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MHCEpitopeEnergy, a Flexible Rosetta-Based Biotherapeutic Deimmunization Platform
Author(s) -
Brahm J. Yachnin,
Vikram Khipple Mulligan,
Sagar D. Khare,
Chris BaileyKellogg
Publication year - 2021
Publication title -
journal of chemical information and modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 160
eISSN - 1549-960X
pISSN - 1549-9596
DOI - 10.1021/acs.jcim.1c00056
Subject(s) - epitope , benchmark (surveying) , computer science , computational biology , protein design , sequence (biology) , function (biology) , suite , bioinformatics , machine learning , artificial intelligence , biology , protein structure , immunology , antibody , biochemistry , genetics , geodesy , archaeology , evolutionary biology , geography , history
As non-"self" macromolecules, biotherapeutics can trigger an immune response that can reduce drug efficacy, require patients to be taken off therapy, or even cause life-threatening reactions. To enable the flexible and facile design of protein biotherapeutics while reducing the prevalence of T-cell epitopes that drive immune recognition, we have integrated into the Rosetta protein design suite a new scoring term that allows design protocols to account for predicted or experimentally identified epitopes in the optimized objective function. This flexible scoring term can be used in any Rosetta design trajectory, can be targeted to specific regions of a protein, and can be readily extended to work with a variety of epitope predictors. By performing extensive design runs with varied design parameter choices for three case study proteins as well as a larger diverse benchmark, we show that the incorporation of this scoring term enables the effective exploration of an alternative, deimmunized sequence space to discover diverse proteins that are potentially highly deimmunized while retaining physical and chemical qualities similar to those yielded by equivalent nondeimmunizing sequence design protocols.

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