Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan.

ABSTRACT: The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype-fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise a

SUBMITTER: Fernandez-de-Cossio-Diaz J 

PROVIDER: S-EPMC7783173 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature