ArticlePloS one2018
De novo protein structure prediction using ultra-fast molecular dynamics simulation.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Advances in bioinformatics and multi-omics integration: transforming viral infectious disease research in veterinary medicine.Virology journal · 2025Review
- Advancements in small molecule drug design: A structural perspective.Drug discovery today · 2023Review
- Exposing structural variations in SARS-CoV-2 evolution.Scientific reports · 2021Article
- Exposing Structural Variations in SARS-CoV-2 Evolution.Research square · 2021Article
- Oxidative Damage? Not a Problem! The Characterization of Humanin-like Mitochondrial Peptide in Anoxia Tolerant Freshwater Turtles.The protein journal · 2021Article
- Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.PloS one · 2021Article
- Leri: A web-server for identifying protein functional networks from evolutionary couplings.Computational and structural biotechnology journal · 2021Article
- Reducing Ensembles of Protein Tertiary Structures Generated De Novo via Clustering.Molecules (Basel, Switzerland) · 2020Article
- Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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