Developability issues, such as aggregation, low expression yield, or instability over long-term storage often hamper the development of biologics. Developability is underpinned by a complex interplay of biophysical properties, including conformational stability and solubility. Advances have been made in the optimisation of individual properties, however, multi-trait optimisation remains highly challenging, as mutations that improve one property often negatively impact the others. Here, we introduce a fully automated computational strategy for the rational design of conformationally stable and soluble protein and antibody variants harbouring multiple mutations. This pipeline, called CamSol Combination, leverages phylogenetic information to reduce false positive predictions, and combines a rapid method of predicting solubility changes with an empirical energy function. We experimentally validate the method’s predictions on a nanobody isolated with yeast-display from a synthetic library, by producing 12 designs ranging from single-point mutants to a quadruple mutant. All 12 designed variants retained antigen binding, had improved thermal stability, and decreased propensity to precipitate (increased relative solubility), and 8 also had reduced cross-reactivity. The melting temperature was improved by 13.6 ºC with 4 rationally designed mutations. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.ukSignificanceProtein-based biologics, such as antibodies and enzymes, are crucial reagents in research, industrial biotechnology, and diagnostics, and are increasingly used to treat a wide range of diseases as biotherapeutics. Often, biologics with suitable functionality are discovered, but their development into practically useful molecules is impeded by developability issues. Conformational stability and solubility are arguably the most important biophysical properties underpinning developability potential, as they determine colloidal stability and aggregation, and correlate with expression yield and poly-specificity. Here we introduce and experimentally validate a fully automated computational method and associated webserver for the rational design of proteins and antibodies with improved stability and solubility. Computational methods are rapid, inexpensive, and have no material requirements, which makes their implementation into biologic development pipelines particularly attractive.

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology, diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but their development is impeded by developability issues. Stability and solubility are key biophysical traits underpinning developability potential, as they determine aggregation, correlate with production yield and poly-specificity, and are essential to access parenteral and oral delivery. While advances for the optimisation of individual traits have been made, the co-optimization of multiple traits remains highly problematic and time-consuming, as mutations that improve one property often negatively impact others. In this work, we introduce a fully automated computational strategy for the simultaneous optimisation of conformational stability and solubility, which we experimentally validate on six antibodies, including two approved therapeutics. Our results on 42 designs demonstrate that the computational procedure is highly effective at improving developability potential, while not affecting antigen-binding. We make the method available as a webserver at www-cohsoftware.ch.cam.ac.uk.