Computational re-design of protein structures to improve solubility

ABSTRACT

Introduction: 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.

Area covered: 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.

Expert opinion: 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 in silico 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.

KEYWORDS:

• Protein aggregation
• bioinformatics
• biotechnology
• protein stability
• protein structure
• protein design
• protein solubility
• therapeutic proteins

Article highlights

• Protein aggregation is a major bottleneck in the development of protein-based therapeutics.

• The aggregation potential of proteins is concentrated at specific sequential and structural regions, known as aggregation-prone regions (APRs).

• First generation, sequence-based, programs find difficulties predicting APRs in folded globular proteins, failing to detect APRs when residues are not contiguous in sequence or mistaking APRs for the buried hydrophobic core.

• Second generation, structure-based programs accurately predict the modulation of protein intrinsic aggregation properties by the structural context, the impact of mutations on both protein aggregation and structural stability, and the influence of conformational fluctuations in the aggregation propensities of native protein ensembles.

• Structure-based aggregation predictors are efficient and cost-effective tools for the rational design of soluble, stable, active and long-lasting protein therapeutics.

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Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer Disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This work was funded by the Spanish Ministry of Economy and Competitiveness (award No. BIO2016-78310-R to S Ventura) and by the Institució Catalana de Recerca i Estudis Avançats (ICREA) through ICREA-Academia 2015 (also awarded to S Ventura).