![]() Īmino acid sequences contain enough information for specifying their three-dimensional structures, thus which provides the principle for predicting three-dimensional structure from its sequence. Since it requires numerous time and relatively expensive efforts, experimental determination of protein structures is lagging behind, and the gap between sequences and structures is widening rather than diminishing. Despite tremendous efforts of community-wide in structural genomics, protein structures determined by experiments, such as X-ray crystallography, NMR spectroscopy or Cryo-EM, cannot keep the pace with the explosive growth of protein sequences. Nowadays, more and more protein sequences are being produced by genomics sequencing techniques. In modern biology and medicine, it is a major challenge to determine a protein tertiary structure from its primary amino acid sequence, and it has significant and profound consequences, such as understanding protein function, engineering new proteins, designing drugs or for environmental engineering. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. 2018010089 and the Korean Government Ministry of Trade, Industry and Energy N0001822. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: This work was supported by DGIST start-up fund No. Received: JAccepted: OctoPublished: November 20, 2018Ĭopyright: © 2018 Cheung, Yu. 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.Ĭitation: Cheung NJ, Yu W (2018) De novo protein structure prediction using ultra-fast molecular dynamics simulation. The proposed approach is demonstrated by calculations on a set of eighteen large proteins from different fold classes. Combining with evolutionary-based residue-contacts, the presented predictor can predict the tertiary structures of a number of target proteins with remarkable accuracy. 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. Apparently, computational biology is playing a more important role in protein structure prediction than ever. 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.
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