Ejlali Nasim, Faghihi Mohammad Reza, Sadeghi Mehdi
, Faculty of Mathematical Sciences.
.
Stat Appl Genet Mol Biol. 2017 Sep 26;16(4):243-257. doi: 10.1515/sagmb-2016-0014.
An important topic in bioinformatics is the protein structure alignment. Some statistical methods have been proposed for this problem, but most of them align two protein structures based on the global geometric information without considering the effect of neighbourhood in the structures. In this paper, we provide a Bayesian model to align protein structures, by considering the effect of both local and global geometric information of protein structures. Local geometric information is incorporated to the model through the partial Procrustes distance of small substructures. These substructures are composed of β-carbon atoms from the side chains. Parameters are estimated using a Markov chain Monte Carlo (MCMC) approach. We evaluate the performance of our model through some simulation studies. Furthermore, we apply our model to a real dataset and assess the accuracy and convergence rate. Results show that our model is much more efficient than previous approaches.
生物信息学中的一个重要主题是蛋白质结构比对。针对这个问题已经提出了一些统计方法，但其中大多数基于全局几何信息比对两个蛋白质结构，而没有考虑结构中邻域的影响。在本文中，我们通过考虑蛋白质结构的局部和全局几何信息的影响，提供了一个用于比对蛋白质结构的贝叶斯模型。局部几何信息通过小子结构的部分普罗克汝斯距离纳入模型。这些子结构由侧链中的β碳原子组成。使用马尔可夫链蒙特卡罗（MCMC）方法估计参数。我们通过一些模拟研究评估了我们模型的性能。此外，我们将我们的模型应用于一个真实数据集，并评估其准确性和收敛速度。结果表明，我们的模型比以前的方法效率高得多。
