Multi-Self-Overlap Ensemble for Protein Folding: Ground State Search and Thermodynamics

Chikenji, George; Kikuchi, Macoto; Iba, Yukito

doi:10.1103/physrevlett.83.1886

Cited by 83 publications

(92 citation statements)

References 34 publications

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“…7(a) we read off that the distributions possess two peaks at temperatures within that region where the ground-state -globule transition takes place. This is interpreted as indication of a "first-order-like" transition [19]. The behaviour in the vicinity of the globule -random coil transition is less spectacular as can be seen in Fig.…”

mentioning

confidence: 81%

“…Since some results regarding the ground states and thermodynamic properties were available [19] it was a good candidate for testing our algorithm and for checking the performance of our method. For this sequence we analysed in detail the temperature-dependent behaviour of radius of gyration, end-to-end distance, as well as their fluctuations, and compared it with the specific heat in order to elaborate relations between characteristic properties of these curves (peaks, "shoulders") and conformational transitions not being transitions in a strict thermodynamic sense due to the impossibility to formulate a thermodynamic limit for proteins.…”

Section: Discussionmentioning

confidence: 99%

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Thermodynamics of lattice heteropolymers

Bachmann

Janke

2004

The Journal of Chemical Physics

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We calculate thermodynamic quantities of HP lattice proteins by means of a multicanonical chain growth algorithm that connects the new variants of the Pruned-Enriched Rosenbluth Method (nPERM) and flat histogram sampling of the entire energy space. Since our method directly simulates the density of states, we obtain results for thermodynamic quantities of the system for all temperatures. In particular, this algorithm enables us to accurately simulate the usually difficult accessible low-temperature region. Therefore, it becomes possible to perform detailed analyses of the low-temperature transition between ground states and compact globules.

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confidence: 81%

Section: Discussionmentioning

confidence: 99%

Thermodynamics of lattice heteropolymers

Bachmann

Janke

2004

The Journal of Chemical Physics

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Schiemann

Bachmann

Janke

2005

The Journal of Chemical Physics

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We have exactly enumerated all sequences and conformations of HP proteins with chains of up to 19 monomers on the simple cubic lattice. For two variants of the hydrophobic-polar (HP) model, where only two types of monomers are distinguished, we determined and statistically analyzed designing sequences, i.e., sequences that have a non-degenerate ground state. Furthermore we were interested in characteristic thermodynamic properties of HP proteins with designing sequences. In order to be able to perform these exact studies, we applied an efficient enumeration method based on contact sets.

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“…Here, we introduce another variant of this idea, dubbed by us "model hopping" (MH), where as in the Hamilton Exchange method [4] or the Multi-Self-Overlap-Ensemble [5] the random walk in temperatures is replaced by one through an ensemble of models with slightly altered energy functions. For this we assume that the energy function can be separated in two terms: E = E A + aE B .…”

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confidence: 99%

Efficient Sampling of Protein Structures by Model Hopping

Kwak

Hansmann

2005

Phys. Rev. Lett.

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We introduce a novel simulation method, model hopping, that enhances sampling of low-energy configurations in complex systems. The approach is illustrated for a protein folding problem. Thermodynamic quantities of proteins with up to 46 residues are evaluated from all-atom simulations with this method.The solution of many pharmacological and medical problems, such as rational drug design or the pathology of diseases associated with the mis-folding of proteins, requires a detailed understanding of the relation between chemical composition and structure of proteins. However, despite more than two decades of research, this so-called protein-folding problem has remained a hard computational task. This is because proteins in an all-atom representation are characterized by a rough energy landscape with a huge number of local minima separated by high energy barriers. Various numerical techniques exist that can alleviate this multiple minima problem. One popular example is parallel tempering (also known as replica exchange method) [1], a technique that was first introduced to protein studies in Ref. [2]. In its most common form, one considers an artificial system built up of N non-interacting copies of a molecule, each at a different temperature T i . In addition to standard Monte Carlo or molecular dynamics moves that affect only one copy, parallel tempering allows also the exchange of conformations between two copies i and j = i + 1 with probability w(C old → C new ) = min(1, exp(ΔβΔE)). The resulting random walk in temperature enables configurations to cross energy barriers and move out of local minima leading in this way to an enhanced sampling of lowenergy structures. Variants of parallel tempering introducing non-canonical weights have been proposed [2,3].Here, we introduce another variant of this idea, dubbed by us "model hopping" (MH), where as in the Hamilton Exchange method [4] or the Multi-Self-Overlap-Ensemble [5] the random walk in temperatures is replaced by one through an ensemble of models with slightly altered energy functions. For this we assume that the energy function can be separated in two terms: E = E A + aE B . As in parallel tempering, MH considers N non-interacting copies of the molecule, but copies are now exchanged according toHere, Δa = a j − a i and ΔE B = E B (C j ) − E B (C i ). Due to this exchange move configurations perform a random walk on a ladder of models with a 1 = 1 > a 2 > a 3 > …. > a N that differ by the relative contributions of E B to the total energy E of the molecule. For instance, barriers in the energy landscape of proteins often arise from van der Waals repulsion between atoms that come too close. Generalized-ensemble techniques circumvent the problem by performing a random walk in energy that allows crossing of these barriers. On the other hand, in MH the protein walks randomly up and down on a ladder of models with successively smaller contributions from the van der Waals energy. While the "physical" system is on one side of the ladder (at a 1 = 1), the (non-p...

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Multi-Self-Overlap Ensemble for Protein Folding: Ground State Search and Thermodynamics

Cited by 83 publications

References 34 publications

Thermodynamics of lattice heteropolymers

Thermodynamics of lattice heteropolymers

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

Efficient Sampling of Protein Structures by Model Hopping

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