Faculty, Staff and Student Publications
Publication Date
7-15-2022
Journal
iScience
Abstract
With the advent of new artificial intelligence and machine learning algorithms, predictive modeling can, in some cases, produce structures on par with experimental methods. The combination of predictive modeling and experimental structure determination by electron cryomicroscopy (cryoEM) offers a tantalizing approach for producing robust atomic models of macromolecular assemblies. Here, we apply AlphaFold2 to a set of community standard data sets and compare the results with the corresponding reference maps and models. Moreover, we present three unique case studies from previously determined cryoEM density maps of viruses. Our results show that AlphaFold2 can not only produce reasonably accurate models for analysis and additional hypotheses testing, but can also potentially yield incorrect structures if not properly validated with experimental data. Whereas we outline numerous shortcomings and potential pitfalls of predictive modeling, the obvious synergy between predictive modeling and cryoEM will undoubtedly result in new computational modeling tools.
Keywords
Computational molecular modelling, Biochemistry, Structural biology
DOI
10.1016/j.isci.2022.104496
PMID
35733789
PMCID
PMC9207676
PubMedCentral® Posted Date
5-30-2022
PubMedCentral® Full Text Version
Post-print
Graphical Abstract
Published Open-Access
yes