Kanou Kazuhiko, Iwadate Mitsuo, Hirata Tomoko, Terashi Genki, Umeyama Hideaki, Takeda-Shitaka Mayuko
Kitasato University, Shirokane, Minato-ku, Tokyo, Japan.
Chem Pharm Bull (Tokyo). 2009 Dec;57(12):1335-42. doi: 10.1248/cpb.57.1335.
The prediction of a protein three-dimensional (3D) structure is one of the most important challenges in computational structural biology. We have developed an automatic protein 3D structure prediction method called FAMSD. FAMSD is based on a comparative modeling method which consists of the following four steps: (1) generating and selecting sequence alignments between target and template proteins; (2) constructing 3D structure models based on each selected alignment; (3) selecting the best 3D structure model and (4) refining the selected model. In the FAMSD method, sequence alignment programs such as a series of BLAST programs, SP3 and SPARKS2 programs, the homology modeling program FAMS (Full Automatic Modeling System), the model quality estimation program CIRCLE and the molecular dynamics program APRICOT were used in combination to construct high quality protein models. To assess the FAMSD method we have participated in the 8th Critical Assessment of Techniques for Protein Structure Prediction (CASP8) experiment. The results of our original assessment indicate that the FAMSD method offers excellent capability in packing side-chains with the correct torsion angles while avoiding the formation of atom-atom collisions. Since side-chain packing plays a significant role in defining the biological function of proteins, this method is a valuable resource in biological, pharmaceutical and medicinal research efforts.
蛋白质三维(3D)结构预测是计算结构生物学中最重要的挑战之一。我们开发了一种名为FAMSD的蛋白质3D结构自动预测方法。FAMSD基于一种比较建模方法,该方法包括以下四个步骤:(1)生成并选择目标蛋白与模板蛋白之间的序列比对;(2)基于每个选定的比对构建3D结构模型;(3)选择最佳的3D结构模型;(4)优化选定的模型。在FAMSD方法中,一系列BLAST程序、SP3和SPARKS2程序等序列比对程序、同源建模程序FAMS(全自动建模系统)、模型质量评估程序CIRCLE和分子动力学程序APRICOT被结合使用以构建高质量的蛋白质模型。为了评估FAMSD方法,我们参加了第八届蛋白质结构预测技术关键评估(CASP8)实验。我们最初的评估结果表明,FAMSD方法在以正确的扭转角堆积侧链同时避免原子间碰撞的形成方面具有出色的能力。由于侧链堆积在定义蛋白质的生物学功能中起着重要作用,该方法在生物学、制药和医学研究工作中是一种宝贵的资源。