Devin K. Schweppe

The paper develops a deep learning-enhanced fragment ion indexing method to reduce memory usage in tandem mass spectrometry searches while maintaining search accuracy and speed.

The paper identifies and characterizes human protein-coding duplicated genes using long-read genomic and transcriptomic data, revealing new gene models and constraint patterns.

This study evaluates how detergent selection and timing affect thermal stability proteomics results, finding that DDM improves detection of some drug-target interactions but may obscure others.

The paper introduces POCA, a photosensitizer platform for proximity labeling of proteins, RNA, and DNA in fixed cells without genetic engineering, demonstrating its utility across multiple molecular targets.

The paper identifies age-related molecular changes in mouse spleens using proteomics and transcriptomics, showing cell-type-specific remodeling and gender differences in post-transcriptional buffering.

DNA O-MAP identifies molecular neighborhoods around specific genomic loci using programmable probes and proteomics.

This study compares the effects of two IL-6 inhibitors on serum extracellular vesicles using proteomics, finding distinct protein pathway alterations.

The paper profiles molecular responses to HDAC inhibitors in diverse cell lines, revealing heterogeneous protein remodeling and cell-type specific effects.

This paper designs a synthetic ligand, H2F, that induces HER2-FGFR proximity to reprogram fibroblasts into myogenic cells and promote myogenic regeneration.

Nova is a C# library for handling mass spectrometry data, offering native support and extensibility for real-time analysis and instrument control applications.

This paper enhances the Comet search engine with fragment-ion indexing, improving peptide spectral matching speeds by up to 94% while maintaining accuracy in proteomics applications.

The paper designs TrkA agonists that activate neuronal differentiation and neurite outgrowth without triggering pain-related gene expression.

The paper uses O-MAP to study a cardiac-specific RNA factory around TTN RNA, revealing its chromatin interactions, RNA composition, and role of RBM20 in gene regulation.

The paper identifies 2884 calcium-regulated proteins across human, mouse, and yeast proteomes using thermostability assays and reveals novel calcium engagement in metabolic enzymes and the spliceosome.

This study uses quantitative proteomics to analyze human and mouse gastruloids at four stages, revealing protein dynamics, discordance with transcriptomes, and gene networks linked to developmental defects.

This study uses proteome-wide thermal shift assays to analyze over a million thermal stability measurements from 96 compounds in cells and 70 in lysates, revealing target engagement and off-target effects.

The paper analyzes proteomic changes across 10 tissues in mice at different ages, finding age-related increases in immune proteins and tissue-specific alterations in protein complexes.

The paper introduces O-MAP, a method for discovering RNA-interacting proteins, transcripts, and genomic loci in situ with high precision and without genetic manipulation.

This paper introduces a deep learning model to reduce sequence-specific biases in quantitative mass spectrometry data, improving correlation with mRNA expression.

The paper creates two large-scale cell-line-specific human interactomes using affinity-purification mass spectrometry and identifies shared and cell-specific protein interactions.

The paper introduces Monocle, an algorithm that improves monoisotopic mass estimation in proteomics, increasing peptide identifications by up to two-fold.

The paper improves protein-protein docking by integrating cross-linking data, enhancing prediction accuracy and reducing false positives.