Testing a new Monte Carlo algorithm for protein folding

Two improved versions of the pruned-enriched-Rosenbluth method ͑PERM͒ are proposed and tested on simple models of lattice heteropolymers. Both are found to outperform not only the previous version of PERM, but also all other stochastic algorithms which have been employed on this problem, except for the core directed chain growth method ͑CG͒ of Beutler and Dill. In nearly all test cases they are faster in finding low-energy states, and in many cases they found new lowest energy states missed in previous papers. The CG method is superior to our method in some cases, but less efficient in others. On the other hand, the CG method uses heavily heuristics based on presumptions about the hydrophobic core and does not give thermodynamic properties, while the present method is a fully blind general purpose algorithm giving correct Boltzmann-Gibbs weights, and can be applied in principle to any stochastic sampling problem.

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“…We should stress that PERM as used in Ref. 8 was blind for all cases except this 64-mer, in contrast to wrong statements made in Ref. 10.…”

Section: Resultsmentioning

confidence: 73%

“…In order to fold also this chain, we would have either to start from the middle of the chain ͑as done in Ref. 8 for some sequences͒ or use some other heuristics which help formation of the hydrophobic core. Since we wanted our algorithm to be as general and ''blind'' as possible, we did not incorporate such tricks.…”

Section: Resultsmentioning

confidence: 99%

Growth algorithms for lattice heteropolymers at low temperatures

Hsu

Mehra

Nadler

et al. 2003

The Journal of Chemical Physics

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138

133

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“…Highly simplified models of proteins were studied by Grassberger and coworkers 17, by Liu and coworkers 19,21,39, Liang and coworkers 20,22,40,41, and others 16,18. Garel, Orland, and coworkers applied polymer growth methods to all-atom peptide models — but their work employed extremely high-temperature sampling (T=1000 K) followed by energy minimization 11,23–25.…”

Section: Introductionmentioning

confidence: 99%

Rapid sampling of all‐atom peptides using a library‐based polymer‐growth approach

Mamonov¹,

Zhang²,

Zuckerman³

2010

J Comput Chem

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We adapted existing polymer growth strategies for equilibrium sampling of peptides described by modern atomistic forcefields with a simple uniform dielectric solvent. The main novel feature of our approach is the use of pre-calculated statistical libraries of molecular fragments. A molecule is sampled by combining fragment configurations – of single residues in this study – which are stored in the libraries. Ensembles generated from the independent libraries are reweighted to conform with the Boltzmann-factor distribution of the forcefield describing the full molecule. In this way, high-quality equilibrium sampling of small peptides (4–8 residues) typically requires less than one hour of single-processor wallclock time and can be significantly faster than Langevin simulations. Furthermore, approximate, clash-free ensembles can be generated for larger peptides (up to 32 residues in this study) in less than a minute of single-processor computing. We discuss possible applications of our growth procedure to free energy calculation, fragment assembly protein-structure prediction protocols, and to “multi-resolution” sampling.

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“…A number of well-known heuristic optimisation methods have been applied to the 2D and 3D HP Protein Folding Problem, including Evolutionary Algorithms (EAs) [11-15] and Monte Carlo (MC) algorithms [16-22]. The latter have been found to be particularly robust and effective for finding high-quality solutions to the HP Protein Folding Problem [18].…”

Section: Introductionmentioning

confidence: 99%

“…Their non-standard EA incorporates characteristics of Monte Carlo methods. Currently among the best known algorithms for the HP Protein Folding problem are various Monte Carlo algorithms, including the 'pruned-enriched Rosenbluth method' (PERM) of Grassberger et al [16,18]. PERM is a biased chain growth algorithm that evaluates partial conformations and employs pruning and enrichment strategies to explore promising partial solutions.…”

Section: Introductionmentioning

confidence: 99%

An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem

2005

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Background: The protein folding problem is a fundamental problems in computational molecular biology and biochemical physics. Various optimisation methods have been applied to formulations of the ab-initio folding problem that are based on reduced models of protein structure, including Monte Carlo methods, Evolutionary Algorithms, Tabu Search and hybrid approaches. In our work, we have introduced an ant colony optimisation (ACO) algorithm to address the non-deterministic polynomial-time hard (NP-hard) combinatorial problem of predicting a protein's conformation from its amino acid sequence under a widely studied, conceptually simple model -the 2-dimensional (2D) and 3-dimensional (3D) hydrophobic-polar (HP) model.

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Testing a new Monte Carlo algorithm for protein folding

Cited by 82 publications

References 46 publications

Growth algorithms for lattice heteropolymers at low temperatures

Growth algorithms for lattice heteropolymers at low temperatures

Rapid sampling of all‐atom peptides using a library‐based polymer‐growth approach

An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem

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