基于 MELDxMD 的 NMR 辅助蛋白质结构预测。
NMR-assisted protein structure prediction with MELDxMD.
机构信息
Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York.
Department of Chemistry, Stony Brook University, Stony Brook, New York.
出版信息
Proteins. 2019 Dec;87(12):1333-1340. doi: 10.1002/prot.25788. Epub 2019 Aug 8.
Abstract
We describe the performance of MELD-accelerated molecular dynamics (MELDxMD) in determining protein structures in the NMR-data-assisted category in CASP13. Seeded from web server predictions, MELDxMD was found best in the NMR category, over 17 targets, outperforming the next-best groups by a factor of ~4 in z-score. MELDxMD gives ensembles, not single structures; succeeds on a 326-mer, near the current upper limit for NMR structures; and predicts structures that match experimental residual dipolar couplings even though the only NMR-derived data used in the simulations was NOE-based ambiguous atom-atom contacts and backbone dihedrals. MELD can use noisy and ambiguous experimental information to reduce the MD search space. We believe MELDxMD is a promising method for determining protein structures from NMR data.
摘要
我们描述了 MELD 加速分子动力学（MELDxMD）在 CASP13 中确定 NMR 数据辅助类别中的蛋白质结构的性能。从网络服务器预测开始，MELDxMD 在 NMR 类别中表现最佳，在 17 个目标中，其 z 分数比下一个最佳组高出约 4 倍。MELDxMD 提供的是集合，而不是单个结构；在一个 326 个残基的蛋白质上取得成功，接近目前 NMR 结构的上限；并且预测的结构与实验残余偶极耦合相匹配，尽管在模拟中仅使用基于 NMR 的不明确原子-原子接触和骨架二面角作为 NMR 衍生数据。MELD 可以使用嘈杂和不明确的实验信息来缩小 MD 搜索空间。我们相信 MELDxMD 是一种从 NMR 数据确定蛋白质结构的很有前途的方法。
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