Mathematical Sciences: Development and Implementation of Parallel Algorithms for Protein Inverse Folding Problem

数学科学：蛋白质逆折叠问题并行算法的开发和实现

• 批准号: 9318771
• 负责人: Jill Mesirov
• 金额: $ 4.72万
• 依托单位: Thinking Machines Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-09-01 至 1996-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9318771&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Development; Implementation; Parallel

The investigators develop and implement parallel algorithms on the Connection Machine CM-5 for solving the protein folding problem faster and more accurately than is currently possible. In the inverse folding approach, the assumption that the sequence being analyzed adopts one of the already known protein folds is tested. The energy, or a score, of the new sequence forced to adopt structures of other proteins is calculated for all known protein structures. Structures that are not compatible with the sequence are dismissed. This approach is not a complete solution to the folding problem since it is possible for a sequence to adopt a novel, previously uncharacterized topology. Still, the number of examples where nonhomologous proteins adopt very similar structures is large and still growing. This and the extension of the inverse folding approach to the supersecondary structure elements provide a very general protein structure prediction method. The method is computationally very intensive. The investigators expect that the increase in performance from the use of appropriate parallel algorithms and architecture will allow them to reduce some approximations that are made in the current approach, and thus increase the utility and predictive power of the approach. The protein folding problem, i.e. predicting a protein's three-dimensional structure from its amino acid sequence, is a fundamental, unsolved problem in molecular biology. Kowledge of a protein's three-dimensional structure is necessary to understand its function and interactions with other agents. Inverse folding refers to a more tractable approach to the general problem of protein structure prediction. The inverse protein folding problem is the problem of determining whether a newly sequenced protein can fold into one of the known protein topologies. This problem has many applications in drug design and protein structure prediction. The investigators use high performance parallel comput ing technology to increase the performance and utility of this methodology. The algorithms and software packages resulting from this project will be available for commercialization and industrial applications.

研究人员在连接机CM-5上开发并实现并行算法，以比目前可能的更快、更准确地解决蛋白质折叠问题。在反折叠方法中，假设被分析的序列采用已知的蛋白质折叠之一进行了测试。对于所有已知的蛋白质结构，计算新序列被迫采用其他蛋白质结构的能量或分数。与序列不兼容的结构将被忽略。这种方法不是折叠问题的完整解决方案，因为序列有可能采用新颖的、以前未表征的拓扑结构。尽管如此，非同源蛋白采用非常相似结构的例子仍然很多，而且还在不断增加。这一方法和反折叠方法在超二级结构元素上的推广提供了一种非常通用的蛋白质结构预测方法。这种方法的计算量非常大。研究人员期望通过使用适当的并行算法和架构来提高性能，这将使他们能够减少当前方法中所做的一些近似，从而提高方法的实用性和预测能力。蛋白质折叠问题，即从氨基酸序列预测蛋白质的三维结构，是分子生物学中一个基本的、尚未解决的问题。了解蛋白质的三维结构对于理解其功能和与其他因子的相互作用是必要的。逆折叠是一种更容易处理的方法来解决蛋白质结构预测的一般问题。蛋白质逆折叠问题是确定新测序的蛋白质是否可以折叠成已知蛋白质拓扑结构之一的问题。这个问题在药物设计和蛋白质结构预测中有许多应用。研究人员使用高性能并行计算技术来提高该方法的性能和效用。该项目产生的算法和软件包将可用于商业化和工业应用。