COMPUTER SIMULATION THEORY OF GLOBULAR PROTEIN DYNA

球状蛋白质动态的计算机模拟理论

• 批准号: 2178769
• 负责人: JEFFREY SKOLNICK
• 金额: $ 17.58万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 1986
• 资助国家: 美国
• 起止时间: 1986-12-01 至 1997-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2178769
• 关键词: amylases; artificial intelligence; chemical models; computer simulation; cytochrome c; globular protein; hemerythrin; hydrogen bond; mathematics; model design /development; molecular dynamics; molecular energy level; myoglobin; protein folding; retinoid binding proteins

The prediction of the three dimensional structure of a globular protein from its amino acid sequence along with the mechanism by which protein folding occurs are among the most important unsolved problems of contemporary molecular biology. The overall objectives of this proposal are the continued development and refinement of algorithms which not only can predict protein tertiary structure using only sequence information as input but also may provide insights into the folding pathway. To achieve these goals, this proposal focuses on the lattice based aspects of a hierarchical approach to protein folding. High resolution lattice models of proteins, comprised of an alpha-carbon plus reduced off lattice, side chain description, will provide the overall folding pathways and folded conformations. The resulting folded lattice structures are estimated for the alpha-carbons to have a 2-4 angstroms rms deviation from the native state. Turning to the folding pathways, the predicted molten globule states and their free energy landscape will be characterized in detail. The factors responsible for side chain fixation on passage from the molten globule to the native state will be explored, with particular attention focused on the interplay of protein sequence and side chain packing. Specifically this proposal will address the following. (1). A new high coordination lattice model of proteins will be refined, different side chain realizations will be examined and the dynamic Monte Carlo algorithms parallelized. (2). Better empirical free energy functions will be developed. These include better methods for predicting the propensities for secondary structure and generalization of the hydrogen bond scheme to include backbone-side chain hydrogen bonds. To help eliminate misfolded structures, additional very robust knowledge based rules, such as the connections in supersecondary structural elements do not cross, will be included in the interaction scheme. Sequence specific tertiary interactions including a local burial turn, pair interactions and generalized cooperative multibody side chain contact templates will be self consistently derived in the presence of predicted secondary structure propensities. Then, a recently developed neural network which can recognize whether 7 by 7 subfragments of sidechain contact maps are protein like or not will be extended to include sequence specific preferences for subsequences to adopt specific patterns. This information will be obtained from a neural network trained on both homologous and non homologous subsequences that adopt these patterns. Thus, it should be general and not simply applicable to homologous sequence fragments. (3). The folding of representative motifs of globular proteins will be undertaken. Included are the helical proteins such as cytochrome c, whose predicted folding pathway will be compared to experiment, myohemerythrin, myoglobin and complement factor, 1c5a. The mixed motif proteins include ubiquitin, flavodoxin and PRA isomerase, and the beta-proteins include the 16th complement control protein of factor H, 1hcc, alpha-amylase, plastocyanin and retinol binding protein. (4). To validate the methodology, additional blind predictions of proteins whose structures are unknown will be undertaken. Likely candidates include rusticyanin and erythropoietin.

球状蛋白质三维结构的预测 从它的氨基酸序列沿着与蛋白质 折叠发生是最重要的未解决的问题之一， 当代分子生物学 本提案的总体目标 是算法的不断发展和完善， 可以预测蛋白质的三级结构只使用序列信息， 输入，但也可以提供对折叠途径的见解。 实现 为了实现这些目标，该建议侧重于基于格的方面， 蛋白质折叠的分层方法。 高分辨率晶格模型 蛋白质，由α-碳加上减少了晶格，侧 链描述，将提供整体折叠路径和折叠 构象 由此产生的折叠晶格结构估计为 α-碳与天然碳的均方根偏差为2-4埃 状态 转向折叠路径，预测的熔融球 国家和他们的自由能景观将详细描述。 侧链固定的因素，从熔融 将特别关注地探索球体到自然状态 专注于蛋白质序列和侧链包装的相互作用。 具体而言，这项建议将解决以下问题。 （一）. 新高 将蛋白质的配位晶格模型进行细化， 链实现将被检查和动态蒙特卡罗算法 并行化。 （二）、 更好的经验自由能函数将是 开发 这些包括更好的方法来预测倾向 对于二级结构和氢键方案的推广， 包括主链-侧链氢键。 帮助消除错误折叠 结构，其他非常强大的基于知识的规则，如 超二级结构元素中的连接不会交叉， 包括在交互方案中。 序列特异性三级 相互作用，包括本地埋葬转向，对相互作用和 广义合作多体侧链接触模板将是 在存在预测二级结构的情况下自洽衍生 倾向 然后，最近开发的神经网络， 识别侧链接触图的7乘7亚片段是否 类似或不类似的蛋白质将被扩展以包括序列特异性的 偏好服从采取特定的模式。 这些信息 将从同源和非同源神经网络训练获得 采用这些模式的同源序列。 因此， 一般性的，而不是简单地适用于同源序列片段。 （三）、 球状蛋白的代表性基序的折叠将是 进行。 包括螺旋蛋白质，如细胞色素c，其 将预测的折叠途径与实验，肌红蛋白， 肌红蛋白和补体因子，1C 5a。 混合基序蛋白包括 泛素、黄素氧还蛋白和PRA异构酶，β-蛋白包括 因子H的第16补体控制蛋白，1hcc，α-淀粉酶， 质体蓝素和视黄醇结合蛋白。 （四）、 验证 方法，额外的盲目预测蛋白质的结构， 未知的将进行。 可能的候选人包括rusticyanin和 促红细胞生成素