Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India.
J Comput Chem. 2013 Aug 15;34(22):1925-36. doi: 10.1002/jcc.23339. Epub 2013 Jun 3.
One of the major challenges for protein tertiary structure prediction strategies is the quality of conformational sampling algorithms, which can effectively and readily search the protein fold space to generate near-native conformations. In an effort to advance the field by making the best use of available homology as well as fold recognition approaches along with ab initio folding methods, we have developed Bhageerath-H Strgen, a homology/ab initio hybrid algorithm for protein conformational sampling. The methodology is tested on the benchmark CASP9 dataset of 116 targets. In 93% of the cases, a structure with TM-score ≥ 0.5 is generated in the pool of decoys. Further, the performance of Bhageerath-H Strgen was seen to be efficient in comparison with different decoy generation methods. The algorithm is web enabled as Bhageerath-H Strgen web tool which is made freely accessible for protein decoy generation (http://www.scfbio-iitd.res.in/software/Bhageerath-HStrgen1.jsp).
蛋白质三级结构预测策略的主要挑战之一是构象采样算法的质量，它可以有效地、轻易地搜索蛋白质折叠空间，生成接近天然构象的结构。为了通过充分利用可用的同源性以及折叠识别方法和从头折叠方法来推进这一领域的发展，我们开发了 Bhageerath-H Strgen，这是一种用于蛋白质构象采样的同源/从头折叠混合算法。该方法在基准 CASP9 数据集上进行了测试，该数据集包含 116 个目标。在 93%的情况下，在诱饵池中生成了 TM-score ≥ 0.5 的结构。此外，与不同的诱饵生成方法相比，Bhageerath-H Strgen 的性能表现也更加高效。该算法可通过 Bhageerath-H Strgen 网络工具进行访问，该工具可免费用于蛋白质诱饵生成（http://www.scfbio-iitd.res.in/software/Bhageerath-HStrgen1.jsp）。
