iScore: An MPI supported software for ranking protein–protein docking models based on a random walk graph kernel and support vector machines
iScore: An MPI supported software for ranking protein–protein docking models based on a random walk graph kernel and support vector machines
复制标题
iScore：一款 MPI 支持的软件，用于基于随机游走图内核和支持向量机对蛋白质对接模型进行排名
DOI：
10.1016/j.softx.2020.100462
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发表时间：
2020
期刊：
影响因子：
3.4
通讯作者：
Xue, Li C.
中科院分区：
文献类型：
--
作者：
Renaud, Nicolas;Jung, Yong;Honavar, Vasant;Geng, Cunliang;Bonvin, Alexandre M.J.J.;Xue, Li C.
计算对接是模拟蛋白质-蛋白质复合体三维结构的一种很有前途的工具，它提供了对细胞生命中蛋白质功能的基本见解。从巨大的生成对接模型池中挑选出接近本地的模型(称为评分问题)仍然是计算对接中的主要挑战。我们最近发布了iScore，这是一种新的基于图内核的评分函数。IScore根据对接模型的界面图与培训界面图集的相似性对其进行排名。IScore使用支持向量机方法和随机游走图核对蛋白质-蛋白质界面进行分类和排序。在这里，我们介绍iScore的软件。该软件提供了完全自动化计算工作流程的可执行脚本。此外，接口图的创建和分析可以使用消息传递接口(MPI)分布在不同的进程中，并且可以通过专用的CUDA内核卸载到GPU。
Computational docking is a promising tool to model three-dimensional (3D) structures of protein–protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph similarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein–protein interfaces. Here, we present the software for iScore. The software provides executable scripts that fully automate the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.
影响因子：
5.6
作者：
Vreven T;Moal IH;Vangone A;Pierce BG;Kastritis PL;Torchala M;Chaleil R;Jiménez-García B;Bates PA;Fernandez-Recio J;Bonvin AM;Weng Z
通讯作者：
Weng Z
Docking Benchmark 4、5 和 CAPRI Score_set 的对接模型
影响因子：
5.8
作者：
C. Geng;L. Xue;A. Bonvin
通讯作者：
A. Bonvin
影响因子：
3.7
作者：
Bourquard T;Bernauer J;Azé J;Poupon A
通讯作者：
Poupon A