Le Guilloux Vincent, Schmidtke Peter, Tuffery Pierre
ICOA - Institut de chimie organique et analytique - UMR CNRS 6005, Div. of chemoinformatics and molecular modeling, University of Orléans, Orléans, France.
BMC Bioinformatics. 2009 Jun 2;10:168. doi: 10.1186/1471-2105-10-168.
Virtual screening methods start to be well established as effective approaches to identify hits, candidates and leads for drug discovery research. Among those, structure based virtual screening (SBVS) approaches aim at docking collections of small compounds in the target structure to identify potent compounds. For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein surfaces.
Fpocket is an open source pocket detection package based on Voronoi tessellation and alpha spheres built on top of the publicly available package Qhull. The modular source code is organised around a central library of functions, a basis for three main programs: (i) Fpocket, to perform pocket identification, (ii) Tpocket, to organise pocket detection benchmarking on a set of known protein-ligand complexes, and (iii) Dpocket, to collect pocket descriptor values on a set of proteins. Fpocket is written in the C programming language, which makes it a platform well suited for the scientific community willing to develop new scoring functions and extract various pocket descriptors on a large scale level. Fpocket 1.0, relying on a simple scoring function, is able to detect 94% and 92% of the pockets within the best three ranked pockets from the holo and apo proteins respectively, outperforming the standards of the field, while being faster.
Fpocket provides a rapid, open source and stable basis for further developments related to protein pocket detection, efficient pocket descriptor extraction, or drugablity prediction purposes. Fpocket is freely available under the GNU GPL license at http://fpocket.sourceforge.net.
虚拟筛选方法已开始成为药物发现研究中识别活性化合物、候选化合物和先导化合物的有效方法。其中，基于结构的虚拟筛选（SBVS）方法旨在将小分子化合物集合对接至目标结构中以识别强效化合物。对于SBVS而言，蛋白质结构中候选口袋的识别是一个关键特征，并且近年来人们对开发蛋白质表面口袋和空腔检测方法的兴趣日益浓厚。
Fpocket是一个基于Voronoi镶嵌和alpha球体的开源口袋检测软件包，它构建于公开可用的Qhull软件包之上。模块化源代码围绕一个中央函数库组织，该函数库是三个主要程序的基础：（i）Fpocket，用于进行口袋识别；（ii）Tpocket，用于在一组已知蛋白质-配体复合物上组织口袋检测基准测试；（iii）Dpocket，用于收集一组蛋白质上的口袋描述符值。Fpocket用C编程语言编写，这使其成为一个非常适合科学界的平台，科学界可以在此平台上开发新的评分函数并大规模提取各种口袋描述符。Fpocket 1.0依靠一个简单的评分函数，能够分别从全蛋白和无配体蛋白的排名前三口袋中检测出94%和92%的口袋，优于该领域的标准，而且速度更快。
Fpocket为与蛋白质口袋检测、高效口袋描述符提取或成药可能性预测相关的进一步发展提供了一个快速、开源且稳定的基础。Fpocket根据GNU GPL许可在http://fpocket.sourceforge.net上免费提供。
