DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces
DiAMoNDBack: Diffusion-Denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces
复制标题
DiAMoNDBack：Cα 蛋白迹线非确定性反向映射的扩散去噪自回归模型
DOI：
10.1021/acs.jctc.3c00840
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发表时间：
2023
影响因子：
5.5
通讯作者：
Ferguson, Andrew L.
中科院分区：
文献类型：
--
作者：
Jones, Michael S.;Shmilovich, Kirill;Ferguson, Andrew L.
粗粒度的蛋白质分子模型允许使用全原子模型无法达到的长度和时间尺度，并模拟发生在长时间尺度上的过程，如聚集和折叠。降低的分辨率实现了计算加速，但原子表示对于完整理解机械细节至关重要。反向映射是将所有原子分辨率恢复到粗粒度分子模型的过程。在这项工作中，我们报告DiAMoNDBack（非确定性Backmapping的扩散去噪自回归模型）作为自回归去噪扩散概率模型，将所有原子细节恢复为仅保留Cα坐标的粗粒度蛋白质表示。自回归生成过程以逐个残基的方式从蛋白质N-末端到C-末端进行，条件是Cα迹线和先前在局部邻域内的反向映射的主链和侧链原子。我们模型的局部和自回归性质使其在蛋白质之间可转移。去噪扩散过程的随机性意味着该模型生成与粗粒度Cα迹线一致的主链和侧链全原子构型的真实系综。我们从蛋白质数据库（PDB）中训练了超过65 k+结构的DiAMoNDBack，并在应用程序中验证了它，以保持PDB测试集，来自蛋白质Encoder数据库（PED）的内在无序蛋白质结构，来自DE Shaw Research的快速折叠迷你蛋白质的分子动力学模拟，以及粗粒度模拟数据。我们在正确的键形成、避免侧链冲突以及所生成的侧链构型状态的多样性方面实现了最先进的重建性能。我们将DiAMoNDBack模型作为免费和开源的Python包公开提供。
Coarse-grained molecular models of proteins permit access to length and time scales unattainable by all-atom models and the simulation of processes that occur on long time scales, such as aggregation and folding. The reduced resolution realizes computational accelerations, but an atomistic representation can be vital for a complete understanding of mechanistic details. Backmapping is the process of restoring all-atom resolution to coarse-grained molecular models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping) as an autoregressive denoising diffusion probability model to restore all-atom details to coarse-grained protein representations retaining only Cα coordinates. The autoregressive generation process proceeds from the protein N-terminus to C-terminus in a residue-by-residue fashion conditioned on the Cα trace and previously backmapped backbone and side-chain atoms within the local neighborhood. The local and autoregressive nature of our model makes it transferable between proteins. The stochastic nature of the denoising diffusion process means that the model generates a realistic ensemble of backbone and side-chain all-atom configurations consistent with the coarse-grained Cα trace. We train DiAMoNDBack over 65k+ structures from the Protein Data Bank (PDB) and validate it in applications to a hold-out PDB test set, intrinsically disordered protein structures from the Protein Ensemble Database (PED), molecular dynamics simulations of fast-folding mini-proteins from DE Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art reconstruction performance in terms of correct bond formation, avoidance of side-chain clashes, and the diversity of the generated side-chain configurational states. We make the DiAMoNDBack model publicly available as a free and open-source Python package.