|
Special Issue:
SPECIAL TOPIC — Modeling and simulations for the structures and functions of proteins and nucleic acids
|
| SPECIAL TOPIC—Modeling and simulations for the structures and functions of proteins and nucleic acids |
Prev
Next
|
|
|
Different potential of mean force of two-state protein GB1 and downhill protein gpW revealed by molecular dynamics simulation |
| Xiaofeng Zhang(张晓峰)1, Zilong Guo(郭子龙)1, Ping Yu(余平)1, Qiushi Li(李秋实)2, Xin Zhou(周昕)2, Hu Chen(陈虎)1 |
1 Research Institute for Biomimetics and Soft Matter, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Department of Physics, Xiamen University, Xiamen 361005, China;
2 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China |
|
|
|
|
Abstract Two-state folding and down-hill folding are two kinds of protein folding dynamics for small single domain proteins. Here we apply molecular dynamics (MD) simulation to the two-state protein GB1 and down-hill folding protein gpW to reveal the relationship of their free energy landscape and folding/unfolding dynamics. Results from the steered MD simulations show that gpW is much less mechanical resistant than GB1, and the unfolding process of gpW has more variability than that of GB1 according to their force-extension curves. The potential of mean force (PMF) of GB1 and gpW obtained by the umbrella sampling simulations shows apparent difference: PMF of GB1 along the coordinate of extension exhibits a kink transition point where the slope of PMF drops suddenly, while PMF of gpW increases with extension smoothly, which are consistent with two-state folding dynamics of GB1 and downhill folding dynamics of gpW, respectively. Our results provide insight to understand the fundamental mechanism of different folding dynamics of two-state proteins and downhill folding proteins.
|
Received: 30 March 2020
Revised: 20 April 2020
Accepted manuscript online:
|
|
PACS:
|
87.14.E-
|
(Proteins)
|
| |
87.10.Tf
|
(Molecular dynamics simulation)
|
| |
87.15.A-
|
(Theory, modeling, and computer simulation)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 11874309, 11474237, and 11574310) and the 111 Project, China (Grant No. B16029). |
Corresponding Authors:
Xin Zhou, Hu Chen
E-mail: xzhou@ucas.ac.cn;chenhu@xmu.edu.cn
|
Cite this article:
Xiaofeng Zhang(张晓峰), Zilong Guo(郭子龙), Ping Yu(余平), Qiushi Li(李秋实), Xin Zhou(周昕), Hu Chen(陈虎) Different potential of mean force of two-state protein GB1 and downhill protein gpW revealed by molecular dynamics simulation 2020 Chin. Phys. B 29 078701
|
| [1] |
Honig B 1999 J. Mol. Biol. 293 283
|
| [2] |
Kuhlman B, Dantas G, Ireton G C, Varani G, Stoddard B L and Baker D 2003 Science 302 1364
|
| [3] |
Huang P S, Boyken S E and Baker D 2016 Nature 537 320
|
| [4] |
Soto C 2003 Nat. Rev. Neurosci. 4 49
|
| [5] |
McCallister E L, Alm E and Baker D 2000 Nat. Struct. Mol. Biol. 7 669
|
| [6] |
Chen H, Fu H, Zhu X, Cong P, Nakamura F and Yan J 2011 Biophys. J. 100 517
|
| [7] |
Beck D A C and Daggett V 2004 Methods 34 112
|
| [8] |
Fersht A R and Daggett V 2002 Cell 108 573
|
| [9] |
Leopold P E, Montal M and Onuchic J N 1992 Proc. Natl. Acad. Sci. USA 89 8721
|
| [10] |
Bryngelson J D, Onuchic J N, Socci N D and Wolynes P G 1995 Proteins 21 167
|
| [11] |
Jackson S E and Fersht A R 1991 Biochemistry (Mosc.) 30 10428
|
| [12] |
Garcia-Mira M M, Sadqi M, Fischer N, Sanchez-Ruiz J M and Munoz V 2002 Science 298 2191
|
| [13] |
Sadqi M, Fushman D and Munoz V 2006 Nature 442 317
|
| [14] |
Zhang J, Li W, Wang J, Qin M and Wang W 2008 Proteins 72 1038
|
| [15] |
Ding K, Louis J M and Gronenborn A M 2004 J. Mol. Biol. 335 1299
|
| [16] |
Schmidt H L, Sperling L J, Gao Y G, Wylie B J, Boettcher J M, Wilson S R and Rienstra C M 2007 J. Phys. Chem. B 111 14362
|
| [17] |
De Sancho D, Mittal J and Best R B 2013 J. Chem. Theory Comput. 9 1743
|
| [18] |
Cao Y and Li H 2007 Nat. Mater. 6 109
|
| [19] |
Jackson S E 1998 Fold. Des. 3 81
|
| [20] |
Barrick D 2009 Phys. Biol. 6 015001
|
| [21] |
Li H, Wang H C, Cao Y, Sharma D and Wang M 2008 J. Mol. Biol. 379 871
|
| [22] |
Puchner E M and Gaub H E 2009 Curr. Opin. Struct. Biol. 19 605
|
| [23] |
Murialdo H, Xing X, Tzamtzis D, Haddad A and Gold M 2003 Biochem. Cell Biol. 81 307
|
| [24] |
Sborgi L, Verma A, Munoz V and de Alba E 2011 PLoS One 6 e26409
|
| [25] |
Fung A, Li P, Godoy-Ruiz R, Sanchez-Ruiz J M and Munoz V 2008 J. Am. Chem. Soc. 130 7489
|
| [26] |
Lu H and Schulten K 1999 Proteins 35 453
|
| [27] |
Lu H, Isralewitz B, Krammer A, Vogel V and Schulten K 1998 Biophys. J. 75 662
|
| [28] |
Kaestner J 2011 Wiley Interdisciplinary Reviews-Computational Molecular Science 1 932
|
| [29] |
Xu W, Li Y and Zhang Z 2012 Chin. Phys. Lett. 29 068702
|
| [30] |
Souaille M and Roux B 2001 Comput. Phys. Commun. 135 40
|
| [31] |
Torrie G M and Valleau J P 1977 J. Comput. Phys. 23 187
|
| [32] |
Kumar S, Bouzida D, Swendsen R H, Kollman P A and Rosenberg J M 1992 J. Comput. Chem. 13 1011
|
| [33] |
Kumar S, Rosenberg J M, Bouzida D, Swendsen R H and Kollman P A 1995 J. Comput. Chem. 16 1339
|
| [34] |
Chen H, Yuan G, Winardhi R S, Yao M, Popa I, Fernandez J M and Yan J 2015 J. Am. Chem. Soc. 137 3540
|
| [35] |
Yuan G, Le S, Yao M, Qian H, Zhou X, Yan J and Chen H 2017 Angew. Chem. 129 5582
|
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
