Recent Advances in Protein Folding Pathway Prediction through
Computational Methods

Kailong      Zhao; Fang      Liang; Yuhao      Xia; Minghua      Hou; Guijun      Zhang
doi:10.2174/0109298673265249231004193520
Abstract

The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through specific structures, providing insights into the treatment and prevention of diseases. With the advancement of AI technology in the field of protein structure prediction, computational methods have become increasingly important and promising for studying protein folding mechanisms. In this review, we retrospect the current progress in the field of protein folding mechanisms by computational methods from four perspectives: simulation of an inverse folding pathway from native state to unfolded state; prediction of early folding residues by machine learning; exploration of protein folding pathways through conformational sampling; prediction of protein folding intermediates based on templates. Finally, the challenges and future perspectives of the protein folding problem by computational methods are also discussed.
Keywords: Protein folding pathway, computational methods, AI technology, machine learning, conformational sampling, remote templates.
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DOI https://dx.doi.org/10.2174/0109298673265249231004193520	Print ISSN 0929-8673
Publisher Name Bentham Science Publisher	Online ISSN 1875-533X
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