High-accuracy modeling of antibody structures by a search for minimum-energy recombination of backbone fragments
(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.
(Less)
- author
- Norn, Christoffer H. LU ; Lapidoth, Gideon and Fleishman, Sarel J.
- publishing date
- 2017
- 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 <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}}, }