Research
For billions of years, nature has been conducting the greatest experiment of all time. Imagine one-day gaining access to the detailed notes from these experiments. Today, with worldwide expeditions to collect samples from all habitats, single-cell sequencing of unculturable microbes and the rapid drop in sequencing costs, we can finally tap into nature and gain access to these notes.
Making use of this data, our lab is interested in:
Developing a unified statistical model of protein evolution that integrates phylogenetics, genomic, structural, and functional constraints.
Recent Publications
Hettiarachchi R, et al. 2023. 🌐Github
Differentiable Search of Evolutionary Trees from Leaves.Hwang Y, et al. 2023. 🌐Github
Genomic language model predicts protein co-regulation and function.Petti S, et al. 2022. 🌐GitHub
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.
Zhang Z, Wayment-Steele HK, et al. 2024. 🌐Github
Protein language models learn evolutionary statistics of interacting sequence motifs.Wang H, et al. 2022.🌐Github
Disentanglement of Entropy and Coevolution using Spectral Regularization.Bhattacharya N, et al. 2021. 🌐Github
Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention.Marshall D, et al. 2020. 🌐Github
The structure-fitness landscape of pairwise relations in generative sequence models.Dauparas J, et al. 2019. 🌐Github
Unified framework for modeling multivariate distributions in biological sequences.
Explicit modeling of the protein conformational (and/or folding) landscape for protein structure prediction and design.
Recent Publications
Roney JP, et al. 2022. 🌐Github
State-of-the-Art estimation of protein model accuracy using AlphaFold.Ovchinnikov S, et al. 2021.
Structure-based protein design with deep learning.Norn C, et al. 2021. 🌐Github
Protein sequence design by conformational landscape optimization.
(image credit Basile Wicky)
Applying the models to mine metagenomic “dark matter” sequences to discover new protein families, functions, and protein-protein interactions. Probing evolution of multicellularity through comparison of structures and interactions in the early tree of life.
Recent Publications
Pavlopoulos GA, et al. 2023.🌐Database
Unraveling the functional dark matter through global metagenomics.Trinquier J, et al. 2022.🌐Github
SWAMPNN: End-to-end protein structures alignment.Ovchinnikov S, et al., 2017.🌐Github
Protein structure determination using metagenome sequence data.
All Publications