Matt: local flexibility aids protein multiple structure alignment.
Matt: local flexibility aids protein multiple structure alignment.
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DOI：
10.1371/journal.pcbi.0040010
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发表时间：
2008-01
影响因子：
4.3
通讯作者：
Cowen, Lenore
中科院分区：
文献类型：
--
作者：
Menke, Matthew;Berger, Bonnie;Cowen, Lenore
即使在蛋白质多结构比对应该优化什么样的度量上存在一致意见，找到最佳比对在计算上也是禁止的。许多先前的方法使用的一种方法是对齐片段对链，其中来自所有蛋白质的短结构片段彼此最佳地对齐，并且最终的对齐以几何一致的方式将这些连接在一起。Ye和Godzik最近提出，增加几何灵活性可能有助于在各种情况下更好地建模蛋白质结构。我们介绍的程序马特（多重对齐与平移和扭曲），对齐的片段对链接算法，在中间步骤，允许片段之间的局部灵活性：小的平移和旋转暂时允许使对齐的片段集更接近，即使它们是物理上不可能的刚体变换。在由这些“弯曲”对齐引导的动态编程装配之后，在输出对齐之前的最后一步中恢复几何一致性。Matt在流行的Homstrad和SABmark基准数据集上与其他最近的多蛋白质结构比对程序进行了测试。Matt的全局性能与Homstrad上的其他程序具有竞争力，但优于SABmark上的其他程序，SABmark是具有更远同源性的蛋白质的多结构比对的基准。在这两个数据集上，Matt展示了更好地对齐α-螺旋和β-链末端的能力，这是任何结构对齐程序的一个重要特征，旨在帮助构建用于反向蛋白质折叠问题的线程方法的结构模板库。马特对齐是否可以用来区分远同源结构对不同源的蛋白质对的相关问题也被认为是。为此，基于Matt比对的共同核心长度和平均均方根偏差（RMSD）的p值得分显示出在SABmark基准数据集中很大程度上将诱饵与同源蛋白质结构分开。我们假设，马特的强大的性能来自于它的能力，在不同的构象状态的蛋白质模型，也许更重要的是，它的能力，在更遥远的相关蛋白质模型的骨干扭曲。蛋白质折叠成复杂的高度不对称的三维形状。当发现一种蛋白质折叠的形状与已知功能的其他蛋白质足够相似时，这可以显着帮助预测新蛋白质的功能。此外，在一组这样的类似蛋白质中结构高度保守的区域可以指示保守区域的功能或结构重要性。给定一组蛋白质结构，蛋白质结构对齐问题是确定这些蛋白质结构的骨架的叠加，使尽可能多的结构处于紧密的空间对齐中。我们引入了一种算法，允许在结构中的局部灵活性时，它使它们更接近对齐。当待对齐的结构高度相似时，该算法的性能与竞争对手一样好，并且随着相似性的降低，该算法的性能越来越好。此外，对于相关的分类问题，即询问两种蛋白质之间的结构相似程度是否意味着它们可能从共同的祖先进化而来，评分函数基于为每对蛋白质结构生成的最佳比对来评估它们是否应该被宣布为结构上足够相似。该评分可用于预测两种蛋白质何时具有足够相似的形状以可能共享功能特征。
Even when there is agreement on what measure a protein multiple structure alignment should be optimizing, finding the optimal alignment is computationally prohibitive. One approach used by many previous methods is aligned fragment pair chaining, where short structural fragments from all the proteins are aligned against each other optimally, and the final alignment chains these together in geometrically consistent ways. Ye and Godzik have recently suggested that adding geometric flexibility may help better model protein structures in a variety of contexts. We introduce the program Matt (Multiple Alignment with Translations and Twists), an aligned fragment pair chaining algorithm that, in intermediate steps, allows local flexibility between fragments: small translations and rotations are temporarily allowed to bring sets of aligned fragments closer, even if they are physically impossible under rigid body transformations. After a dynamic programming assembly guided by these “bent” alignments, geometric consistency is restored in the final step before the alignment is output. Matt is tested against other recent multiple protein structure alignment programs on the popular Homstrad and SABmark benchmark datasets. Matt's global performance is competitive with the other programs on Homstrad, but outperforms the other programs on SABmark, a benchmark of multiple structure alignments of proteins with more distant homology. On both datasets, Matt demonstrates an ability to better align the ends of α-helices and β-strands, an important characteristic of any structure alignment program intended to help construct a structural template library for threading approaches to the inverse protein-folding problem. The related question of whether Matt alignments can be used to distinguish distantly homologous structure pairs from pairs of proteins that are not homologous is also considered. For this purpose, a p-value score based on the length of the common core and average root mean squared deviation (RMSD) of Matt alignments is shown to largely separate decoys from homologous protein structures in the SABmark benchmark dataset. We postulate that Matt's strong performance comes from its ability to model proteins in different conformational states and, perhaps even more important, its ability to model backbone distortions in more distantly related proteins. Proteins fold into complicated highly asymmetrical 3-D shapes. When a protein is found to fold in a shape that is sufficiently similar to other proteins whose functional roles are known, this can significantly aid in predicting function in the new protein. In addition, the areas where structure is highly conserved in a set of such similar proteins may indicate functional or structural importance of the conserved region. Given a set of protein structures, the protein structural alignment problem is to determine the superimposition of the backbones of these protein structures that places as much of the structures as possible into close spatial alignment. We introduce an algorithm that allows local flexibility in the structures when it brings them into closer alignment. The algorithm performs as well as its competitors when the structures to be aligned are highly similar, and outperforms them by a larger and larger margin as similarity decreases. In addition, for the related classification problem that asks if the degree of structural similarity between two proteins implies if they likely evolved from a common ancestor, a scoring function assesses, based on the best alignment generated for each pair of protein structures, whether they should be declared sufficiently structurally similar or not. This score can be used to predict when two proteins have sufficiently similar shapes to likely share functional characteristics.
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影响因子：
1.7
作者：
Eidhammer, I;Jonassen, I;Taylor, WR
通讯作者：
Taylor, WR
DOI：
10.1073/pnas.0404383101
发表时间：
2004-08-17
影响因子：
11.1
作者：
Kolodny, R;Linial, N
通讯作者：
Linial, N
影响因子：
14.9
作者：
Echols, N;Milburn, D;Gerstein, M
通讯作者：
Gerstein, M
DOI：
10.1093/protein/13.8.535
发表时间：
2000-08-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
Jung, J;Lee, B
通讯作者：
Lee, B
影响因子：
5.6
作者：
BARTON, GJ;STERNBERG, MJE
通讯作者：
STERNBERG, MJE