溶解度
蛋白质稳定性
理论(学习稳定性)
抗体
蛋白质结构
化学
计算生物学
计算机科学
生物化学
生物
遗传学
物理化学
机器学习
作者
Angelo Rosace,Anja Bennett,Marc Oeller,Mie M. Mortensen,Laila Sakhnini,Nikolai Lorenzen,Christian Poulsen,Pietro Sormanni
标识
DOI:10.1038/s41467-023-37668-6
摘要
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.
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