Lai Jhih-Siang, Burley Stephen K, Duarte Jose M
Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, United States.
Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, United States.
Bioinform Adv. 2024 Jul 25;4(1):vbae111. doi: 10.1093/bioadv/vbae111. eCollection 2024.
Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein-protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a "big data" science requiring more efficient analysis tools.
This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics.
ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.
体积三维物体分析正应用于结构生物信息学、生物物理学和结构生物学等研究领域，并有可能集成人工智能/机器学习（AI/ML）技术。一种这样的方法，即三维泽尼克矩，已被证明在分析蛋白质结构方面很有价值（例如，蛋白质折叠分类、蛋白质-蛋白质相互作用分析和分子动力学模拟）。它们的紧凑性和效率使其适用于大规模分析。然而，现有的推导三维泽尼克矩的方法可能效率低下，特别是在需要高阶项时，这阻碍了更广泛的应用。随着实验和计算预测的蛋白质结构信息的数量不断增加，结构生物学已成为一门“大数据”科学，需要更高效的分析工具。
本应用笔记介绍了一个基于Python的软件包ZMPY3D，通过将数学公式向量化并使用图形处理单元（GPU）来加速三维泽尼克矩的计算。该软件包提供了流行的GPU支持库，如CuPy和TensorFlow以及NumPy实现，旨在提高计算效率、适应性和未来算法开发的灵活性。ZMPY3D软件包可以从PyPI安装，源代码可从GitHub获得。基于体积的蛋白质三维结构相似性分数和叠加功能的变换矩阵都已实现，创建了一个强大的计算工具，将使研究界能够将三维泽尼克矩与现有的AI/ML工具结合起来，推动蛋白质结构生物信息学的研究和教育。
用Python实现的ZMPY3D可在GitHub（https://github.com/tawssie/ZMPY3D）和PyPI上获得，根据GPL许可证发布。
