Joshi Rajani R, Jyothi S
BJM School of Bioscience and Bioengineering, Indian Institute of Technology, Powai, 400076, Mumbai, India.
Comput Biol Chem. 2003 Jul;27(3):241-52. doi: 10.1016/s0097-8485(02)00074-8.
We have formulated the ab-initio prediction of the 3D-structure of proteins as a probabilistic programming problem where the inter-residue 3D-distances are treated as random variables. Lower and upper bounds for these random variables and the corresponding probabilities are estimated by nonparametric statistical methods and knowledge-based heuristics. In this paper we focus on the probabilistic computation of the 3D-structure using these distance interval estimates. Validation of the predicted structures shows our method to be more accurate than other computational methods reported so far. Our method is also found to be computationally more efficient than other existing ab-initio structure prediction methods. Moreover, we provide a reliability index for the predicted structures too. Because of its computational simplicity and its applicability to any random sequence, our algorithm called PROPAINOR (PROtein structure Prediction by AI an Nonparametric Regression) has significant scope in computational protein structural genomics.
我们已将蛋白质三维结构的从头预测表述为一个概率编程问题，其中残基间的三维距离被视为随机变量。这些随机变量的上下界以及相应概率通过非参数统计方法和基于知识的启发式方法进行估计。在本文中，我们专注于使用这些距离区间估计来进行三维结构的概率计算。对预测结构的验证表明，我们的方法比迄今报道的其他计算方法更准确。我们还发现我们的方法在计算上比其他现有的从头结构预测方法更高效。此外，我们还为预测结构提供了一个可靠性指标。由于其计算简单且适用于任何随机序列，我们的算法PROPAINOR（通过人工智能和非参数回归进行蛋白质结构预测）在计算蛋白质结构基因组学中有很大的应用范围。
