Evidence map›Paper›PMID 42741978›Full record

ArticleProtein science : a publication of the Protein Society2026

EvoMut: A computational framework for engineering oxidative stability in proteins.

Seyed Shahriar Arab, Chenlin Hsieh, Nathan E Lewis

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. EvoMut: A computational framework for engineering oxidative stability in proteins.Protein science : a publication of the Protein Society · 2026
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Seyed Shahriar ArabCenter for Molecular Medicine, University of Georgia, Athens, Georgia, USA.ORCID 0000-0002-2739-1617
Chenlin HsiehCenter for Molecular Medicine, University of Georgia, Athens, Georgia, USA.ORCID 0009-0000-9239-8711
Nathan E LewisCenter for Molecular Medicine, University of Georgia, Athens, Georgia, USA.ORCID 0000-0001-7700-3654

Funding

Unraveling the mammalian secretory pathway through systems biology and algorithm developmentR35GM119850 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEWIS, NATHAN ENOCH · 2016 to 2025
$4.3M
Glycoengineering of CHO cells to express recombinant alpha-1 antitrypsinR41HL164260 · NHLBI · NEUIMMUNE BIOLOGICS, INC. · PI LEWIS, NATHAN ENOCH · 2022 to 2022
$274k
Georgia Research AllianceNHLBI NIH HHS R41 HL164260NIGMS NIH HHS R35 GM119850Novo Nordisk Foundation NNF20SA0066621
6 · The paper itself

Abstract

Amino acid oxidation is a major cause of protein instability and loss of function in therapeutic and industrial settings. Although methionine, cysteine, tryptophan, tyrosine, histidine, lysine, and arginine residues are widely recognized as oxidation-prone, only a subset of such residues is dominant functional hotspots, and not all are suitable targets for mutation. Identifying these vulnerable, yet engineerable, sites remains a major challenge. Here, we present EvoMut, a residue-level analytical framework for evaluating both oxidative vulnerability and mutation feasibility. EvoMut estimates oxidation risk by integrating structural features, local functional context, intrinsic chemical susceptibility, and evolutionary conservation. A central feature of the framework is the explicit separation of oxidation risk from mutation feasibility. Specifically, candidate substitutions are evaluated only after high-risk residues are identified and ranked by evolutionary substitution patterns. Application of EvoMut to multiple proteins, and evaluation with experimental data, showed that oxidation-prone residues differ markedly in their engineering potential. EvoMut distinguishes residues that are both oxidation-sensitive and evolutionarily permissive from those that are chemically vulnerable but functionally constrained. By providing residue-level mechanistic insight, EvoMut offers a practical framework for the rational design of oxidation-resistant proteins. EvoMut is freely available as a web server at https://proteus.cmm.uga.edu/evomut.

Indexed as

Computational BiologyProtein EngineeringProteinsModels, MolecularMutationOxidation-ReductionProtein StabilityProteinsevolutionary conservationoxidative hotspot predictionoxidative stability engineeringprotein engineeringprotein oxidationstructural bioinformatics

Identifiers

PMID42741978
PMCPMC13575976

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.