Difference between revisions of "2023Si DeNovo"

Latest revision as of 16:45, 23 August 2023

Citation

Si, Dong / Chen, Jason / Nakamura, Andrew / Chang, Luca / Guan, Haowen. Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps. 2023. J. Molecular Biology, Vol. 435, No. 9, p. 167967

Abstract

The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learningbased methods surrounding macromolecule modeling applications.

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Links

https://www.sciencedirect.com/science/article/pii/S0022283623000232

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