钙蛋白酶Calpain酶切位点预测工具 —— GPS-CCD 1.0

本文介绍了一种名为GPS-CCD的新型计算程序,用于预测钙激活蛋白酶(calpain)的切割位点。通过收集实验验证的368个切割位点数据,利用Group-based Prediction System (GPS)算法开发了此软件。经过验证,该系统的准确性达到89.98%,敏感性为60.87%,特异性为90.07%。此外,还对196个未经验证的潜在底物进行了预测,并提供在线服务。

For publication of results, please cite the following article:

 GPS-CCD: GPS-CCD: A novel computational program for the prediction of calpain cleavage sites
  Zexian Liu , Jun Cao, Xinjiao Gao, Qian Ma,Jian Ren and Yu Xue .
 PLoS ONE , 2011, (Accepted )

  [Abstract] [Full Text] [PDF]

Calpains constitute an important family of the Ca2+-dependent cysteine proteases, which contain a nucleophilic cysteine in the catalytically active site (Goll et al , 2003 ; Franco et al , 2005 ; Zatz et al , 2005 ; Liu et al , 2008 ). Calpains are widely expressed in mammalians and conserved across eukaryotes (Futai et al , 2001 ; Croall et al , 2007 ). For instance, in budding yeast, at least one calpain-like protease, Rim13/Cpl1, has been identified, although its functions are still elusive (Hayashi et al , 2001 ). In humans, there are over 14 distinct members of the calpain superfamily, some of which are tissue specific. Calpain 1 (μ-calpain, micromolar Ca2+-requiring) and Calpain 2 (m-calpain, millimolar Ca2+-requiring) are ubiquitously expressed and well characterized isoforms (Huang et al , 2005 ). Through spatial and temporal cleavage of a variety of substrates to change their conformation, function and stability (Glading et al , 2002 ), Ca2+-activated calpains play an important role in numerous biological processes, including the regulation of gene expression, signal transduction, cell death/apoptosis, remodeling cytoskeletal attachments during cell fusion/motility and cell cycle progression (Squier et al , 1999 ; Tan et al , 2006 ; Yousefi et al , 2006 ). Moreover, calpain aberrancies are frequently implicated in a variety of diseases and cancers (Arrington et al , 2006 ; Williams et al , 2008 ). Although many studies have tried to dissect the regulatory roles and molecular mechanisms of calpain-dependent cleavage, in fact our understanding of calpain is still fragmentary.


In this work, we collected 368 experimentally verified calpain cleavage sites in 130 proteins. With a previously developed algorithm of GPS (Group-based Prediction System)(Xue et al , 2008 ), we developed a novel software package of GPS-CCD (Calpain Cleavage Detector) for the prediction of calpain cleavage sites. The leave-one-out validation and 4-, 6-, 8-, 10-fold cross-validations were performed to evaluate the performance of the prediction system. By comparison, the GPS 2.0 algorithm was employed for its outstanding prediction performance, with an accuracy 89.98% , sensitivity 60.87% and specificity 90.07% . Furthermore, there are many proteins experimentally identified as calpain substrates for which the exact cleavage sites have not been verified, and we collected 196 such proteins from PubMed. As an application, we predicted potential calpain cleavage sites for these targets. These prediction results might be a useful resource for further experimental investigation. Finally, the online service and local packages of GPS-CCD 1.0 were implemented in JAVA 1.5 (J2SE 5.0) and are freely available for academic researchers at: http://ccd.biocuckoo.org/ .

GPS-CCD 1.0 User Interface

【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
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