Institute of Science and Technology Austria, Klosterneuburg, Austria
Aubin Fleiss
Synthetic Biology Group, MRC London Institute of Medical Sciences, London, United Kingdom; Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College London, London, United Kingdom
Department of Chemistry, Center for Structural Biology, Vanderbilt University, Nashville, United States; Institute for Drug Discovery, Medical School, Leipzig University, Leipzig, Germany
Synthetic Biology Group, MRC London Institute of Medical Sciences, London, United Kingdom; Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College London, London, United Kingdom
Synthetic Biology Group, MRC London Institute of Medical Sciences, London, United Kingdom; Institute of Clinical Sciences, Faculty of Medicine and Imperial College Centre for Synthetic Biology, Imperial College London, London, United Kingdom; Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russian Federation
Institute of Science and Technology Austria, Klosterneuburg, Austria; Evolutionary and Synthetic Biology Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
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.
