A New Method for Coarse-Grained Elastic Normal-Mode Analysis

Lu, Mingyang; Poon, Billy K.; Ma, Jianpeng

doi:10.1021/ct050307u

Cited by 85 publications

(102 citation statements)

References 37 publications

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“…Interestingly, in order to achieve the amount of flexing of the anticodon stem-loop that is observed in the PCs, the ANM modes require the counterbalance of a significantly exaggerated motion of the acceptor arm (mode 1). While the acceptor arm may appear to have exaggerated motions here, similar to the "tip effect" noted by the Ma group (Lu et al 2006), nonetheless, its large motion does appear to meet the required deformation it undergoes when binding inside the ribosome. .…”

Section: Trnasupporting

confidence: 65%

Elastic network models capture the motions apparent within ensembles of RNA structures

Zimmermann

Jernigan

2014

RNA

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The role of structure and dynamics in mechanisms for RNA becomes increasingly important. Computational approaches using simple dynamics models have been successful at predicting the motions of proteins and are often applied to ribonucleoprotein complexes but have not been thoroughly tested for well-packed nucleic acid structures. In order to characterize a true set of motions, we investigate the apparent motions from 16 ensembles of experimentally determined RNA structures. These indicate a relatively limited set of motions that are captured by a small set of principal components (PCs). These limited motions closely resemble the motions computed from low frequency normal modes from elastic network models (ENMs), either at atomic or coarse-grained resolution. Various ENM model types, parameters, and structure representations are tested here against the experimental RNA structural ensembles, exposing differences between models for proteins and for folded RNAs. Differences in performance are seen, depending on the structure alignment algorithm used to generate PCs, modulating the apparent utility of ENMs but not significantly impacting their ability to generate functional motions. The loss of dynamical information upon coarse-graining is somewhat larger for RNAs than for globular proteins, indicating, perhaps, the lower cooperativity of the less densely packed RNA. However, the RNA structures show less sensitivity to the elastic network model parameters than do proteins. These findings further demonstrate the utility of ENMs and the appropriateness of their application to well-packed RNA-only structures, justifying their use for studying the dynamics of ribonucleo-proteins, such as the ribosome and regulatory RNAs.

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Section: Trnasupporting

confidence: 65%

Elastic network models capture the motions apparent within ensembles of RNA structures

Zimmermann

Jernigan

2014

RNA

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“…Presumably, this result is largely attributable to the elimination of structural shifts caused by initial energy minimization. Thus, we expect that modes calculated by the MNM will perform in normal-mode-based x-ray refinement better than those in our previous studies (29,36,37).…”

Section: Discussionmentioning

confidence: 71%

“…The most notable types include the rotations-translations of blocks (RTB) method (15) [also called block normal mode analysis (16)], allatom-derived coarse-grained methods (17, 18), and different variations of the elastic network model (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). The latter constructs the Hessian matrix from a highly simplistic Hamiltonian rather than a realistic molecular force field.…”

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confidence: 99%

A minimalist network model for coarse-grained normal mode analysis and its application to biomolecular x-ray crystallography

Ma²

2008

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

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In this article, we report a method for coarse-grained normal mode analysis called the minimalist network model. The main features of the method are that it can deliver accurate low-frequency modes on structures without undergoing initial energy minimization and that it also retains the details of molecular interactions. The method does not require any additional adjustable parameters after coarse graining and is computationally very fast. Tests on modeling the experimentally measured anisotropic displacement parameters in biomolecular x-ray crystallography demonstrate that the method can consistently perform better than other commonly used methods including our own one. We expect this method to be effective for applications such as structural refinement and conformational sampling.all-atom normal modes ͉ elastic network model ͉ energy minimization ͉ normal mode analysis ͉ x-ray refinement N ormal mode analysis is a powerful tool for describing the global, collective, and functional motions of protein complexes (1-5). In this approach, the potential energy function of a protein is assumed to be harmonic so that protein motions can be described as a linear combination of a set of independent harmonic modes. Typically, only a few lowest-frequency modes with the largest amplitudes are sufficient to account for a majority of experimentally observed conformational fluctuations.Conventional normal mode analysis requires the calculation of an all-atom second-derivative matrix, the Hessian matrix, by using typical molecular mechanics force fields such as CHARMM (6-8) or AMBER (9-12). Thus, it requires substantial computer memory and processing power to perform the matrix diagonalization, which becomes a severe bottleneck in studies of supramolecular complexes. Moreover, to satisfy the harmonic approximation, the conventional method requires a lengthy initial energy-minimization step.To reduce the computational cost, many types of coarse-grained normal mode analyses have been developed (5,13,14). The most notable types include the rotations-translations of blocks (RTB) method (15) [also called block normal mode analysis (16)], allatom-derived coarse-grained methods (17, 18), and different variations of the elastic network model (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29). The latter constructs the Hessian matrix from a highly simplistic Hamiltonian rather than a realistic molecular force field. Although elastic network models have been very successful, the quality of their modes is not optimal according to some recent studies, probably because of the usage of oversimplified Hamiltonians. In several reports that evaluated the use of low-frequency modes for interpreting crystallographic thermal parameters (28,30,31), the RTB approach with the CHARMM force field predicts anisotropic displacement parameters (ADPs) better than various elastic network models (28).Another limitation of the elastic network model is that it ignores molecular interaction details, which can be very important for certain cases (32). Also, in many a...

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“…1JSM). An improved method of elastic normal mode analysis was used for computing the C␣-based motional pattern (50). The reason for computing the modes based on the monomeric subunit instead of the trimeric HA is that the intrinsic deformational pattern of the individual subunit is more likely utilized in evolution to achieve different structures within the HA family (46,54).…”

Section: Methodsmentioning

confidence: 99%

Crystal Structure of Unliganded Influenza B Virus Hemagglutinin

Wang

Cheng

et al. 2008

J Virol

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123

141

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Here we report the crystal structure of hemagglutinin (HA) from influenza B/Hong Kong/8/73 (B/HK) virus determined to 2.8 Å. At a sequence identity of ϳ25% to influenza A virus HAs, B/HK HA shares a similar overall structure and domain organization. More than two dozen amino acid substitutions on influenza B virus HAs have been identified to cause antigenicity alteration in site-specific mutants, monoclonal antibody escape mutants, or field isolates. Mapping these substitutions on the structure of B/HK HA reveals four major epitopes, the 120 loop, the 150 loop, the 160 loop, and the 190 helix, that are located close in space to form a large, continuous antigenic site. Moreover, a systematic comparison of known HA structures across the entire influenza virus family reveals evolutionarily conserved ionizable residues at all regions along the chain and subunit interfaces. These ionizable residues are likely the structural basis for the pH dependence and sensitivity to ionic strength of influenza HA and hemagglutinin-esterase fusion proteins.Influenza virus is a negative-stranded RNA virus belonging to the Orthomyxoviridae family. Three types of influenza viruses have been detected: A, B, and C. They all have a major surface glycoprotein, hemagglutinin (HA) for influenza A and B viruses and hemagglutinin-esterase fusion (HEF) protein for influenza C virus (2,20,85,98). Infections caused by influenza A and B viruses remain a major source of human morbidity and mortality worldwide (13, 90).Influenza B virus circulates exclusively in humans and seals (66), with no subtypes. The first isolated influenza B virus strain was B/Lee/40 (44). Currently, there are two major phylogenetic lineages found in circulation: B/Victoria/2/87 (B/VI) like and B/Yamagata/16/88 (B/YM) like (41,77,83). Despite the lack of subtypes, influenza B virus undergoes antigenic variation through genetic reassortment among cocirculating strains of different lineages and antigenic drift from cumulative mutations (17,48,49,56,70,83,101). Influenza B virus HAs have a mutational rate about five times slower than that observed for influenza A virus HAs (3,10,15,19,34,41,44,45,81,94,101). The antigenic structures of influenza B virus HAs have been studied by sequence analysis of both naturally occurring variants and antibody-selected escape variants (5,6,10,35,44,45,51,76,77,94,96), using the structure of influenza A H3 HA as a reference (100). However, given the rather low sequence identity between influenza A and B virus HAs, ϳ20% for HA 1 , which is the primary target for antigenic variation, it is hard to define accurately the antigenic sites in this fashion.We have recently reported two crystal structures of influenza B/Hong Kong/8/73 virus HA (referred to as B/HK HA herein) in complex with human and avian receptor analogs (95). Comparison of these structures with those of influenza A virus HAs provided a structural basis for the receptor-binding properties of influenza A and B virus HAs and suggested the role of residue 222 (H3 numbering) as a key an...

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A New Method for Coarse-Grained Elastic Normal-Mode Analysis

Cited by 85 publications

References 37 publications

Elastic network models capture the motions apparent within ensembles of RNA structures

Elastic network models capture the motions apparent within ensembles of RNA structures

A minimalist network model for coarse-grained normal mode analysis and its application to biomolecular x-ray crystallography

Crystal Structure of Unliganded Influenza B Virus Hemagglutinin

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