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Journal article

Antibody side chain conformations are position-dependent

Abstract:
Side chain prediction is an integral component of computational antibody design and structure prediction. Current antibody modelling tools use backbone-dependent rotamer libraries with conformations taken from general proteins. Here we present our antibody-specific rotamer library, where rotamers are binned according to their IMGT position, rather than their local backbone geometry. We find that for some amino acid types at certain positions, only a restricted number of side chain conformations are ever observed. Using this information, we are able to reduce the breadth of the rotamer sampling space. Based on our rotamer library, we built a side chain predictor, PEARS. On a blind test set of 95 antibody model structures, PEARS had the highest average ϰ1 and ϰ1 + 2 accuracy (78.7% and 64.8%) compared to three leading backbone-dependent side chain predictors. Our use of IMGT position, rather than backbone ϕ/ψ, meant that PEARS was more robust to errors in the backbone of the model structure. PEARS also achieved the lowest number of side chain-side chain clashes. PEARS is freely available as a web application at http://opig.stats.ox.ac.uk/webapps/pears. This article is protected by copyright. All rights reserved.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/prot.25453

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Role:
Author
ORCID:
0000-0002-7817-3644
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Kellogg College
Role:
Author
ORCID:
0000-0003-1388-2252


More from this funder
Funding agency for:
Leem, J
Deane, C
Grant:
EP/L016044/1
EP/L016044/1
More from this funder
Funding agency for:
Leem, J
Deane, C
Grant:
EP/L016044/1
EP/L016044/1


Publisher:
Wiley
Journal:
Proteins More from this journal
Volume:
86
Issue:
4
Pages:
383-392
Publication date:
2018-01-10
Acceptance date:
2018-01-05
DOI:
EISSN:
1097-0134
ISSN:
0887-3585
Pmid:
29318667


Language:
English
Keywords:
Pubs id:
pubs:817678
UUID:
uuid:8b768ec8-ce50-4b7f-8f1e-036c08777169
Local pid:
pubs:817678
Source identifiers:
817678
Deposit date:
2018-01-17

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