| 1 | 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 |
| 2 | 三维氨基酸描述子(SVTD)在HLA-A^*0201限制性CTL表位肽QSAR中的应用显示文摘应用新型的三维氨基酸描述子-SVTD对65个HLA-A^*0201限制性CTL表位肽进行结构表征,通过多元线性回归对其结构与活性的关系建立定量构效关系QSAR模型,采用内外双重验证的办法分析验证模型的稳定性,建模计算值,留一法(leave—one—out,LOO)交互检验预测值和外部样本预测值的相关系数R,Rcv,Qext分别为:0.949,0.899,0.922。表明SVTD描述子能够系统地表征肽与生物活性相关的结构,且所产生的定量构效关系模型,预测HLA-A^*0201活性的能力较好,可以为改造CTL表位肽结构提供理论基础。 | 陈婷 李美萍 张生万 | 2011 | 计算机与应用化学2011，28，1： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员