Kristen M. Naegle

ProteomeScout v4 provides an open-source ecosystem for aggregating and harmonizing post-translational modification data with web and code-based access tools.

The paper develops a theoretical framework to predict avidity in bivalent protein interactions using structure-informed modeling and experimental validation.

KSTAR v1.2 improves speed and accessibility for kinase activity inference from phosphoproteomic data without compromising accuracy.

SpY-C classifies phosphopeptide sites as potential SH2 domain binders using supervised learning, improving annotation of pY function and informing downstream machine learning approaches.

This study investigates how phosphorylation at specific tyrosine sites within SH2 domains affects ligand binding, finding that phosphorylation at Y63 in PTPN11 reduces binding of physiologically relevant substrates.

The paper investigates crosstalk between splicing factors and kinase networks using phosphoproteomics and RNA-seq data, showing significant changes in phosphorylation and kinase activity upon splicing factor knockdown.

This study identifies cell competition as a key driver of gliomagenesis by showing that pre-malignant OPCs outcompete wildtype cells, and inhibiting this process reduces tumor growth.

This paper introduces PTM-POSE, a computational pipeline to analyze how alternative splicing affects post-translational modifications, and finds that 32% of PTMs are excluded from at least one Ensembl isoform.

The paper introduces a synthetic toolkit for efficiently producing tyrosine phosphorylated proteins by leveraging signaling-inspired targeting interactions.

This paper introduces DANSy, a method converting proteins into linguistic networks using domain architecture to uncover functional relationships in proteomes, showing it complements traditional interaction networks.

The paper develops CoDIAC, a tool for analyzing domain interfaces, revealing new insights into SH2 domain function and regulation through phosphorylation and acetylation.

The paper uses high-content imaging and a new computational model to predict how drugs affect cardiac fibroblast phenotype and identify potential therapeutic mechanisms.

KSTAR predicts patient-specific kinase activities from phosphoproteomic data, improving comparison across labs and identifying tissue-specific profiles and treatment responses.

KinPred unifies and sustains kinase-substrate predictions by standardizing reference proteomes, mapping substrates and kinases, and filtering for the human phosphoproteome.

The paper improves the analysis of SH2-pTyr domain-peptide interactions through a new pipeline that enhances affinity measurement accuracy and reduces false negatives.

ProteoClade processes multi-species proteomic data for taxa-specific analysis and quantitation, enabling efficient workflows for complex microbiota samples.

ASPEN improves ancestor divergence reconstruction by using ensemble models to integrate over topological diversity from subsampled sequence alignments.