Literature DB >> 30951584

AbPredict 2: a server for accurate and unstrained structure prediction of antibody variable domains.

Gideon Lapidoth1, Jake Parker2,3, Jaime Prilusky4, Sarel J Fleishman1.   

Abstract

SUMMARY: Methods for antibody structure prediction rely on sequence homology to experimentally determined structures. Resulting models may be accurate but are often stereochemically strained, limiting their usefulness in modeling and design workflows. We present the AbPredict 2 web-server, which instead of using sequence homology, conducts a Monte Carlo-based search for low-energy combinations of backbone conformations to yield accurate and unstrained antibody structures.
AVAILABILITY AND IMPLEMENTATION: We introduce several important improvements over the previous AbPredict implementation: (i) backbones and sidechains are now modeled using ideal bond lengths and angles, substantially reducing stereochemical strain, (ii) sampling of the rigid-body orientation at the light-heavy chain interface is improved, increasing model accuracy and (iii) runtime is reduced 20-fold without compromising accuracy, enabling the implementation of AbPredict 2 as a fully automated web-server (http://abpredict.weizmann.ac.il). Accurate and unstrained antibody model structures may in some cases obviate the need for experimental structures in antibody optimization workflows.
© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Year:  2019        PMID: 30951584      PMCID: PMC6542689          DOI: 10.1093/bioinformatics/bty822

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  19 in total

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4.  PIGS: automatic prediction of antibody structures.

Authors:  Paolo Marcatili; Alessandra Rosi; Anna Tramontano
Journal:  Bioinformatics       Date:  2008-07-19       Impact factor: 6.937

5.  High affinity anti-inorganic material antibody generation by integrating graft and evolution technologies: potential of antibodies as biointerface molecules.

Authors:  Takamitsu Hattori; Mitsuo Umetsu; Takeshi Nakanishi; Takanari Togashi; Nozomi Yokoo; Hiroya Abe; Satoshi Ohara; Tadafumi Adschiri; Izumi Kumagai
Journal:  J Biol Chem       Date:  2009-12-31       Impact factor: 5.157

Review 6.  Development trends for monoclonal antibody cancer therapeutics.

Authors:  Janice M Reichert; Viia E Valge-Archer
Journal:  Nat Rev Drug Discov       Date:  2007-05       Impact factor: 84.694

7.  Novel affinity tag system using structurally defined antibody-tag interaction: application to single-step protein purification.

Authors:  Terukazu Nogi; Takeshi Sangawa; Sanae Tabata; Masamichi Nagae; Keiko Tamura-Kawakami; Ayako Beppu; Mitsuharu Hattori; Norihisa Yasui; Junichi Takagi
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8.  The association of heavy and light chain variable domains in antibodies: implications for antigen specificity.

Authors:  Anna Chailyan; Paolo Marcatili; Anna Tramontano
Journal:  FEBS J       Date:  2011-06-28       Impact factor: 5.542

9.  RosettaAntibody: antibody variable region homology modeling server.

Authors:  Aroop Sircar; Eric T Kim; Jeffrey J Gray
Journal:  Nucleic Acids Res       Date:  2009-05-20       Impact factor: 16.971

10.  MolProbity: all-atom structure validation for macromolecular crystallography.

Authors:  Vincent B Chen; W Bryan Arendall; Jeffrey J Headd; Daniel A Keedy; Robert M Immormino; Gary J Kapral; Laura W Murray; Jane S Richardson; David C Richardson
Journal:  Acta Crystallogr D Biol Crystallogr       Date:  2009-12-21
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Journal:  PLoS Comput Biol       Date:  2019-08-23       Impact factor: 4.475

Review 4.  VHH Structural Modelling Approaches: A Critical Review.

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5.  NanoNet: Rapid and accurate end-to-end nanobody modeling by deep learning.

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6.  RosettaCM for antibodies with very long HCDR3s and low template availability.

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Journal:  Proteins       Date:  2021-07-07

Review 7.  Macromolecular modeling and design in Rosetta: recent methods and frameworks.

Authors:  Julia Koehler Leman; Brian D Weitzner; Steven M Lewis; Jared Adolf-Bryfogle; Nawsad Alam; Rebecca F Alford; Melanie Aprahamian; David Baker; Kyle A Barlow; Patrick Barth; Benjamin Basanta; Brian J Bender; Kristin Blacklock; Jaume Bonet; Scott E Boyken; Phil Bradley; Chris Bystroff; Patrick Conway; Seth Cooper; Bruno E Correia; Brian Coventry; Rhiju Das; René M De Jong; Frank DiMaio; Lorna Dsilva; Roland Dunbrack; Alexander S Ford; Brandon Frenz; Darwin Y Fu; Caleb Geniesse; Lukasz Goldschmidt; Ragul Gowthaman; Jeffrey J Gray; Dominik Gront; Sharon Guffy; Scott Horowitz; Po-Ssu Huang; Thomas Huber; Tim M Jacobs; Jeliazko R Jeliazkov; David K Johnson; Kalli Kappel; John Karanicolas; Hamed Khakzad; Karen R Khar; Sagar D Khare; Firas Khatib; Alisa Khramushin; Indigo C King; Robert Kleffner; Brian Koepnick; Tanja Kortemme; Georg Kuenze; Brian Kuhlman; Daisuke Kuroda; Jason W Labonte; Jason K Lai; Gideon Lapidoth; Andrew Leaver-Fay; Steffen Lindert; Thomas Linsky; Nir London; Joseph H Lubin; Sergey Lyskov; Jack Maguire; Lars Malmström; Enrique Marcos; Orly Marcu; Nicholas A Marze; Jens Meiler; Rocco Moretti; Vikram Khipple Mulligan; Santrupti Nerli; Christoffer Norn; Shane Ó'Conchúir; Noah Ollikainen; Sergey Ovchinnikov; Michael S Pacella; Xingjie Pan; Hahnbeom Park; Ryan E Pavlovicz; Manasi Pethe; Brian G Pierce; Kala Bharath Pilla; Barak Raveh; P Douglas Renfrew; Shourya S Roy Burman; Aliza Rubenstein; Marion F Sauer; Andreas Scheck; William Schief; Ora Schueler-Furman; Yuval Sedan; Alexander M Sevy; Nikolaos G Sgourakis; Lei Shi; Justin B Siegel; Daniel-Adriano Silva; Shannon Smith; Yifan Song; Amelie Stein; Maria Szegedy; Frank D Teets; Summer B Thyme; Ray Yu-Ruei Wang; Andrew Watkins; Lior Zimmerman; Richard Bonneau
Journal:  Nat Methods       Date:  2020-06-01       Impact factor: 28.547

8.  Computational approaches to therapeutic antibody design: established methods and emerging trends.

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9.  The AbDesign computational pipeline for modular backbone assembly and design of binders and enzymes.

Authors:  Rosalie Lipsh-Sokolik; Dina Listov; Sarel J Fleishman
Journal:  Protein Sci       Date:  2020-10-28       Impact factor: 6.725

  9 in total

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