An extended dead‐end elimination algorithm to determine gap‐free lists of low energy states

Kloppmann, Edda; Ullmann, G. Matthias; Becker, Torsten

doi:10.1002/jcc.20749

Cited by 11 publications

(7 citation statements)

References 38 publications

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“…Overall, we conclude that for the larger protein design problems, the DEE‐based methods presented in Ref 31. and32 are not applicable, since even finding the single lowest energy rotamer assignment for these problems using the sophisticated DEE criteria28, 29 of the HERO algorithm was not possible within a more than reasonable amount of time. We also emphasize that, since we actually wish to obtain the top 100 sequences, a non‐zero DEE threshold would still be required for these problems, which would leave the rotamer space resulting from the application of DEE even larger than that listed in Table I (“DEE” column), that is, making A* even less likely to be feasible.…”

Section: Resultsmentioning

confidence: 91%

“…A more recently devised method for providing successive low‐energy assignments is that of X‐DEE,32 which successively applies DEE to disjoint subspaces until a specified number of lowest energy rotamer assignments are found. However, X‐DEE requires very many runs of DEE on large subspaces, and for cases where the search space is very large as for protein design (unlike the case of two‐state variables tested in Ref 32…”

Section: Previous Workmentioning

confidence: 99%

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Accurate prediction for atomic‐level protein design and its application in diversifying the near‐optimal sequence space

Fromer

Yanover

2008

Proteins

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The task of engineering a protein to assume a target three-dimensional structure is known as protein design. Computational search algorithms are devised to predict a minimal energy amino acid sequence for a particular structure. In practice, however, an ensemble of low-energy sequences is often sought. Primarily, this is performed because an individual predicted low-energy sequence may not necessarily fold to the target structure because of both inaccuracies in modeling protein energetics and the nonoptimal nature of search algorithms employed. Additionally, some low-energy sequences may be overly stable and thus lack the dynamic flexibility required for biological functionality. Furthermore, the investigation of low-energy sequence ensembles will provide crucial insights into the pseudo-physical energy force fields that have been derived to describe structural energetics for protein design. Significantly, numerous studies have predicted low-energy sequences, which were subsequently synthesized and demonstrated to fold to desired structures. However, the characterization of the sequence space defined by such energy functions as compatible with a target structure has not been performed in full detail. This issue is critical for protein design scientists to successfully continue using these force fields at an ever-increasing pace and scale. In this paper, we present a conceptually novel algorithm that rapidly predicts the set of lowest energy sequences for a given structure. Based on the theory of probabilistic graphical models, it performs efficient inspection and partitioning of the near-optimal sequence space, without making any assumptions of positional independence. We benchmark its performance on a diverse set of relevant protein design examples and show that it consistently yields sequences of lower energy than those derived from state-of-the-art techniques. Thus, we find that previously presented search techniques do not fully depict the low-energy space as precisely. Examination of the predicted ensembles indicates that, for each structure, the amino acid identity at a majority of positions must be chosen extremely selectively so as to not incur significant energetic penalties. We investigate this high degree of similarity and demonstrate how more diverse near-optimal sequences can be predicted in order to systematically overcome this bottleneck for computational design. Finally, we exploit this in-depth analysis of a collection of the lowest energy sequences to suggest an explanation for previously observed experimental design results. The novel methodologies introduced here accurately portray the sequence space compatible with a protein structure and further supply a scheme to yield heterogeneous low-energy sequences, thus providing a powerful instrument for future work on protein design.

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Section: Resultsmentioning

confidence: 91%

Section: Previous Workmentioning

confidence: 99%

Accurate prediction for atomic‐level protein design and its application in diversifying the near‐optimal sequence space

Fromer

Yanover

2008

Proteins

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“…When this occurs, the combined process is heuristic and the provable guarantee is lost. Other modifications to DEE include X-DEE (extended DEE), which gives gap-free lists of low-energy states for a given energy range and was applied to the determination of protonation states of a protein [26], and type-variant DEE, which can be used in multistate protein design [27]. Further descriptions of DEE modifications and successes can be found in Fung et al .…”

Section: Methodsmentioning

confidence: 99%

Computational Methods for De novo Protein Design and its Applications to the Human Immunodeficiency Virus 1, Purine Nucleoside Phosphorylase, Ubiquitin Specific Protease 7, and Histone Demethylases

Bellows¹,

Floudas²

2010

CDT

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This paper provides an overview of computational de novo protein design methods, highlighting recent advances and successes. Four protein systems are described that are important targets for drug design: human immunodeficiency virus 1, purine nucleoside phosphorylase, ubiquitin specific protease 7, and histone demethylases. Target areas for drug design for each protein are described, along with known inhibitors, focusing on peptidic inhibitors, but also describing some smallmolecule inhibitors. Computational design methods that have been employed in elucidating these inhibitors for each protein are outlined, along with steps that can be taken in order to apply computational protein design to a system that has mainly used experimental methods to date. KeywordsComputational protein design; human immunodeficiency virus 1; purine nucleoside phosphorylase; ubiquitin specific protease 7; histone demethylases

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“…where P m (t) denotes the probability that the system is in charge state m at time t, k ml denotes the probability per unit time that the system will change its state from l to m. The summation runs over all possible states l. In order to restrict the number of states and only consider states that are accessible in a certain energy range, methods like extended Dead End Elimination (Kloppmann et al 2007) can be used. Simulating charge transfer by Eq.…”

Section: Simulating Chemical Reactions Using a Master Equation Approachmentioning

confidence: 99%

Investigating the mechanisms of photosynthetic proteins using continuum electrostatics

et al. 2008

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Computational methods based on continuum electrostatics are widely used in theoretical biochemistry to analyze the function of proteins. Continuum electrostatic methods in combination with quantum chemical and molecular mechanical methods can help to analyze even very complex biochemical systems. In this article, applications of these methods to proteins involved in photosynthesis are reviewed. After giving a short introduction to the basic concepts of the continuum electrostatic model based on the Poisson-Boltzmann equation, we describe the application of this approach to the docking of electron transfer proteins, to the comparison of isofunctional proteins, to the tuning of absorption spectra, to the analysis of the coupling of electron and proton transfer, to the analysis of the effect of membrane potentials on the energetics of membrane proteins, and to the kinetics of charge transfer reactions. Simulations as those reviewed in this article help to analyze molecular mechanisms on the basis of the structure of the protein, guide new experiments, and provide a better and deeper understanding of protein functions.

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An extended dead‐end elimination algorithm to determine gap‐free lists of low energy states

Cited by 11 publications

References 38 publications

Accurate prediction for atomic‐level protein design and its application in diversifying the near‐optimal sequence space

Accurate prediction for atomic‐level protein design and its application in diversifying the near‐optimal sequence space

Computational Methods for De novo Protein Design and its Applications to the Human Immunodeficiency Virus 1, Purine Nucleoside Phosphorylase, Ubiquitin Specific Protease 7, and Histone Demethylases

Investigating the mechanisms of photosynthetic proteins using continuum electrostatics

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