An Analysis of Core Deformations in Protein Superfamilies

Leo‐Macias, Alejandra; López-Romero, Pedro; Lupyan, Dmitry; Zerbino, Daniel R.; Ortíz, Ángel R.

doi:10.1529/biophysj.104.052449

Cited by 113 publications

(104 citation statements)

References 41 publications

(42 reference statements)

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“…The overall structure of unliganded B/HK HA is very similar to the receptor-bound structures reported earlier (95). However, several regions [HA 1 (43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62), HA 1 (73)(74)(75)(76)(77)(78)(79), HA 1 (139-152), HA 1 (226-241), HA 1 (283-289), and HA 2 (15)(16)(17)(18)(19)(20)(21)] display large deviations between the unliganded and avian receptorbound structures and between the avian and human receptorbound structures when the full-length proteins are aligned (Fig. 1c).…”

Section: Resultssupporting

confidence: 75%

“…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%

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Crystal Structure of Unliganded Influenza B Virus Hemagglutinin

Wang

Cheng

et al. 2008

J Virol

118

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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Section: Resultssupporting

confidence: 75%

Section: Methodsmentioning

confidence: 99%

Crystal Structure of Unliganded Influenza B Virus Hemagglutinin

Wang

Cheng

et al. 2008

J Virol

118

141

View full text Add to dashboard Cite

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“…The overlap between the space spanned by the first I PCs and the first J low-frequency ENM modes was defined by the root mean square inner product in Leo-Macias et al (2005) and is shown here in Equation 5.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

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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“…Despite these drawbacks, ANM and related models can be successfully applied to a number of problems: interpolation methods to decompose proteins in structural domains (Zheng and Thirumalai, 2009), describing conformational transitions (Kim, 2002;Petrone and Pande, 2006;Zheng, 2008), describing structural differences between related proteins (Leo-Macias et al, 2005;Echave, 2008), analyzing the deformation of proteins under mechanical stress (Eyal and Bahar, 2008), and protein vibrational dynamics (Yang et al, 2009;Micheletti et al, 2004;Ming and Wall, 2005).…”

Section: Elastic Network Modelsmentioning

confidence: 99%

Automating the search of molecular motor templates by evolutionary methods

Fernández

Vico

2011

Biosystems

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Biological molecular motors are nanoscale devices capable of transforming chemical energy into mechanical work, which are being researched in many scientific disciplines. From a computational point of view, the characteristics and dynamics of these motors are studied at multiple time scales, ranging from very detailed and complex molecular dynamics simulations spanning a few microseconds, to extremely simple and coarse-grained theoretical models of their working cycles. However, this research is performed only in the (relatively few) instances known from molecular biology. In this work, results from elastic network analysis and behaviour-finding methods are applied to explore a subset of the configuration space of template molecular structures that are able to transform chemical energy into directed movement. While using methods based on elastic networks limits the scope of our results, it enables the implementation of computationally lightweight methods, in a way that evolutionary search techniques can be applied to discover novel molecular motor templates. The results show that molecular motion can be attained from a variety of structural configurations. Additionally, these methods enable a new computational way to test hypotheses about molecular motors.

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An Analysis of Core Deformations in Protein Superfamilies

Cited by 113 publications

References 41 publications

Crystal Structure of Unliganded Influenza B Virus Hemagglutinin

Crystal Structure of Unliganded Influenza B Virus Hemagglutinin

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

Automating the search of molecular motor templates by evolutionary methods

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