Matthew K. Howard

Mapping the sequence determinants of constitutive activity in GPCRs

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

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.

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.

Molecular basis of proton-sensing by G protein-coupled receptors

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.

A Suite of Foundation Models Captures the Contextual Interplay Between Codons

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.

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.

Profiling the proximal proteome of the mu opioid receptor identifies novel regulators of receptor signaling and trafficking

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