Computational counterselection identifies nonspecific therapeutic biologic candidates.

ABSTRACT: Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and indiv

SUBMITTER: Saksena SD 

PROVIDER: S-EPMC9308162 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature