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A fast, stochastic threading algorithm for proteins.

机译:一种快速,随机的蛋白质穿行算法。

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摘要

MOTIVATION: Sequences for new proteins are being determined at a rapid rate, as a result of the Human Genome Project, and related genome research. The ability to predict the three-dimensional structure of proteins from sequence alone would be useful in discovering and understanding their function. Threading, or fold recognition, aims to predict the tertiary structure of a protein by aligning its amino acid sequence with a large number of structures, and finding the best fit. This approach depends on obtaining good performance from both the scoring function, which simulates the free energy for given trial alignments, and the threading algorithm, which searches for the lowest-score alignment. It appears that current scoring functions and threading algorithms need improvement. RESULTS: This paper presents a new threading algorithm. Numerical tests demonstrate that it is more powerful than two popular approximate algorithms, and much faster than exact methods.
机译:动机:人类基因组计划和相关基因组研究的结果,正在迅速确定新蛋白质的序列。仅从序列中预测蛋白质三维结构的能力将有助于发现和理解其功能。穿线或折叠识别旨在通过将氨基酸序列与大量结构进行比对并预测最佳拟合来预测蛋白质的三级结构。这种方法取决于从评分函数(该函数模拟给定的试验比对的自由能)和线程算法(该算法搜索最低分数的比对)中获得良好的性能。似乎当前的评分功能和线程算法需要改进。结果:本文提出了一种新的线程算法。数值测试表明,它比两种流行的近似算法更强大,并且比精确方法快得多。

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