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De novo protein structure prediction using ultra-fast molecular dynamics simulation

Title
De novo protein structure prediction using ultra-fast molecular dynamics simulation
Authors
Cheung, J. NgaamYu, Wookyung
DGIST Authors
Yu, Wookyung
Issue Date
2018-11
Citation
Plos One, 13(11), e0205819
Type
Article
Article Type
Article
Keywords
CONTACT PREDICTIONSSEQUENCECOEVOLUTION
ISSN
1932-6203
Abstract
Modern genomics sequencing techniques have provided a massive amount of protein sequences, but experimental endeavor in determining protein structures is largely lagging far behind the vast and unexplored sequences. Apparently, computational biology is playing a more important role in protein structure prediction than ever. Here, we present a system of de novo predictor, termed NiDelta, building on a deep convolutional neural network and statistical potential enabling molecular dynamics simulation for modeling protein tertiary structure. Combining with evolutionary-based residue-contacts, the presented predictor can predict the tertiary structures of a number of target proteins with remarkable accuracy. The proposed approach is demonstrated by calculations on a set of eighteen large proteins from different fold classes. The results show that the ultra-fast molecular dynamics simulation could dramatically reduce the gap between the sequence and its structure at atom level, and it could also present high efficiency in protein structure determination if sparse experimental data is available.
URI
http://hdl.handle.net/20.500.11750/9470
DOI
10.1371/journal.pone.0205819
Publisher
PLOS
Related Researcher
  • Author Yu, Wookyung Laboratory of Protein Biophysics
  • Research Interests protein biophysics; protein folding; protein dynamics and conformational change
Files:
There are no files associated with this item.
Collection:
Department of Brain and Cognitive SciencesLaboratory of Protein Biophysics1. Journal Articles


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