Jyothi Subramanian, Mustafi Sourajit M, Chary Kandala V R, Joshi Rajani R
Department Mathematics, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India.
J Mol Model. 2005 Nov;11(6):481-8. doi: 10.1007/s00894-005-0256-7. Epub 2005 Aug 11.
PROPAINOR is a new algorithm developed for ab initio prediction of the 3D structures of proteins using knowledge-based nonparametric multivariate statistical methods. This algorithm is found to be most efficient in terms of computational simplicity and prediction accuracy for single-domain proteins as compared to other ab initio methods. In this paper, we have used the algorithm for the atomic structure prediction of a multi-domain (two-domain) calcium-binding protein, whose solution structure has been deposited in the PDB recently (PDB ID: 1JFK). We have studied the sensitivity of the predicted structure to NMR distance restraints with their incorporation as an additional input. Further, we have compared the predicted structures in both these cases with the NMR derived solution structure reported earlier. We have also validated the refined structure for proper stereochemistry and favorable packing environment with good results and elucidated the role of the central linker.
PROPAINOR是一种新开发的算法，用于使用基于知识的非参数多元统计方法从头预测蛋白质的三维结构。与其他从头预测方法相比，该算法在计算简单性和单结构域蛋白质预测准确性方面最为高效。在本文中，我们使用该算法对一种多结构域（双结构域）钙结合蛋白进行原子结构预测，该蛋白的溶液结构最近已存入蛋白质数据库（PDB ID：1JFK）。我们研究了预测结构对NMR距离约束的敏感性，并将其作为额外输入纳入其中。此外，我们将这两种情况下的预测结构与先前报道的NMR衍生溶液结构进行了比较。我们还对优化后的结构进行了正确立体化学和良好堆积环境的验证，结果良好，并阐明了中央连接体的作用。
