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Kevin Wiehe

Bio: Kevin Wiehe is an academic researcher from Duke University. The author has contributed to research in topics: Antibody & Epitope. The author has an hindex of 31, co-authored 83 publications receiving 5387 citations. Previous affiliations of Kevin Wiehe include University of Massachusetts Medical School & Durham University.
Topics: Antibody, Epitope, Virology, Biology, Antigen


Papers
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Journal ArticleDOI
TL;DR: This paper presents a user-friendly protein docking server, based on the rigid-body docking programs ZDOCK and M-ZDOCK, to predict structures of protein-protein complexes and symmetric multimers, and provides options for users to guide the scoring and the selection of output models.
Abstract: Summary: Protein–protein interactions are essential to cellular and immune function, and in many cases, because of the absence of an experimentally determined structure of the complex, these interactions must be modeled to obtain an understanding of their molecular basis. We present a user-friendly protein docking server, based on the rigid-body docking programs ZDOCK and M-ZDOCK, to predict structures of protein–protein complexes and symmetric multimers. With a goal of providing an accessible and intuitive interface, we provide options for users to guide the scoring and the selection of output models, in addition to dynamic visualization of input structures and output docking models. This server enables the research community to easily and quickly produce structural models of protein–protein complexes and symmetric multimers for their own analysis. Availability: The ZDOCK server is freely available to all academic and non-profit users at: http://zdock.umassmed.edu. No registration is required. Contact: ude.demssamu@ecreip.nairb or ude.demssamu@gnew.gnipihz

1,228 citations

Journal ArticleDOI
25 Apr 2013-Nature
TL;DR: The isolation, evolution and structure of a broadly neutralizing antibody from an African donor followed from the time of infection and its co-crystal structure revealed a new loop-based mechanism of CD4-binding-site recognition.
Abstract: Current human immunodeficiency virus-1 (HIV-1) vaccines elicit strain-specific neutralizing antibodies. However, cross-reactive neutralizing antibodies arise in approximately 20% of HIV-1-infected individuals, and details of their generation could provide a blueprint for effective vaccination. Here we report the isolation, evolution and structure of a broadly neutralizing antibody from an African donor followed from the time of infection. The mature antibody, CH103, neutralized approximately 55% of HIV-1 isolates, and its co-crystal structure with the HIV-1 envelope protein gp120 revealed a new loop-based mechanism of CD4-binding-site recognition. Virus and antibody gene sequencing revealed concomitant virus evolution and antibody maturation. Notably, the unmutated common ancestor of the CH103 lineage avidly bound the transmitted/founder HIV-1 envelope glycoprotein, and evolution of antibody neutralization breadth was preceded by extensive viral diversification in and near the CH103 epitope. These data determine the viral and antibody evolution leading to induction of a lineage of HIV-1 broadly neutralizing antibodies, and provide insights into strategies to elicit similar antibodies by vaccination.

989 citations

Journal ArticleDOI
24 Jan 2013-Immunity
TL;DR: Four V2 monoclonal antibodies from RV144 vaccinees are isolated that recognize residue 169, neutralize laboratory-adapted HIV-1, and mediate killing of field-isolate HIV- 1-infected CD4(+) T cells, providing vaccine designers with new options.

382 citations

Journal ArticleDOI
15 Nov 2007-Proteins
TL;DR: This work shows that it can reduce the complexity of model representation and thus make the computation tractable with minimal loss of predictive performance and introduces a pair‐wise statistical potential suitable for docking that builds on previous work and is incorporated into the fast fourier transform‐based docking algorithm ZDOCK.
Abstract: The biophysical study of protein-protein interactions and docking has important implications in our understanding of most complex cellular signaling processes. Most computational approaches to protein docking involve a tradeoff between the level of detail incorporated into the model and computational power required to properly handle that level of detail. In this work, we seek to optimize that balance by showing that we can reduce the complexity of model representation and thus make the computation tractable with minimal loss of predictive performance. We also introduce a pair-wise statistical potential suitable for docking that builds on previous work and show that this potential can be incorporated into our fast fourier transform-based docking algorithm ZDOCK. We use the Protein Docking Benchmark to illustrate the improved performance of this potential compared with less detailed other scoring functions. Furthermore, we show that the new potential performs well on antibody-antigen complexes, with most predictions clustering around the Complementarity Determining Regions of antibodies without any manual intervention.

310 citations

Journal ArticleDOI
01 Aug 2005-Proteins
TL;DR: A new version of the Protein–Protein Docking Benchmark is presented, reconstructed from the bottom up to include more complexes, particularly focusing on more unbound–unbound test cases, and provides a platform for evaluating the progress of docking methods on a wide variety of targets.
Abstract: We present a new version of the Protein-Protein Docking Benchmark, reconstructed from the bottom up to include more complexes, par- ticularly focusing on more unbound- unbound test cases. SCOP (Structural Classification of Proteins) was used to assess redundancy between the com- plexes in this version. The new benchmark consists of 72 unbound- unbound cases, with 52 rigid-body cases, 13 medium-difficulty cases, and 7 high-difficulty cases with substantial conformational change. In addition, we retained 12 antibody-antigen test cases with the antibody structure in the bound form. The new bench- mark provides a platform for evaluating the progress of docking methods on a wide variety of targets. The new version of the benchmark is available to the public at http://zlab.bu.edu/benchmark2. Proteins 2005;60:214 -216. © 2005 Wiley-Liss, Inc.

295 citations


Cited by
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Journal ArticleDOI
TL;DR: This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results of the ClusPro server.
Abstract: The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

1,699 citations

Journal ArticleDOI
26 May 2020-Nature
TL;DR: In a study of antibodies isolated from patients infected with SARS-CoV-2, antibodies that potently neutralized the virus competed with angiotensin-converting enzyme 2 for binding to the receptor-binding domain of the viral spike protein, suggesting that antibodies that disrupt this interaction could be developed to treat Sars-Cov-2 infection.
Abstract: The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a global health emergency that is in urgent need of intervention1-3. The entry of SARS-CoV-2 into its target cells depends on binding between the receptor-binding domain (RBD) of the viral spike protein and its cellular receptor, angiotensin-converting enzyme 2 (ACE2)2,4-6. Here we report the isolation and characterization of 206 RBD-specific monoclonal antibodies derived from single B cells from 8 individuals infected with SARS-CoV-2. We identified antibodies that potently neutralize SARS-CoV-2; this activity correlates with competition with ACE2 for binding to RBD. Unexpectedly, the anti-SARS-CoV-2 antibodies and the infected plasma did not cross-react with the RBDs of SARS-CoV or Middle East respiratory syndrome-related coronavirus (MERS-CoV), although there was substantial plasma cross-reactivity to their trimeric spike proteins. Analysis of the crystal structure of RBD-bound antibody revealed that steric hindrance inhibits viral engagement with ACE2, thereby blocking viral entry. These findings suggest that anti-RBD antibodies are largely viral-species-specific inhibitors. The antibodies identified here may be candidates for development of clinical interventions against SARS-CoV-2.

1,438 citations

Journal ArticleDOI
TL;DR: This paper presents a user-friendly protein docking server, based on the rigid-body docking programs ZDOCK and M-ZDOCK, to predict structures of protein-protein complexes and symmetric multimers, and provides options for users to guide the scoring and the selection of output models.
Abstract: Summary: Protein–protein interactions are essential to cellular and immune function, and in many cases, because of the absence of an experimentally determined structure of the complex, these interactions must be modeled to obtain an understanding of their molecular basis. We present a user-friendly protein docking server, based on the rigid-body docking programs ZDOCK and M-ZDOCK, to predict structures of protein–protein complexes and symmetric multimers. With a goal of providing an accessible and intuitive interface, we provide options for users to guide the scoring and the selection of output models, in addition to dynamic visualization of input structures and output docking models. This server enables the research community to easily and quickly produce structural models of protein–protein complexes and symmetric multimers for their own analysis. Availability: The ZDOCK server is freely available to all academic and non-profit users at: http://zdock.umassmed.edu. No registration is required. Contact: ude.demssamu@ecreip.nairb or ude.demssamu@gnew.gnipihz

1,228 citations