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Using factor analysis scales of generalized amino acid information for prediction and characteristic analysis of β-turns in proteins based on a support vector machine model

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science China Chemistry 2010年第5期53卷 1160-1166页

作者： liang guizhao & zhao wei key laboratory of biorheological science and technology(Chongqing University),ministry of education Bioengineering College,Chongqing University,Chongqing 400044,China 1. key laboratory of biorheological science and technology (Chongqing University) Ministry of Education Bioengineering College Chongqing University Chongqing 400044 China

This paper offers a new combined approach to predict and characterize β-turns in *** approach includes two key steps,i.e.,how to represent the features of β-turns and how to develop a *** first step is to use factor analysis scales of generalized amino acid information(FASGAI),involving hydrophobicity,alpha and turn propensities,bulky properties,compositional characteristics,local flexibility and electronic properties,to represent the features of β-turns in *** second step is to construct a support vector machine(SVM) predictor of β-turns based on 426 training proteins by a sevenfold cross validation *** SVM predictor thus predicted β-turns on 547 and 823 proteins by an external validation test,*** results are compared with the previously best known β-turn prediction methods and are shown to give comparative *** significantly,the SVM model provides some information related to β-turn residues in *** results demonstrate that the present combination approach may be used in the prediction of protein structures.

关键词： β-turns factor analysis scales of generalized amino acid information support vector machine

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A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

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science China Chemistry 2011年第7期54卷 1064-1071页

作者： zhao wei liang guizhao CHEN YuZhen YANG Li key laboratory of biorheological science and technology (Chongqing University) Ministry of Education Bioengineering College Chongqing University Chongqing 400044 China Department of Mathematics Henan Institute of Science and Technology Xinxiang 453003 China

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships （QSRR） models. First, the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties （SGBP）, involving physicochemical, quantum chemical, topological, spatial structural properties, etc.; thereafter, the sequence data are transformed into a uniform matrix by auto cross covariance （ACC）. ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence; hence, this method adequately takes the neighboring effect into account. Then, a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonuclcotides. Finally, a support vector machine is used to develop QSRR models to predict chromatographic retention behavior. The whole dataset is divided into pairs of training sets and test sets with different proportions; as a result, it has been found that the QSRR models using more than 26 training samples have an appropriate external power, and can accurately represent the relationship between the features of sequences and structures, and the retention times. The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information, easy manipulation and high characterization competence. Moreover, the method can further be applied to predict chromatographic retention behavior of oligonucleotides.

关键词： oligonucleotide quantitative structure-retention relationship scores of generalized base properties auto cross covariance genetic algorithm support vector machine

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类黄酮抑制IP6Ks的3D-QSAR研究

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分子科学学报 2020年第3期36卷 191-197页

作者：王海香秦东亚岳一珂伯维晨郑鑫梁桂兆贵阳护理职业学院贵州贵阳550081 重庆大学生物工程学院生物流变科学与技术教育部重点实验室重庆400044

采用基于R基团搜索技术的Topomer CoMFA建立了14个类黄酮类肌醇六磷酸激酶抑制剂的3D-QSAR模型,研究了类黄酮化合物对肌醇六磷酸激酶(IP6Ks)活性的抑制作用.该模型的主成分数为3,拟合与留一法交互验证的复相关系数以及F检验值分别为q2=0... 详细信息

采用基于R基团搜索技术的Topomer CoMFA建立了14个类黄酮类肌醇六磷酸激酶抑制剂的3D-QSAR模型,研究了类黄酮化合物对肌醇六磷酸激酶(IP6Ks)活性的抑制作用.该模型的主成分数为3,拟合与留一法交互验证的复相关系数以及F检验值分别为q2=0.842,qst2=0.26;r2=0.965,rst2=0.12;F=91.519.在此基础上通过Topomer Search进行分子片段筛选,对化合物8,14和6进行重新拼接设计,其预测活性可以分别提高12.76倍、9.27倍和62%.运用Surflex-dock分子对接法研究了实验数据中活性最高的化合物6和活性最低的化合物10与IP6Ks的PDB结构的作用机制,发现并验证了之前所建立的Topomer CoMFA模型构效关系分析研究的结果,进一步阐明了化合物6抑制活性更高的原因.结果表明,在类黄酮分子结构的C(5),C(7)和C(4′)位上,取代基团的大小和静电性质对其抑制活性产生重要的影响.本研究可能对以天然产物设计和合成具有更好生物活性的IP6Ks抑制剂具有指导作用.

关键词：类黄酮肌醇六磷酸激酶 Topomer CoMFA Topomer Search 分子对接

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