ArticlePloS one2017
Protein asparagine deamidation prediction based on structures with machine learning methods.
Article in PloS one, 2017. 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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 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
- Accurate prediction of asparagine deamidation in biologics using advanced machine learning models.Briefings in bioinformatics · 2026Article
- Insights into the role of internal catalysts in asparagine-to-succinimide conversion in a hyperthermophilic glutamine amidotransferase.The Journal of biological chemistry · 2026Article
- Expanding the Chemoproteomic Toolkit to Asparagine and Glutamine.ACS chemical biology · 2026Article
- Developability Evaluation of Single-Domain Antibody-Chelator Conjugates for Diagnostic Radiotracers.Antibodies (Basel, Switzerland) · 2026Article
- Mechanism-Driven Features Enable Asn Deamidation Reactivity Prediction via Machine Learning Methods.Journal of chemical information and modeling · 2025Article
- Crucial aspects for maintaining rAAV stability.Scientific reports · 2024Article
- The Accurate Prediction of Antibody Deamidations by Combining High-Throughput Automated Peptide Mapping and Protein Language Model-Based Deep Learning.Antibodies (Basel, Switzerland) · 2024Article
- Stability of Protein Pharmaceuticals: Recent Advances.Pharmaceutical research · 2024Review
- Heat-induced structural and chemical changes to a computationally designed miniprotein.Protein science : a publication of the Protein Society · 2024Article
- LAP: Liability Antibody Profiler by sequence & structural mapping of natural and therapeutic antibodies.PLoS computational biology · 2024Article
- Article
- Conformational Control of Fast Asparagine Deamidation in a Norovirus Capsid Protein.Biochemistry · 2023Article
- Approaches to Avoid Proteolysis During Protein Expression and Purification.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Machine learning prediction of methionine and tryptophan photooxidation susceptibility.Molecular therapy. Methods & clinical development · 2021Article
- Mechanisms of Deamidation of Asparagine Residues and Effects of Main-Chain Conformation on Activation Energy.International journal of molecular sciences · 2020Article
- Machine Learning Enables Accurate Prediction of Asparagine Deamidation Probability and Rate.Molecular therapy. Methods & clinical development · 2019Article
- Utility of High Resolution NMR Methods to Probe the Impact of Chemical Modifications on Higher Order Structure of Monoclonal Antibodies in Relation to Antigen Binding.Pharmaceutical research · 2019Article
- Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc. A more accurate prediction method for potential hotspot residues would allow their elimination or reduction as early as possible in the drug discovery process. In this work, we focus on prediction models for asparagine (Asn) deamidation. Sequence-based prediction method simply identifies the NG motif (amino acid asparagine followed by a glycine) to be liable to deamidation. It still dominates deamidation evaluation process in most pharmaceutical setup due to its convenience. However, the simple sequence-based method is less accurate and often causes over-engineering a protein. We introduce structure-based prediction models by mining available experimental and structural data of deamidated proteins. Our training set contains 194 Asn residues from 25 proteins that all have available high-resolution crystal structures. Experimentally measured deamidation half-life of Asn in penta-peptides as well as 3D structure-based properties, such as solvent exposure, crystallographic B-factors, local secondary structure and dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated residues while effectively eliminated false positive predictions. It is possible that such quantitative protein structure-function relationship tools can also be applied to other protein hotspot predictions. In addition, we extensively discussed metrics being used to evaluate the performance of predicting unbalanced data sets such as the deamidation case.
Indexed as
Identifiers
What OpenQuestion holds
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.