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High-accuracy modeling of antibody structures by a search for minimum-energy recombination of backbone fragments

Norn, Christoffer H. LU ; Lapidoth, Gideon and Fleishman, Sarel J. (2017) In Proteins: Structure, Function and Bioinformatics 85(1). p.30-38
Abstract

Current methods for antibody structure prediction rely on sequence homology to known structures. Although this strategy often yields accurate predictions, models can be stereo-chemically strained. Here, we present a fully automated algorithm, called AbPredict, that disregards sequence homology, and instead uses a Monte Carlo search for low-energy conformations built from backbone segments and rigid-body orientations that appear in antibody molecular structures. We find cases where AbPredict selects accurate loop templates with sequence identity as low as 10%, whereas the template of highest sequence identity diverges substantially from the query's conformation. Accordingly, in several cases reported in the recent Antibody Modeling... (More)

Current methods for antibody structure prediction rely on sequence homology to known structures. Although this strategy often yields accurate predictions, models can be stereo-chemically strained. Here, we present a fully automated algorithm, called AbPredict, that disregards sequence homology, and instead uses a Monte Carlo search for low-energy conformations built from backbone segments and rigid-body orientations that appear in antibody molecular structures. We find cases where AbPredict selects accurate loop templates with sequence identity as low as 10%, whereas the template of highest sequence identity diverges substantially from the query's conformation. Accordingly, in several cases reported in the recent Antibody Modeling Assessment benchmark, AbPredict models were more accurate than those from any participant, and the models' stereo-chemical quality was consistently high. Furthermore, in two blind cases provided to us by crystallographers prior to structure determination, the method achieved <1.5 Ångstrom overall backbone accuracy. Accurate modeling of unstrained antibody structures will enable design and engineering of improved binders for biomedical research directly from sequence. Proteins 2016; 85:30–38.

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Please use this url to cite or link to this publication:
author
; and
publishing date
type
Contribution to journal
publication status
published
subject
keywords
AbDesign, combinatorial-backbone modeling, loop prediction, protein structure prediction, rosetta
in
Proteins: Structure, Function and Bioinformatics
volume
85
issue
1
pages
9 pages
publisher
John Wiley & Sons Inc.
external identifiers
  • pmid:27717001
  • scopus:85000402235
ISSN
0887-3585
DOI
10.1002/prot.25185
language
English
LU publication?
no
id
397e1aa6-ce57-48d0-8065-8cb77c894f93
date added to LUP
2020-04-22 14:32:37
date last changed
2024-07-11 17:34:51
@article{397e1aa6-ce57-48d0-8065-8cb77c894f93,
  abstract     = {{<p>Current methods for antibody structure prediction rely on sequence homology to known structures. Although this strategy often yields accurate predictions, models can be stereo-chemically strained. Here, we present a fully automated algorithm, called AbPredict, that disregards sequence homology, and instead uses a Monte Carlo search for low-energy conformations built from backbone segments and rigid-body orientations that appear in antibody molecular structures. We find cases where AbPredict selects accurate loop templates with sequence identity as low as 10%, whereas the template of highest sequence identity diverges substantially from the query's conformation. Accordingly, in several cases reported in the recent Antibody Modeling Assessment benchmark, AbPredict models were more accurate than those from any participant, and the models' stereo-chemical quality was consistently high. Furthermore, in two blind cases provided to us by crystallographers prior to structure determination, the method achieved &lt;1.5 Ångstrom overall backbone accuracy. Accurate modeling of unstrained antibody structures will enable design and engineering of improved binders for biomedical research directly from sequence. Proteins 2016; 85:30–38.</p>}},
  author       = {{Norn, Christoffer H. and Lapidoth, Gideon and Fleishman, Sarel J.}},
  issn         = {{0887-3585}},
  keywords     = {{AbDesign; combinatorial-backbone modeling; loop prediction; protein structure prediction; rosetta}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{30--38}},
  publisher    = {{John Wiley & Sons Inc.}},
  series       = {{Proteins: Structure, Function and Bioinformatics}},
  title        = {{High-accuracy modeling of antibody structures by a search for minimum-energy recombination of backbone fragments}},
  url          = {{http://dx.doi.org/10.1002/prot.25185}},
  doi          = {{10.1002/prot.25185}},
  volume       = {{85}},
  year         = {{2017}},
}