Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning
Open accessAbstract
MOTIVATION: The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay that is often used to quantify the hydrophobicity of an antibody to assess downstream risks. Earlier studies have shown that retention times in this assay can be correlated to amino-acid or atomic propensities weighted by the surface areas obtained from protein 3-dimensional structures. The goal of this study is to develop models to enable prediction of delayed HIC retention times directly from sequence. RESULTS: We utilize the randomforest machine learning approach to estimate the surface exposure of amino-acid side-chains in the variable region directly from the antibody sequence. We obtain mean-absolute errors of 4.6% for the prediction of surface exposure. Using experimental HIC data along with the estimated surface areas, we derive an amino-acid propensity scale that enables prediction of antibodies likely to have delayed retention times in the assay. We achieve a cross-validation Area Under Curve of 0.85 for the Receiver Operating Characteristic curve of our model. The low computational expense and high accuracy of this approach enables real-time assessment of hydrophobic character to enable prioritization of antibodies during the discovery process and rational engineering to reduce hydrophobic liabilities. AVAILABILITY AND IMPLEMENTATION: Structure data, aligned sequences, experimental data and prediction scores for test-cases, and R scripts used in this work are provided as part of the Supplementary Material. CONTACT: tushar.jain@adimab.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Jain and colleagues developed a surface accessible area predictor that can be applied to an antibody sequence to create a propensity score that could be correlated with aggregation risk.
“This was addressed by Jain and colleagues who developed a surface accessible area predictor that can be applied to an antibody sequence to further create a propensity score that could be correlated with aggregation risk [149].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [149]
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- Authors
- Jain, T., Boland, T., Lilov, A., Burnina, I., Brown, M., Xu, Y., & Vásquez, M.
- Affiliations
- Adimab (United States) (US)
- Corresponding author
- Jain, Tushar
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-08-16
- Volume
- 33
- Issue
- 23
- Pages
- 3758-3766
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2749723472
- Cited by
- 61
- References
- 94
- Citations by year
- 2026: 8 · 2025: 5 · 2024: 8 · 2023: 4 · 2022: 13 · 2021: 9 · 2020: 6 · 2019: 6 · 2018: 2
- FWCI
- 1.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Keywords
- hydrophobic interaction chromatography, monoclonal antibodies, delayed retention, hydrophobicity, variable region, amino acid propensities, sequence-based prediction, random forest, antibody developability, antibody engineering
- MeSH terms
- Machine Learning, Amino Acids, Antibodies, Monoclonal, Chromatography, Immunoglobulin Variable Region, ROC Curve, Sequence Analysis, Protein, Hydrophobic and Hydrophilic Interactions
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