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Folding simulations

The energy functions for folding simulations include atom-based potentials from molecular mechanics packages [164] such as CHARMM [81], AMBER [165], and ECEPP... [Pg.289]

Y Cm, RS Chen, WEI Wong. Protein folding simulation with genetic algorithm and supersecondary structure constraints. Proteins 31 247-257, 1998. [Pg.309]

AR Dinner, M Karplus. A metastable state m folding simulations of a protein model. Nature Struct Biol 5 236-241, 1998. [Pg.390]

FOLD SIMULATIONS OF PROTON TRANSFER REACTIONS IN ENZYMES 145... [Pg.145]

Figure 10-5. Representative conformations of the (5 amyloid peptide (10-42) under different pH conditions. The conformations were obtained as centroids of the most populated clusters from the replica-exchange CPHMD folding simulations [43, 44]. The N-terminal residues 10-28 are shown in blue the C-terminal residues 29-42 are shown in red. In the most aggregation-prone state (pH 6), the side chains of the central hydrophobic cluster Leu-17, Val-18, Phe-19, Phe-20 and Ala-21 are shown as van der Waals spheres in pink, grey, cyan, purple and green, respectively... Figure 10-5. Representative conformations of the (5 amyloid peptide (10-42) under different pH conditions. The conformations were obtained as centroids of the most populated clusters from the replica-exchange CPHMD folding simulations [43, 44]. The N-terminal residues 10-28 are shown in blue the C-terminal residues 29-42 are shown in red. In the most aggregation-prone state (pH 6), the side chains of the central hydrophobic cluster Leu-17, Val-18, Phe-19, Phe-20 and Ala-21 are shown as van der Waals spheres in pink, grey, cyan, purple and green, respectively...
Another interesting application area of PHMD simulations is to investigate electrostatic interactions in the unfolded states of proteins. A traditional view that unfolded proteins adopt random conformational states that are devoid of electrostatic and hydrophobic interactions, are recently challenged by experimental data [20, 69], REX-CPHMD folding simulations of the 35 residue C-terminal subdomain of the villin headpiece domain revealed a significant deviation from the standard pKa values for several titratable residues. Additional simulations, in which a charged group is neutralized confirmed the existence of specific electrostatic interactions in the unfolded states (JK and CLB, manuscript in preparation). [Pg.277]

Rhee, Y. M. Pande, V. S., Multiplexed-replica exchange molecular dynamics method for protein folding simulation, Biophys. J. 2003, 84, 775-786. [Pg.501]

Ulmschneider, J.P., Ulmschneider, M.B., Di Nola, A. Monte Carlo vs molecular dynamics for all-atom polypeptide folding simulations. J. Phys. Chem. B 2006, 110, 16733 12. [Pg.71]

C. Simmerling, B. Strockbine, and A. Roitberg. All-atom strucutre prediction and folding simulations of a stable protein. J. Am. Chem. Soc., 124 11258-11259, 2002. [Pg.570]

Wen EZ, Hsieh MJ, Kollman PA, Luo R (2004) Enhanced ab initio protein folding simulations in Poisson-Boltzmann molecular dynamics with self guiding forces, J Mol Graph Model, 22 415—424... [Pg.335]

Figure 28 Diagrams of a sequence of conformations of poly(L-alanine) encountered at various successive stages on a conformational pathway during a folding simulation, starting from a fully extended conformation.190 The final structure is the same as shown in Figure 29e. Figure 28 Diagrams of a sequence of conformations of poly(L-alanine) encountered at various successive stages on a conformational pathway during a folding simulation, starting from a fully extended conformation.190 The final structure is the same as shown in Figure 29e.
Figure 30 Stereo views of some conformations of Met-enkephalin along a conformational pathway in the electrostatically driven Monte Carlo procedure during a folding simulation, starting from a randomly generated structure. The structure in (d)191 is the same as that in Figure 23. Figure 30 Stereo views of some conformations of Met-enkephalin along a conformational pathway in the electrostatically driven Monte Carlo procedure during a folding simulation, starting from a randomly generated structure. The structure in (d)191 is the same as that in Figure 23.
Most protein folding simulations using explicit solvent consist of 80 or more percent water, and it turns out that the calculation of the water interaction indeed also takes more than 80% of the CPU time. Some MD packages improve on this by using a special routine for the water interaction [42]. Nevertheless, it seems a waste that most of the computer time is spent on solvent molecules... [Pg.405]

S. Gnanakaran, H. Nymeyer, J. Portman, K. Y. Sanbonmatsu, A. E. Garcia (2003) Peptide folding simulations. Curr. Opin. Struc. Biol. 13, pp. 168-174... [Pg.428]

V. S. Pande, I. Baker, J. Chapman, S. P. Elmer, S. Khahq, S. M. Larson, Y. M. Rhee, M. R. Shirts, C. D. Snow, E. J. Sorin, B. Zagrovic (2003) Atomistic protein folding simulations on the submillisecond time scale using worldwide distributed computing. Biopolymers 68, pp. 91-109... [Pg.431]

B. Zagrovic, E. Sorin, V. S. Pande (2001) Beta-hairpin folding simulations in atomistic detail using an implicit solvent model. J. Mol. Biol. 313, pp. 151-169... [Pg.433]

The protein folding process and its determining factors are still poorly understood. But even if all factors could be modeled appropriately, folding simulations can span only a tiny fraction of the actual time required to fold a protein which is on the order of milliseconds to seconds. In addition, the contributing factors need to be modeled extremely accurately in order to avoid error propagation during the vast amount of computation needed for completely folding a protein. Also the structure prediction problem appears to be difficult [172-174]. [Pg.273]

Ortiz, A. R., A. Kolinski, and J. Skolnick, Nativelike topology assembly of small proteins using predicted restraints in Monte Carlo folding simulations. Proc Natl Acad Sci USA, 1998. 95(3) p. 1020-5. [Pg.322]

This review will present an overview of current molecular mechanics techniques and discuss some of their limitations. We will then look at knowledge-based protein prediction strategies and examine the incorporation of such empirical rules into refinement methodologies for model protein systems. More comprehensive reviews of molecular dynamics simulations, knowledge-based protein modeling, and protein folding simulations are available. - " ... [Pg.58]

Secondary structure prediction methods have been complemented by packing analyses of amino acid residues in globular proteins. Packing arrangements have been examined extensively [13, 14] in attempts to identify preferred interaction patterns between non-contiguous amino acid residues. While there is no straightforward way to cast this information into a scheme for prediction of protein structure from sequence, it can certainly be used for plausibility checks on hypothetical protein models or to score protein models obtained by protein folding simulations on lattices [15]. [Pg.686]

Gnanakaran, S., Nymeyer, H., Portman, J., Sanbonmatsu, K.Y., Garcia, A.E. Peptide folding simulations. Curr. Opin. Struct. Biol. 2003,13,168-74. [Pg.118]

Totrov, M., Abagyan, R. Rapid boundary element solvation electrostatics calculations in folding simulations Successful folding of a 23-residue peptide. Biopolymers 2001,60(2), 124-33. [Pg.135]


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See also in sourсe #XX -- [ Pg.382 ]




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