7lg4

Publication Abstract from PubMed

Studies of protein fitness landscapes reveal biophysical constraints guiding protein evolution and empower prediction of functional proteins. However, generalisation of these findings is limited due to scarceness of systematic data on fitness landscapes of proteins with a defined evolutionary relationship. We characterized the fitness peaks of four orthologous fluorescent proteins with a broad range of sequence divergence. While two of the four studied fitness peaks were sharp, the other two were considerably flatter, being almost entirely free of epistatic interactions. Mutationally robust proteins, characterized by a flat fitness peak, were not optimal templates for machine-learning-driven protein design - instead, predictions were more accurate for fragile proteins with epistatic landscapes. Our work paves insights for practical application of fitness landscape heterogeneity in protein engineering.

Heterogeneity of the GFP fitness landscape and data-driven protein design.,Gonzalez Somermeyer L, Fleiss A, Mishin AS, Bozhanova NG, Igolkina AA, Meiler J, Alaball Pujol ME, Putintseva EV, Sarkisyan KS, Kondrashov FA Elife. 2022 May 5;11. pii: 75842. doi: 10.7554/eLife.75842. PMID:35510622^[1]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.