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ThreaDom: extracting protein domain boundary information from multiple threading alignments

机译:ThreaDom:从多个线程比对中提取蛋白质域边界信息

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摘要

>Motivation: Protein domains are subunits that can fold and evolve independently. Identification of domain boundary locations is often the first step in protein folding and function annotations. Most of the current methods deduce domain boundaries by sequence-based analysis, which has low accuracy. There is no efficient method for predicting discontinuous domains that consist of segments from separated sequence regions. As template-based methods are most efficient for protein 3D structure modeling, combining multiple threading alignment information should increase the accuracy and reliability of computational domain predictions.>Result: We developed a new protein domain predictor, ThreaDom, which deduces domain boundary locations based on multiple threading alignments. The core of the method development is the derivation of a domain conservation score that combines information from template domain structures and terminal and internal alignment gaps. Tested on 630 non-redundant sequences, without using homologous templates, ThreaDom generates correct single- and multi-domain classifications in 81% of cases, where 78% have the domain linker assigned within ±20 residues. In a second test on 486 proteins with discontinuous domains, ThreaDom achieves an average precision 84% and recall 65% in domain boundary prediction. Finally, ThreaDom was examined on 56 targets from CASP8 and had a domain overlap rate 73, 87 and 85% with the target for Free Modeling, Hard multiple-domain and discontinuous domain proteins, respectively, which are significantly higher than most domain predictors in the CASP8. Similar results were achieved on the targets from the most recently CASP9 and CASP10 experiments.>Availability: .>Contact: >Supplementary information: are available at Bioinformatics online.
机译:>动机:蛋白质结构域是可以独立折叠和进化的亚基。域边界位置的识别通常是蛋白质折叠和功能注释的第一步。当前大多数方法通过基于序列的分析来推导域边界,其准确性较低。没有有效的方法来预测由来自分离的序列区域的片段组成的不连续域。由于基于模板的方法对于蛋白质3D结构建模最有效,因此结合多个线程对齐信息应该可以提高计算域预测的准确性和可靠性。>结果:我们开发了一种新的蛋白质域预测因子ThreaDom,基于多个线程对齐推导域边界位置。方法开发的核心是推导结合了来自模板域结构以及末端和内部比对间隙的信息的域保守评分。在不使用同源模板的情况下,对630个非冗余序列进行了测试,ThreaDom可以在81%的情况下生成正确的单域和多域分类,其中78%的域接头分配在±20个残基内。在对具有不连续域的486种蛋白质的第二次测试中,ThreaDom在域边界预测中达到了84%的平均精度和65%的召回率。最后,对来自CASP8的56个靶标检测了ThreaDom,它们的域重叠率分别为Free Modeling,Hard多重域和不连续域蛋白的域重叠率分别为73%,87%和85%,这显着高于大多数的域预测因子。 CASP8。在最近的CASP9和CASP10实验中,在目标上也获得了相似的结果。>可用性:。>联系方式: >补充信息:可从在线生物信息学获得。

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