Evidence map›Paper›PMID 40957401›Full record

ArticleCell reports methods2025

A structural machine learning approach for rapid prediction of thermodynamically destabilizing tyrosine phosphorylations.

Jaie Woodard, Zhengqing Liu, Atena Malemir Chegini, Jian Tian, Rupa Bhowmick, Subramaniam Pennathur, Alireza Mashaghi, Jeffrey R Brender, Sriram Chandrasekaran

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Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Jaie WoodardDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Zhengqing LiuDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Atena Malemir CheginiMedical Systems Biophysics and Bioengineering, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, 2333CC Leiden, the Netherlands.
Jian TianState Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
Rupa BhowmickDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Subramaniam PennathurDepartment of Medicine and Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA.
Alireza MashaghiMedical Systems Biophysics and Bioengineering, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, 2333CC Leiden, the Netherlands.
Jeffrey R BrenderMolecular Imaging Program, National Institutes of Health, Bethesda, MD 20892, USA; Clinical Cancer Metabolism Facility, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Sriram ChandrasekaranDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: csriram@umich.edu.

Funding

Linking metabolic activity with drug sensitivity using metabolic influence networksR35GM137795 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sriram Chandrasekaran · 2020 to 2026
$2.6M
NIGMS NIH HHS R35 GM137795
6 · The paper itself

Abstract

Tyrosine phosphorylations are a prominent characteristic of numerous diseases, yet it is challenging to identify potentially (dys)functional phosphorylations among thousands of phospho-proteins. Here, we propose a machine learning method to predict the thermodynamic stability change resulting from tyrosine phosphorylation. Our approach, based on the prediction of phosphomimetic stability (ΔΔG) from structural features, strongly correlates with experimental phosphorylation stability and mutational scanning cDNA proteolysis data (R = 0.55-0.67). We apply our approach to predict the potential destabilizing effects of all 384,858 tyrosine residues from the Alphafold2 database, the PhosphoSitePlus database, and on a pan-cancer phosphoproteomics dataset with 11 cancer subtypes. We predict destabilizing phosphorylations in both oncogenes and tumor suppressors, and ΔΔG values and local protein circuit topology features are able to distinguish phospho-proteins that are known to be dysregulated in cancer. Our approach can enable rapid screening of destabilizing phosphorylations and phosphomimetic mutations.

Indexed as

Machine LearningTyrosineHumansMutationNeoplasmsPhosphoproteinsPhosphorylationThermodynamicsPhosphoproteinsTyrosinecomputational biophysicsCP: computational biologyphosphorylationpost-translational modificationsprotein stabilitysystems biology

Identifiers

PMID40957401
PMCPMC12539237

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