计算生物学  >> Vol. 4 No. 2 (June 2014)

柔性多肽片段–蛋白质相互作用的全局对接方法
Global Docking Method for Flexible Peptide Segment-Protein Interactions

DOI: 10.12677/HJCB.2014.42005, PDF, HTML, 下载: 2,837  浏览: 11,953  科研立项经费支持

作者: 来瑞颖, 万 波, 黄 强:复旦大学生命科学学院,上海

关键词: 多肽–蛋白质相互作用多肽结合位点分子对接Protein-Peptide Interaction Peptide-Binding Site Molecular Docking

摘要: 多肽–蛋白质的相互作用在生物细胞中发挥着各种各样重要的作用。通常情况下,它们之间的结合信息是未知的。所以,利用计算方法预测结合位点具有重要意义。而以Rosetta为代表的常用对接软件通常具有很强的初始位置依赖性。为克服这一局限性,本研究提出了一种全局对接的方法,以受体蛋白为球状系统的中心,将多肽平均地分布在球面26个位置上;同时定义了一个区分天然结合构象和非天然结合构象的筛选参数。用上述方法预测多肽–蛋白质的结合构象,结果显示该方法能成功预测蛋白质的结合位点,且多数多肽的预测构象的Cα-RMSD在5.5 Å以下。因此,研究结果表明,所发展的方法在蛋白质多肽结合位点预测方面有很好的应用价值。
Abstract: Protein-peptide binding plays various important roles in living cells. In many cases, the peptide- binding sites of proteins are not known in prior. Then, computational prediction of the peptide- binding sites is desirable. Popular programs for protein-peptide docking usually depend strongly on the initial positions of peptides, such as Rosetta. To overcome this limitation, here we develop a global docking approach in which the peptide is initially distributed evenly on 26 surface locations of a virtual sphere around the protein, and define a selection parameter for discriminating native-like binding site from non-native sites. We used this approach to predict the native-like binding conformations of peptide-protein complexes, and in most cases the peptide-binding sites were correctly predicted, with Cα-RMSDs below 5.5 Å with respect to the crystal structures of peptides. The results of this study suggested that our approach may be very useful for the identification of peptide-binding sites of proteins.

文章引用: 来瑞颖, 万波, 黄强. 柔性多肽片段–蛋白质相互作用的全局对接方法[J]. 计算生物学, 2014, 4(2): 42-52. http://dx.doi.org/10.12677/HJCB.2014.42005

参考文献

[1] Petsalaki, E. and Russell, R. (2008) Peptide-mediated interactions in biological systems: New discoveries and applica- tions. Current Opinion in Biotechnology, 19, 344-350.
[2] Karanicolas, J. and Kuhlman, B. (2009) Computational design of affinity and specificity at protein-protein interfaces. Current Opinion in Structural Biology, 19, 458-463.
[3] Hao, J., Serohijos, A., Newton, G., Tassone, G., Wang, Z., Sgroi, D., Dokholyan, N. and Basilion, J. (2008) Identifica- tion and rational redesign of peptide ligands to CRIP1, a novel biomarker for cancers. PLOS Com-putational Biology, 4, e1000138.
[4] Vlieghe, P., Lisowski, V., Martinez, J. and Khrestchatisky, M. (2010) Synthetic therapeutic peptides: Science and market. Journal of Neuroscience Methods, 15, 40-56.
[5] Doyle, D., Lee, A., Lewis, J., Kim, E., Sheng, M. and MacKinnon, R. (1996) Crystal structures of a complexed and peptide-free membrane pro-tein-binding domain: Molecular basis of peptide recognition by PDZ. Cell, 85, 1067-1076.
[6] Morris, G., Goodsell, D., Huey, R. and Olson, A. (1996) Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4. Journal of Computer-Aided Molecular Design, 10, 293-304.
[7] Raveh, B., London, N., Zimmerman, L. and Schueler-Furman, O. (2011) Rosetta FlexPepDock ab-initio: Simultaneous folding, docking and refinement of peptides onto their receptors. Plos One, 6, e18934.
[8] Shoichet, B. and Kuntz, I. (1993) Matching chemistry and shape in molecular docking. Protein Engineering Design & Selection, 6, 723-732.
[9] Hetényi, C. and van der Spoel, D. (2009) Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Science, 11, 1729-1737.
[10] Aita, T., Nishigaki, K. and Husimi, Y. (2010) Toward the fast blind docking of a peptide to a target protein by using a four-body statistical pseudo-potential. Computational Biology and Chemistry, 34, 53-62.
[11] Coleman, R. and Sharp, K. (2010) Protein pockets: Inventory, shape, and comparison. Journal of Chemical Informa- tion and Modeling, 50, 589-603.
[12] Dagliyan, O., Proctor, E., D’Auria, K., Ding, F. and Dokholyan, N. (2011) Structural and dynamic determinants of protein-peptide recognition. Structure, 19, 1837-1845.
[13] Vallee-Belisle, A., Ricci, F. and Plaxco, K. (2009) Thermodynamic basis for the optimization of binding-induced bio- molecular switches and structure-switching biosensors. Proceedings of the National Academy of Sciences India Section B, 106, 13802-13807.
[14] Uversky, V. and Dunker, A. (2010) Understanding protein non-folding. BBA-Proteins Proteom, 1804, 1231-1264.
[15] Humphris, E. and Kortemme, T. (2008) Prediction of protein-protein interface sequence diversity using flexible back- bone computational protein design. Structure, 16, 1777-1788.
[16] Ding, F., Yin, S. and Dokholyan, N. (2010) Rapid flexible docking using a stochastic rotamer library of ligands. Jour- nal of Chemical Information and Modeling, 50, 1623-1632.
[17] Chou, S., Upton, H., Bao, K., Schulze-Gahmen, U., Samelson, A., He, N., Nowak, A., Lu, H., Krogan, N., Zhou, Q. and Alber, T. (2012) HIV-1 tat recruits transcription elongation factors dispersed along a flexible AFF4 scaffold. Pro- ceedings of the National Academy of Sciences India Section B, 110, E123-E131.
[18] Schulze-Gahmen, U., Upton, H., Birnberg, A., Bao, K., Chou, S., Krogan, N. and Zhou, Q. (2013) Building a super elon- gation complex for HIV. Elife, 2, e00577.
[19] Bradley, P. (2005) Toward high-resolution de novo structure prediction for small proteins. Science, 309, 1868-1871.
[20] Zhu, J., Yang, Q., Dai, D. and Huang, Q. (2013) X-ray crystal structure of phosphodiesterase 2 in complex with a highly selective, nanomolar inhibitor reveals a binding-induced pocket important for selectivity. Journal of the Ameri- can Chemical Society, 135, 11708-11711.
[21] Yan, Z., Zheng, X., Wang, E. and Wang, J. (2013) Thermodynamic and kinetic specificities of ligand binding. Chemi- cal Science, 4, 2387.
[22] Yan, Z. and Wang, J. (2012) Specificity quantification of biomolecular recognition and its implication for drug discov- ery. Science Report-UK, 2, srep00309.