从头预测蛋白质骨架的一种并行蚁群方法及其在CASP8/9中的应用

从低同源关系的氨基酸序列预测蛋白质的三维结构被称为从头预测,它是计算生物学领域中的挑战之一.蛋白质骨架预测是从头预测的必要先导步骤.本文应用一种基于共享信息素的并行蚁群优化算法,在现有能量函数指导下,通过不同能量项之间的定性互补,构建具有最低能量的蛋白质骨架结构,并通过聚类选择构象候选集合中具有最低自由能的构象.在CASP8/9所公布的从头建模目标上应用了该方法,CASP8的13个从头建模目标中,模型1中有2个目标的预测结果超过CASP8中最好的结果,7个位列前10名;CASP9的29个从头建模目标中,候选集中的最佳结果中有20个进入Server组的前10名,模型1中有11个进入前10名.本文的结果说明融合多个不同的能量函数指导并行搜索,可以更好地模拟天然蛋白质的折叠行为.同时,在本算法载体上实现了不同种类搜索策略的融合并行,对于用非确定性算法解决类似的优化问题来说也是一种新颖的方法.

A parallel ant colonies approach to de novo prediction of protein backbone in CASP8/9

Predicting the three-dimensional structures of proteins from amino acid sequences with only a few remote homologs,or de novo prediction,remains a major challenge in computational biology.The de novo modeling of the protein backbone is the prerequisite stage of a protein structure prediction process.Using a parallel ant colony optimization based on sharing one pheromone matrix,this paper proposes a parallel approach to predicte the structure of a protein backbone.The parallel approach combines various sources of energy functions and generates protein backbones with the lowest energies jointly determined by the various energy functions.All the free modeling targets in CASP8/9 are used to evaluate the performance of the method.For 13 targets in CASP8,two out of the predicted model1s selected by our approach are the best of the published CASP8 results,and seven out of the model1s are ranked in the top 10.For 29 targets in CASP9,20 out of the best models from our predictions are ranked in the top 10,and 11 out of the model1s are ranked in the top 10.The solution described in this paper mimics the nature behavior of native protein folding by simultaneously minimizing the values of multiple energy functions.It also provides a general framework to combine different search strategies in parallel platform,which is a novel approach to solving the similar optimization problems with non-deterministic algorithms.