Please wait a minute...
Chin. Phys. B, 2018, Vol. 27(2): 020503    DOI: 10.1088/1674-1056/27/2/020503
Special Issue: SPECIAL TOPIC — Soft matter and biological physics
SPECIAL TOPIC—Soft matter and biological physics Prev   Next  

Optimizing the atom types of proteins through iterative knowledge-based potentials

Xin-Xiang Wang(汪心享), Sheng-You Huang(黄胜友)
School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China
Abstract  Knowledge-based scoring functions have been widely used for protein structure prediction, protein-small molecule, and protein-nucleic acid interactions, in which one critical step is to find an appropriate representation of protein structures. A key issue is to determine the minimal protein representations, which is important not only for developing of scoring functions but also for understanding the physics of protein folding. Despite significant progresses in simplifying residues into alphabets, few studies have been done to address the optimal number of atom types for proteins. Here, we have investigated the atom typing issue by classifying the 167 heavy atoms of proteins through 11 schemes with 1 to 20 atom types based on their physicochemical and functional environments. For each atom typing scheme, a statistical mechanics-based iterative method was used to extract atomic distance-dependent potentials from protein structures. The atomic distance-dependent pair potentials for different schemes were illustrated by several typical atom pairs with different physicochemical properties. The derived potentials were also evaluated on a high-resolution test set of 148 diverse proteins for native structure recognition. It was found that there was a crossover around the scheme of four atom types in terms of the success rate as a function of the number of atom types, which means that four atom types may be used when investigating the basic folding mechanism of proteins. However, it was revealed by a close examination of typical potentials that 14 atom types were needed to describe the protein interactions at atomic level. The present study will be beneficial for the development of protein related scoring functions and the understanding of folding mechanisms.
Keywords:  atom types      knowledge-based potentials      statistical mechanics      iteration  
Received:  28 September 2017      Revised:  30 December 2017      Accepted manuscript online: 
PACS:  05.20.-y (Classical statistical mechanics)  
  87.14.E- (Proteins)  
  87.15.ad (Analytical theories)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 31670724), the National Key Research and Development Program of China (Grant Nos. 2016YFC1305800 and 2016YFC1305805), and the Startup Grant of Huazhong University of Science and Technology, China.
Corresponding Authors:  Sheng-You Huang     E-mail:  huangsy@hust.edu.cn
About author:  05.20.-y; 87.14.E-; 87.15.ad

Cite this article: 

Xin-Xiang Wang(汪心享), Sheng-You Huang(黄胜友) Optimizing the atom types of proteins through iterative knowledge-based potentials 2018 Chin. Phys. B 27 020503

[1] Wang J and Wang W 1999 Nat. Struct. Mol. Biol. 6 1033
[2] Wang J and Wang W 2000 Phys. Rev. E 61 6981
[3] Fan K and Wang W 2003 J. Mol. Biol. 328 921
[4] Dill K A 1985 Biochemistry 24 1501
[5] Riddle D S, Santiago J V, Bray-Hall S T, Doshi N, Grantcharova V P, Yi Q and Baker D 1997 Nat. Struct. Biol. 4 805
[6] Launay G, Mendez R, Wodak S and Simonson T 2007 BMC Bioinformatics 8 270
[7] Luthra A, Jha A N, Ananthasuresh G K and Vishveswara S 2007 J. Biosci. 32 883
[8] Li T, Fan K, Wang J and Wang W 2003 Protein Eng. 16 323
[9] Walter K U, Vamvaca K and Hilvert D 2005 J. Biol. Chem. 280 37742
[10] Akanuma S, Kigawa T and Yokoyama S 2002 Proc. Natl. Acad. Sci. USA 99 13549
[11] Peterson E L, Kondev J, Theriot J A and Phillips R 2009 Bioinformatics 25 1356
[12] Albayrak A, Out H H and Sezerman U O 2010 BMC Bioinformatics 11 428
[13] Etchebest C, Benros C, Bornot A, Camproux A C and de Brevern A G 2007 Eur. Biophys. J. 36 1059
[14] Melo F and Marti-Renom M A 2006 Proteins 63 986
[15] Cannata N, Toppo S, Romualdi C and Valle G 2002 Bioinformatics 18 1102
[16] Shi Y Z, Wu Y Y, Wang F H and Tan Z J 2015 Chin. Phys. B 24 116802
[17] Zhang W, Sun Z B and Zou X W 2005 Chin. Phys. Lett. 22 2133
[18] Li W F, Zhang J, Wang J and Wang W 2015 Acta Phys. Sin. 64 098701(in Chinese)
[19] Solis A D 2015 Proteins 83 2198
[20] Huang J T, Wang T, Huang S R and Li X 2015 Proteins 83 631
[21] Levitt M and Warshel A 1975 Nature 253 694
[22] Wang J and Wang W 2002 Phys. Rev. E 65 041911
[23] Chen H, Zhou X and Ou-Yang Z C 2002 Phys. Rev. E 65 061907
[24] Liu Y, Zeng J and Gong H 2014 Proteins 82 2383
[25] Yang Y and Zhou Y 2008 Protein Sci. 17 1212
[26] Yang Y and Zhou Y 2008 Proteins 72 793
[27] Mintseris J and Weng Z 2004 Genome Informatics 15 160
[28] Mintseris J, Pierce B, Wiehe K, Anderson R, Chen R and Weng Z 2007 Proteins 69 511
[29] Zhao Y, Cheng T and Wang R 2007 J. Chem. Inf. Model. 47 1379
[30] Jiang L, Gao Y, Mao F, Liu Z and Lai L 2002 Proteins 46 190
[31] Zhang M, Chen C, He Y and Xiao Y 2005 Phys. Rev. E 72 051919
[32] Huang S Y and Zou X 2014 Nucleic Acids Res. 42 e55
[33] Huang S Y and Zou X 2014 Proteins 72 557
[34] Huang S Y and Zou X 2006 J. Comput. Chem. 27 1866
[35] Huang S Y and Zou X 2011 Proteins 79 2648
[36] Zhou H and Zhou Y 2002 Protein Sci. 11 2714
[37] Zhou H and Skolnick J 2011 Biophys J. 101 2043
[38] Mitchell J, Alex A and Snarey M 1999 J. Chem. Inf. Comput. Sci. 39 751
[39] Labute P 2005 J. Chem. Inf. Model. 45 215
[40] Rajgaria R, McAllister S R and Floudas C A 2008 Proteins 70 950
[41] Lu M, Dousis A D and Ma J 2008 J. Mol. Biol. 376 288
[42] Huang S Y and Zou X 2010 J. Chem. Inf. Model. 50 263
[43] Kadukova M and Grudinin S 2016 J. Chem. Inf. Model. 56 1410
[44] Xu W X, Li Y and Zhang J Z 2012 Chin. Phys. Lett. 29 068702
[45] Yan Y M, Zhang D, Zhou P, Li B T and Huang S Y 2017 Nucleic Acids Res. 45 w365
[46] Huang S Y 2014 Drug Discov. Today 19 1081
[47] Lensink M F, Velankar S, Kryshtafovych A, Huang S Y, et al. 2016 Proteins 84 323
[48] Zhang Y and Skolnick J 2004 Proc. Natl. Acad. Sci. USA 101 7594
[1] Variational quantum simulation of thermal statistical states on a superconducting quantum processer
Xue-Yi Guo(郭学仪), Shang-Shu Li(李尚书), Xiao Xiao(效骁), Zhong-Cheng Xiang(相忠诚), Zi-Yong Ge(葛自勇), He-Kang Li(李贺康), Peng-Tao Song(宋鹏涛), Yi Peng(彭益), Zhan Wang(王战), Kai Xu(许凯), Pan Zhang(张潘), Lei Wang(王磊), Dong-Ning Zheng(郑东宁), and Heng Fan(范桁). Chin. Phys. B, 2023, 32(1): 010307.
[2] Iterative filtered ghost imaging
Shao-Ying Meng(孟少英), Mei-Yi Chen(陈美伊), Jie Ji(季杰), Wei-Wei Shi(史伟伟), Qiang Fu(付强), Qian-Qian Bao(鲍倩倩), Xi-Hao Chen(陈希浩), and Ling-An Wu(吴令安). Chin. Phys. B, 2022, 31(2): 028702.
[3] Computational prediction of RNA tertiary structures using machine learning methods
Bin Huang(黄斌), Yuanyang Du(杜渊洋), Shuai Zhang(张帅), Wenfei Li(李文飞), Jun Wang (王骏), and Jian Zhang(张建)†. Chin. Phys. B, 2020, 29(10): 108704.
[4] Weighted total variation using split Bregman fast quantitative susceptibility mapping reconstruction method
Lin Chen(陈琳), Zhi-Wei Zheng(郑志伟), Li-Jun Bao(包立君), Jin-Sheng Fang(方金生), Tian-He Yang(杨天和), Shu-Hui Cai(蔡淑惠), Cong-Bo Cai(蔡聪波). Chin. Phys. B, 2018, 27(8): 088701.
[5] Bound states resulting from interaction of the non-relativistic particles with the multiparameter potential
Ahmet Taş, Ali Havare. Chin. Phys. B, 2017, 26(10): 100301.
[6] Numerical simulation of the magnetoresistance effect controlled by electric field in p-n junction
Pan Yang(杨盼), Wen-Jie Chen(谌文杰), Jiao Wang(王娇), Zhao-Wen Yan(闫兆文), Jian-Li Qiao(乔坚栗), Tong Xiao(肖彤), Xin Wang(王欣), Zheng-Peng Pang(庞正鹏), Jian-Hong Yang(杨建红). Chin. Phys. B, 2016, 25(4): 047306.
[7] Application of asymptotic iteration method to a deformed well problem
Hakan Ciftci, H F Kisoglu. Chin. Phys. B, 2016, 25(3): 030201.
[8] A universal function of creep rate
Li Jing-Tian (李菁田), Rong Xi-Ming (荣曦明), Wang Jian-Lu (王建录), Zhang Bang-Qiang (张邦强), Ning Xi-Jing (宁西京). Chin. Phys. B, 2015, 24(9): 093401.
[9] Numerical solution of the imprecisely defined inverse heat conduction problem
Smita Tapaswini, S. Chakraverty, Diptiranjan Behera. Chin. Phys. B, 2015, 24(5): 050203.
[10] Knowledge-based potentials in bioinformatics: From a physicist's viewpoint
Zheng Wei-Mou (郑伟谋). Chin. Phys. B, 2015, 24(12): 128701.
[11] Simulation studies of multi-line line-of-sight tunable-diode-laserabsorption spectroscopy performance in measuring temperature probability distribution function
Zhang Guang-Le (张光乐), Liu Jian-Guo (刘建国), Kan Rui-Feng (阚瑞峰), Xu Zhen-Yu (许振宇). Chin. Phys. B, 2014, 23(12): 124207.
[12] Relativistic symmetries with the trigonometric Pöschl-Teller potential plus Coulomb-like tensor interaction
Babatunde J. Falaye, Sameer M. Ikhdair. Chin. Phys. B, 2013, 22(6): 060305.
[13] Solutions of the Duffin Kemmer Petiau equation in the presence of Hulthén potential in (1+2) dimensions for unity spin particles using the asymptotic iteration method
Z. Molaee, M. K. Bahar, F. Yasuk, H. Hassanabadi. Chin. Phys. B, 2013, 22(6): 060306.
[14] Incomplete nonextensive statistics and the zeroth law of thermodynamics
Huang Zhi-Fu (黄志福), Ou Cong-Jie (欧聪杰), Chen Jin-Can (陈金灿). Chin. Phys. B, 2013, 22(4): 040501.
[15] Relativistic treatment of the spin-zero particles subject to the second Pöschl–Teller-like potential
Ekele V. Aguda, Amos S. Idowu. Chin. Phys. B, 2013, 22(10): 100303.
No Suggested Reading articles found!