Dukka Bahadur K C, Tomita Etsuji, Suzuki Jun'ichi, Akutsu Tatsuya
Graduate School of Informatics & Bioinformatics Center Kyoto University, Kyoto, 611-0001, Japan.
J Bioinform Comput Biol. 2005 Feb;3(1):103-26. doi: 10.1142/s0219720005000904.
"Protein Side-chain Packing" has an ever-increasing application in the field of bio-informatics, dating from the early methods of homology modeling to protein design and to the protein docking. However, this problem is computationally known to be NP-hard. In this regard, we have developed a novel approach to solve this problem using the notion of a maximum edge-weight clique. Our approach is based on efficient reduction of protein side-chain packing problem to a graph and then solving the reduced graph to find the maximum clique by applying an efficient clique finding algorithm developed by our co-authors. Since our approach is based on deterministic algorithms in contrast to the various existing algorithms based on heuristic approaches, our algorithm guarantees of finding an optimal solution. We have tested this approach to predict the side-chain conformations of a set of proteins and have compared the results with other existing methods. We have found that our results are favorably comparable or better than the results produced by the existing methods. As our test set contains a protein of 494 residues, we have obtained considerable improvement in terms of size of the proteins and in terms of the efficiency and the accuracy of prediction.
“蛋白质侧链堆积”在生物信息学领域的应用日益广泛,从早期的同源建模方法到蛋白质设计以及蛋白质对接。然而,已知这个问题在计算上是NP难的。在这方面,我们开发了一种新颖的方法来解决这个问题,该方法使用最大边权团的概念。我们的方法基于将蛋白质侧链堆积问题有效地简化为一个图,然后通过应用我们的合著者开发的高效团查找算法来求解简化后的图以找到最大团。由于我们的方法基于确定性算法,与现有的各种基于启发式方法的算法形成对比,我们的算法保证能找到最优解。我们已经测试了这种方法来预测一组蛋白质的侧链构象,并将结果与其他现有方法进行了比较。我们发现我们的结果与现有方法产生的结果相比具有优势或更好。由于我们的测试集包含一个494个残基的蛋白质,我们在蛋白质大小以及预测效率和准确性方面都取得了显著的改进。