L. Sesta

Inference of annealed protein fitness landscapes with AnnealDCA

This paper introduces AnnealDCA, a machine-learning framework to infer protein fitness landscapes from sequencing data, demonstrating its effectiveness on antibody Rep-Seq and screening experiments.

AMaLa: analysis of Directed Evolution experiments via Annealed Mutational approximated Landscape

AMaLa infers fitness landscapes from Directed Evolution experiments by modeling mutation and selection dynamics across sequenced rounds, outperforming existing methods.