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.

中文翻译：

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精细化的模板选择组合算法显着提高了基于模板的建模精度

与从头算蛋白质建模方法相比，比较建模被认为是最流行和最可靠的蛋白质结构建模算法。但是，选择最佳模板集仍然是一项重大挑战。开发了一种有效的模板排序算法来有效地选择可靠的命中来预测蛋白质结构。该算法采用模板命中的成对以及多序列比对来排列和选择最佳可能的模板集。它捕获模板命中的几个关键序列和结构信息，并将其转换为分数以有效地对它们进行排名。这组选定的模板用于对目标建模。该算法的建模精度在包含 CASP8、CASP9 和 CASP10 目标的 TBM-HA 域上进行了测试和评估。平均而言，该模板排名和选择算法分别将 GDT-TS、GDT-HA 和 TM_Score 提高了 3.531、4.814 和 0.022。此外，已经表明，包含具有丰富构象多样性的结构相似模板对于建模算法最大程度地以及可靠地跨越目标序列并构建其近乎原生的模型至关重要。最佳模型采样也是预测最佳目标结构的关键。