Protein Design by Algorithm

TL;DR

This paper reviews algorithms for protein design, highlighting their mathematical structure, guarantees, and potential impact on developing new therapeutics through computational methods.

Contribution

It provides a comprehensive overview of protein design algorithms, emphasizing their mathematical foundations, guarantees, and significance in therapeutic development.

Findings

Algorithms have provable guarantees of accuracy and optimality.

Protein design algorithms bridge discrete and continuous computation.

Potential to significantly impact health through computational therapeutics.

Abstract

We review algorithms for protein design in general. Although these algorithms have a rich combinatorial, geometric, and mathematical structure, they are almost never covered in computer science classes. Furthermore, many of these algorithms admit provable guarantees of accuracy, soundness, complexity, completeness, optimality, and approximation bounds. The algorithms represent a delicate and beautiful balance between discrete and continuous computation and modeling, analogous to that which is seen in robotics, computational geometry, and other fields in computational science. Finally, computer scientists may be unaware of the almost direct impact of these algorithms for predicting and introducing molecular therapies that have gone in a short time from mathematics to algorithms to software to predictions to preclinical testing to clinical trials. Indeed, the overarching goal of these algorithms is to enable the development of new therapeutics that might be impossible or too expensive to discover using experimental methods. Thus the potential impact of these algorithms on individual, community, and global health has the potential to be quite significant.