Congratulations to all the authors Aleksander Durumeric, Sean McCarty, Jay Smith, @jonkhler.argmin.xyz, @katarinaelez.bsky.social, Lluís Raich and Patricia Suriana!!

These findings inspire a number of questions about how to translate fitness landscapes to prospective evolution predictions. Therefore, we introduce mavenets: github.com/SztainLab/ma... for reproducing and building upon this work.

MCMC simulations predict high fitness combinatorial variants with sequence profiles matching variants of concern, predicting omicron mutations using a model trained only near WT sequences.

We systematically evaluate performance on held out data within a single DMS experiment, data from independent DMS libraries, and epidemiological data, maintaining accuracy at edit distances much further from the training data, including omicron variants.

These models predict the impact of combinatorial mutations more accurately than linearly combining the impact of point mutants, suggesting an understanding of epistatic relationships.

We combined machine learning, deep mutational scanning data, and Markov chain Monte Carlo simulations to characterize the SARS-CoV-2 RBD fitness landscape.

Positions are open to both Assistant and Associate Professor ranks!

The AViDD centers were incredibly important for making scientific discoveries that improve human health. Hopefully they will be re-instated in some form

We have multiple ongoing projects related to protein engineering and antiviral drug resistance. Our lab uses a variety of dynamic computational and experimental techniques to unravel molecular mechanisms for rational design. Please send a CV and cover letter to be considered.