High-throughput computational pipeline for 3-D structure preparation and in silico protein surface property screening: A case study on HBcAg dimer structures

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

“…3-D structurebased methods include the prediction of soluble expression by molecular dynamics (MD)-simulated unfolding combined with a support vector machine (SVM) architecture (Schaller et al, 2015), dynamic exposure of hydrophobic patches in MD simulations (Chennamsetty et al, 2009;Jamroz et al, 2014), and projection of sequence-based methods onto 3-D structures (Sormanni et al, 2015;Zambrano et al, 2015). Although high-throughput 3-D structure generation of VLP building blocks has been described previously (Klijn et al, 2019), the computational cost of creating 3-D structures is still high, limiting the applicability of this approach in candidate selection for several hundred molecules. Amino acid sequence-based methods can be distinguished into amino acid composition-based algorithms such as machine learning approaches using SVM or random forest classifiers (Magnan et al, 2009;Agostini et al, 2012;Samak et al, 2012;Xiaohui et al, 2014;Yang et al, 2016) and sliding-window-based algorithms, such as AGGRESCAN, Zyggregator, and CamSol (Conchillo-Sole et al, 2007;Tartaglia et al, 2008;Sormanni et al, 2015).…”

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

“…Based on zeta potential measurements, this behavior could be related to surface charge. In another study, a high-throughput 3-D structure generation workflow was developed that we applied on exactly these three constructs in their disassembled form to calculate a surface charge that correlated well with the zeta potential measurements ( Klijn et al, 2019 ). This is a good example of in silico representations of physicochemical properties, which pave the way for model-assisted rather than empirical process development.…”