Elastic models of conformational transitions in macromolecules

Kim, Moon K.; Chirikjian, Gregory S.; Jernigan, Robert L.

doi:10.1016/s1093-3263(02)00143-2

Cited by 119 publications

(106 citation statements)

References 14 publications

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“…This landscape is required for a complete knowledge of functional mechanisms and therefore it has implications for drug discovery. Advances in computational approaches have been promising in sampling such conformational intermediates (2,3). However, in general, computational methods are limited by uncertainties in protein models as well as insufficient computational resources to achieve proper sampling.…”

mentioning

confidence: 99%

Coevolutionary signals across protein lineages help capture multiple protein conformations

Morcos

Jana

Hwa

et al. 2013

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

175

195

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A long-standing problem in molecular biology is the determination of a complete functional conformational landscape of proteins. This includes not only proteins' native structures, but also all their respective functional states, including functionally important intermediates. Here, we reveal a signature of functionally important states in several protein families, using direct coupling analysis, which detects residue pair coevolution of protein sequence composition. This signature is exploited in a protein structure-based model to uncover conformational diversity, including hidden functional configurations. We uncovered, with high resolution (mean ∼1.9 Å rmsd for nonapo structures), different functional structural states for medium to large proteins (200-450 aa) belonging to several distinct families. The combination of direct coupling analysis and the structure-based model also predicts several intermediates or hidden states that are of functional importance. This enhanced sampling is broadly applicable and has direct implications in protein structure determination and the design of ligands or drugs to trap intermediate states.A s demonstrated by Anfinsen in 1973 (1) for small and intermediate-size proteins, amino acid sequences contain all of the necessary information to determine their native structure and function. In principle, a complete physical understanding of all molecular interactions should be sufficient to uncover not only the proteins' native structures, but also all their respective functional states, including functionally important intermediates. This landscape is required for a complete knowledge of functional mechanisms and therefore it has implications for drug discovery. Advances in computational approaches have been promising in sampling such conformational intermediates (2, 3). However, in general, computational methods are limited by uncertainties in protein models as well as insufficient computational resources to achieve proper sampling. Experimental techniques such as crystallography or NMR spectroscopy have been successful in identifying functional protein structures but only for a fraction of the complete set of known protein sequences (4, 5). Additionally, the determination of functionally important intermediate states using such methods has been challenging due to their transient nature. One idea to confront this challenge is to search for clues in genomic data (6-10). Functional states under conformational selection should leave a trace in the evolutionary history of proteins. Recent results inspired by this hypothesis have led to the development of the powerful "direct coupling analysis" (DCA), which was able to predict a large number of direct structural contacts between residues from sequences alone (11). Other useful methods have been developed to define coupling among residue pairs (12, 13). Others have also looked into correlated electrostatic mutations to study the evolution of protein topology toward minimized interaction frustration (14). Integrating the DCA-predicted co...

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mentioning

confidence: 99%

Coevolutionary signals across protein lineages help capture multiple protein conformations

Morcos

Jana

Hwa

et al. 2013

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

175

195

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“…The EN models developed for polymer networks have been successfully applied to proteins in many different applications by us (20, and many others (53,54,. The basic difference between elastomeric polymer networks and proteins is that a protein is a collapsed polymer with many non-bonded interactions, while for polymers these non-bonded interactions could be (as for the phantom chain models) completely neglected.…”

Section: Elastic Network Models Of Proteins -Gaussian Network Modelmentioning

confidence: 99%

Packing Regularities in Biological Structures Relate to Their Dynamics

Jernigan

Kloczkowski

Protein Folding Protocols

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The high packing density inside proteins leads to certain geometric regularities and also is one of the most important contributors to the high extent of cooperativity manifested by proteins in their cohesive domain motions. The orientations between neighboring non-bonded residues in proteins substantially follow the similar geometric regularities, regardless of whether the residues are on the surface or buried -a direct result of hydrophobicity forces. These orientations are relatively fixed and correspond closely to small deformations from those of the face-centered cubic lattice, which is the way in which identical spheres pack at the highest density. Packing density also is related to the extent of conservation of residues, and we show this relationship for residue packing densities by averaging over a large sample or residue packings. There are three regimes: 1) over a broad range of packing densities the relationship between sequence entropy and inverse packing density is nearly linear, 2) over a limited range of low packing densities the sequence entropy is nearly constant, and 3) at extremely low packing densities the sequence entropy is highly variable. These packing results provide important justification for the simple elastic network models that have been shown for a large number of proteins to represent protein dynamics so successfully, even when the models are extremely coarse-grained. Elastic network models for polymeric chains are simple and could be combined with these protein elastic networks to represent partially denatured parts of proteins. Finally, we show results of applications of the elastic network model to study the functional motions of the ribosome, based on its known structure. These results indicate expected correlations among its components for the step-wise processing steps in protein synthesis, and suggest ways to use these elastic network models to develop more detailed mechanisms -an important possibility, since most experiments yield only static structures.

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“…Partial unfolding is involved in the operation of some protein machines. Although Gō-like models are often the first choice for such systems, and indeed used for a mechanical stretching survey of thousands of proteins [30], variants of ENMs can be also considered to reduce computational costs [31][32][33][34].…”

Section: Automated Screening and Its Applicationsmentioning

confidence: 99%

Screening for mechanical responses of proteins using coarse-grained elastic network models

Togashi

2016

NOLTA

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Abstract:Owing to recent progress in structural biology, the structures of a number of proteins have been resolved and deposited into databases such as the Protein Data Bank (PDB). Many proteins function as molecular machines or show allosteric effects, which require integrated communication between different parts of the protein structure; however, the general principles underlying such communication are not yet clear. All-atom molecular dynamics simulation is a powerful tool for tracking molecular motion, but is still far too computationally costly to be applied widely. In this paper, we present a simple method for assessing the properties of mechanical communications using coarse-grained steered molecular dynamics simulations with elastic network models. The method was tested by screening for a certain mechanical property among protein structures in the PDB.

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Elastic models of conformational transitions in macromolecules

Cited by 119 publications

References 14 publications

Coevolutionary signals across protein lineages help capture multiple protein conformations

Coevolutionary signals across protein lineages help capture multiple protein conformations

Packing Regularities in Biological Structures Relate to Their Dynamics

Screening for mechanical responses of proteins using coarse-grained elastic network models

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