Jingyou Rao

Learning inhibitor interactions from protein deep mutational scanning data and deep learning models

The paper benchmarks AI models for predicting mutational effects and drug-target affinity using deep mutational scanning data, finding that current models fail to capture inhibitor-dependent fitness landscapes.

Distinct activation mechanisms underlie ligand efficacy at a GPCR

This study uses deep mutational pharmacology to show how different residues in the -opioid receptor determine the efficacy of opioids by shaping distinct activation mechanisms.

Accurate variant effect estimation in FACS-based deep mutational scanning data with Lilace

Lilace improves variant effect estimation in FACS-based DMS by providing uncertainty quantification and better FDR control.

Rosace-AA: Enhancing Interpretation of Deep Mutational Scanning Data with Amino Acid Substitution and Position-Specific Insights

Cosmos: A Position-Resolution Causal Model for Direct and Indirect Effects in Protein Functions

Cosmos is a Bayesian framework that identifies direct and indirect effects of protein variants in multi-phenotype DMS data by inferring causal relationships and enabling counterfactual interpretation.

Mapping kinase domain resistance mechanisms for the MET receptor tyrosine kinase via deep mutational scanning

The paper uses deep mutational scanning to analyze MET kinase domain mutations and their resistance to 11 inhibitors, identifying resistance and sensitizing mutations and improving prediction with a protein language model.

Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage

Rosace improves analysis of deep mutational scanning data by using Bayesian shrinkage and position information, outperforming existing methods in simulations and real data.