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Ensembles of Hydrophobicity Scales as Potent Classifiers for Chimeric Virus-Like Particle Solubility – An Amino Acid Sequence-Based Machine Learning Approach

Vormittag, Philipp; Klamp, Thorsten; Hubbuch, Jürgen

Virus-like particles (VLPs) are protein-based nanoscale structures that show high potential as immunotherapeutics or cargo delivery vehicles. Chimeric VLPs are decorated with foreign peptides resulting in structures that confer immune responses against the displayed epitope. However, insertion of foreign sequences often results in insoluble proteins, calling for methods capable of assessing a VLP candidate’s solubility in silico. The prediction of VLP solubility requires a model that can identify critical hydrophobicity-related parameters, distinguishing between VLP-forming aggregation and aggregation leading to insoluble virus protein clusters. Therefore, we developed and implemented a soft ensemble vote classifier (sEVC) framework based on chimeric hepatitis B core antigen (HBcAg) amino acid sequences and 91 publicly available hydrophobicity scales. Based on each hydrophobicity scale, an individual decision tree was induced as classifier in the sEVC. An embedded feature selection algorithm and stratified sampling proved beneficial for model construction. With a learning experiment, model performance in the space of model training set size and number of included classifiers in the sEVC was explored. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000120529
Veröffentlicht am 24.06.2020
DOI: 10.3389/fbioe.2020.00395
Zitationen: 2
Web of Science
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Bio- und Lebensmitteltechnik (BLT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2296-4185
KITopen-ID: 1000120529
Erschienen in Frontiers in Bioengineering and Biotechnology
Verlag Frontiers Media
Band 8
Seiten Art. Nr.: 395
Vorab online veröffentlicht am 05.05.2020
Schlagwörter virus-like particles, solubility, hydrophobicity, hydrophobicity scales, machine learning, feature selection
Nachgewiesen in Web of Science
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