AUTHOR=Gomes Priscila S. F. C. , Gomes Diego E. B. , Bernardi Rafael C. TITLE=Protein structure prediction in the era of AI: Challenges and limitations when applying to in silico force spectroscopy JOURNAL=Frontiers in Bioinformatics VOLUME=2 YEAR=2022 URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2022.983306 DOI=10.3389/fbinf.2022.983306 ISSN=2673-7647 ABSTRACT=
Mechanoactive proteins are essential for a myriad of physiological and pathological processes. Guided by the advances in single-molecule force spectroscopy (SMFS), we have reached a molecular-level understanding of how mechanoactive proteins sense and respond to mechanical forces. However, even SMFS has its limitations, including the lack of detailed structural information during force-loading experiments. That is where molecular dynamics (MD) methods shine, bringing atomistic details with femtosecond time-resolution. However, MD heavily relies on the availability of high-resolution structural data, which is not available for most proteins. For instance, the Protein Data Bank currently has 192K structures deposited, against 231M protein sequences available on Uniprot. But many are betting that this gap might become much smaller soon. Over the past year, the AI-based AlphaFold created a buzz on the structural biology field by being able to predict near-native protein folds from their sequences. For some, AlphaFold is causing the merge of structural biology with bioinformatics. Here, using an