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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Refined template selection and combination algorithm significantly improves template-based modeling accuracy
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Refined template selection and combination algorithm significantly improves template-based modeling accuracy

机译：精细模板选择和组合算法显着提高了基于模板的建模精度

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摘要

In contrast to ab-initio protein modeling methodologies, comparative modeling is considered as the most popular and reliable algorithm to model protein structure. However, the selection of the best set of templates is still a major challenge. An effective template-ranking algorithm is developed to efficiently select only the reliable hits for predicting the protein structures. The algorithm employs the pairwise as well as multiple sequence alignments of template hits to rank and select the best possible set of templates. It captures several key sequences and structural information of template hits and converts into scores to effectively rank them. This selected set of templates is used to model a target. Modeling accuracy of the algorithm is tested and evaluated on TBM-HA domain containing CASP8, CASP9 and CASP10 targets. On an average, this template ranking and selection algorithm improves GDT-TS, GDT-HA and TM_Score by 3.531, 4.814 and 0.022, respectively. Further, it has been shown that the inclusion of structurally similar templates with ample conformational diversity is crucial for the modeling algorithm to maximally as well as reliably span the target sequence and construct its near-native model. The optimal model sampling also holds the key to predict the best possible target structure.
机译：与AB-Initio蛋白质建模方法相反，对比建模被认为是模型蛋白质结构最受欢迎和最可靠的算法。但是，选择最佳模板的选择仍然是一个重大挑战。开发了一种有效的模板排序算法，以有效地选择用于预测蛋白质结构的可靠点。该算法采用成对的模板命中的多个序列对齐，以排名并选择最佳可能的模板集。它捕获了模板命中的几个关键序列和结构信息，并转换成分数以有效地对它们进行排序。此选定的一组模板用于模拟目标。在包含CASP8，CASP9和CASP10目标的TBM-HA域测试和评估算法的建模准确度。平均而言，该模板排名和选择算法分别通过3.531,4.814和0.022改善GDT-TS，GDT-HA和TM_Score。此外，已经表明，将具有充分构象分集的结构相似的模板包含在建模算法最大值并且可靠地跨越目标序列并构造其近乎天然模型的重要性。最佳模型采样还可以保持钥匙以预测最佳目标结构。

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