COMPUTATIONAL PREDICTION OF BETA-SHEET ARRANGEMENT

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 人们普遍认为，一些人类蛋白质从局部螺旋结构到分子间β-折叠的转变是神经退行性疾病的主要原因，包括一些最可怕和最昂贵的疾病，如阿尔茨海默病、帕金森病、亨廷顿病、传染性海绵状脑病(TSE)。然而，构象转变的确切机制目前还不清楚，由于这一因素，该过程的致病产物是非晶态的，不能溶解，因此不可能用X射线结晶学和溶液核磁共振来确定结构。在这个非常活跃的神经科学研究领域，对这种转变的计算预测可能会提供一种看似合理的方法。与只涉及局部氨基酸的α-螺旋不同，β-折叠可能涉及参与链之间的远程相互作用，这些相互作用在氨基酸序列中可能不一定是连续的。因此，很难预测纸张排列，因此预测的成功非常有限。然而，这些长程相互作用提供了有关蛋白质序列所采用的拓扑结构的信息。预测α-链的排列以形成β-折叠很可能是从氨基酸序列预测蛋白质三维结构的关键步骤。更好地了解形成β-折叠的β链的排列，不仅将为防止导致这些神经退行性疾病的分子间β-折叠的形成提供可能的解决方案，而且还有助于三维结构预测的成功。这个项目通过开发新的计算模型来解决这个问题，以预测两个Beta链在Beta-Sheet中相邻存在的可能性和产品的比对。具体目标包括：1)开发蛋白质贝塔片长距离相互作用的数据库；2)基于支持向量机算法开发新的计算模型，以预测两条贝塔链在贝塔片中相邻存在的可能性；3)开发新的计算模型，以预测两条贝塔链的排列，如果它们如目标2所述相互作用的话。长期目标：更好地理解分子间和分子内的远程相互作用。将所学知识应用于神经科学研究，寻找神经退行性疾病的解决方案，并预测蛋白质的三维结构。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. It is widely believed that the transformation from local ¿-helix structures to intermolecular beta-sheets of some human proteins is the main cause of neurodegenerative diseases including some of the most feared and costly diseases such as Alzheimers disease, Parkinsons disease, huntingtons disease, transmissible spongiform encephalopathies (TSEs). However, the exact mechanism of conformation transformation is not clear yet due to the factor the pathogenic products of the process are noncrystalline and insoluble, and therefore it is not possible to use x-ray crystallography and solution NMR to determine the structures. Computational prediction of the transformation may present a plausible approach in this very active neuroscience research area. Unlike alpha-helices, which involve only local amino acids, the beta-sheets may involve long-range interactions between participating strands that may not be necessarily successive in the amino acid sequences. Thus it is difficult to predict sheet arrangement and consequently the predictions have had very limited success. However, these long-range interactions provide information about the topology adopted by a protein sequence. The prediction of arrangement of alpha-strands to form beta-sheet may well be an essential step to predict the 3-D structure of proteins from amino acid sequences. A better understanding of the arrangement of beta-strands to form beta-sheets will not only provide possible solutions to prevent intermolecular beta-sheet formation that causes these neurodegenerative diseases but also contribute to the success of 3-D structure prediction. This project addresses this issue by developing novel computational models to predict the likelihood of two beta-strands to exist adjacently in a beta-sheet and the alignment of the product. Specific aims include: 1) The development of a database of long-range interactions in protein beta-sheets; 2) The development of new computational models based on the support vector machine (SVM) algorithm to predict the likelihood of two beta-strands to exist adjacently in a beta-sheet; 3) The development of new computational models to predict the alignment of two beta-strands if they are predicted to interact with each other as stated in aim 2. Long-term objectives: To better understand inter- and intra- molecular long-range interactions. To apply gained knowledge in neuroscience research, finding solutions for neurodegenerative diseases, and predicting protein 3-D structure.