Protein folding Monte Carlo simulation

AR Ortiz, A Kolinski, J Skolnick. Fold assembly of small proteins using Monte Carlo simulations driven by restraints derived from multiple sequence alignments. J Mol Biol 277 419-448, 1998. 

Ortiz A R, A Kolinski and J Skoltuck 1998 Fold Assembly of Small Proteins Using Monte Carlo Simulations Driven by Restraints Derived from Multiple Sequence Alignments. Journal of Molecular Biology 277 419-446... 

A Kolinski, J Skolmck. Monte Carlo simulations of protein folding. I. Lattice model and interaction scheme. Pi-otem 18 338-352, 1994. 

Shimada J, Shakhnovich El. The ensemble folding kinetics of protein G from an all-atom Monte Carlo simulation. Proc Natl Acad Sci USA 2002 99 11175-80. 

Irback, A., Mohanty, S. PROFASI a Monte Carlo simulation package for protein folding and aggregation. J. Comput. Chem. 2006, 27,1548-55. 

O Toole, E.M., Panagiotopoulos, A.Z. Monte Carlo simulation of folding transitions of simple model proteins using a chain growth algorithm. J. Chem. Phys. 1992, 97, 8644-52. 

Skolnick, J, Kolinski, A Dynamics monte carlo simulations of a new lattice model of globular protein folding, structure and dynamics J. Mol. Biol. 1991 221, 499-531. 

E.I. Shakhnovich, G. Farztdinov, A.M. Gutin, and M. Karplus, Protein Folding Bottlenecks A Lattice Monte Carlo Simulation, Phys. Rev. Lett., 67 (1991) 1665. 

Kolinski, A. and Skolnick, J., Monte Carlo simulations of protein folding. 11. Application to protein A, ROP, and crambin. Proteins, 18, 353, 1994. 

J. Skolnick and A. Kolinski, J. Mol. Biol., 212, 787 (1990). Dynamic Monte Carlo Simulations of Globular Protein Folding/Unfolding Pathways. I. Six-Member, Greek Key Beta-Barrel Proteins. 

In summary, the recent developments allow Monte Carlo simulations of protein dynamics in their denatured state, in the intermediate states and in the folded state. In the latter case, where the fine details are of major interest, standard MD techniques are usually superior to the Monte Carlo reduced model approaches. 

M. H. Hao and H. A. Scheraga, Monte Carlo Simulation of a First-Order Transition for Protein Folding, J. Phys. Chem. 98,4940-4948 (1994). 

We still use the corn protein data described in Section 4.1. Here we do not consider all the variables but only the 28 wavelengths selected by CARS. For the proposed method, the number of Monte Carlo simulations is set to 1000. At each simulation 60% samples selected randomly are used as training samples and the remaining serve as test samples. The number of latent variables (LVs) for PCR, PLS and ECR (a = 0.5) is chosen using 5-fold cross validation. 

The two-dimensional square lattice protein folding model discussed earlier provides a simple basis for probing this issue. The model has the advantage of allowing one to carry out many exact calculations to check the predictions from first-order sensitivity theory. Unlike molecular dynamics or Monte Carlo simulations, there are no statistical errors or convergence problems associated with the calculations of the properties, and their parametric derivatives, of a model polypeptide on a two-dimensional square lattice. 

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