Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen.

ABSTRACT: Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg

SUBMITTER: Hutchinson M 

PROVIDER: S-EPMC11195458 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature