I. Anishchenko

Atomic context-conditioned protein sequence design using LigandMPNN

This paper introduces LigandMPNN, a deep learning method for protein sequence design that models non-protein atomic contexts, achieving better performance than existing methods in recovering interacting residues.

Essential and virulence-related protein interactions of pathogens revealed through deep learning

This study uses deep learning to predict protein-protein interactions in bacterial pathogens, identifying new complexes involving essential genes and virulence factors, with half of tested predictions validated.

De novo design of diverse small molecule binders and sensors using Shape Complementary Pseudocycles

The paper designs nanomolar binders for diverse small molecules using a pseudocycle-based shape complementarity method, enabling direct conversion into sensing systems.

Small-molecule binding and sensing with a designed protein family

This paper designs a protein family that binds and senses small molecules using deep learning and physics-based methods, achieving nanomolar sensitivity for cortisol.

Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom

This paper introduces RFAA and RFdiffusionAA, models that can predict and design complex biomolecular assemblies including proteins, nucleic acids, small molecules, and covalent modifications.

Efficient and accurate prediction of protein structure using RoseTTAFold2

This paper introduces RoseTTAFold2, a protein structure prediction method that achieves AlphaFold2-level accuracy without using its key features, demonstrating the potential for diverse model architectures.

Robust deep learning based protein sequence design using ProteinMPNN

A deep learning method for protein sequence design shows high performance in both simulations and experiments, applicable to various design challenges.

Deep learning methods for designing proteins scaffolding functional sites

This paper introduces two deep learning methods for designing proteins with specific functional sites, showing they can generate diverse proteins with desired properties.

Structures of core eukaryotic protein complexes

The paper uses coevolution analysis and deep learning to model core eukaryotic protein complexes in yeast, identifying 104 new assemblies and structurally characterizing 608 previously unknown complexes.

Protein sequence design by explicit energy landscape optimization

This paper designs protein sequences by optimizing an energy landscape to ensure they fold into a desired structure, showing improved performance over traditional methods.

De novo protein design by deep network hallucination

The paper uses deep networks to design new proteins by inverting structure prediction models, resulting in 27 stable folded proteins with diverse structures.

Structural basis for autophagy inhibition by the human Rubicon-Rab7 complex

This study determines the crystal structure of the Rubicon RH domain bound to Rab7-GTP, revealing its mechanism of autophagy inhibition and validating it as a therapeutic target.