Recent advances in automated protein design and its future challenges

ABSTRACT

Introduction: Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques.

Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design.

Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.

KEYWORDS:

• Protein design
• automated protein design
• ab initio design
• scoring function
• protein folding
• conformational search

Article highlights

• The idea of having control over protein function and stability has become the main driving force for the field of automated protein design.

• Automated protein design is particularly useful in protein engineering where a large number of interdependent mutations must be considered or screening by directed evolution is difficult.

• Challenges in automated protein design include the enormously large dimensional spaces for searching the optimal sequence and conformation within a defined structure or function and the development of a fast yet accurate scoring functions along with an efficient optimization algorithm.

• One of the remaining challenges is to be able to evaluate the stability of a proposed design model quickly through computational or experimental means to iteratively improve scoring functions.

• Solubility and immunogenicity issues continue to be a particular concern of proteins engineered by automated protein design and currently limit clinical development.

• The future development of accurate scoring function for protein design will involve a collaborative effort between different disciplines, including informatics and data science.

• In turn, this may give useful feedback to the studies of protein stability, protein folding, and protein structure and function prediction, and may open up the possibilities beyond backbone-based protein design, e.g. the design of intrinsically disordered proteins.

This box summarizes key points contained in the article.

Acknowledgments

The authors thank Dr Smita Mohanty and Dr Xiaoqiang Huang for their fruitful discussions and suggestions. We also thank Dr Xinqiu Yao for proofreading the manuscript.

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

Additional information

Funding

This work was supported by the National Institute of General Medical Sciences (GM083107 and GM116960) as well the US National Science Foundation (DBI1564756).