A dynamic Monte Carlo algorithm for exploration of dense conformational spaces in heteropolymers

Abstract

Articles you may be interested inThe effect of sequence on the conformational stability of a model heteropolymer in explicit water Monte Carlo studies of the thermodynamics and kinetics of reduced protein models: Application to small helical, β, and α/β proteins

Exact sequence analysis for three-dimensional hydrophobic-polar lattice proteins

“…Therefore, sophisticated algorithms were applied to find lowest energy states for chains of up to 136 monomers. The methods applied are based on very different algorithms, ranging from exact enumeration in two dimensions [9,10] and three dimensions on cuboid (compact) lattices [2,11], and hydrophobic core construction methods [12,13] over genetic algorithms [14,15,16,17,18], Monte Carlo simulations with different types of move sets [19,20,21,22], and generalized ensemble approaches [23] to Rosenbluth chain growth methods [24] of the 'Go with the Winners' type [25,26,27,28,29,30]. With some of these algorithms, thermodynamic quantities of lattice heteropolymers were studied as well [23,27,29,30,31].…”

Thermodynamics of Protein Folding from Coarse-Grained Models’ Perspectives

“…The reason is that the native fold, i.e., the ground-state or lowest-energy conformation, plays an essential role in protein science and that it is, in the discrete lattice representation, non-or low-degenerate. Monte Carlo simulations with move sets consisting of semilocal conformational updates like end flips, corner flips, and "crank shafts" [23][24][25][26], as well as nonlocal pivot updates [45], are inefficient in sampling the dominating dense conformations in the low-temperature region. It turned out that a different method, Rosenbluth chain growth [28] combined with a 'Go with the winners' strategy [29], is much more efficient in sampling highly dense conformations.…”

“…Therefore, sophisticated algorithms were developed to find lowest-energy states for chains of up to 136 monomers. The methods applied are based on very different algorithms, ranging from exact enumeration in two dimensions [11,12] and three dimensions on cuboid (compact) lattices [4,[13][14][15], and hydrophobic-core construction methods [16,17] over genetic algorithms [18][19][20][21][22], Monte Carlo simulations with different types of move sets [23][24][25][26], and generalized ensemble approaches [27] to Rosenbluth chaingrowth methods [28] of the 'Go with the Winners' type [29][30][31][32][33][34][35]. With some of these algorithms, thermodynamic quantities of lattice heteropolymers were studied as well [14,27,31,[34][35][36].…”

Crystallization of a polymer on a surface

“…33 We also make occasional bond-flipping moves which, although they do not change the shape of the volume occupied by the polymer, change the path of the polymer through that volume. 28,34,35 These moves speed up equilibration in the dense phases. During the simulation we always constrain the polymer to have at least one unit in contact with the surface.…”