Alexey Nesvizhskii

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University of Michigan

United States

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About

Professor Alexey Nesvizhskii is a faculty member at the University of Michigan in the United States. His research primarily focuses on proteomics, with significant contributions to protein quantification, multiplexed proteomics, and data analysis methodologies. Recent publications include work on enhancing sensitivity in peptide identification and exploring interactions crucial for lung fibrosis.

Recent Grants

Grant: Close

Computational tools for mass spectrometry-based interactome data

Open Date: 2010-09-01

Close Date: 2018-08-31

Grant: Close

MRI: Development of ion-mobility enhanced electron-transfer dissociation for elucidating biological networks using a systems biology approach

Open Date: 2009-09-01

Close Date: 2014-08-31

Grant: Close

Advanced Proteome Informatics of Cancer

Open Date: 2009-07-01

Close Date: 2020-07-31

Grant: Close

Analysis and Statistical Validation of Proteomic Datasets

Open Date: 2006-09-27

Close Date: 2012-07-31

Articles (27)

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Collaborators (17)

Fengchao Yu

University of Michigan

UNITED STATES

Alexey Nesvizhskii

Research Interests

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About

Recent Grants

Articles (27)

SynchroSep-MS: Parallel LC Separations for Multiplexed Proteomics

ProMix: Enhancing Protein Quantification through Experimental Design and Statistical Normalization

PAI-1 Interaction with Sortilin Related Receptor-1 is Required for Lung Fibrosis

MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search

Light‐Activated Reactivity of Nitrones with Amino Acids and Proteins

Light‐Activated Reactivity of Nitrones with Amino Acids and Proteins

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing

Are Internal Fragments Observable in Electron Based Top-Down Mass Spectrometry?

Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform.

MSBooster: improving peptide identification rates using deep learning-based features

Detecting diagnostic features in MS/MS spectra of post-translationally modified peptides

Unraveling the glycosylated immunopeptidome with HLA-Glyco

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

Solid-Phase Compatible Silane-Based Cleavable Linker Enables Custom Isobaric Quantitative Chemoproteomics

Sulfoproteomics Workflow with Precursor Ion Accurate Mass Shift Analysis Reveals Novel Tyrosine Sulfoproteins in the Golgi.

Mass Spectrometric Profiling of HLA-B44 Peptidomes Provides Evidence for Tapasin-Mediated Tryptophan Editing

Efficient Analysis of Proteome-Wide FPOP Data by FragPipe

Identification of LMAN1- and SURF4-Dependent Secretory Cargoes

The Intrinsically Disordered N Terminus in Atg12 from Yeast Is Necessary for the Functional Structure of the Protein

Glycoproteomics

SP3-enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State Dependent Redox-Sensitive Cysteines

Multi-attribute Glycan Identification and FDR Control for Glycoproteomics

Kinesin-binding protein remodels the kinesin motor to prevent microtubule binding

The antiviral enzyme viperin inhibits cholesterol biosynthesis

Collaborators (17)

Fengchao Yu

Adnan Halim

Zhenyu Tan

Morten Thaysen-Andersen

Vadim Demichev

Yanzhuang Wang

Markus Ralser

Kiyoko F. Aoki-Kinoshita

Malini Raghavan

Stacy Malaker

kai li

Keriann M. Backus

Michael A Cianfrocco

David Sept

Jason Stumpff

Ieva Bagdonaite

Nicholas Riley

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