Citation: LI Yong, ZHOU Wei, DAI Zhi-Jun, CHEN Yuan, WANG Zhi-Ming, YUAN Zhe-Ming. Predicting the Protein Folding Rate Based on Sequence Feature Screening and Support Vector Regression[J]. Acta Physico-Chimica Sinica, ;2014, 30(6): 1091-1098. doi: 10.3866/PKU.WHXB201404091 shu

Predicting the Protein Folding Rate Based on Sequence Feature Screening and Support Vector Regression

1.
Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Hunan Agricultural University, Changsha 410128, P. R. China; Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Hunan Agricultural University, Changsha 410128, P. R. China

Received Date: 12 December 2013
Available Online: 9 April 2014
Fund Project:

Folding rate prediction plays an important role in clarifying the protein folding mechanism. In this work, we collected 115 protein samples with known folding rates including two-, multi-, and mixed-state proteins. To characterize the primary structure information of the protein molecules more comprehensively, we considered sequence length, residue components with different scales, k-space features for pair residues, and geostatistics association features among different locations of the residues substituted with corresponding physical-chemical properties. Each protein sequence was represented by a numeric vector containing 9357 numbers. We selected 23 features with a clear meaning from the above-mentioned high-dimensional features for each sample, after conducting an improved binary matrix shuffling filter and a worst descriptor elimination multi-round method. We constructed a nonlinear support vector regression (SVR) model based on the folding rate and the 23 retained features. The correlation coefficient of the Jackknife cross validation was 0.95. Our prediction accuracy was superior to other results from the literature and other reference feature selection methods. Finally, we established an interpretability system for SVR, and our data showed that the nonlinear regression relationship between the folding rates and the reserved features was highly significant. By further analyzing the effects of each retained descriptor on protein folding rates, the results showed that the protein folding rate might be closely related to the sequence length, the features associated with the medium-and short-range, the triplet residues component features, etc.
- Protein folding,
- Folding rate prediction,
- High-dimensional feature,
- Feature screening,
- Support vector regression
1. [1]
  
  (1) Guo, J. X.; Rao, N. N.; Liu, G. X.; Li, J.;Wang, Y. H. Prog. Biochem. Biophys. 2010, 37 (12), 1331. [郭建秀, 饶妮妮, 刘广雄, 李杰, 王云鹤. 生物化学与生物物理进展, 2010, 37 (12), 1331.]
2. [2]
  (2) Xi, L. L.; Li, S. Y.; Liu, H. X.; Li, J. Z.; Lei, B. L.; Yao, X. J. J. Theor. Biol. 2010, 264 (4), 1159. doi: 10.1016/j.jtbi.2010.03.042
3. [3]
  (3) Plaxco, K.W.; Simons, K. T.; Baker, D. J. Mol. Biol. 1998, 277 (4), 985. doi: 10.1006/jmbi.1998.1645
4. [4]
  (4) Ivankov, D. N.; Garbuzynskiy, S. O.; Alm, E.; Plaxco, K.W.; Baker, D.; Finkelstein, A. V. Protein Sci. 2003, 12 (9), 2057. doi: 10.1110/ps.0302503
5. [5]
  (5) Weikl, T. R.; Dill, K. A. J. Mol. Biol. 2003, 332 (4), 953. doi: 10.1016/S0022-2836(03)00884-2
6. [6]
  (6) Zhang, L. X.; Li, J.; Jiang, Z. T.; Xia, A. G. Polymer 2003, 44 (5), 1751. doi: 10.1016/S0032-3861(03)00021-1
7. [7]
  (7) Capriotti, E.; Casadio, R. Bioinformatics 2007, 23 (3), 385. doi: 10.1093/bioinformatics/btl610
8. [8]
  (8) Ivankov, D. N.; Bogatyreva, N. S.; Lobanov, M. Y.; Galzitskaya, O. V. PLoS One 2009, 4 (8), e6476.
9. [9]
  (9) ng, H. P.; Isom, D. G.; Srinivasan, R.; Rose, G. D. J. Mol. Biol. 2003, 327 (5), 1149. doi: 10.1016/S0022-2836(03)00211-0
10. [10]
  (10) Ivankov, D. N.; Finkelstein, A. V. Proc. Natl. Acad. Sci. U. S. A. 2004, 101 (24), 8942. doi: 10.1073/pnas.0402659101
11. [11]
  (11) Ma, B. G.; Guo, J. X.; Zhang, H. Y. Proteins: Struct., Funct., Bioinf. 2006, 65 (2), 362. doi: 10.1002/prot.21140
12. [12]
  (12) Jiang, Y. F.; Iglinski, P.; Kurgan, L. J. Comput. Chem. 2009, 30 (5), 772. doi: 10.1002/jcc.21096
13. [13]
  (13) Gao, J. Z.; Zhang, T.; Zhang, H.; Shen, S. Y.; Ruan, J. S.; Kurgan, L. Proteins: Struct., Funct., Bioinf. 2010, 78 (9), 2114.
14. [14]
  (14) Shen, H. B.; Song, J. N.; Chou, K. C. J. Biomed. Sci. Eng. 2009, 2 (3), 136. doi: 10.4236/jbise.2009.23024
15. [15]
  (15) Cheng, X.; Xiao, X.;Wu, Z. C.;Wang, P.; Lin,W. Z. Proteins: Struct., Funct., Bioinf. 2013, 81 (1), 140. doi: 10.1002/prot.24171
16. [16]
  (16) Zhang, H. Y.;Wang, H. Y.; Dai, Z. J.; Chen, M. S.; Yuan, Z. M. BMC Bioinformatics 2012, 13 (1), 298. doi: 10.1186/1471-2105-13-298
17. [17]
  (17) Han, N.; Yuan, Z. M.; Chen, Y.; Dai, Z. J.;Wang, Z. M. Acta Phys. -Chim. Sin. 2013, 29 (9), 1945. [韩娜, 袁哲明, 陈渊, 代志军, 王志明. 物理化学学报, 2013, 29 (9), 1945.] doi: 10.3866/PKU.WHXB201306182
18. [18]
  (18) Guo, J. X.; Rao, N. N.; Liu, G. X.; Yang, Y.;Wang, G. J. Comput. Chem. 2011, 32 (8), 1612. doi: 10.1002/jcc.21740
19. [19]
  (19) Galzitskaya, O. V.; Garbuzynskiy, S. O.; Ivankov, D. N.; Finkelstein, A. V. Proteins: Struct., Funct., Genet. 2003, 51 (2), 162. doi: 10.1002/prot.10343
20. [20]
  (20) Kawashima, S.; Pokarowski, P.; Pokarowska, M.; Kolinski, A.; Katayama, T.; Kanehisa, M. Nucl. Acids Res. 2008, 36 (suppl. 1), D202.
21. [21]
  (21) Gromiha, M, M.; Selvaraj, S. Prep. Biochem. Biotechnol. 1999, 29 (4), 339. doi: 10.1080/10826069908544933
22. [22]
  (22) Zhou, P.; Tian, F. F.; Li, B.;Wu, S. R.; Li, Z. L. Acta Chim. Sin. 2006, 64 (7), 691. [周鹏, 田菲菲, 李波, 吴世容, 李志良. 化学学报, 2006, 64 (7), 691.]
23. [23]
  (23) Tan, X. S.;Wang, Z. M.; Tan, S. Q.; Yuan, Z. M.; Xiong, X. Y. J. Syst. Simul. 2009, 21 (24), 7795. [谭显胜, 王志明, 谭泗桥, 袁哲明, 熊兴耀. 系统仿真学报, 2009, 21 (24), 7795.]
24. [24]
  (24) Dai, Z. J.; Zhou,W.; Yuan, Z. M. Acta Phys. -Chim. Sin. 2011, 27 (7), 1654. [代志军, 周玮, 袁哲明. 物理化学学报, 2011, 27 (7), 1654.] doi: 10.3866/PKU.WHXB20110735
25. [25]
  (25) Chang, C. C.; Lin, C. J. ACM TIST. 2011, 2 (3), 27.
26. [26]
  (26) Chen, Y.; Yuan, Z. M.; Zhou,W.; Xiong, X. Y. Acta Phys. -Chim. Sin. 2009, 25 (8), 1587. [陈渊, 袁哲明, 周玮, 熊兴耀. 物理化学学报, 2009, 25 (8), 1587.] doi: 10.3866/PKU.WHXB20090752
27. [27]
  (27) Leardi, R. J. Chemometr. 2000, 14 (5-6), 643. doi: 10.1002/1099-128X(200009/12)14:5/6<643::AID-CEM621>3.0.CO;2-E
28. [28]
  (28) Wang, Z. M.; Han, N.; Yuan, Z. M.;Wu, Z. H. Acta Phys. -Chim. Sin. 2013, 29 (3), 498. [王志明, 韩娜, 袁哲明, 伍朝华. 物理化学学报, 2013, 29 (3), 498.] doi: 10.3866/PKU.WHXB201301042
29. [29]
  (29) Ouyang, Z.; Liang, J. Protein Sci. 2008, 17 (7), 1256. doi: 10.1110/ps.034660.108
1. [1]
  Zhi Zheng , Feiyang Liu , Junlong Zhao . D-Amino Acids and Mirror-Image Proteins. University Chemistry, 2026, 41(2): 353-359. doi: 10.12461/PKU.DXHX202505017
2. [2]
  Zhichao ZHANG , Jun ZHOU , Jinhui WANG , Haocheng ZHAO , Yi WANG , Chenqi JIA , Jifan HU . Cascaded prediction of the performance of Eu²⁺-doped phosphors with whitlockite-type structure based on compositional features and luminescent properties of Ca₉Y(PO₄)₇∶Eu²⁺ phosphors. Chinese Journal of Inorganic Chemistry, 2026, 42(8): 1685-1698. doi: 10.11862/CJIC.20250343
3. [3]
  Xinran Zhang , Siqi Liu , Yichi Chen , Qingli Zou , Qinghong Xu , Yaqin Huang . From Protein to Energy Storage Materials: Edible Gelatin Jelly Electrolyte. University Chemistry, 2025, 40(7): 255-266. doi: 10.12461/PKU.DXHX202408104
4. [4]
  Xinyi Hong , Tailing Xue , Zhou Xu , Enrong Xie , Mingkai Wu , Qingqing Wang , Lina Wu . Non-Site-Specific Fluorescent Labeling of Proteins as a Chemical Biology Experiment. University Chemistry, 2024, 39(4): 351-360. doi: 10.3866/PKU.DXHX202310010
5. [5]
  Zhibei Qu , Changxin Wang , Lei Li , Jiaze Li , Jun Zhang . Organoid-on-a-Chip for Drug Screening and the Inherent Biochemistry Principles. University Chemistry, 2024, 39(7): 278-286. doi: 10.3866/PKU.DXHX202311039
6. [6]
  Hao Wu , Fengqi Li , Xinwei Shi , Haifeng Bian , Qing Zhou , Shunshun Jia , Yujie Ma , Jian Gu , Jingzi Zhang , Shuijian He , Xiangkang Meng . Machine-learning guides discovery of multi-principal element alloys as electrocatalyst for hydrogen evolution reaction. Acta Physico-Chimica Sinica, 2026, 42(8): 100227-0. doi: 10.1016/j.actphy.2025.100227
7. [7]
  Zhi Dou , Huiyu Duan , Yixi Lin , Yinghui Xia , Mingbo Zheng , Zhenming Xu . High-Throughput Screening Lithium Alloy Phases and Investigation of Ion Transport for Solid Electrolyte Interphase Layer. Acta Physico-Chimica Sinica, 2024, 40(3): 2305039-0. doi: 10.3866/PKU.WHXB202305039
8. [8]
  Yucheng Shan , Liming Xu , Peng Sun , Zhijing Zhu , Chenglong Wang , Jinliang Li , Guang Yang , Likun Pan . A data-driven approach for rapid revealing of metal doping in MnO₂ cathodes for high-performance aqueous zinc-ion batteries. Acta Physico-Chimica Sinica, 2026, 42(7): 100232-. doi: 10.1016/j.actphy.2025.100232
9. [9]
  Shuying Zhu , Shuting Wu , Ou Zheng . Improvement and Expansion of the Experiment for Determining the Rate Constant of the Saponification Reaction of Ethyl Acetate. University Chemistry, 2024, 39(4): 107-113. doi: 10.3866/PKU.DXHX202310117
10. [10]
  Heng Zhang . Determination of All Rate Constants in the Enzyme Catalyzed Reactions Based on Michaelis-Menten Mechanism. University Chemistry, 2024, 39(4): 395-400. doi: 10.3866/PKU.DXHX202310047
11. [11]
  Jian Huang , Mingjue Zhang , Shangchu Ma , Jia Dong , Guanzi Wu , Aiming Wen , Zhuoliang Liu . Data-Driven Approach for the Determination of Chemical Reaction Rate Constant. University Chemistry, 2026, 41(1): 213-226. doi: 10.12461/PKU.DXHX202505110
12. [12]
  Yujia Luo , Yunpeng Qi , Huiping Xing , Yuhu Li . The Use of Viscosity Method for Predicting the Life Expectancy of Xuan Paper-based Heritage Objects. University Chemistry, 2024, 39(8): 290-294. doi: 10.3866/PKU.DXHX202401037
13. [13]
  Haiyu Zhu , Zhuoqun Wen , Wen Xiong , Xingzhan Wei , Zhi Wang . 二维半金属/硅异质结中肖特基势垒高度的准确高效预测. Acta Physico-Chimica Sinica, 2025, 41(7): 100078-0. doi: 10.1016/j.actphy.2025.100078
14. [14]
  Xinghai Li , Zhisen Wu , Lijing Zhang , Shengyang Tao . Machine Learning Enables the Prediction of Amide Bond Synthesis Based on Small Datasets. Acta Physico-Chimica Sinica, 2025, 41(2): 100010-0. doi: 10.3866/PKU.WHXB202309041
15. [15]
  Jian Cao , Chang Liu , Danling Wang , Haichao Li , Lina Xu , Hongping Xiao , Shaoqi Zhan , Xiao He , Guoyong Fang . Machine learning potentials for property predictions of two-dimensional group-Ⅲ nitrides. Acta Physico-Chimica Sinica, 2026, 42(4): 100224-0. doi: 10.1016/j.actphy.2025.100224
16. [16]
  Kai PENG , Xinyi ZHAO , Zixi CHEN , Xuhai ZHANG , Yuqiao ZENG , Jianqing JIANG . Progress in the application of high-entropy alloys and high-entropy ceramics in water electrolysis. Chinese Journal of Inorganic Chemistry, 2025, 41(7): 1257-1275. doi: 10.11862/CJIC.20240454
17. [17]
  Ying Zhang , Fang Ge , Zhimin Luo . AI-Driven Biochemical Teaching Research: Predicting the Functional Effects of Gene Mutations. University Chemistry, 2025, 40(3): 277-284. doi: 10.12461/PKU.DXHX202412104
18. [18]
  Xiaodong Chen , Yumin Zhang . An Improved Simulated Annealing Algorithm for Predicting the Molecular Formulas of Organic Compounds. University Chemistry, 2025, 40(9): 19-24. doi: 10.12461/PKU.DXHX202408095
19. [19]
  Heng Zhang , Ying Ma , Shiling Yuan . Machine Learning-based Prediction of Antifouling Performance in Polymer Materials: An Integrated Molecular Simulation Experiment. University Chemistry, 2026, 41(1): 346-353. doi: 10.12461/PKU.DXHX202506015
20. [20]
  Yichang Liu , Li An , Dan Qu , Zaicheng Sun . “双碳”背景下的综合设计实验——以PbCrO₄催化甲基蓝的光降解速率常数测定为例. University Chemistry, 2025, 40(6): 222-229. doi: 10.12461/PKU.DXHX202407105

Get Citation

PDF

XML

Metrics

PDF Downloads(665)
Abstract views(1368)
HTML views(127)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142