Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York.
Department of Chemistry, Stony Brook University, Stony Brook, New York.
Proteins. 2019 Dec;87(12):1333-1340. doi: 10.1002/prot.25788. Epub 2019 Aug 8.
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.
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。
