Willow Coyote‐Maestas

The paper measures basal activity across the human GPCR family on a single scale and identifies key residues that influence constitutive signaling.

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.

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.

The paper identifies cardiomyocyte-specific vulnerabilities to LMNA mutations using saturation mutagenesis in hiPSCs and cardiomyocytes, revealing how lamin polymer disruption leads to disease.

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

The paper introduces a platform to characterize GPCR expression and uses machine learning to identify structural features affecting their biosynthesis.

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.

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.

The paper identifies proton-sensing mechanisms in GPCRs using cryo-EM and deep mutational scanning, revealing a network of residues rather than a single site for proton recognition.

The paper analyzes variant classification disparities between European-like and non-European-like genetic ancestries and shows that MAVE data can reduce these disparities in VUS reclassification.

The paper introduces cdsFM, a suite of codon-resolution large language models that predict the functional consequences of synonymous codon choices and identify pathogenic variants.

This study performs a deep mutational scan on EccD3 in M. smegmatis to understand its role in the ESX-3 secretion system and its importance for mycobacterial survival.

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

The paper introduces DIMPLE, a method for generating indel and missense mutation libraries, and demonstrates its application in studying the impact of mutations on protein function.

The paper identifies novel regulators of mu opioid receptor signaling and trafficking using proximity labeling and proteomics.