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Optimization of a Soft Ensemble Vote Classifier for the Prediction of Chimeric Virus-Like Particle Solubility and Other Biophysical Properties

Vormittag, Philipp; Klamp, Thorsten; Hubbuch, Jürgen

Abstract:
Chimeric virus-like particles (cVLPs) are protein-based nanostructures applied as investigational vaccines against infectious diseases, cancer, and immunological disorders. Low solubility of cVLP vaccine candidates is a challenge that can prevent development of these very substances. Solubility of cVLPs is typically assessed empirically, leading to high time and material requirements. Prediction of cVLP solubility in silico can aid in reducing this effort. Protein aggregation by hydrophobic interaction is an important factor driving protein insolubility. In this article, a recently developed soft ensemble vote classifier (sEVC) for the prediction of cVLP solubility was used based on 91 literature amino acid hydrophobicity scales. Optimization algorithms were developed to boost model performance, and the model was redesigned as a regression tool for ammonium sulfate concentration required for cVLP precipitation. The present dataset consists of 568 cVLPs, created by insertion of 71 different peptide sequences using eight different insertion strategies. Two optimization algorithms were developed that (I) modified the sEVC with regard to systematic misclassification based on the different insertion strategies, and (II) modified the amino acid hydrophobicity scale tables to improve classification. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000123516
Veröffentlicht am 25.09.2020
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: 1000123516
Erschienen in Frontiers in Bioengineering and Biotechnology
Band 8
Seiten Art.-Nr.: 881
Schlagwörter virus-like particles, solubility, hydrophobicity scales, machine learning, precipitation, optimization
Nachgewiesen in Scopus
Web of Science
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