| 2 | 一种新的氨基酸描述符SVHEHS在生物活性肽QSAR中的应用研究显示文摘对20种氨基酸的457种性质参数按疏水性质、电性特征、氢键贡献和立体特征进行分类后,并各自进行主成分分析(PCA),得到一种新的氨基酸结构描述符SVHEHS(score vector of hydrophilicity,electronic,hydrogen bondcontribution and steric properties)。用该描述符分别对一系列血管紧张素转化酶抑制二肽以及苦味二肽进行序列表征,并用来与生物活性建立多元线性回归模型。血管紧张素转化酶抑制二肽、苦味二肽模型的相关系数、交叉验证相关系数、均方根误差分别为0.936、0.854、0.259和0.949、0.886、0.136,同时还对所得模型进行了外部验证。结果表明,该描述符建立的模型具有较好的拟合与预测能力,用于生物活性肽的定量构效关系研究是理想的。 | 彭剑秋 刘静 管骁 | 2012 | 食品科学2012，33，7： | 2 |
| 3 | A novel vector of topological and structural information for amino acids and its QSAR applications for peptides and analogues显示文摘A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/ QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting en-zyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK) , the R2, cross-validation R2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R2cu≥0.82, Q2cu≥0.77, Ermse≤0.44 (GPLS+SVM); ESCK, R2cu≥0.84, Q2cu≥0.82, Ermse≤0.20 (GPLS+INN), respectively. | LI ZhiLiang LI GenRong SHU Mao SUN JiaYing YANG ShanBin MEI Hu ZHANG MengJun ZHOU Ping WU ShiRong CHEN GuoHua LU FengLin LU TingTing | 2008 | Science China Chemistry2008，51，10： | 2 |
| 5 | Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED)显示文摘Only from the primary structures of peptides, a new set of descriptors called the molecular electro-negativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the auto-mated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1― 14) Ad and 14 other restricted activities assigned as '1'(+) and '0'(-), respectively. The latter comprised 6 Ab(15-20), 3 Ak(21-23), 2 Ek(24-26), 2 H-2k(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction(none wrong for 8 testing samples); while con-trastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross valida-tions were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility an-tigen (MHC) epitope of human. It will be useful in immune identification and recognition of pro-teins and genes and in the design and devel-opment of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with anti-genic activity and heptapeptide sequences with tachykinin activity through quantitative se-quence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oli-gopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drwan: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields. | LI ZhiLiang1,2, WU ShiRong1,2, CHEN ZeCong1,2, YE Nancy1,2, YANG ShengXi1,2, LIAO ChunYang1,2, ZHANG MengJun1,2,3, YANG Li1,2, MEI Hu1,2,4, YANG Yan1,2, ZHAO Na1,2, ZHOU Yuan1,2, ZHOU Ping1,2, XIONG Qing1,2, XU Hong1,2, LIU ShuShen1,2, LING ZiHua1,2, CHEN Gang1,2,4 & LI GenRong1,2 1 College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing 400044, China 2 State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha 410012, China 3 Department of Medical Analysis/PLA Center of Bioinformatics Immunology, Surgeon Third University, Chongqing 400031, China 4 Technology Centre for Life Sciences, Singapore Polytechnic, 500 Dover Road, Singapore 139651, Singapore | 2007 | Science China(Life Sciences)2007，50，5： | 1 |
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