Vibrational dynamics of transfer RNAs: comparison of the free and synthetase-bound forms

A cholesterol analog stabilizes the human β2-adrenergic receptor nonlinearly with temperature

In cell membranes, G protein-coupled receptors (GPCRs) interact with cholesterol, which modulates their assembly, stability, and conformation. Previous studies have shown how cholesterol modulates the structural properties of GPCRs at ambient temperature. Here, we characterized the mechanical, kinetic, and energetic properties of the human β2-adrenergic receptor (β2AR) in the presence and absence of the cholesterol analog cholesteryl hemisuccinate (CHS) at room temperature (25°C), at physiological temperature (37°C), and at high temperature (42°C). We found that CHS stabilized various structural regions of β2AR differentially, which changed nonlinearly with temperature. Thereby, the strongest effects were observed for structural regions that are important for receptor signaling. Moreover, at 37°C, but not at 25° or 42°C, CHS caused β2AR to increase and stabilize conformational substates to adopt to basal activity. These findings indicate that the nonlinear, temperature-dependent action of CHS in modulating the structural and functional properties of this GPCR is optimized for 37°C.

Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0

New computational biophysics pipelines for analysing the global dynamics of structural ensembles and large, dynamic complexes resolved by cryoEM are reviewed.

Cryo-electron microscopy (cryoEM) has become a well established technique with the potential to produce structures of large and dynamic supramolecular complexes that are not amenable to traditional approaches for studying structure and dynamics. The size and low resolution of such molecular systems often make structural modelling and molecular dynamics simulations challenging and computationally expensive. This, together with the growing wealth of structural data arising from cryoEM and other structural biology methods, has driven a trend in the computational biophysics community towards the development of new pipelines for analysing global dynamics using coarse-grained models and methods. At the centre of this trend has been a return to elastic network models, normal mode analysis (NMA) and ensemble analyses such as principal component analysis, and the growth of hybrid simulation methodologies that make use of them. Here, this field is reviewed with a focus on ProDy, the Python application programming interface for protein dynamics, which has been developed over the last decade. Two key developments in this area are highlighted: (i) ensemble NMA towards extracting and comparing the signature dynamics of homologous structures, aided by the recent SignDy pipeline, and (ii) pseudoatom fitting for more efficient global dynamics analyses of large and low-resolution supramolecular assemblies from cryoEM, revisited in the CryoDy pipeline. It is believed that such a renewal and extension of old models and methods in new pipelines will be critical for driving the field forward into the next cryoEM revolution.

Low-Frequency Harmonic Perturbations Drive Protein Conformational Changes

Protein dynamics has been investigated since almost half a century, as it is believed to constitute the fundamental connection between structure and function. Elastic network models (ENMs) have been widely used to predict protein dynamics, flexibility and the biological mechanism, from which remarkable results have been found regarding the prediction of protein conformational changes. Starting from the knowledge of the reference structure only, these conformational changes have been usually predicted either by looking at the individual mode shapes of vibrations (i.e., by considering the free vibrations of the ENM) or by applying static perturbations to the protein network (i.e., by considering a linear response theory). In this paper, we put together the two previous approaches and evaluate the complete protein response under the application of dynamic perturbations. Harmonic forces with random directions are applied to the protein ENM, which are meant to simulate the single frequency-dependent components of the collisions of the surrounding particles, and the protein response is computed by solving the dynamic equations in the underdamped regime, where mass, viscous damping and elastic stiffness contributions are explicitly taken into account. The obtained motion is investigated both in the coordinate space and in the sub-space of principal components (PCs). The results show that the application of perturbations in the low-frequency range is able to drive the protein conformational change, leading to remarkably high values of direction similarity. Eventually, this suggests that protein conformational change might be triggered by external collisions and favored by the inherent low-frequency dynamics of the protein structure.

Oncogenic mutations on Rac1 affect global intrinsic dynamics underlying GTP and PAK1 binding

Rac1 is a small member of the Rho GTPase family. One of the most important downstream effectors of Rac1 is a serine/threonine kinase, p21-activated kinase 1 (PAK1). Mutational activation of PAK1 by Rac1 has oncogenic signaling effects. Here, although we focus on Rac1-PAK1 interaction by atomic-force-microscopy-based single-molecule force spectroscopy experiments, we explore the effect of active mutations on the intrinsic dynamics and binding interactions of Rac1 by Gaussian network model analysis and molecular dynamics simulations. We observe that Rac1 oncogenic mutations are at the hinges of three global modes of motion, suggesting the mechanical changes as potential markers of oncogenicity. Indeed, the dissociation of wild-type Rac1-PAK1 complex shows two distinct unbinding dynamic states that are reduced to one with constitutively active Q61L and oncogenic Y72C mutant Rac1, as revealed by single-molecule force spectroscopy experiments. Q61L and Y72C mutations change the mechanics of the Rac1-PAK1 complex by increasing the elasticity of the protein and slowing down the transition to the unbound state. On the other hand, Rac1's intrinsic dynamics reveal more flexible GTP and PAK1-binding residues on switches I and II with Q61L, Y72C, oncogenic P29S and Q61R, and negative T17N mutations. The cooperativity in the fluctuations of GTP-binding sites around the p-loop and switch I decreases in all mutants, mostly in Q61L, whereas some PAK1-binding residues display enhanced coupling with GTP-binding sites in Q61L and Y72C and within each other in P29S. The predicted binding free energies of the modeled Rac1-PAK1 complexes show that the change in the dynamic behavior likely means a more favorable PAK1 interaction. Overall, these findings suggest that the active mutations affect intrinsic functional dynamic events and alter the mechanics underlying the binding of Rac1 to GTP and upstream and downstream partners including PAK1.

Computational Ways to Enhance Protein Inhibitor Design

Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials.

Local and Global Motions Underlying Antibiotic Binding in Bacterial Ribosome

The bacterial ribosome is one of the most important targets in the treatment of infectious diseases. As antibiotic resistance in bacteria poses a growing threat, a significant amount of effort is concentrated on exploring new drug-binding sites where testable predictions are of significance. Here, we study the dynamics of a ribosomal complex and 67 small and large subunits of the ribosomal crystal structures (64 antibiotic-bound, 3 antibiotic-free) from Deinococcus radiodurans, Escherichia coli, Haloarcula marismortui, and Thermus thermophilus by the Gaussian network model. Interestingly, a network of nucleotides coupled in high-frequency fluctuations reveals known antibiotic-binding sites. These sites are seen to locate at the interface of dynamic domains that have an intrinsic dynamic capacity to interfere with functional globular motions. The nucleotides and the residues fluctuating in the fast and slow modes of motion thus have promise for plausible antibiotic-binding and allosteric sites that can alter antibiotic binding and resistance. Overall, the present analysis brings a new dynamic perspective to the long-discussed link between small-molecule binding and large conformational changes of the supramolecule.

Computational Analysis of Dynamical Fluctuations of Oncoprotein E7 (HPV 16) for the Hot Spot Residue Identification Using Elastic Network Model

Aims:

To find out Potential Drug targets against HPV E7.

Background:

Oncoprotein E7 of Human Papilloma Virus (HPV-16), after invading human body alter host protein-protein interaction networks caused by the fluctuations of amino acid residues present in E7. E7 interacts with Rb protein of human host with variable residual fluctuations, leading towards the progression of cervical cancer.

Objective:

Our study was focused our computational analysis of the binding and competing interactions of the E7 protein of HPV with Rb protein.

Methods:

Our study is based on analysis of dynamic fluctuations of E7 in host cell and correlation analysis of specific residue found in motif of LxCxE, that is the key region in stabilizing interaction between E7 and Rb.

Results and Discussion:

Cysteine, Leucine and Glutamic acid have been identified as hot spot residues of E7 which can provide platform for drug designing and understanding of pathogenesis of cervical cancer, in future. Our study shows validation of the vitality of linear binding motifs LxCxE of E7 of HPV in interacting with Rb as an important event in propagation of HPV in human cells and transformation of infection into cervical cancer.

Conclusion:

Our study shows validation of the vitality of linear binding motifs LxCxE of E7 of HPV in interacting with Rb as an important event in propagation of HPV in human cells and transformation of infection into cervical cancer.

Other:

E7 interacts with Rb protein of human host with variable residual fluctuations, leading towards the progression of cervical cancer.

A study on allosteric communication in U1A-snRNA binding interactions: network analysis combined with molecular dynamics data

The allosteric regulation during the binding interactions between small nuclear RNAs (snRNAs) and the associated protein factors is critical to the function of spliceosomes in alternative RNA splicing. Although network models combined with molecular dynamics simulations have shown to be powerful tools for the analysis of protein allostery, the atomic-level simulations are, however, too expensive and with limited accuracy for the large-size systems. In this work, we use a residual network model combined with a coarse-grained Gaussian network model (GNM) to investigate the binding interactions between the snRNA and the human U1A protein which is a major component of the spliceosomal U1 small nuclear ribonucleoprotein particle, and to identify the residues that play an important role in the allosteric communication in U1A during this process. We also utilize the Girvan-Newman method to detect the structural organization in U1A-snRNA recognition and interactions. Our results reveal that: (Ι) not only the residues at the binding sites that are traditionally considered to play a major role in U1A-snRNA association, but those residues that are far away from the RNA binding interface participate in the U1A's allosteric signal transmission induced by the RNA binding; (Π) the structure of U1A protein is well organized with different communities acting different roles for its RNA binding and allosteric regulation. The study demonstrates that the combination of the residual network and elastic network models is an effective and efficient method which can be readily extended to the investigation of the allosteric communication for other macromolecular interaction systems.

Breaking a single hydrogen bond in the mitochondrial tRNAPhe -PheRS complex leads to phenotypic pleiotropy of human disease

Various pathogenic variants in both mitochondrial tRNA^Phe and Phenylalanyl‐tRNA synthetase mitochondrial protein coding gene (FARS2) gene encoding for the human mitochondrial PheRS have been identified and associated with neurological and/or muscle‐related pathologies. An important Guanine‐34 (G34)A anticodon mutation associated with myoclonic epilepsy with ragged red fibers (MERRF) syndrome has been reported in hmit‐tRNA^Phe. The majority of G34 contacts in available aaRSs‐tRNAs complexes specifically use that base as an important tRNA identity element. The network of intermolecular interactions providing its specific recognition also largely conserved. However, their conservation depends also on the invariance of the residues in the anticodon binding domain (ABD) of human mitochondrial Phenylalanyl‐tRNA synthetase (hmit‐PheRS). A defect in recognition of the anticodon of tRNA^Phe may happen not only because of G34A mutation, but also due to mutations in the ABD. Indeed, a pathogenic mutation in FARS2 has been recently reported in a 9‐year‐old female patient harboring a p.Asp364Gly mutation. Asp364 is hydrogen bonded (HB) to G34 in WT hmit‐PheRS. Thus, there are two pathogenic variants disrupting HB between G34 and Asp364: one is associated with G34A mutation, and the other with Asp364Gly mutation. We have measured the rates of tRNA^Phe aminoacylation catalyzed by WT hmit‐PheRS and mutant enzymes. These data ranked the residues making a HB with G34 according to their contribution to activity and the signal transduction pathway in the hmit‐PheRS‐tRNA^Phe complex. Furthermore, we carried out extensive MD simulations to reveal the interdomain contact topology on the dynamic trajectories of the complex, and gaining insight into the structural and dynamic integrity effects of hmit‐PheRS complexed with tRNA^Phe.

Database

Structural data are available in PDB database under the accession number(s): 3CMQ, 3TUP, 5MGH, 5MGV.

Interpreting the Dynamics of Binding Interactions of snRNA and U1A Using a Coarse-Grained Model

The binding interactions of small nuclear RNAs (snRNA) and the associated protein factors are critical to the function of spliceosomes in alternatively splicing primary RNA transcripts. Although molecular dynamics simulations are a powerful tool to interpret the mechanism of biological processes, the atomic-level simulations are, however, too expensive and with limited accuracy for the large-size systems, such as snRNA-protein complexes. We extend the coarse-grained Gaussian network model, which models the RNA-protein complexes as a harmonic chain of Cα, P, and O4' atoms, to investigating the impact of the snRNA-binding interaction on the dynamic stability of the human U1A protein, which is a major component of the spliceosomal U1 small nuclear ribonucleoprotein particle. The results reveal that the first and third loops and the C-terminal helix regions of the U1A domain undergo a significant loss of flexibility upon the RNA binding due to the forming of mostly electrostatic and hydrogen bond interactions with RNA 5' stem and loop. By examining the residues whose mutations significantly change the binding free energy between U1A and snRNA, the Gaussian network model-based calculations show that not only the residues at the binding sites that are traditionally considered to play a major role in U1A-RNA association but also those residues that are far away from the RNA-binding interface can participate in the long-range allosteric signal transmission; these calculations are quantitatively consistent with the data observed in the recent snRNA binding experiments. The study demonstrates a useful avenue to utilize the simplified elastic network model to investigate the dynamics characteristics of the biologically important macromolecular interactions.

Barnaba: software for analysis of nucleic acid structures and trajectories

RNA molecules are highly dynamic systems characterized by a complex interplay between sequence, structure, dynamics, and function. Molecular simulations can potentially provide powerful insights into the nature of these relationships. The analysis of structures and molecular trajectories of nucleic acids can be nontrivial because it requires processing very high-dimensional data that are not easy to visualize and interpret. Here we introduce Barnaba, a Python library aimed at facilitating the analysis of nucleic acid structures and molecular simulations. The software consists of a variety of analysis tools that allow the user to (i) calculate distances between three-dimensional structures using different metrics, (ii) back-calculate experimental data from three-dimensional structures, (iii) perform cluster analysis and dimensionality reductions, (iv) search three-dimensional motifs in PDB structures and trajectories, and (v) construct elastic network models for nucleic acids and nucleic acids-protein complexes. In addition, Barnaba makes it possible to calculate torsion angles, pucker conformations, and to detect base-pairing/base-stacking interactions. Barnaba produces graphics that conveniently visualize both extended secondary structure and dynamics for a set of molecular conformations. The software is available as a command-line tool as well as a library, and supports a variety of file formats such as PDB, dcd, and xtc files. Source code, documentation, and examples are freely available at https://github.com/srnas/barnaba under GNU GPLv3 license.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

Optimization of a novel biophysical model using large scale in vivo antisense hybridization data displays improved prediction capabilities of structurally accessible RNA regions

Current approaches to design efficient antisense RNAs (asRNAs) rely primarily on a thermodynamic understanding of RNA–RNA interactions. However, these approaches depend on structure predictions and have limited accuracy, arguably due to overlooking important cellular environment factors. In this work, we develop a biophysical model to describe asRNA–RNA hybridization that incorporates in vivo factors using large-scale experimental hybridization data for three model RNAs: a group I intron, CsrB and a tRNA. A unique element of our model is the estimation of the availability of the target region to interact with a given asRNA using a differential entropic consideration of suboptimal structures. We showcase the utility of this model by evaluating its prediction capabilities in four additional RNAs: a group II intron, Spinach II, 2-MS2 binding domain and glgC 5΄ UTR. Additionally, we demonstrate the applicability of this approach to other bacterial species by predicting sRNA–mRNA binding regions in two newly discovered, though uncharacterized, regulatory RNAs.

Understanding the Relative Flexibility of RNA and DNA Duplexes: Stretching and Twist-Stretch Coupling

The flexibility of double-stranded (ds) RNA and dsDNA is crucial for their biological functions. Recent experiments have shown that the flexibility of dsRNA and dsDNA can be distinctively different in the aspects of stretching and twist-stretch coupling. Although various studies have been performed to understand the flexibility of dsRNA and dsDNA, there is still a lack of deep understanding of the distinctive differences in the flexibility of dsRNA and dsDNA helices as pertains to their stretching and twist-stretch coupling. In this work, we have explored the relative flexibility in stretching and twist-stretch coupling between dsRNA and dsDNA by all-atom molecular dynamics simulations. The calculated stretch modulus and twist-stretch coupling are in good accordance with the existing experiments. Our analyses show that the differences in stretching and twist-stretch coupling between dsRNA and dsDNA helices are mainly attributed to their different (A- and B-form) helical structures. Stronger basepair inclination and slide in dsRNA is responsible for the apparently weaker stretching rigidity versus that of dsDNA, and the opposite twist-stretch coupling for dsRNA and dsDNA is also attributed to the stronger basepair inclination in dsRNA than in dsDNA. Our calculated macroscopic elastic parameters and microscopic analyses are tested and validated by different force fields for both dsRNA and dsDNA.

Intrinsic Dynamics Analysis of a DNA Octahedron by Elastic Network Model

DNA is a fundamental component of living systems where it plays a crucial role at both functional and structural level. The programmable properties of DNA make it an interesting building block for the construction of nanostructures. However, molecular mechanisms for the arrangement of these well-defined DNA assemblies are not fully understood. In this paper, the intrinsic dynamics of a DNA octahedron has been investigated by using two types of Elastic Network Models (ENMs). The application of ENMs to DNA nanocages include the analysis of the intrinsic flexibilities of DNA double-helices and hinge sites through the calculation of the square fluctuations, as well as the intrinsic collective dynamics in terms of cross-collective map calculation coupled with global motions analysis. The dynamics profiles derived from ENMs have then been evaluated and compared with previous classical molecular dynamics simulation trajectories. The results presented here revealed that ENMs can provide useful insights into the intrinsic dynamics of large DNA nanocages and represent a useful tool in the field of structural DNA nanotechnology.

Relating the Structure of HIV-1 Reverse Transcriptase to Its Processing Step

Abstract By treating an enzyme as a coarse-grained uniform block of material, utilizing only the α-Carbon positions, the normal modes of motion can be obtained. For reverse transcriptase the slower of these motions are suggestive of being involved in the processing step, where the RNA or DNA strand is copied onto a new DNA strand at a polymerase site, and the RNA strand is subsequently cut up at the distant Ribonuclease H site. The slowest mode of motion involves hinge bending about a site midway between the polymerase and Ribonuclease H sites, suggesting that it can push or pull the RNA strand between these two sites. Pulling the nucleic acid strand would require tight binding to the RNase H site. The next slowest mode involves a hinge that opens and closes the protein like a clamp, which could facilitate the release of the nucleic acids for their step-wise progression. The third mode could rotate the substrate. An overall description of the step-wise processing step would involve close coordination among these steps. Results suggest that the smaller p51 subunit serves only as ballast to support the various modes of motion involving the different parts of the p66 subunit.

Conservation of structural fluctuations in homologous protein kinases and its implications on functional sites

Our aim is to explore the similarities in structural fluctuations of homologous kinases. Gaussian Network Model based Normal Mode Analysis was performed on 73 active conformation structures in Ser/Thr/Tyr kinase superfamily. Categories of kinases with progressive evolutionary divergence, viz. (i) Same kinase with many crystal structures, (ii) Within-Subfamily, (iii) Within-Family, (iv) Within-Group, and (v) Across-Group, were analyzed. We identified a flexibility signature conserved in all kinases involving residues in and around the catalytic loop with consistent low-magnitude fluctuations. However, the overall structural fluctuation profiles are conserved better in closely related kinases (Within-Subfamily and Within-family) than in distant ones (Within-Group and Across-Group). A substantial 65.4% of variation in flexibility was not accounted by variation in sequences or structures. Interestingly, we identified substructural residue-wise fluctuation patterns characteristic of kinases of different categories. Specifically, we recognized statistically significant fluctuations unique to families of protein kinase A, cyclin-dependent kinases, and nonreceptor tyrosine kinases. These fluctuation signatures localized to sites known to participate in protein-protein interactions typical of these kinase families. We report for the first time that residues characterized by fluctuations unique to the group/family are involved in interactions specific to the group/family. As highlighted for Src family, local regions with differential fluctuations are proposed as attractive targets for drug design. Overall, our study underscores the importance of consideration of fluctuations, over and above sequence and structural features, in understanding the roles of sites characteristic of kinases. Proteins 2016; 84:957-978. © 2016 Wiley Periodicals, Inc.

Ribosome Mechanics Informs about Mechanism

The essential aspects of the ribosome's mechanism can be extracted from coarse-grained simulations, including the ratchet motion, the movement together of critical bases at the decoding center, and movements of the peptide tunnel lining that assist in the expulsion of the synthesized peptide. Because of its large size, coarse graining helps to simplify and to aid in the understanding of its mechanism. Results presented here utilize coarse-grained elastic network modeling to extract the dynamics, and both RNAs and proteins are coarse grained. We review our previous results, showing the well-known ratchet motions and the motions in the peptide tunnel and in the mRNA tunnel. The motions of the lining of the peptide tunnel appear to assist in the expulsion of the growing peptide chain, and clamps at the ends of the mRNA tunnel with three proteins ensure that the mRNA is held tightly during decoding and essential for the helicase activity at the entrance. The entry clamp may also assist in base recognition to ensure proper selection of the incoming tRNA. The overall precision of the ribosome machine-like motions is remarkable.

Computing the Amino Acid Specificity of Fluctuations in Biomolecular Systems

We developed a new amino acid specific method for the computation of spatial fluctuations of proteins around their native structures. We show the consistency with experimental values and the increased performance in comparison to an established model, based on statistical estimates for a set of test proteins. We apply the new method to HIV-1 protease in its wild-type form and to a V82F-I84V mutant that shows resistance to protease inhibitors. We further show how the method can be successfully used to explain the molecular biophysics of drug resistance of the mutant.

Elastic network models for RNA: a comparative assessment with molecular dynamics and SHAPE experiments

Elastic network models (ENMs) are valuable and efficient tools for characterizing the collective internal dynamics of proteins based on the knowledge of their native structures. The increasing evidence that the biological functionality of RNAs is often linked to their innate internal motions poses the question of whether ENM approaches can be successfully extended to this class of biomolecules. This issue is tackled here by considering various families of elastic networks of increasing complexity applied to a representative set of RNAs. The fluctuations predicted by the alternative ENMs are stringently validated by comparison against extensive molecular dynamics simulations and SHAPE experiments. We find that simulations and experimental data are systematically best reproduced by either an all-atom or a three-beads-per-nucleotide representation (sugar-base-phosphate), with the latter arguably providing the best balance of accuracy and computational complexity.

Allosteric Dynamic Control of Binding

Proteins have a highly dynamic nature and there is a complex interrelation between their structural dynamics and binding behavior. By assuming various conformational ensembles, they perform both local and global fluctuations to interact with other proteins in a dynamic infrastructure adapted to functional motion. Here, we show that there is a significant association between allosteric mutations, which lead to high-binding-affinity changes, and the hinge positions of global modes, as revealed by a large-scale statistical analysis of data in the Structural Kinetic and Energetic Database of Mutant Protein Interactions (SKEMPI). We further examined the mechanism of allosteric dynamics by conducting studies on human growth hormone (hGH) and pyrin domain (PYD), and the results show how mutations at the hinge regions could allosterically affect the binding-site dynamics or induce alternative binding modes by modifying the ensemble of accessible conformations. The long-range dissemination of perturbations in local chemistry or physical interactions through an impact on global dynamics can restore the allosteric dynamics. Our findings suggest a mechanism for the coupling of structural dynamics to the modulation of protein interactions, which remains a critical phenomenon in understanding the effect of mutations that lead to functional changes in proteins.

A Gaussian network model study suggests that structural fluctuations are higher for inactive states than active states of protein kinases

Protein kinases participate extensively in cellular signalling. Using Gaussian normal mode analysis of kinases in active and diverse inactive forms, authors show that structural fluctuations are significantly higher in inactive forms and are localized in functionally sensitive sites.

Lightweight object oriented structure analysis: tools for building tools to analyze molecular dynamics simulations

LOOS (Lightweight Object Oriented Structure-analysis) is a C++ library designed to facilitate making novel tools for analyzing molecular dynamics simulations by abstracting out the repetitive tasks, allowing developers to focus on the scientifically relevant part of the problem. LOOS supports input using the native file formats of most common biomolecular simulation packages, including CHARMM, NAMD, Amber, Tinker, and Gromacs. A dynamic atom selection language based on the C expression syntax is included and is easily accessible to the tool-writer. In addition, LOOS is bundled with over 140 prebuilt tools, including suites of tools for analyzing simulation convergence, three-dimensional histograms, and elastic network models. Through modern C++ design, LOOS is both simple to develop with (requiring knowledge of only four core classes and a few utility functions) and is easily extensible. A python interface to the core classes is also provided, further facilitating tool development.

Topological constraints are major determinants of tRNA tertiary structure and dynamics and provide basis for tertiary folding cooperativity

Recent studies have shown that basic steric and connectivity constraints encoded at the secondary structure level are key determinants of 3D structure and dynamics in simple two-way RNA junctions. However, the role of these topological constraints in higher order RNA junctions remains poorly understood. Here, we use a specialized coarse-grained molecular dynamics model to directly probe the thermodynamic contributions of topological constraints in defining the 3D architecture and dynamics of transfer RNA (tRNA). Topological constraints alone restrict tRNA's allowed conformational space by over an order of magnitude and strongly discriminate against formation of non-native tertiary contacts, providing a sequence independent source of folding specificity. Topological constraints also give rise to long-range correlations between the relative orientation of tRNA's helices, which in turn provides a mechanism for encoding thermodynamic cooperativity between distinct tertiary interactions. These aspects of topological constraints make it such that only several tertiary interactions are needed to confine tRNA to its native global structure and specify functionally important 3D dynamics. We further show that topological constraints are conserved across tRNA's different naturally occurring secondary structures. Taken together, our results emphasize the central role of secondary-structure-encoded topological constraints in defining RNA 3D structure, dynamics and folding.

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

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 ribonucleo-protein 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.

Specific non-local interactions are not necessary for recovering native protein dynamics

The elastic network model (ENM) is a widely used method to study native protein dynamics by normal mode analysis (NMA). In ENM we need information about all pairwise distances, and the distance between contacting atoms is restrained to the native value. Therefore ENM requires O(N²) information to realize its dynamics for a protein consisting of N amino acid residues. To see if (or to what extent) such a large amount of specific structural information is required to realize native protein dynamics, here we introduce a novel model based on only O(N) restraints. This model, named the ‘contact number diffusion’ model (CND), includes specific distance restraints for only local (along the amino acid sequence) atom pairs, and semi-specific non-local restraints imposed on each atom, rather than atom pairs. The semi-specific non-local restraints are defined in terms of the non-local contact numbers of atoms. The CND model exhibits the dynamic characteristics comparable to ENM and more correlated with the explicit-solvent molecular dynamics simulation than ENM. Moreover, unrealistic surface fluctuations often observed in ENM were suppressed in CND. On the other hand, in some ligand-bound structures CND showed larger fluctuations of buried protein atoms interacting with the ligand compared to ENM. In addition, fluctuations from CND and ENM show comparable correlations with the experimental B-factor. Although there are some indications of the importance of some specific non-local interactions, the semi-specific non-local interactions are mostly sufficient for reproducing the native protein dynamics.

Elastic Network Models of Nucleic Acids Flexibility

Elastic network models (ENMs) are a useful tool for describing large scale motions in protein systems. While they are well validated in the context of proteins, relatively little is known about their applicability to nucleic acids, whose different architecture does not necessarily warrant comparable performance. In this study we thoroughly evaluate and optimize the efficiency of popular ENMs for capturing RNA and DNA flexibility. We also introduce two alternative models in which the strength of elastic connections at a coarse-grained level is governed by distance distribution at atomic resolution. For each of the considered ENMs we report the optimal length of spring connections as well as the scaling of elastic force constants that provides the best agreement of vibrational frequencies with normal modes based on atomic force field. In order to determine the absolute values of force constants we introduce a novel method based on the overlap of pseudoinverse of Hessian matrices.

The elastic network model reveals a consistent picture on intrinsic functional dynamics of type II restriction endonucleases

The vibrational dynamics of various type II restriction endonucleases, in complex with cognate/non-cognate DNA and in the apo form, are investigated with the elastic network model in order to reveal common functional mechanisms in this enzyme family. Scissor-like and tong-like motions observed in the slowest modes of all enzymes and their complexes point to common DNA recognition and cleavage mechanisms. Normal mode analysis further points out that the scissor-like motion has an important role in differentiating between cognate and non-cognate sequences at the recognition site, thus implying its catalytic relevance. Flexible regions observed around the DNA-binding site of the enzyme usually concentrate on the highly conserved β-strands, especially after DNA binding. These β-strands may have a structurally stabilizing role in functional dynamics for target site recognition and cleavage. In addition, hot spot residues based on high-frequency modes reveal possible communication pathways between the two distant cleavage sites in the enzyme family. Some of these hot spots also exist on the shortest path between the catalytic sites and are highly conserved.

Enhanced dynamics of HIV gp120 glycoprotein by small molecule binding

HIV cell entry and infection are driven by binding events to the CD4 and chemokine receptors with associated conformational change of the viral glycoprotein, gp120. Scyllatoxin miniprotein CD4 mimetics and a small molecule inhibitor of CD4 binding, NBD-556, also effectively induce gp120 conformational change. In this study we examine the fluctuation profile of gp120 in context of CD4, a miniprotein mimetic, and NBD-556 with the aim of understanding the effect of ligand binding on gp120 conformational dynamics. Analysis of molecular dynamics trajectories indicate that NBD-556 binding in the Phe 43 cavity enhances the overall mobility of gp120, especially in the outer domain in comparison to CD4 or miniprotein bound complex. Interactions with the more flexible bridging sheet strengthen upon NBD-556 binding and may contribute to gp120 restructuring. The enhanced mobility of D368, E370, and I371 with NBD-556 bound in the Phe 43 cavity suggests that interactions with α3-helix in the outer domain are not optimal, providing further insights into gp120--small molecule interactions that may impact small molecule designs.

Fluctuation dynamics analysis of gp120 envelope protein reveals a topologically based communication network

Human Immunodeficiency Virus (HIV) infection is initiated by binding of the viral glycoprotein gp120, to the cellular receptor CD4. On CD4 binding, gp120 undergoes conformational change, permitting binding to the chemokine receptor. Crystal structures of gp120 ternary complex reveal the CD4 bound conformation of gp120. We report here the application of the Gaussian network model (GNM) to the crystal structures of gp120 bound to CD4 or CD4 mimic and 17b, to study the collective motions of the gp120 core and determine the communication propensities of the residue network. The GNM fluctuation profiles identify residues in the inner domain and outer domain that may facilitate conformational change or stability, respectively. Communication propensities delineate a residue network that is topologically suited for signal propagation from the Phe43 cavity throughout the gp120 outer domain. These results provide a new context for interpreting gp120 core envelope structure-function relationships.

Group theory and biomolecular conformation: I. Mathematical and computational models

Biological macromolecules, and the complexes that they form, can be described in a variety of ways ranging from quantum mechanical and atomic chemical models, to coarser grained models of secondary structure and domains, to continuum models. At each of these levels, group theory can be used to describe both geometric symmetries and conformational motion. In this survey, a detailed account is provided of how group theory has been applied across computational structural biology to analyze the conformational shape and motion of macromolecules and complexes.

Experimental and computational determination of tRNA dynamics

As the molecular representation of the genetic code, tRNA plays a central role in the translational machinery where it interacts with several proteins and other RNAs during the course of protein synthesis. These interactions exploit the dynamic flexibility of tRNA. In this minireview, we discuss the effects of modified bases, ions, and proteins on tRNA structure and dynamics and the challenges of observing its motions over the cycle of translation.

Focused functional dynamics of supramolecules by use of a mixed-resolution elastic network model

The mixed-resolution elastic network model was introduced previously for computing the motions of a structure, which is described at different levels of detail in different parts, for example, with atomistic and residue-level regions. This method has proved to be an efficient tool to explore the collective dynamics of proteins with some atomistic details, which would be difficult to obtain with either conventional full-atom approaches or fully coarse-grained models. Understanding function often requires atomic detail, but not necessarily for the entire structure. In this study, the calculation of the interaction forces between different resolution regions for the hierarchical levels of coarse-graining is further elaborated on in the new approach by considering explicitly the atomic contacts in the crystal structure. The collective dynamics of the enzyme triosephosphate isomerase and its active site together with loop 6 motions are considered in detail. The supramolecular assemblage ribosome and local atomic motions in its "interesting" functional part-the decoding center-are investigated for the low frequency range of the spectrum with high computational efficiency. This new atom-based mixed coarse-graining approach can be effectively used to generate realistic high-resolution conformations of extremely large protein-DNA or RNA complexes by performing energy minimization on structures deformed along the normal modes of the elastic network model. The new model permits focusing on specific functional parts that move in coordination and response to the remainder of the entire structure.

Global motions of the nuclear pore complex: insights from elastic network models

The nuclear pore complex (NPC) is the gate to the nucleus. Recent determination of the configuration of proteins in the yeast NPC at approximately 5 nm resolution permits us to study the NPC global dynamics using coarse-grained structural models. We investigate these large-scale motions by using an extended elastic network model (ENM) formalism applied to several coarse-grained representations of the NPC. Two types of collective motions (global modes) are predicted by the ENMs to be intrinsically favored by the NPC architecture: global bending and extension/contraction from circular to elliptical shapes. These motions are shown to be robust against tested variations in the representation of the NPC, and are largely captured by a simple model of a toroid with axially varying mass density. We demonstrate that spoke multiplicity significantly affects the accessible number of symmetric low-energy modes of motion; the NPC-like toroidal structures composed of 8 spokes have access to highly cooperative symmetric motions that are inaccessible to toroids composed of 7 or 9 spokes. The analysis reveals modes of motion that may facilitate macromolecular transport through the NPC, consistent with previous experimental observations.

A coarse-grained protein force field for folding and structure prediction

We have revisited the protein coarse-grained optimized potential for efficient structure prediction (OPEP). The training and validation sets consist of 13 and 16 protein targets. Because optimization depends on details of how the ensemble of decoys is sampled, trial conformations are generated by molecular dynamics, threading, greedy, and Monte Carlo simulations, or taken from publicly available databases. The OPEP parameters are varied by a genetic algorithm using a scoring function which requires that the native structure has the lowest energy, and the native-like structures have energy higher than the native structure but lower than the remote conformations. Overall, we find that OPEP correctly identifies 24 native or native-like states for 29 targets and has very similar capability to the all-atom discrete optimized protein energy model (DOPE), found recently to outperform five currently used energy models.

Protein elastic network models and the ranges of cooperativity

Elastic network models (ENMs) are entropic models that have demonstrated in many previous studies their abilities to capture overall the important internal motions, with comparisons having been made against crystallographic B-factors and NMR conformational variabilities. ENMs have become an increasingly important tool and have been widely used to comprehend protein dynamics, function, and even conformational changes. However, reliance upon an arbitrary cutoff distance to delimit the range of interactions has presented a drawback for these models, because the optimal cutoff values can differ somewhat from protein to protein and can lead to quirks such as some shuffling in the order of the normal modes when applied to structures that differ only slightly. Here, we have replaced the requirement for a cutoff distance and introduced the more physical concept of inverse power dependence for the interactions, with a set of elastic network models that are parameter-free, with the distance cutoff removed. For small fluctuations about the native forms, the power dependence is the inverse square, but for larger deformations, the power dependence may become inverse 6th or 7th power. These models maintain and enhance the simplicity and generality of the original ENMs, and at the same time yield better predictions of crystallographic B-factors (both isotropic and anisotropic) and of the directions of conformational transitions. Thus, these parameter-free ENMs can be models of choice whenever elastic network models are used.

Constraint counting on RNA structures: linking flexibility and function

RNA structures are highly flexible biomolecules that can undergo dramatic conformational changes required to fulfill their diverse functional roles. Constraint counting on a topological network representation of an RNA structure can provide very efficiently detailed insights into the intrinsic flexibility characteristics of the biomolecule. In the network, vertices represent atoms and edges represent covalent and strong non-covalent bonds and angle constraints. Initially, the method has been successfully applied to identify rigid and flexible regions in proteins. Here, we present recent progress in extending the approach to RNA structures. As a case study, we analyze stability characteristics of the ribosomal exit tunnel and relate these findings to the tunnel's active role in co-translational processes.

The ribosome structure controls and directs mRNA entry, translocation and exit dynamics

The protein-synthesizing ribosome undergoes large motions to effect the translocation of tRNAs and mRNA; here, the domain motions of this system are explored with a coarse-grained elastic network model using normal mode analysis. Crystal structures are used to construct various model systems of the 70S complex with/without tRNA, elongation factor Tu and the ribosomal proteins. Computed motions reveal the well-known ratchet-like rotational motion of the large subunits, as well as the head rotation of the small subunit and the high flexibility of the L1 and L7/L12 stalks, even in the absence of ribosomal proteins. This result indicates that these experimentally observed motions during translocation are inherently controlled by the ribosomal shape and only partially dependent upon GTP hydrolysis. Normal mode analysis further reveals the mobility of A- and P-tRNAs to increase in the absence of the E-tRNA. In addition, the dynamics of the E-tRNA is affected by the absence of the ribosomal protein L1. The mRNA in the entrance tunnel interacts directly with helicase proteins S3 and S4, which constrain the mRNA in a clamp-like fashion, as well as with protein S5, which likely orients the mRNA to ensure correct translation. The ribosomal proteins S7, S11 and S18 may also be involved in assuring translation fidelity by constraining the mRNA at the exit site of the channel. The mRNA also interacts with the 16S 3' end forming the Shine-Dalgarno complex at the initiation step; the 3' end may act as a 'hook' to reel in the mRNA to facilitate its exit.

Effects of protein subunits removal on the computed motions of partial 30S structures of the ribosome

The Anisotropic Network Model (ANM) is used to study motions of the 30S small ribosomal subunit. The effect of the absence of certain subunits on the motions of the remaining partial structures was investigated by removing one protein, pairs of proteins and selected sets of proteins at a time. Our results show that the removal of some proteins doesn't change the large-scale dynamics of the partial structures, but the removal of certain subunits does cause significant changes in motion of the remaining structure, and these changes can be reverted by the removal of other subunits, which indicates interdependence between motions of various parts of the 30S ribosomal structure. We further found that the subunits showing such interdependence have strong positive correlation of their motions, which indicates that these subunits function as a unit block in the 30S small ribosomal subunit. Dynamically interdependent subunit pairs identified in this paper are consistent with previous experimental observations that suggested dimerization of those subunits.

Conformational transition pathways explored by Monte Carlo simulation integrated with collective modes

Conformational transitions between open/closed or free/bound states in proteins possess functional importance. We propose a technique in which the collective modes obtained from an anisotropic network model (ANM) are used in conjunction with a Monte Carlo (MC) simulation approach, to investigate conformational transition pathways and pathway intermediates. The ANM-MC technique is applied to adenylate kinase (AK) and hemoglobin. The iterative method, in which normal modes are continuously updated during the simulation, proves successful in accomplishing the transition between open-closed conformations of AK and tense-relaxed forms of hemoglobin (C(alpha)-root mean square deviations between two end structures of 7.13 A and 3.55 A, respectively). Target conformations are reached by root mean-square deviations of 2.27 A and 1.90 A for AK and hemoglobin, respectively. The intermediate conformations overlap with crystal structures from the AK family within a 3.0-A root mean-square deviation. In the case of hemoglobin, the transition of tense-to-relaxed passes through the relaxed state. In both cases, the lowest-frequency modes are effective during transitions. The targeted Monte Carlo approach is used without the application of collective modes. Both the ANM-MC and targeted Monte Carlo techniques can explore sequences of events in transition pathways with an efficient yet realistic conformational search.

Influence of oligomerization on the dynamics of G-protein coupled receptors as assessed by normal mode analysis

The recently discovered impact of oligomerization on G-protein coupled receptor (GPCR) function further complicates the already challenging goal of unraveling the molecular and dynamic mechanisms of these receptors. To help understand the effect of oligomerization on the dynamics of GPCRs, we have compared the motion of monomeric, dimeric, and tetrameric arrangements of the prototypic GPCR rhodopsin, using an approximate-yet powerful-normal mode analysis (NMA) technique termed elastic network model (ENM). Moreover, we have used ENM to discriminate between putative dynamic mechanisms likely to account for the recently observed conformational rearrangement of the TM4,5-TM4,5 dimerization interface of GPCRs that occurs upon activation. Our results indicate: (1) significant perturbation of the normal modes (NMs) of the rhodopsin monomer upon oligomerization, which is mainly manifested at interfacial regions; (2) increased positive correlation among the transmembrane domains (TMs) and between the extracellular loop (EL) and TM regions of the rhodopsin protomer; (3) highest interresidue positive correlation at the interfaces between protomers; and (4) experimentally testable hypotheses of differential motional changes within different putative oligomeric arrangements.

Polyelectrolyte behavior and kinetics of aminoacyl-tRNA on the ribosome

The torque acting on cognate (three base pairs that are matched) "ternary complex" consisting of elongation factor-Tu, guanosine-5'-triphosphate GTP, and aminoacyl-transfer RNA due to induced wrapping of the 30S subunit of the ribosome and the speed with which the ternary complex samples the space allowed by diffusion is determined. Under appropriate conditions, mode coupling speeds up the barrier crossing rate for cognate relative to near-cognate ternary complexes. We determine the flexibility of the ternary complex relative to transfer RNA (tRNA) by a coarse-grained model. We predict the magnesium binding sites in the ternary complex at low magnesium concentration and unravel the nature of the interaction energy of magnesium with site-specific tRNAPhe bases.

Analyzing the flexibility of RNA structures by constraint counting

RNA requires conformational dynamics to undergo its diverse functional roles. Here, a new topological network representation of RNA structures is presented that allows analyzing RNA flexibility/rigidity based on constraint counting. The method extends the FIRST approach, which identifies flexible and rigid regions in atomic detail in a single, static, three-dimensional molecular framework. Initially, the network rigidity of a canonical A-form RNA is analyzed by counting on constraints of network elements of increasing size. These considerations demonstrate that it is the inclusion of hydrophobic contacts into the RNA topological network that is crucial for an accurate flexibility prediction. The counting also explains why a protein-based parameterization results in overly rigid RNA structures. The new network representation is then validated on a tRNA(ASP) structure and all NMR-derived ensembles of RNA structures currently available in the Protein Data Bank (with chain length >/=40). The flexibility predictions demonstrate good agreement with experimental mobility data, and the results are superior compared to predictions based on two previously used network representations. Encouragingly, this holds for flexibility predictions as well as mobility predictions obtained by constrained geometric simulations on these networks. Potential applications of the approach to analyzing the flexibility of DNA and RNA/protein complexes are discussed.