Artificial intelligence-based methods for protein structure prediction: a survey

Abstract

Protein structure prediction (PSP) is a meaningful problem that has drawn worldwide attention, where various artificial intelligence (AI) techniques, such as evolutionary computation (EC)-based and neural networks (NNs)-based methods, have been applied to PSP and have obtained promising results in recent years. Considering the rapid and significant advances of AI-based methods for PSP, it is vital to make a survey on this progress to summarize the existing research experience and to provide guidelines for further development of related research fields. With these aims, a broad survey of AI-based methods for solving PSP problems is provided in this article. First, EC-based PSP methods are reviewed, which are organized by three key steps involved in using EC-based methods for PSP. Second, NNs-based PSP methods are reviewed. More specifically, typical NNs-based methods to predict protein structural features are described and state-of-the-art NNs-based methods with end-to-end architecture and attention mechanism are reviewed. Third, the accuracy, interpretability, accessibility, and ethical challenges of AI-based methods are discussed. Last, the future directions including hybrid AI paradigm, protein language models, and the prediction of protein complexes and biomolecular interactions are given, and the conclusion is drawn. This survey is expected to draw attention, raise discussions, and inspire new ideas in the wonderful interdisciplinary field of biology and AI.