Nakamura Tsukasa, Oda Toshiyuki, Fukasawa Yoshinori, Tomii Kentaro
Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba, 277-8562, Japan.
Proteins. 2018 Mar;86 Suppl 1(Suppl Suppl 1):274-282. doi: 10.1002/prot.25432. Epub 2017 Dec 5.
Proteins often exist as their multimeric forms when they function as so-called biological assemblies consisting of the specific number and arrangement of protein subunits. Consequently, elucidating biological assemblies is necessary to improve understanding of protein function. Template-Based Modeling (TBM), based on known protein structures, has been used widely for protein structure prediction. Actually, TBM has become an increasingly useful approach in recent years because of the increased amounts of information related to protein amino acid sequences and three-dimensional structures. An apparently similar situation exists for biological assembly structure prediction as protein complex structures in the PDB increase, although the inference of biological assemblies is not a trivial task. Many methods using TBM, including ours, have been developed for protein structure prediction. Using enhanced profile-profile alignments, we participated in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12), as the FONT team (Group # 480). Herein, we present experimental procedures and results of retrospective analyses using our approach for the Quaternary Structure Prediction category of CASP12. We performed profile-profile alignments of several types, based on FORTE, our profile-profile alignment algorithm, to identify suitable templates. Results show that these alignment results enable us to find templates in almost all possible cases. Moreover, we have come to understand the necessity of developing a model selection method that provides improved accuracy. Results also demonstrate that, to some extent, finding templates of protein complexes is useful even for MEDIUM and HARD assembly prediction.
当蛋白质作为由特定数量和排列的蛋白质亚基组成的所谓生物组装体发挥功能时，它们通常以多聚体形式存在。因此，阐明生物组装体对于增进对蛋白质功能的理解是必要的。基于已知蛋白质结构的基于模板的建模（TBM）已被广泛用于蛋白质结构预测。实际上，由于与蛋白质氨基酸序列和三维结构相关的信息量增加，近年来TBM已成为一种越来越有用的方法。随着PDB中蛋白质复合物结构的增加，生物组装体结构预测也存在明显类似的情况，尽管推断生物组装体并非易事。已经开发了许多使用TBM的方法，包括我们的方法，用于蛋白质结构预测。我们作为FONT团队（第480组），使用增强的profile-profile比对，参与了第12届蛋白质结构预测技术关键评估社区范围实验（CASP12）。在此，我们展示了使用我们的方法对CASP12四级结构预测类别的回顾性分析的实验程序和结果。我们基于我们的profile-profile比对算法FORTE进行了几种类型的profile-profile比对，以识别合适的模板。结果表明，这些比对结果使我们能够在几乎所有可能的情况下找到模板。此外，我们已经认识到开发一种能提高准确性的模型选择方法的必要性。结果还表明，在某种程度上，找到蛋白质复合物的模板即使对于中等难度和高难度的组装预测也是有用的。
