Recent advances in the integration of protein mechanics and machine learning
Journal
Extreme Mechanics Letters
Journal Volume
72
Start Page
102236
ISSN
2352-4316
Date Issued
2024-11
Author(s)
Yen-Lin Chen
DOI
10.1016/j.eml.2024.102236
Abstract
Mechanics underlies protein properties and behavior. From a theoretical standpoint, it is possible to derive these based on physical rules. This is appealing because they provide insights into physiology and disease, as well as aid in protein engineering; however, the convoluted nature of the biological system and current computational speeds limit its feasibility. Machine learning (ML) architectures are known for their ability to make inferences on complex data, such as the relationship between protein mechanics, properties, and behavior. Substantial efforts have been made to learn such correlations in tasks such as the prediction of structure, stability, natural frequency, mechanical strength, folding rate, solubility, and function. Each of these properties is interconnected through protein mechanics, and it is not surprising that the methods used in these tasks overlap highly in model input and architecture. In this review, we evaluate ML methods for the seven aforementioned prediction tasks to identify current trends in ML research in the field of protein sciences, focusing on the input and model architecture of each method. A short overview of de novo protein design is also provided. Finally, we highlight trends in the application of ML methods in the field of protein science, as well as directions for future improvements.
Subjects
Machine learning
Protein mechanics
Protein property prediction
Publisher
Elsevier BV
Type
journal article