Dynameomics: design of a computational lab workflow and scientific data repository for protein simulations

Simms, Andrew M.; Toofanny, Rudesh D.; Kehl, Catherine; Benson, Noah C.; Daggett, Valerie

doi:10.1093/protein/gzn012

Cited by 41 publications

(60 citation statements)

References 15 publications

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“…Ramachandran plots of native protein simulations were obtained from our ongoing Dynameomics project (www.dynameomics.org), in which proteins and domains representing the most common folds (36) are being simulated by using a standard protocol (24) and loaded into a hybrid relational multidimensional database (37,38). Here, we report data from simulations of 188 different proteins in water at 298 K. The simulations are all at least 21 ns long with a mean simulation time of 30 ns.…”

Section: Methodsmentioning

confidence: 99%

The intrinsic conformational propensities of the 20 naturally occurring amino acids and reflection of these propensities in proteins

Beck

Alonso

Inoyama

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

127

183

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Here, we compare the distributions of main chain (⌽,⌿) angles (i.e., Ramachandran maps) of the 20 naturally occurring amino acids in three contexts: (i) molecular dynamics (MD) simulations of GlyGly-X-Gly-Gly pentapeptides in water at 298 K with exhaustive sampling, where X ‫؍‬ the amino acid in question; (ii) 188 independent protein simulations in water at 298 K from our Dynameomics Project; and (iii) static crystal and NMR structures from the Protein Data Bank. The GGXGG peptide series is often used as a model of the unstructured denatured state of proteins. The sampling in the peptide MD simulations is neither random nor uniform. Instead, individual amino acids show preferences for particular conformations, but the peptide is dynamic, and interconversion between conformers is facile. For a given amino acid, the (⌽,⌿) distributions in the protein simulations and the Protein Data Bank are very similar and often distinct from those in the peptide simulations. Comparison between the peptide and protein simulations shows that packing constraints, solvation, and the tendency for particular amino acids to be used for specific structural motifs can overwhelm the ''intrinsic propensities'' of amino acids for particular (⌽,⌿) conformations. We also compare our helical propensities with experimental consensus values using the host-guest method, which appear to be determined largely by context and not necessarily the intrinsic conformational propensities of the guest residues. These simulations represent an improved coil library free from contextual effects to better model intrinsic conformational propensities and provide a detailed view of conformations making up the ''random coil'' state.coil library ͉ Dynameomics ͉ molecular dynamics ͉ protein folding ͉ host-guest P rotein secondary structure was predicted before the atomic structures of protein were determined (1-3). Conformational preferences of the amino acids were also estimated very early on, beginning with Ramachandran's ''map'' in 1963, ''based solely on repulsive van der Waals'' forces in dipeptides (4, 5). Remarkably, these predictions regarding structure and conformational preferences were later largely validated in protein crystal structures (6-8).In the protein folding field, these preferences are seen as both means of excluding regions of conformational space and as driving forces for the formation of secondary structure, both of which limit and bias the necessary search of conformational space required during protein folding.(⌽,⌿) dihedral angle distributions are increasingly used to check the validity of structures. Although there can be no doubt about the general tendency of amino acids in globular proteins to populate some regions of (⌽,⌿) space relative to others, the use of such distributions to judge and refine structures leads to dangerous circular reasoning. That is, (⌽,⌿) preferences are used as tests of crystal structures, and those very crystal structures are then used to define and support the Ramachandran (⌽,⌿) angle distributions.Many exper...

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Section: Methodsmentioning

confidence: 99%

The intrinsic conformational propensities of the 20 naturally occurring amino acids and reflection of these propensities in proteins

Beck

Alonso

Inoyama

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

127

183

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“…Geometrical Parameters and Occurrence Probability of the Selected AA Side Chain Rotamers54,55 with the Exception of Ala and Gly a a g and t stand for gauche and trans, respectively.…”

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confidence: 99%

Coarse Point Charge Models For Proteins From Smoothed Molecular Electrostatic Potentials

Leherte

Vercauteren

2009

J. Chem. Theory Comput.

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To generate coarse electrostatic models of proteins, we developed an original approach to hierarchically locate maxima and minima in smoothed molecular electrostatic potentials. A charge-fitting program was used to assign charges to the so-obtained reduced representations. Templates are defined to easily generate coarse point charge models for protein structures, in the particular cases of the Amber99 and Gromos43A1 force fields. Applications to four small peptides and to the ion channel KcsA are presented. Electrostatic potential values generated by the reduced models are compared with the corresponding values obtained using the original sets of atomic charges.

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“…The Dynameomics database Simms et al 2008) currently contains ;450 proteins, each of which has been simulated for at least 21 ns at a temperature of 298 K. Additionally, it contains at least two unfolding simulations of each protein at 498 K for 31 ns and at least three short (2 ns) simulations at 498 K. These simulated target proteins form a data set that spans a considerable portion of the protein universe, representing >75% of all known protein folds.…”

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confidence: 99%

Dynameomics: Large‐scale assessment of native protein flexibility

Benson

Daggett

2008

Protein Science

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Structure is only the first step in understanding the interactions and functions of proteins. In this paper, we explore the flexibility of proteins across a broad database of over 250 solvated protein molecular dynamics simulations in water for an aggregate simulation time of ;6 ms. These simulations are from our Dynameomics project, and these proteins represent approximately 75% of all known protein structures. We employ principal component analysis of the atomic coordinates over time to determine the primary axis and magnitude of the flexibility of each atom in a simulation. This technique gives us both a database of flexibility for many protein fold families and a compact visual representation of a particular protein's native-state conformational space, neither of which are available using experimental methods alone. These tools allow us to better understand the nature of protein motion and to describe its relationship to other structural and dynamical characteristics. In addition to reporting general properties of protein flexibility and detailing many dynamic motifs, we characterize the relationship between protein native-state flexibility and early events in thermal unfolding and show that flexibility predicts how a protein will begin to unfold. We provide evidence that fold families have conserved flexibility patterns, and family members who deviate from the conserved patterns have very low sequence identity. Finally, we examine novel aspects of highly inflexible loops that are as important to structural integrity as conventional secondary structure. These loops, which are difficult if not impossible to locate without dynamic data, may constitute new structural motifs.Keywords: protein; flexibility; folding; stability; families Much scientific effort has been spent attempting to catalog, describe, observe, and understand protein structure and function. Even when the structure of a protein is known, this knowledge is often not sufficient to elucidate details of the protein's function or its mode of action, both of which are pieces of information that are frequently of much greater importance to biologists than structure. As biologists increasingly seek to understand and modify aspects of cellular behavior and as protein databases gather more high-resolution three-dimensional structures, the ability to understand key features of a protein's dynamic behavior becomes more important.Flexibility is critical in determining protein behavior and function. Because proteins are not static entities (as they are represented in structural databases) and because crystal structures do not necessarily represent a protein in its active conformation, any attempt to determine potential biochemical interactions of a protein from these data suffers from a lack of information about its motion. A quantitative description of a protein's flexibility provides a summary of its dominant dynamical modes and significant information about potential conformations available to it. Flexibility may also provide insight into unfoldin...

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Dynameomics: design of a computational lab workflow and scientific data repository for protein simulations

Cited by 41 publications

References 15 publications

The intrinsic conformational propensities of the 20 naturally occurring amino acids and reflection of these propensities in proteins

The intrinsic conformational propensities of the 20 naturally occurring amino acids and reflection of these propensities in proteins

Coarse Point Charge Models For Proteins From Smoothed Molecular Electrostatic Potentials

Dynameomics: Large‐scale assessment of native protein flexibility

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