ArticlemAbs
Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method.
Article in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 53 citations in OpenAlex.
- A systematic review of recent trends in research on therapeutically significant L-asparaginase and acute lymphoblastic leukemia.Molecular biology reports · 2022Pooled it
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- Mechanistic insight into the oxidative degradation of monoclonal antibodies: relevance to developability and the design of stable pharmaceutical formulations.Biochemical Society transactions · 2026Review
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- Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.Journal of computer-aided molecular design · 2024Article
- Stability of Protein Pharmaceuticals: Recent Advances.Pharmaceutical research · 2024Review
- Understanding the Specific Implications of Amino Acids in the Antibody Development.The protein journal · 2024Review
- Evaluating the chaos game representation of proteins for applications in machine learning models: prediction of antibody affinity and specificity as a case study.Journal of molecular modeling · 2023Article
- Accurate profiling of full-length Fv in highly homologous antibody libraries using UMI tagged short reads.Nucleic acids research · 2023Article
- Developability assessment at early-stage discovery to enable development of antibody-derived therapeutics.Antibody therapeutics · 2023Review
- Protein folding stabilities are a major determinant of oxidation rates for buried methionine residues.The Journal of biological chemistry · 2022Article
- A novel method for in silico assessment of Methionine oxidation risk in monoclonal antibodies: Improvement over the 2-shell model.PloS one · 2022Article
- Machine learning prediction of methionine and tryptophan photooxidation susceptibility.Molecular therapy. Methods & clinical development · 2021Article
- Predicting antibody affinity changes upon mutations by combining multiple predictors.Scientific reports · 2020Article
- QSAR Implementation for HIC Retention Time Prediction of mAbs Using Fab Structure: A Comparison between Structural Representations.International journal of molecular sciences · 2020Article
- Machine Learning-Guided Prediction of Antigen-Reactive In Silico Clonotypes Based on Changes in Clonal Abundance through Bio-Panning.Biomolecules · 2020Article
- Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.Molecular therapy. Methods & clinical development · 2019Article
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- Review
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Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monoclonal antibodies (mAbs) have become a major class of protein therapeutics that target a spectrum of diseases ranging from cancers to infectious diseases. Similar to any protein molecule, mAbs are susceptible to chemical modifications during the manufacturing process, long-term storage, and in vivo circulation that can impair their potency. One such modification is the oxidation of methionine residues. Chemical modifications that occur in the complementarity-determining regions (CDRs) of mAbs can lead to the abrogation of antigen binding and reduce the drug's potency and efficacy. Thus, it is highly desirable to identify and eliminate any chemically unstable residues in the CDRs during the therapeutic antibody discovery process. To provide increased throughput over experimental methods, we extracted features from the mAbs' sequences, structures, and dynamics, used random forests to identify important features and develop a quantitative and highly predictive in silico methionine oxidation model.
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Registered trials
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.