Optimization of Stuctures and Networks of Proteins

描述（由申请人提供）：本申请有三个主要目的，即利用数学编程工具解决生物制药学问题。第一个目标是从同源性获得的蛋白质结构的细化。基于同源性的结构很少有原子分辨率，而原子分辨率对于蛋白质功能的详细建模和药物分子的设计是必要的。基于数学规划的方法，一类新的能量函数，适用于细化问题，将被设计。第二个目标是研究一种新型的网络：蛋白质结构之间的序列流网络。我们研究了点突变产生的新序列保留在当前折叠中或切换到其他稳定结构（我们排除了没有稳定折叠的序列）的过程。基于稳定性准则，接受或拒绝对结构的调整。网。工作定义了一个主方程，它使得研究过去的（古代的）褶皱和作为吸引子的未来结构成为可能。第三个目标是研究蛋白质-蛋白质（物理）相互作用的网络，并将用生物信息学和生物物理学方法进行研究。将比较序列流网络和蛋白质-蛋白质相互作用网络，以更好地理解生物机制和识别的进化。 设计新的算法来预测蛋白质结构和蛋白质-蛋白质相互作用可能有助于设计新药分子。

DESCRIPTION (provided by applicant): This application has three major aims that exploit the use of mathematical programming tools for biophysically motivated problems. The first goal is the refinement of protein structures that are obtained from homology. Structures based on homology are rarely of atomic resolution; a resolution which is necessary for detailed modeling of protein function, and for the design of drug molecules. Based on mathematical programming approach a new class of energy functions, suitable for the refinement problem, will be designed. The second goal is to investigate a new type of network; the network of sequence flow between protein structures. We examine the process in which new sequences, generated by point mutations, remain in the current fold, or switch to other stable structures (we exclude sequences with no stable fold). The adjustments to the structure are accepted or rejected based on stability criterion. The net. work defines a master equation which makes it possible to investigate past (ancient) folds, and future structures that serve as attractors. The third aim examines the network of protein-protein (physical) interactions and will be examined with bioinformatics and biophysical approaches. Comparison will be made between the network of sequence flow and the network of protein-protein interactions to better understand the evolution of biological mechanisms and recognition. PUBLIC HEALTH RELEVANCE The design of new algorithms to predict protein structures and protein-protein interactions is likely to help with the design of new drug molecules.