ArticleComputational and structural biotechnology journal2019
Residue Adjacency Matrix Based Feature Engineering for Predicting Cysteine Reactivity in Proteins.
Article in Computational and structural biotechnology journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- EvoMut: A computational framework for engineering oxidative stability in proteins.Protein science : a publication of the Protein Society · 2026Article
- CIAA: Integrated Proteomics and Structural Modeling for Understanding Cysteine Reactivity with Iodoacetamide Alkyne.ACS chemical biology · 2025Article
- Predicting cysteine reactivity changes upon phosphorylation using XGBoost.FEBS open bio · 2024Article
- Thiol redox proteomics: Characterization of thiol-based post-translational modifications.Proteomics · 2023Review
- PSSMCOOL: a comprehensive R package for generating evolutionary-based descriptors of protein sequences from PSSM profiles.Biology methods & protocols · 2022Article
- Stoichiometric Thiol Redox Proteomics for Quantifying Cellular Responses to Perturbations.Antioxidants (Basel, Switzerland) · 2021Review
- Article
- Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture.Computational and structural biotechnology journal · 2019Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Free radicals that form from reactive species of nitrogen and oxygen can react dangerously with cellular components and are involved with the pathogenesis of diabetes, cancer, Parkinson's, and heart disease. Cysteine amino acids, due to their reactive nature, are prone to oxidation by these free radicals. Determining which cysteines oxidize within proteins is crucial to our understanding of these chronic diseases. Wet lab techniques, like differential alkylation, to determine which cysteines oxidize are often expensive and time-consuming. We utilize machine learning as a fast and inexpensive approach to identifying cysteines with oxidative capabilities. We created the original features RAMmod and RAMseq for use in classification. We also incorporated well-known features such as PROPKA, SASA, PSS, and PSSM. Our algorithm requires only the protein sequence to operate; however, we do use template matching by MODELLER to acquire 3D coordinates for additional feature extraction. There was a mean improvement of RAM over N6C by 22.04% MCC. It was statistically significant with a
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