Applying forces to elastic network models of large biomolecules using a haptic feedback device.
Elastic network models of biomolecules have proved to be relatively good at predicting global conformational changes particularly in large systems. Software that facilitates rapid and intuitive exploration of conformational change in elastic network models of large biomolecules in response to externally applied forces would therefore be of considerable use, particularly if the forces mimic those that arise in the interaction with a functional ligand. We have developed software that enables a user to apply forces to individual atoms of an elastic network model of a biomolecule through a haptic feedback device or a mouse. With a haptic feedback device the user feels the response to the applied force whilst seeing the biomolecule deform on the screen. Prior to the interactive session normal mode analysis is performed, or pre-calculated normal mode eigenvalues and eigenvectors are loaded. For large molecules this allows the memory and number of calculations to be reduced by employing the idea of the important subspace, a relatively small space of the first M lowest frequency normal mode eigenvectors within which a large proportion of the total fluctuation occurs. Using this approach it was possible to study GroEL on a standard PC as even though only 2.3% of the total number of eigenvectors could be used, they accounted for 50% of the total fluctuation. User testing has shown that the haptic version allows for much more rapid and intuitive exploration of the molecule than the mouse version.
School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.
M B
MB
Stocks
S D
SD
Laycock
S
S
Hayward
D000818:Animals; D030562:Databases, Protein; D004548:Elasticity; D006801:Humans; D056004:Molecular Dynamics Simulation; D011506:Proteins / Q000737:chemistry*; D012984:Software; D014584:User-Computer Interface
D011506:Proteins
Protein Eng. 2001 Jan;14(1):1-6
11287673
Bioinformatics. 2007 Jul 1;23(13):i408-17
17646324
Fold Des. 1997;2(3):173-81
9218955
J Struct Biol. 2004 Sep;147(3):302-14
15450299
J Mol Graph Model. 2007 Mar;25(6):801-5
16987676
Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W610-4
15215461
Biophys J. 2006 Sep 1;91(5):1823-31
16714351
Phys Rev Lett. 1992 Apr 27;68(17):2696-2699
10045464
Curr Opin Struct Biol. 1999 Apr;9(2):164-9
10322205
Biophys J. 2008 May 1;94(9):3424-35
18223005
J Mol Biol. 2003 Feb 14;326(2):485-92
12559916
BMC Struct Biol. 2009 Oct 27;9:69
19860901
J Mol Graph Model. 2010 Sep;29(2):280-9
20727801
Proteins. 1987;2(4):308-29
3448606
Biophys J. 2001 Jan;80(1):505-15
11159421
Annu Rev Phys Chem. 1995;46:223-50
24329489
Structure. 2009 Dec 9;17(12):1605-1613
20004164
PLoS Comput Biol. 2009 Sep;5(9):e1000496
19730674
Proteins. 1993 Dec;17(4):412-25
8108382
Phys Rev Lett. 1996 Aug 26;77(9):1905-1908
10063201
Phys Rev Lett. 2005 Feb 25;94(7):078102
15783858
Chem Rev. 2010 Mar 10;110(3):1463-97
19785456
Biophys Chem. 1990 Jan;35(1):105-12
2328273
PLoS Comput Biol. 2009 Oct;5(10):e1000544
19851447
Curr Opin Struct Biol. 2005 Oct;15(5):586-92
16143512
Bioinformatics. 2002 Jan;18(1):140-6
11836222
J Mol Biol. 2004 Jun 11;339(4):1001-21
15165865
J Mol Biol. 1993 Dec 20;234(4):1207-17
7505336