Evaluation of an Affinity-Enhanced Anti-SARS-CoV2 Nanobody Design Workflow Using Machine Learning and Molecular Dynamics.

ABSTRACT: In silico optimization of protein binding has received a great deal of attention in the recent years. Since in silico prefiltering of strong binders is fast and cheap compared to in vitro library screening methods, the advent of powerful hardware and advanced machine learning algorithms has made this strategy more accessible and preferred. These advances have already impacted the global response to pandemic threats. In this study, we proposed and tested a workflow for designing nanobodies targeting the SARS-CoV-2 spike protein receptor binding domain (S-RBD) using machine learning techniques complemented by molecular dynamics simulations. We evaluated the feasibility of this workflow using a test set of 3 different nanobodies and 2 different S-RBD variants, from in

SUBMITTER: Fazekas Z 

PROVIDER: S-EPMC11481066 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature