ArticlePeerJ2019
Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Structural Features of a Tiny Viral Protein, ORF7b of SARS-CoV-2.International journal of molecular sciences · 2026Article
- Hypervariable loop profiling decodes sequence determinants of antibody stability.Nature structural & molecular biology · 2026Article
- PROPERMAB: an integrative framework formAbs · 2025Article
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- Unlocking expanded flagellin perception through rational receptor engineering.Nature plants · 2025Article
- Antigen-presenting cell internalization is key for understanding and evaluating therapeutic antibodies' immunogenicity.Frontiers in immunology · 2025Review
- Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data.NPJ systems biology and applications · 2024Article
- Targeting Efficient Features of Urate Oxidase to Increase Its Solubility.Applied biochemistry and biotechnology · 2024Article
- DOTAD: A Database of Therapeutic Antibody Developability.Interdisciplinary sciences, computational life sciences · 2024Article
- Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors.Journal of computer-aided molecular design · 2024Article
- Combining Scattering Experiments and Colloid Theory to Characterize Charge Effects in Concentrated Antibody Solutions.Molecular pharmaceutics · 2024Article
- Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum.NPJ systems biology and applications · 2024Article
- A New Phenolic Acid Decarboxylase from the Brown-Rot FungusBioengineering (Basel, Switzerland) · 2024Article
- Biophysical Analysis of Therapeutic Antibodies in the Early Development Pipeline.Biologics : targets & therapy · 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
- Embedding Dynamics in Intrinsic Physicochemical Profiles of Market-Stage Antibody-Based Biotherapeutics.Molecular pharmaceutics · 2023Article
- Article
- How can we discover developable antibody-based biotherapeutics?Frontiers in molecular biosciences · 2023Review
- FSH-blocking therapeutic for osteoporosis.eLife · 2022Article
- Machine learning prediction of methionine and tryptophan photooxidation susceptibility.Molecular therapy. Methods & clinical development · 2021Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Improved understanding of properties that mediate protein solubility and resistance to aggregation are important for developing biopharmaceuticals, and more generally in biotechnology and synthetic biology. Recent acquisition of large datasets for antibody biophysical properties enables the search for predictive models. In this report, machine learning methods are used to derive models for 12 biophysical properties. A physicochemical perspective is maintained in analysing the models, leading to the observation that models cluster largely according to charge (cross-interaction measurements) and hydrophobicity (self-interaction methods). These two properties also overlap in some cases, for example in a new interpretation of variation in hydrophobic interaction chromatography. Since the models are developed from differences of antibody variable loops, the next stage is to extend models to more diverse protein sets. AVAILABILITY: The web application for the sequence-based algorithms are available on the protein-sol webserver, at https://protein-sol.manchester.ac.uk/abpred, with models and virtualisation software available at https://protein-sol.manchester.ac.uk/software.
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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.