Automated optimisation of solubility and conformational stability of antibodies and proteins

Abstract

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.

Automated optimization of the solubility of a hyper-stable α-amylase

“…Conversely, the selected mutations had a substantial effect on relative solubility and, crucially, left stability and activity unaffected. A similar observation was made when applying CamSol Combination to the Fv region of the therapeutic antibodies adalimumab and golimumab [5]. In a study that characterized illustrating the mutations contained in the selected models, and structure of B. licheniformis α-amylase (BLA, PDB ID: 1BLI) used as input.…”

“…The output consists of several designs harbouring single or multiple mutations predicted to improve solubility and stability. The pipeline is described in detail by Rosace et al [5]. and illustrated in figure 1a.…”

Quantitative size-resolved characterization of mRNA nanoparticles by in-line coupling of asymmetrical-flow field-flow fractionation with small angle X-ray scattering

“…Control of aggregation state is of outstanding importance for quality control of biologics. Understanding protein misfolding as well as oligomerization processes are essential for optimal process design and formulation development 34 , 35 . Elucidation of oligomerization processes and the formation of aggregates has additionally become a hot topic ever since their role in the manifestation of various neurodegenerative diseases has become evident 36 .…”

FLAb: Benchmarking deep learning methods for antibody fitness prediction

“…Jain et al define the characteristics that comprise antibody developability, which includes (1) highlevel of expression, (2) high conformational and colloidal stability, (3) low immunogenicity, (4) high binding affinity towards the target antigen, (5) a low propensity for aggregation, and (6) low polyreactivity [16]. To assess the efficacy of protein design models in capturing essential characteristics of therapeutic antibodies, we have compiled a collection (Table 1) of 17 mutational landscapes of distinct antibody families with a total of 13,384 associated fitness metrics relevant to Jain et al's definition of antibody developability [12,17,20,24,27,30,16,15]. Each sequence is associated with at least one fitness label pertaining to the six aforementioned developability factors.…”