Stochastic Protein Folding Simulation in the d-Dimensional HP-Model

Steinhöfel, Kathleen; Skaliotis, Alexandros; Albrecht, Andreas

doi:10.1007/978-3-540-71233-6_30

Cited by 9 publications

(9 citation statements)

References 49 publications

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“…The HP-model in the unrestricted 3D-cubic lattice was chosen due to its prevalence in previous protein studies Steinhöfel et al, 2007;Wolfinger et al, 2006;Thachuk et al, 2007) and the abundance of reasonable sequence sets. Often the used benchmark sets consider degeneracy only and thus (with the exception of do not reflect a reasonable protein-likeness definition based on kinetic properties.…”

Section: Resultsmentioning

confidence: 99%

“…For global folding, where the whole fold space is explored, we utilize the Pull-move set (Lesh et al, 2003). This set is often used (Thachuk et al, 2007) and has been shown to yield realistic folding times (Steinhöfel et al, 2007). We address the problem of correct folding temperatures essential for reasonable Monte Carlo simulations (see Methods).…”

Section: Resultsmentioning

confidence: 99%

“…In contrast, the application of Pull moves (Lesh et al, 2003) results in more local changes of the structure. It has been shown that this move set is able to reproduce realistic folding times (Steinhöfel et al, 2007) and is therefore well suited for our folding based classification . To our knowledge there is no other ergodic move set for lattice protein models.…”

Section: Latfold-global Folding Simulationsmentioning

confidence: 99%

“…In addition to the applicability of this data set to address relevant biological questions, it serves as the first welldefined benchmark sequence set for folding algorithms (Steinhöfel et al, 2007). So far new methods have usually been tested on random sequences that, with high probability, will not show proteinlike behavior.…”

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confidence: 99%

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Classifying proteinlike sequences in arbitrary lattice protein models using LatPack

et al. 2008

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Knowledge of a protein's three-dimensional native structure is vital in determining its chemical properties and functionality. However, experimental methods to determine structure are very costly and time-consuming. Computational approaches such as folding simulations and structure prediction algorithms are quicker and cheaper but lack consistent accuracy. This currently restricts extensive computational studies to abstract protein models. It is thus essential that simplifications induced by the models do not negate scientific value. Key to this is the use of thoroughly defined proteinlike sequences. In such cases abstract models can allow for the investigation of important biological questions. Here, we present a procedure to generate and classify proteinlike sequence data sets. Our LatPack tools and the approach in general are applicable to arbitrary lattice protein models. Identification is based on thermodynamic kinetic features and incorporates the sequential assembly of proteins by addressing cotranslational folding. We demonstrate the approach in the widely used unrestricted 3D-cubic HP-model. The resulting sequence set is the first large data set for this model exhibiting the proteinlike properties required. Our data tools are freely available and can be used to investigate protein-related problems.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Latfold-global Folding Simulationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Classifying proteinlike sequences in arbitrary lattice protein models using LatPack

et al. 2008

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“…For example, instancespecific landscape parameters such as the maximum value of the minimum escape height from local minima can be utilized to obtain relatively tight bounds for the termination of local search when coupled with a confidence parameter, see [2]. The application of this type of run-time bounds to protein folding simulation exhibits a close correspondence between the simulation time (in number of transitions) and estimates of real folding times (in nanoseconds) of protein sequences [24], [25], which is due to the common source of thermodynamics (simulated annealing, minimizing free energy in protein foldings).…”

mentioning

confidence: 99%

Combinatorial Landscape Analysis for k-SAT Instances

Albrecht¹,

Lane²,

Steinhöfel³

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Abstract-Over the past ten years, methods from statistical physics have provided a deeper inside into the average complexity of hard combinatorial problems, culminating in a rigorous proof for the asymptotic behaviour of the k-SAT phase transition threshold by Achlioptas and Peres in 2004. On the other hand, when dealing with individual instances of hard problems, gathering information about specific properties of instances in a pre-processing phase might be helpful for an appropriate adjustment of local search-based procedures. In the present paper, we address both issues in the context of landscapes induced by k-SAT instances: Firstly, we utilize a sampling method devised by Garnier and Kallel in 2002 for approximations of the number of local maxima in landscapes generated by individual k-SAT instances and a simple neighbourhood relation. The objective function is given by the number of satisfied clauses. Secondly, we outline a method for obtaining upper bounds for the average number of local maxima in k-SAT instances which indicates some kind of phase transition for the neighbourhood-specific ratio m/n = Θ(2 k /k).

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