduced cefixime susceptibility appear amenable to development of sequence-based diagnostic tests, whereas the undefined mechanisms of resistance to ceftriaxone and azithromycin underscore the importance of phenotypic surveillance.
Abstract: Background Treatment of Neisseria gonorrhoeae infection is empirical and based on population-wide susceptibilities. Increasing antimicrobial resistance underscores the potential importance of rapid diagnostic tests, including sequence-based tests, to guide therapy. However, the usefulness of sequence-based diagnostic tests depends on the prevalence and dynamics of the resistance mechanisms. Methods We define the prevalence and dynamics of resistance markers to extended-spectrum cephalosporins, macrolides, and fluoroquinolones in 1102 resistant and susceptible clinical N. gonorrhoeae isolates collected from 2000 to 2013 via the Centers for Disease Control and Prevention's Gonococcal Isolate Surveillance Project. Results Reduced extended-spectrum cephalosporin susceptibility is predominantly clonal and associated with the mosaic penA XXXIV allele and derivatives (sensitivity 98% for cefixime and 91% for ceftriaxone), but alternative resistance mechanisms have sporadically emerged. Reduced azithromycin susceptibility has arisen through multiple mechanisms and shows limited clonal spread; the basis for resistance in 36% of isolates with reduced azithromycin susceptibility is unclear. Quinolone-resistant N. gonorrhoeae has arisen multiple times, with extensive clonal spread. Conclusions Quinolone-resistant N. gonorrhoeae and reduced cefixime susceptibility appear amenable to development of sequence-based diagnostic tests, whereas the undefined mechanisms of resistance to ceftriaxone and azithromycin underscore the importance of phenotypic surveillance. The identification of multidrug-resistant isolates highlights the need for additional measures to respond to the threat of untreatable gonorrhea.

183 citations

Journal ArticleDOI
TL;DR: The EVcouplings framework is presented, a fully integrated open-source application and Python package for coevolutionary analysis that enables generation of sequence alignments, calculation and evaluation of evolutionary couplings, and de novo prediction of structure and mutation effects.
Abstract: Summary Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users. Availability and implementation https://github.com/debbiemarkslab/evcouplings.

161 citations

Journal ArticleDOI
28 Mar 2018-Nature
TL;DR: The crystal structure of Thermus thermophilus RodA is reported, determined using evolutionary covariance-based fold prediction to enable molecular replacement and provides a framework for understanding bacterial cell wall synthesis and SEDS protein function.
Abstract: The shape, elongation, division and sporulation (SEDS) proteins are a large family of ubiquitous and essential transmembrane enzymes with critical roles in bacterial cell wall biology. The exact function of SEDS proteins was for a long time poorly understood, but recent work has revealed that the prototypical SEDS family member RodA is a peptidoglycan polymerase-a role previously attributed exclusively to members of the penicillin-binding protein family. This discovery has made RodA and other SEDS proteins promising targets for the development of next-generation antibiotics. However, little is known regarding the molecular basis of SEDS activity, and no structural data are available for RodA or any homologue thereof. Here we report the crystal structure of Thermus thermophilus RodA at a resolution of 2.9 A, determined using evolutionary covariance-based fold prediction to enable molecular replacement. The structure reveals a ten-pass transmembrane fold with large extracellular loops, one of which is partially disordered. The protein contains a highly conserved cavity in the transmembrane domain, reminiscent of ligand-binding sites in transmembrane receptors. Mutagenesis experiments in Bacillus subtilis and Escherichia coli show that perturbation of this cavity abolishes RodA function both in vitro and in vivo, indicating that this cavity is catalytically essential. These results provide a framework for understanding bacterial cell wall synthesis and SEDS protein function.

102 citations

Journal ArticleDOI
TL;DR: The bPBP pedestal domain is defined as the key allosteric activator of RodA both in vitro and in vivo, explaining how a SEDS–bPBP complex can coordinate its dual enzymatic activities of peptidoglycan polymerization and crosslinking to build the cell wall.
Abstract: The shape, elongation, division and sporulation (SEDS) proteins are a highly conserved family of transmembrane glycosyltransferases that work in concert with class B penicillin-binding proteins (bPBPs) to build the bacterial peptidoglycan cell wall1–6. How these proteins coordinate polymerization of new glycan strands with their crosslinking to the existing peptidoglycan meshwork is unclear. Here, we report the crystal structure of the prototypical SEDS protein RodA from Thermus thermophilus in complex with its cognate bPBP at 3.3 A resolution. The structure reveals a 1:1 stoichiometric complex with two extensive interaction interfaces between the proteins: one in the membrane plane and the other at the extracytoplasmic surface. When in complex with a bPBP, RodA shows an approximately 10 A shift of transmembrane helix 7 that exposes a large membrane-accessible cavity. Negative-stain electron microscopy reveals that the complex can adopt a variety of different conformations. These data define the bPBP pedestal domain as the key allosteric activator of RodA both in vitro and in vivo, explaining how a SEDS–bPBP complex can coordinate its dual enzymatic activities of peptidoglycan polymerization and crosslinking to build the cell wall. The crystal structure of the RodA–PBP2 complex from Thermus thermophilus elucidates how binding between these two proteins regulates their abilities to polymerize and crosslink peptidoglycan during bacterial cell wall synthesis.

79 citations


Cited by
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Journal ArticleDOI
TL;DR: An update to the SWISS-MODEL server is presented, which includes the implementation of a new modelling engine, ProMod3, and the introduction a new local model quality estimation method, QMEANDisCo.
Abstract: Homology modelling has matured into an important technique in structural biology, significantly contributing to narrowing the gap between known protein sequences and experimentally determined structures. Fully automated workflows and servers simplify and streamline the homology modelling process, also allowing users without a specific computational expertise to generate reliable protein models and have easy access to modelling results, their visualization and interpretation. Here, we present an update to the SWISS-MODEL server, which pioneered the field of automated modelling 25 years ago and been continuously further developed. Recently, its functionality has been extended to the modelling of homo- and heteromeric complexes. Starting from the amino acid sequences of the interacting proteins, both the stoichiometry and the overall structure of the complex are inferred by homology modelling. Other major improvements include the implementation of a new modelling engine, ProMod3 and the introduction a new local model quality estimation method, QMEANDisCo. SWISS-MODEL is freely available at https://swissmodel.expasy.org.

7,022 citations

Book
01 Jan 2019
TL;DR: The tabular information presented here represents the most current information for drug selection, interpretation, and QC using the procedures standardized in the most recent editions of M02, M07, and M11, and users should replace outdated editions with the current editions of CLSI documents.
Abstract: The data in the interpretive tables in this supplement are valid only if the methodologies in the following Clinical and Laboratory Standards Institute (CLSI)–approved standards are followed: M02-A12—Performance Standards for Antimicrobial Disk Susceptibility Tests; Approved Standard—Twelfth Edition; M07-A10—Methods for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically; Approved Standard—Tenth Edition; and M11-A8—Methods for Antimicrobial Susceptibility Testing of Anaerobic Bacteria; Approved Standard—Eighth Edition. The standards contain information about both disk (M02) and dilution (M07 and M11) test procedures for aerobic and anaerobic bacteria. Clinicians depend heavily on information from the microbiology laboratory for treatment of their seriously ill patients. The clinical importance of antimicrobial susceptibility test results demands that these tests be performed under optimal conditions and that laboratories have the capability to provide results for the newest antimicrobial agents. The tabular information presented here represents the most current information for drug selection, interpretation, and QC using the procedures standardized in the most current editions of M02, M07, and M11. Users should replace the tables published earlier with these new tables. (Changes in the tables since the previous edition appear in boldface type.) Clinical and Laboratory Standards Institute (CLSI). Performance Standards for Antimicrobial Susceptibility Testing. 27th ed. CLSI supplement M100 (ISBN 1-56238-804-5 [Print]; ISBN 1-56238-805-3 [Electronic]). Clinical and Laboratory Standards Institute, 950 West Valley Road, Suite 2500, Wayne, Pennsylvania 19087 USA, 2017. The Clinical and Laboratory Standards Institute consensus process, which is the mechanism for moving a document through two or more levels of review by the health care community, is an ongoing process. Users should expect revised editions of any given document. Because rapid changes in technology may affect the procedures, methods, and protocols in a standard or guideline, users should replace outdated editions with the current editions of CLSI documents. Current editions are listed in the CLSI catalog and posted on our website at www.clsi.org. If you or your organization is not a member and would like to become one, and to request a copy of the catalog, contact us at: Telephone: +1.610.688.0100; Fax: +1.610.688.0700; E-Mail: customerservice@clsi.org; Website: www.clsi.org. M100S, 26th ed. January 2016 Replaces M100-S25 Performance Standards for Antimicrobial Susceptibility Testing Jean B. Patel, PhD, D(ABMM) Franklin R. Cockerill III, MD George M. Eliopoulos, MD Stephen G. Jenkins, PhD, D(ABMM), F(AAM) James S. Lewis II, PharmD Brandi Limbago, PhD David P. Nicolau, PharmD, FCCP, FIDSA Robin Patel, MD M ir Pow ll, MD, FRCP, FRCPath Sandra S. Richter, MD, D(ABMM) Jana M. Swenson, MMSc Maria M. Traczewski, BS, MT(ASCP) John D. Turnidge, MD Melvi P. Weinstein, MD Barbara L. Zimmer, PhD

3,367 citations

Journal ArticleDOI
TL;DR: The updated version 2.2.2 of the HADDOCK portal is presented, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface.

1,762 citations

Posted ContentDOI
04 Oct 2021-bioRxiv
TL;DR: In this article, an AlphaFold model trained specifically for multimeric inputs of known stoichiometry was proposed, which significantly increases the accuracy of predicted multimimeric interfaces over input-adapted single-chain AlphaFolds.
Abstract: While the vast majority of well-structured single protein chains can now be predicted to high accuracy due to the recent AlphaFold [1] model, the prediction of multi-chain protein complexes remains a challenge in many cases. In this work, we demonstrate that an AlphaFold model trained specifically for multimeric inputs of known stoichiometry, which we call AlphaFold-Multimer, significantly increases accuracy of predicted multimeric interfaces over input-adapted single-chain AlphaFold while maintaining high intra-chain accuracy. On a benchmark dataset of 17 heterodimer proteins without templates (introduced in [2]) we achieve at least medium accuracy (DockQ [3] [≥] 0.49) on 14 targets and high accuracy (DockQ [≥] 0.8) on 6 targets, compared to 9 targets of at least medium accuracy and 4 of high accuracy for the previous state of the art system (an AlphaFold-based system from [2]). We also predict structures for a large dataset of 4,433 recent protein complexes, from which we score all non-redundant interfaces with low template identity. For heteromeric interfaces we successfully predict the interface (DockQ [≥] 0.23) in 67% of cases, and produce high accuracy predictions (DockQ [≥] 0.8) in 23% of cases, an improvement of +25 and +11 percentage points over the flexible linker modification of AlphaFold [4] respectively. For homomeric interfaces we successfully predict the interface in 69% of cases, and produce high accuracy predictions in 34% of cases, an improvement of +5 percentage points in both instances.

1,023 citations

Journal ArticleDOI
TL;DR: How HGT has shaped the web of life is described using examples of HGT among prokaryotes, between proKaryotes and eukaryote, and even between multicellular eukaries, to discuss replacement and additive HGT.
Abstract: Horizontal gene transfer (HGT) is the sharing of genetic material between organisms that are not in a parent-offspring relationship. HGT is a widely recognized mechanism for adaptation in bacteria and archaea. Microbial antibiotic resistance and pathogenicity are often associated with HGT, but the scope of HGT extends far beyond disease-causing organisms. In this Review, we describe how HGT has shaped the web of life using examples of HGT among prokaryotes, between prokaryotes and eukaryotes, and even between multicellular eukaryotes. We discuss replacement and additive HGT, the proposed mechanisms of HGT, selective forces that influence HGT, and the evolutionary impact of HGT on ancestral populations and existing populations such as the human microbiome.

938 citations