Deep learning guided design of protease substrates

Proteases, a class of enzymes that play critical roles in health and disease, exert their function through the cleavage of peptide bonds. Identifying substrates that are efficiently and selectively cl...

biorxiv.org

Atom level enzyme active site scaffolding using RFdiffusion2

De novo enzyme design starts from ideal active site descriptions consisting of constellations of catalytic residue functional groups around reaction transition state(s), and seeks to generate protein ...

biorxiv.org

Computational design of serine hydrolases

The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of ...

science.org

Learning millisecond protein dynamics from what is missing in NMR spectra

Many proteins’ biological functions rely on interconversions between multiple conformations occurring at micro-to millisecond (µs-ms) timescales. A lack of standardized, large-scale experimental data ...

biorxiv.org

Targeting protein–ligand neosurfaces with a generalizable deep learning tool - Nature

A computational deep learning approach is used to design synthetic proteins that target the neosurfaces formed by protein–ligand interactions, with applications in the development of new therapeutic m...

nature.com

Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences

To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from larg...

arxiv.org

Proteina: Scaling Flow-based Protein Structure Generative Models

Recently, diffusion- and flow-based generative models of protein structures have emerged as a powerful tool for de novo protein design. Here, we develop Proteina, a new large-scale flow-based...

openreview.net

ProtComposer: Compositional Protein Structure Generation with 3D...

We develop ProtComposer to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids capturing substructure shapes and semantics. At inference...

openreview.net

Concept Bottleneck Language Models For protein design

We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our architecture offers thr...

arxiv.org

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders

Protein language models (PLMs) have demonstrated remarkable success in protein modeling and design, yet their internal mechanisms for predicting structure and function remain poorly understood. Here w...

biorxiv.org

Mapping the combinatorial coding between olfactory receptors and perception with deep learning

The sense of smell remains poorly understood, especially in contrast to visual and auditory coding. At the core of our sense of smell is the olfactory information flow, in which odorant molecules acti...

biorxiv.org