Computational re-design of protein structures to improve solubility.

Clicks: 495
ID: 66430
2019
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Abstract
: The rapid development of protein therapeutics is providing life-saving therapies for a wide range of human diseases. However, degradation reactions limit the quality and performance of these protein-based drugs. Among them, protein aggregation is the most common and one of the most challenging to prevent. Aggregation impacts biopharmaceutical development at every stage, from discovery to production and storage. In addition, regulators are highly concerned about the impact of protein aggregates on drug product safety. : Herein, the authors review existing protein aggregation prediction approaches, with a special focus on four recently developed algorithms aimed to predict and improve solubility using three-dimensional protein coordinates: SAP, CamSol, Solubis and Aggrescan3D. Furthermore, they illustrate their potential to assist the design of solubility-improved proteins with a number of examples. : Aggregation of protein-based drugs is, traditionally, addressed via wet lab experiments, using trial and error approaches that are expensive, difficult to perform and time-consuming. The structure-based methods we describe here can predict accurately aggregation propensities, allowing researchers to work with pre-selected, well-behaved, protein candidates. These methods should contribute to the reduction of the time to the marketplace along with industrial costs and improve the safety of future therapeutic proteins.
Reference Key
navarro2019computationalexpert Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Navarro, Susanna;Ventura, Salvador;
Journal expert opinion on drug discovery
Year 2019
DOI
10.1080/17460441.2019.1637413
URL
Keywords
Biotechnology protein aggregation protein design protein structure protein stability bioinformatics protein solubility therapeutic proteins

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