Mirny L A, Finkelstein A V, Shakhnovich E I
Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA.
Proc Natl Acad Sci U S A. 2000 Aug 29;97(18):9978-83. doi: 10.1073/pnas.160271197.
In this study, we estimate the statistical significance of structure prediction by threading. We introduce a single parameter epsilon that serves as a universal measure determining the probability that the best alignment is indeed a native-like analog. Parameter epsilon takes into account both length and composition of the query sequence and the number of decoys in threading simulation. It can be computed directly from the query sequence and potential of interactions, eliminating the need for sequence reshuffling and realignment. Although our theoretical analysis is general, here we compare its predictions with the results of gapless threading. Finally we estimate the number of decoys from which the native structure can be found by existing potentials of interactions. We discuss how this analysis can be extended to determine the optimal gap penalties for any sequence-structure alignment (threading) method, thus optimizing it to maximum possible performance.
在本研究中，我们估计了穿线法进行结构预测的统计显著性。我们引入了一个单一参数ε，它作为一种通用度量，用于确定最佳比对确实是类似天然结构的类似物的概率。参数ε考虑了查询序列的长度和组成以及穿线模拟中的诱饵数量。它可以直接从查询序列和相互作用势计算得出，无需进行序列重排和重新比对。尽管我们的理论分析具有一般性，但在此我们将其预测结果与无间隙穿线法的结果进行比较。最后，我们估计了通过现有相互作用势能够找到天然结构的诱饵数量。我们讨论了如何扩展此分析以确定任何序列 - 结构比对（穿线法）方法的最佳空位罚分，从而将其优化至最大可能性能。
