Solubis: a webserver to reduce protein aggregation through mutation

Abstract

Protein aggregation is a major factor limiting the biotechnological and therapeutic application of many proteins, including enzymes and monoclonal antibodies. The molecular principles underlying aggregation are by now sufficiently understood to allow rational redesign of natural polypeptide sequences for decreased aggregation tendency, and hence potentially increased expression and solubility. Given that aggregation-prone regions (APRs) tend to contribute to the stability of the hydrophobic core or to functional sites of the protein, mutations in these regions have to be carefully selected in order not to disrupt protein structure or function. Therefore, we here provide access to an automated pipeline to identify mutations that reduce protein aggregation by reducing the intrinsic aggregation propensity of the sequence (using the TANGO algorithm), while taking care not to disrupt the thermodynamic stability of the native structure (using the empirical force-field FoldX). Moreover, by providing a plot of the intrinsic aggregation propensity score of APRs corrected by the local stability of that region in the folded structure, we allow users to prioritize those regions in the protein that are most in need of improvement through protein engineering. The method can be accessed at http://solubis.switchlab.org/.

Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design

“…To distinguish these differences in mechanisms, Van Durme et al 138 have developed a method termed Solubis that combines TANGO and FoldX. This combination results in a structure-based method to design aggregation-resistant proteins by identifying mutations that reduce the intrinsic aggregation propensity assessed by TANGO while respecting conformational stability computed by FoldX.…”

Understanding and overcoming trade-offs between antibody affinity, specificity, stability and solubility

“…Predictions of mutations that improve one property typically do not consider their potential negative effects on other properties. Nevertheless, encouraging results are emerging for computational design methods to optimize antibody affinity, stability and/or solubility [56, 78–80]. Future efforts will also need to improve structural predictions of antibody CDRs [81, 82] – especially the long and highly variable heavy chain CDR3 – to accurately predict CDR mutations that are beneficial to different antibody properties.…”

In vitro and in silico assessment of the developability of a designed monoclonal antibody library

“…15 Overall, however, the current limitations and challenges that in vitro assays are facing highlight a pressing need for more effective and rapid strategies to assess antibody developability. Given the promising advances in the prediction of solubility, aggregation and viscosity that have been made in the last decade, 19,[24][25][26][27][28][29][30][31][32][33][34][35] in silico predictors could offer convenient alternatives to experimental approaches due to their rapidity and lack of materials requirement. 36 These predictors are typically based on physico-chemical properties derived from the amino acid sequence, and are sometimes processed with machinelearning algorithms, or structure-based algorithms that identify troublesome surface-exposed patches that can compromise the biophysical properties of the antibodies.…”