Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States.
J Chem Inf Model. 2023 Aug 28;63(16):4995-5000. doi: 10.1021/acs.jcim.3c00890. Epub 2023 Aug 7.
We implemented an CCS prediction workflow which incrementally refines generated structures using molecular mechanics, a deep learning potential, conformational clustering, and quantum mechanics (QM). Automating intermediate steps for a high performance computing (HPC) environment allows users to input the SMILES structure of small organic molecules and obtain a Boltzmann averaged collisional cross section (CCS) value as output. The CCS of a molecular species is a metric measured by ion mobility spectrometry (IMS) which can improve annotation of untargeted metabolomics experiments. We report only a minor drop in accuracy when we expedite the CCS calculation by replacing the QM geometry refinement step with a single-point energy calculation. Even though the workflow involves stochastic steps (i.e., conformation generation and clustering), the final CCS value was highly reproducible for multiple iterations on L-carnosine. Finally, we illustrate that the gas phase ensembles modeled for the workflow are intermediate files which can be used for the prediction of other properties such as aqueous phase nuclear magnetic resonance chemical shift prediction. The software is available at the following link: https://github.com/DasSusanta/snakemake_ccs.
我们实现了一个 CCS 预测工作流程，该流程使用分子力学、深度学习势、构象聚类和量子力学（QM）逐步改进生成的结构。为高性能计算（HPC）环境自动化中间步骤允许用户输入小分子的 SMILES 结构，并获得作为输出的玻尔兹曼平均碰撞截面（CCS）值。分子物种的 CCS 是通过离子迁移谱（IMS）测量的度量标准，可提高非靶向代谢组学实验的注释。当我们通过用单点能计算替换 QM 几何精修步骤来加速 CCS 计算时，我们报告的准确性仅略有下降。尽管该工作流程涉及随机步骤（即构象生成和聚类），但对于 L-肌肽的多次迭代，最终的 CCS 值具有高度可重复性。最后，我们说明了为工作流程建模的气相集合是中间文件，可用于预测其他性质，如水性核磁共振化学位移预测。该软件可在以下链接获得：https://github.com/DasSusanta/snakemake_ccs。
