As organisms evolve, the effects of mutations change as a result of epistatic interactions with
other mutations accumulated along the line of descent. This can lead to shifts in adaptability …

Quantitatively linking the composition and function of microbial communities is a major
aspiration of microbial ecology. Microbial community functions emerge from a complex web …

Allosteric communication between distant sites in proteins is central to biological regulation
but still poorly characterized, limiting understanding, engineering and drug development …

The mapping from protein sequence to function is highly complex, making it challenging to
predict how sequence changes will affect a protein's behavior and properties. We present a …

Interactions between mutations (epistasis) can add substantial complexity to genotype-
phenotype maps, hampering our ability to predict evolution. Yet, recent studies have shown …

Deep neural networks (DNNs) have greatly advanced the ability to predict genome function
from sequence. However, elucidating underlying biological mechanisms from genomic …

Epistatic interactions between mutations add substantial complexity to adaptive landscapes
and are often thought of as detrimental to our ability to predict evolution. Yet, patterns of …

Proteins often accumulate neutral mutations that do not affect current functions but can
profoundly influence future mutational possibilities and functions. Understanding such …

We present MoCHI, a tool to fit interpretable models using deep mutational scanning data.
MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for …

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in
evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) …