首页> 美国卫生研究院文献>Protein Science : A Publication of the Protein Society >Statistical significance of hierarchical multi-body potentials based on Delaunay tessellation and their application in sequence-structure alignment.
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Statistical significance of hierarchical multi-body potentials based on Delaunay tessellation and their application in sequence-structure alignment.

机译:基于Delaunay细分的分层多体势的统计意义及其在序列结构比对中的应用。

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摘要

Statistical potentials based on pairwise interactions between C alpha atoms are commonly used in protein threading/fold-recognition attempts. Inclusion of higher order interaction is a possible means of improving the specificity of these potentials. Delaunay tessellation of the C alpha-atom representation of protein structure has been suggested as a means of defining multi-body interactions. A large number of parameters are required to define all four-body interactions of 20 amino acid types (20(4) = 160,000). Assuming that residue order within a four-body contact is irrelevant reduces this to a manageable 8,855 parameters, using a nonredundant dataset of 608 protein structures. Three lines of evidence support the significance and utility of the four-body potential for sequence-structure matching. First, compared to the four-body model, all lower-order interaction models (three-body, two-body, one-body) are found statistically inadequate to explain the frequency distribution of residue contacts. Second, coherent patterns of interaction are seen in a graphic presentation of the four-body potential. Many patterns have plausible biophysical explanations and are consistent across sets of residues sharing certain properties (e.g., size, hydrophobicity, or charge). Third, the utility of the multi-body potential is tested on a test set of 12 same-length pairs of proteins of known structure for two protocols: Sequence-recognizes-structure, where a query sequence is threaded (without gap) through the native and a non-native structure; and structure-recognizes-sequence, where a query structure is threaded by its native and another non-native sequence. Using cross-validated training, protein sequences correctly recognized their native structure in all 24 cases. Conversely, structures recognized the native sequence in 23 of 24 cases. Further, the score differences between correct and decoy structures increased significantly using the three- or four-body potential compared to potentials of lower order.
机译:基于Cα原子之间的成对相互作用的统计势通常用于蛋白质穿线/折叠识别尝试中。包括更高阶的相互作用是改善这些电位的特异性的可能手段。已经提出蛋白质结构的Cα原子表示的Delaunay细分是定义多体相互作用的一种手段。需要大量参数来定义20种氨基酸类型的所有四体相互作用(20(4)= 160,000)。假设四体接触中的残基顺序无关紧要,则使用608种蛋白质结构的非冗余数据集,可以将其降低为可管理的8,855个参数。三行证据支持四体潜力对序列结构匹配的重要性和实用性。首先,与四体模型相比,所有低阶相互作用模型(三体,两体,一体)在统计上均不足以解释残留物接触的频率分布。其次,在四体势的图形表示中可以看到相互作用的连贯模式。许多模式都有合理的生物物理解释,并且在共享某些特性(例如大小,疏水性或电荷)的残基组之间保持一致。第三,在两种协议的已知结构的12对相同长度的蛋白质对的测试集上测试多体电位的效用,以用于两种方案:序列识别结构,其中查询序列通过天然序列(无间隙)穿过和非本地结构;和结构识别序列,其中查询结构由其本机序列和另一个非本机序列组成。使用交叉验证的训练,在所有24个案例中,蛋白质序列都能正确识别其天然结构。相反,在24个案例中,有23个结构识别了天然序列。此外,与低阶电势相比,使用三体或四体电势,正确结构与诱饵结构之间的得分差异显着增加。

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