Neel H. Shah

The paper develops a quantitative statistical mechanical model to predict protein interactions from sequences and uses it to analyze the structure and organization of human signaling networks.

This paper develops a modular strategy to design selective covalent inhibitors of protein tyrosine kinases using substrate-derived peptides, demonstrating selectivity for specific kinases and mutants.

This paper identifies PTP1B substrates using in situ photo-crosslinking, revealing new substrates and interactions, including EGF receptor binding.

This study uses single-cell transcriptomics to analyze how diverse SHP2 variants affect gene expression and cellular phenotypes in relation to disease.

This paper introduces GRIPs, a scalable platform for PTM editing using chimeric molecules, demonstrating functionality in multiple systems and confirming specificity through proteomics.

This paper introduces a method combining bacterial peptide display and deep sequencing to profile tyrosine kinase substrate recognition, showing strong correlation with enzyme kinetics and revealing new substrate specificity insights.

This study uses proximity-labeling proteomics to analyze how SHP2 mutations alter its interactome and localization, revealing functional consequences of different mutation types.

This study identifies a novel active conformation of SHP2 stabilized by the E139D mutation, which alters phosphoprotein binding preferences compared to canonical hyperactive mutants.

The paper uses deep mutational scanning to analyze SHP2 mutations, revealing diverse regulatory mechanisms and potential pathogenicity of uncharacterized variants.

This study shows that a catalytically inactive Shp2 mutation causes hydrocephalus by altering conformation, which can be rescued by allosteric inhibition.

This paper shows that Grb2 binding to PTP1B allosterically enhances its catalytic activity through structural changes, revealing a new regulatory mechanism for PTP1B in cell signaling.

The paper develops a sequence-to-affinity model for SH2 domains using multi-round peptide binding assays and free-energy regression to predict binding specificity and variant effects.

The paper introduces SEC-MX, a method to simultaneously study protein assembly-states and phosphorylation events, and demonstrates its application in HEK293 and HCT116 cells.

The paper introduces ProBound, a machine learning framework that quantifies biophysical parameters of protein-ligand interactions using NGS data, and couples it with Kd-seq to determine absolute affinities.