Optimization of Folding and Threading Proteins

描述(申请人提供)：从基因组序列和蛋白质分类中确定蛋白质的结构和功能仍然是现代计算生物学中最重要的挑战之一。对算法从序列中预测蛋白质形状的能力提出了显著的增强，集中在主要的瓶颈；例如，折叠能量和进行近似匹配的能力。从序列中确定蛋白质形状的算法有两个主要组成部分：第一个组成部分(采样)生成一组看似合理的蛋白质形状；至少一个采样形状预计与正确的折叠相似。第二部分对不同的结构进行评分，并决定最佳模型。能量函数的收敛半径必须足够大，以便也能检测到近似匹配(在线程中，近似匹配可能包括删除和插入)。因此，很明显，不能识别正确折叠的糟糕的评分函数(或能量)可能会降低折叠算法的能力。目前，很容易产生一套会混淆现有能量函数的诱饵(错误)结构。数学编程和机器学习技术(支持向量机)将设计增强的折叠和穿线潜力。通过这些方法进行的训练是自动化的，并且将导致识别作为数据大小的函数的单调改进。为了更有效地覆盖蛋白质空间，目标是在具有(最多)10,000个参数的单个一致势中学习1亿个数据点。在序列和结构信息快速增长的时候，自动化的大规模学习是至关重要的。Http://ser-loopp.tc.cornell.edu/Ioopp.html上的社区现在和将来都可以使用基于新旧潜力的线程预测服务器

DESCRIPTION (provided by applicant): Determining protein structure and function from genomic sequences and protein classification remains one of the most significant challenges in modern computational biology. Significant enhancement to the capacity of algorithms to predict protein shapes from sequences is proposed, focusing on major bottlenecks; e.g., the folding energy and the ability of making approximate matches. Algorithms to determine protein shapes from sequences have two major components: The first component (sampling) generates a set of plausible protein shapes; at least one of the sampled shapes is expected to be similar to the correct fold. The second component scores the different structures and decides on the best model. The radius of convergence of the energy function must be sufficiently large so that approximate matches will be detected as well (in threading approximate matches may include deletions and insertion). It is therefore clear that poor scoring functions (or energies), which are unable to identify the correct fold, are likely to diminish the capacity of the folding algorithm. At present, it is easy to generate a set of decoy (wrong) structures that will confuse existing energy functions. Mathematical programming and machine learning techniques (Support Vector Machines) will design enhanced folding and threading potentials. The training by these methods is automated and will lead to monotonic improvement in recognition as a function of the data size. To more effectively cover protein space, the goal is to learn 100 million data points in a single consistent potential with (at most) 10,000 parameters. The automated large scale learning is crucial at times in which the information on sequences and structures grows rapidly. A threading prediction server, based on the old and the new potentials, is and will be available, to the community at http://ser-loopp.tc.cornell.edu/Ioopp.html