Studies of protein fitness landscapes reveal biophysical constraints guiding protein
evolution and empower prediction of functional proteins. However, generalisation of these …

Proteins—molecular machines that underpin all biological life—are of significant therapeutic
and industrial value. Directed evolution is a high-throughput experimental approach for …

Predictive modelling of protein properties has become increasingly important to the field of
machine-learning guided protein engineering. In one of the two existing approaches …

The structure of fitness landscapes is critical for understanding adaptive protein evolution.
Previous empirical studies on fitness landscapes were confined to either the neighborhood …

Knowing how protein sequence maps to function (the “fitness landscape”) is critical for
understanding protein evolution as well as for engineering proteins with new and useful …

The recent technological advances underlying the screening of large combinatorial libraries
in high-throughput mutational scans deepen our understanding of adaptive protein evolution …

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions,
from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly …

Abstract Machine learning (ML) has transformed protein engineering by constructing models
of the underlying sequence-function landscape to accelerate the discovery of new …

Predicting the effects of mutations on protein function is an important issue in evolutionary
biology and biomedical applications. Computational approaches, ranging from graphical …

High-throughput sequencing has enabled many powerful approaches in biological
research. Here, we review sequencing approaches to measure frequency changes within …