Karolczak Jacek, Przybyłowska Anna, Szewczyk Konrad, Taisner Witold, Heumann John M, Stowell Michael H B, Nowicki Michał, Brzezinski Dariusz
Institute of Computing Science, Poznan University of Technology, Poznan 60-965, Poland.
Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO 80309, United States.
Bioinformatics. 2024 Dec 26;41(1). doi: 10.1093/bioinformatics/btae749.
Accurately identifying ligands plays a crucial role in the process of structure-guided drug design. Based on density maps from X-ray diffraction or cryogenic-sample electron microscopy (cryoEM), scientists verify whether small-molecule ligands bind to active sites of interest. However, the interpretation of density maps is challenging, and cognitive bias can sometimes mislead investigators into modeling fictitious compounds. Ligand identification can be aided by automatic methods, but existing approaches are available only for X-ray diffraction and are based on iterative fitting or feature-engineered machine learning rather than end-to-end deep learning.
Here, we propose to identify ligands using a deep-learning approach that treats density maps as 3D point clouds. We show that the proposed model is on par with existing machine learning methods for X-ray crystallography while also being applicable to cryoEM density maps. Our study demonstrates that electron density map fragments can aid the training of models that can later be applied to cryoEM structures but also highlights challenges associated with the standardization of electron microscopy maps and the quality assessment of cryoEM ligands.
Code and model weights are available on GitHub at https://github.com/jkarolczak/ligands-classification. An accompanying ChimeraX bundle is available at https://github.com/wtaisner/chimerax-ligand-recognizer.
准确识别配体在结构导向药物设计过程中起着关键作用。基于X射线衍射或低温样品电子显微镜（cryoEM）的密度图，科学家们验证小分子配体是否与感兴趣的活性位点结合。然而，密度图的解释具有挑战性，认知偏差有时会误导研究人员对虚拟化合物进行建模。配体识别可以借助自动方法，但现有方法仅适用于X射线衍射，且基于迭代拟合或特征工程机器学习，而非端到端深度学习。
在此，我们提出使用一种深度学习方法来识别配体，该方法将密度图视为三维点云。我们表明，所提出的模型与现有的X射线晶体学机器学习方法相当，同时也适用于低温电子显微镜密度图。我们的研究表明，电子密度图片段有助于训练后续可应用于低温电子显微镜结构的模型，但也凸显了与电子显微镜图标准化及低温电子显微镜配体质量评估相关的挑战。
代码和模型权重可在GitHub上获取，网址为https://github.com/jkarolczak/ligands-classification 。一个配套的ChimeraX包可在https://github.com/wtaisner/chimerax-ligand-recognizer获取。
