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CRAFT:一种基于流转移算法的网络集成腔预测工具。

CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm.

作者信息

Gahlawat Anuj, Singh Anjali, Sandhu Hardeep, Garg Prabha

机构信息

Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar, 160062, Punjab, India.

Department of Computer Science, Kurukshetra University, Kurukshetra, Haryana, India.

出版信息

J Cheminform. 2024 Jan 30;16(1):12. doi: 10.1186/s13321-024-00803-6.

Abstract

Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.

摘要

许多计算方法,包括基于进化、基于能量和基于几何的方法,都被用于识别蛋白质内部的空腔。空腔信息有助于蛋白质功能注释、药物设计、多药理学和变构位点研究。本文介绍了“流转移算法”,用于通过多维空腔扫描快速有效地识别各种蛋白质空腔。首先,它识别分隔符和易感四面体以建立边界区域并提供种子四面体。使用最大圆半径精确扫描种子四面体的面,以将种子流转移到相邻四面体。种子流持续进行,直到被边界或禁止面终止,如果估计的最大圆半径小于或等于用户定义的最大圆半径,则该面被视为禁止面。种子扫描后,对流中涉及的四面体进行聚类以定位空腔。CRAFT网络界面集成了此算法用于蛋白质空腔识别,并增强了用户控制。它支持带有辅因子、氢和配体的蛋白质,并提供诸如3D可视化、空腔物理化学性质、百分比贡献图以及每个空腔的突出显示残基等全面功能。可以通过其网络界面http://pitools.niper.ac.in/CRAFT访问CRAFT,由https://github.com/PGlab-NIPER/CRAFT/提供的命令版本作为补充。科学贡献:流转移算法是一种新颖的几何方法,用于准确可靠地预测各种蛋白质空腔。该算法采用了一个独特的概念,即在3D德劳内三角剖分中使用最大圆半径来处理不同的范德华半径,而现有方法忽略了原子特定的范德华半径或依赖于复杂的加权几何技术。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b52e/10829215/08548c9d5048/13321_2024_803_Fig1_HTML.jpg

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