M. Weigt

Emergent time scales of epistasis in protein evolution

This paper introduces a data-driven epistatic model of protein evolution that reproduces sequence statistics across different time scales and uncovers the collective nature of epistasis.

Understanding epistatic networks in the B1 -lactamases through coevolutionary statistical modeling and deep mutational scanning

The paper uses coevolutionary analysis and deep mutational scanning to study epistatic networks in B1-lactamases, revealing both conserved and specific interactions across homologs.

Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes

The paper predicts polymorphisms in 61,157 Escherichia coli genomes using context-aware models, showing genetic context constrains amino-acid changes in 30-50% of sites.

Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes

The paper predicts mutable sites in SARS-CoV-2 proteins using epistatic models, showing improved accuracy over conservation profiles and identifying overrepresented mutations in variants of concern.

Modeling sequence-space exploration and emergence of epistatic signals in protein evolution

This paper models sequence-space exploration in protein evolution to predict fitness distributions and epistatic signals, enabling the optimization of experimental protocols.

Evolution-based design of chorismate mutase enzymes

This paper demonstrates an evolution-based design process for synthetic enzymes using sequence data, showing that statistical models can generate functional sequences with substantial diversity.

The evolution of the temporal program of genome replication

The paper analyzes replication timing programs across ten Lachancea yeast species, finding linear evolution with divergence and high turnover of active replication origins.