THREADING PROTIEN SEQUENCE THROUGH FOLDING MOTIF

该项目的目标是开发用于最佳对齐的算法 带有蛋白质折叠模体的氨基酸序列。折叠图案是 另一种多肽骨架构象，以 计算机作为成对残基触点的列表。序列的比对 和折叠基序使用残基接触电势来评分，或者 经验自由能函数。可通过以下方式找到最佳路线 列举了所有的可能性，但我们希望开发快速的方法 适用于构象数据库的检索。我们已经开发了统计数据 它们根据氨基酸组成和序列长度的影响进行调整 关于预期的对准分数。这些都提供了量化的基础 对备选路线进行排名。我们还发现，两两接触 势可以划分为线性项和二次项， 与残渣的疏水和成对组分相对应 接触电势。疏水术语在以下方面尤为重要 比对分数，贡献了大约三分之二的信息。vbl.使用 穷举我们还发现，从线性和 二次分量高度相关。总而言之，这些观察结果 暗示最佳排列可以通过隐含的方式快速识别 枚举。我们可以使用动态规划算法来快速排序 关于疏水互补性的替代排列，以及 然后只计算最佳比对的成对接触互补 已确认身份。该项目可能会带来一种新的、实用的方法 蛋白质结构预测，通过识别折叠基序进行预测。 它适用于与其他序列几乎没有同源性的序列 蛋白质。基序识别因此可以检测到遥远的进化 关系，其中序列相似性较低，并可能和扩展 分子建模的可能性。

The goal of this project is to develop algorithms for optimally aligning amino acid sequences with protein folding motifs. Folding motifs are alternative polypeptide backbone conformations, represented in the computer as lists of pairwise residue contacts. Alignments of sequence and folding motif are scored using residue contact potentials, or empirical free energy functions. Optimal alignments may be found by enumeration of all possibilities, but we wish to develop rapid methods suitable for search of a conformer data base. We have developed statistics which adjust for the effects of amino acid composition and sequence length on expected alignment scores. These provide a quantitative basis for ranking alternative alignments. We have also found thatpairwise contact potentials may be partitioned into linear and quadratic terms, corresponding to the hydrophobic and pairwise components of residue contact potentials. The hydrophobic term is particularly important in alignment scores, contributing roughly 2/3 of the information. Using exhaustive enumeration we have also found that scores from linear and quadratic components are highly correlated. Together, these observations suggest that optimal alignments may be identified rapidly by implicit enumeration. We may use dynamic programming algorithms to rapidly rank alternative alignments with respect to hydrophobic complementarity, and then compute pairwise contact complementarity for only thebest alignments identified. This project may lead to a new and practical method for protein structure prediction, prediction by recognition of folding motif. It is applicable to sequences showing little or no homology with other proteins. Motif recognition may thus detect distant evolutionary relationships, where sequence similarity is low, and may and extend possibilities for molecular modeling.