摘要
分子虚拟筛选方法旨在找到一种可以与受体蛋白质进行相互作用并适当修改其生物学行为的活性分子.大多数分子虚拟筛选方法的先决条件是已知蛋白质的结构或小分子结合物.然而对于大多数蛋白质而言,这些信息都是未知的.因此,本文提出了一种名为Screener的基于蛋白质序列比对和活性分子相似性评估的分子虚拟筛选方法.Screener首先从受体蛋白质的序列出发,生成位置特异性频率矩阵特征、二级结构特征以及溶剂可及性特征,利用I-LBR程序对受体蛋白质的潜在结合位点残基进行预测;其次,根据预测的结合位点残基以及相关特征信息构建模板蛋白质库;然后,将所有与任意模板蛋白质相互作用的活性分子收集起来构成潜在的种子分子库;最后,利用分子2D指纹之间的相似性来对待筛选分子集进行排序,完成分子虚拟筛选.在基准测试集DUD40和DUD-E65上,Screener的平均EF^(1%)分别为16.6和25.7,HR^(1%)分别为44.1和67.6.基准测试结果表明Screener的虚拟筛选平均性能优于基于对接的虚拟筛选方法AutoDock Vina及基于结构比对的虚拟筛选方法FINDSITE^(filt)和PoLi.
The purpose of molecular virtual screening method is to find active molecules that can interact with a receptor protein and modify its biological behavior.The prerequisite of most molecular virtual screening methods is the known structure of protein or small molecule conjugate.However,for most proteins,this information is unknown.Therefore,this paper proposes a virtual screening method called Screener based on protein sequence alignment and active molecular similarity evaluation.Screener firstly generates the location-specific frequency matrix features,secondary structure features and solvent accessibility features from the sequence of receptor protein,then uses I-LBR program to predict the potential binding sites of receptor protein.Secondly,according to the predicted binding sites and related feature information,the template protein library is constructed.Then,all active molecules that interact with any template protein are collected to form a potential seed molecule library.Finally,the similarity between molecular 2D fingerprints is used to sort the set of molecules to be screened and complete the molecular virtual screening.On the benchmark datasets DUD40 and DUD-E65,Screener′s average EF^(1%)are 16.6 and 25.7,and HR^(1%)are 44.1 and 67.6,respectively.The benchmark test results show that the average performance of Screener is better than that of AutoDock Vina,a virtual screening method based on docking,and FINDSITE^(filt) and PoLi,virtual screening methods based on structural alignment.
作者
郑琳琳
贾宁欣
张贵军
胡俊
ZHENG Lin-lin;JIA Ning-xin;ZHANG Gui-jun;HU Jun(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《小型微型计算机系统》
CSCD
北大核心
2022年第6期1322-1328,共7页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(61902352,61773346)资助
浙江省自然科学基金项目(LZ20F030002,LY21F020025)资助
浙江省属高校基本科研业务费专项项目(RF-A20200012)资助.
关键词
虚拟筛选
活性分子
结合位点
模板蛋白质
种子分子
virtual screening
active molecules
binding sites
template proteins
seed molecules
