Inference of annealed protein fitness landscapes with AnnealDCA.

ABSTRACT: The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a small fraction of possible protein variants can be tested using these techniques. Computational models that explore the sequence space in-silico to identify the fittest molecules for a given function are needed to overcome this limitation. In this article, we propose AnnealDCA, a machine-learning framework to learn the protein fitness landscape from sequencing data derived from a broad range of experiments that use selection and sequencing to quantify protein activity. We demonstrate the effectiveness of our method by applying it to antibody R

SUBMITTER: Sesta L 

PROVIDER: S-EPMC10878520 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature