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We explore the energy landscape for a four-fold telomere repeat, obtaining interconversion pathways between six experimentally characterised G-quadruplex topologies. The results reveal a multi-funnel system, with a variety of intermediate configurations and misfolded states. This organisation is identified with the intrinsically multi-functional nature of the system, suggesting a new paradigm for the classification of such biomolecules and clarifying issues regarding apparently conflicting experimental results. [ABSTRACT FROM AUTHOR]

A new numerical scheme for RNA (ribonucleic acid) titration based on the Debye-Hückel framework for the salt description is proposed in an effort to reduce the computational costs for further applications to study protein-RNA systems. By means of different sets of Monte Carlo simulations, we demonstrated that this new scheme is able to correctly reproduce the experimental titration behavior and salt pKa shifts. In comparison with other theoretical approaches, similar or even better outcomes are achieved at much lower computational costs. The model was tested on the lead-dependent ribozyme, the branch-point helix, and the domain 5 from Azotobacter vinelandii Intron 5. [ABSTRACT FROM AUTHOR]

Kaposi's sarcoma-associated herpes virus (KSHV) is a human oncovirus. KSHV relies on manipulating the host cell N6-methyl adenosine (m6A) RNA modification pathway to enhance virus replication. Methylation within a RNA stem loop of the open reading frame 50 (ORF50) increases transcript stability via the recruitment of the m6A reader, SND1. In this contribution we explore the energy landscapes of the unmethylated and methylated RNA stem loops of ORF50 to investigate the effect of methylation on the structure of the stem loop. We observe a significant shift upon methylation between an open and closed configuration of the top of the stem loop. In the unmethylated stem loop the closed configuration is much lower in energy, and, as a result, exhibits higher occupancy. Author summary: In this article we present the investigation of the change in structure of an RNA regulatory molecule upon a change in the chemistry of one of its bases. Eukaryotic RNAs contain more than 100 different types of chemical modifications, which can fine-tune the structure and fucntion of RNA. Since RNA systems need to adopt a specific 3D shape to be functional, it is important to understand how a chemical modification impacts the structure adopted. Using the computational technique of energy landscape explorations, that is exploring what structures are available to the system at a given energy, we are able to characterise the RNA before and after the modification, and to understand what the main differences between the ensembles of structures, which can be adopted by the system, are. In this work, we present our results of this investigation on an oncogenic virus-encoded RNA. We show how a chemical modification at a precise location of the native structure affects the system globally, inducing a rearrangement of parts of the structure, which are far away from the modification site. [ABSTRACT FROM AUTHOR]

While RNA folding was originally seen as a simple problem to solve, it has been shown that the promiscuous interactions of the nucleobases result in structural polymorphism, with several competing structures generally observed for non-coding RNA. This inherent complexity limits our understanding of these molecules from experiments alone, and computational methods are commonly used to study RNA. Here, we discuss three advanced sampling schemes, namely Hamiltonian-replica exchange molecular dynamics (MD), ratchet-and-pawl MD and discrete path sampling, as well as the HiRE-RNA coarse-graining scheme, and highlight how these approaches are complementary with reference to recent case studies. While all computational methods have their shortcomings, the plurality of simulation methods leads to a better understanding of experimental findings and can inform and guide experimental work on RNA polymorphism. [ABSTRACT FROM AUTHOR]

The article presents simple rules for developing serious scientific videogames. One rule is that the goal of the game should be defined in a clear way in terms of science and teaching. Another rule is that equilibrium has to be created between scientific accuracy and player accessibility. Another rule is that one should let the players interact with scientific data.

The OPEP coarse-grained protein model has been applied to a wide range of applications since its first release 15 years ago. The model, which combines energetic and structural accuracy and chemical specificity, allows the study of single protein properties, DNA-RNA complexes, amyloid fibril formation and protein suspensions in a crowded environment. Here we first review the current state of the model and the most exciting applications using advanced conformational sampling methods. We then present the current limitations and a perspective on the ongoing developments. [ABSTRACT FROM AUTHOR]

Coarse-grained protein models with various levels ofgranularityand degrees of freedom offer the possibility to explore many phenomenaincluding folding, assembly, and recognition in terms of dynamicsand thermodynamics that are inaccessible to all-atom representationsin explicit aqueous solution. Here, we present a refined version ofthe coarse-grained optimized potential for efficient protein structureprediction (OPEP) based on a six-bead representation. The OPEP version4.0 parameter set, which uses a new analytical formulation for thenonbonded interactions and adds specific side-chain–side-chaininteractions for α-helix, is subjected to three tests. First,we show that molecular dynamics simulations at 300 K preserve theexperimental rigid conformations of 17 proteins with 37–152amino acids within a root-mean-square deviation (RMSD) of 3.1 Åafter 30 ns. Extending the simulation time to 100 ns for five proteinsdoes not change the RMSDs. Second, replica exchange molecular dynamics(REMD) simulations recover the NMR structures of three prototypicalβ-hairpin and α-helix peptides and the NMR three-strandedβ-sheet topology of a 37-residue WW domain, starting from randomlychosen states. Third, REMD simulations on the ccβ peptide showa temperature transition from a three-stranded coiled coil to amyloid-likeaggregates consistent with experiments, while simulations on low molecularweight aggregates of the prion protein helix 1 do not. Overall, thesestudies indicate the effectiveness of our OPEP4 coarse-grained modelfor protein folding and aggregation, and report two future directionsfor improvement. [ABSTRACT FROM AUTHOR]

The volume of an internal protein pocket is fundamental to ligand accessibility. Few programs that compute such volumes manage dynamic data from molecular dynamics (MD) simulations. Limited performance often prohibits analysis of large datasets. We present Epock, an efficient command-line tool that calculates pocket volumes from MD trajectories. A plugin for the VMD program provides a graphical user interface to facilitate input creation, run Epock and analyse the results. [ABSTRACT FROM AUTHOR]

Conformational dynamics is crucial for the biological function of RNA molecules and for their potential as therapeutic targets. This meeting report outlines key "take-home" messages that emerged from the presentations and discussions during the CECAM workshop "RNA dynamics from experimental and computational approaches" in Paris, June 26–28, 2023. [ABSTRACT FROM AUTHOR]

Endoglin (Eng) is an endothelial cell (EC) transmembrane glycoprotein involved in adhesion and angiogenesis. Eng mutations result in vessel abnormalities as observed in hereditary hemorrhagic telangiectasia of type 1. The role of Eng was investigated in endothelial functions and permeability under inflammatory conditions, focusing on the actin dynamic signaling pathway. Endothelial Colony-Forming Cells (ECFC) from human cord blood and mouse lung/aortic EC (MLEC, MAEC) from Eng+/+ and Eng+/− mice were used. ECFC silenced for Eng with Eng-siRNA and ctr-siRNA were used to test tubulogenesis and permeability +/− TNFα and +/− LIM kinase inhibitors (LIMKi). In silico modeling of TNFα–Eng interactions was carried out from PDB IDs 5HZW and 5HZV. Calcium ions (Ca2+) flux was studied by Oregon Green 488 in epifluorescence microscopy. Levels of cofilin phosphorylation and tubulin post-translational modifications were evaluated by Western blot. F-actin and actin–tubulin distribution/co-localization were evaluated in cells by confocal microscopy. Eng silencing in ECFCs resulted in a decrease of cell sprouting by 50 ± 15% (p < 0.05) and an increase in pseudo-tube width (41 ± 4.5%; p < 0.001) compared to control. Upon TNFα stimulation, ECFC Eng–siRNA displayed a significant higher permeability compared to ctr-siRNA (p < 0.01), which is associated to a higher Ca2+ mobilization (p < 0.01). Computational analysis suggested that Eng mitigated TNFα activity. F-actin polymerization was significantly increased in ECFC Eng-siRNA, MAEC+/−, and MLEC+/− compared to controls (p < 0.001, p < 0.01, and p < 0.01, respectively) as well as actin/tubulin distribution (p < 0.01). Furthermore, the inactive form of cofilin (P-cofilin at Ser3) was significantly decreased by 36.7 ± 4.8% in ECFC Eng-siRNA compared to ctr-siRNA (p < 0.001). Interestingly, LIMKi reproduced the absence of Eng on TNFα-induced ECFC-increased permeability. Our data suggest that Eng plays a critical role in the homeostasis regulation of endothelial cells under inflammatory conditions (TNFα), and loss of Eng influences ECFC-related permeability through the LIMK/cofilin/actin rearrangement-signaling pathway. [ABSTRACT FROM AUTHOR]

Electrostatic interactions play a pivotal role in many (bio)molecular association processes. The molecular organization and function in biological systems are largely determined by these interactions from pure Coulombic contributions to more peculiar mesoscopic forces due to ion-ion correlation and proton fluctuations. The latter is a general electrostatic mechanism that gives attraction particularly at low electrolyte concentrations. This charge regulation mechanism due to titrating amino acid and nucleotides residues is discussed here in a purely electrostatic framework. By means of constant-pH Monte Carlo simulations based on a fast coarse-grained titration proton scheme, a new computer molecular model was devised to study protein–RNA interactions. The complexation between the RNA silencing suppressor p19 viral protein and the 19-bp small interfering RNA was investigated at different solution pH and salt conditions. The outcomes illustrate the importance of the charge regulation mechanism that enhances the association between these macromolecules in a similar way as observed for other protein-polyelectrolyte systems typically found in colloidal science. Due to the highly negative charge of RNA, the effect is more pronounced in this system as predicted by the Kirkwood-Shumaker theory. Our results contribute to the general physico-chemical understanding of macromolecular complexation and shed light on the extensive role of RNA in the cell's life. [ABSTRACT FROM AUTHOR]

Alpha 2 -macroglobulin (α 2 M) is a physiological macromolecule that facilitates the clearance of many proteinases, cytokines and growth factors in human. Here, we explored the effect of induced forms of α 2 M on anticoagulant drugs. Gla-domainless factor Xa (GDFXa) and methylamine (MA)-induced α 2 M were prepared and characterized by electrophoresis, immunonephelometry, chromogenic, clot waveform and rotational thromboelastometry assays. Samples from healthy volunteers and anticoagulated patients were included. In vivo neutralization of anticoagulants was evaluated in C57Bl/6JRj mouse bleeding-model. Anticoagulant binding sites on induced α 2 M were depicted by computer-aided energy minimization modeling. GDFXa-induced α 2 M neutralized dabigatran and heparins in plasma and whole blood. In mice, a single IV dose of GDFXa-induced α 2 M following anticoagulant administration significantly reduced blood loss and bleeding time. Being far easier to prepare, we investigated the efficacy of MA-induced α 2 M. It neutralized rivaroxaban, apixaban, dabigatran and heparins in spiked samples in a concentration-dependent manner and in samples from treated patients. Molecular docking analysis evidenced the ability of MA-induced α 2 M to bind non-covalently these compounds via some deeply buried binding sites. Induced forms of α 2 M have the potential to neutralize direct oral anticoagulants and heparins, and might be developed as a universal antidote in case of major bleeding or urgent surgery. [ABSTRACT FROM AUTHOR]

• All-atom molecular dynamics simulations for studying local flexibility of RNA molecules. • Comparison between local flexibility computed from MD simulation and chemical probing reactivities. • A combination of multiple parameters for describing SHAPE reactivity. • DMS and CMCT reactivity more complex than commonly accepted. Determination of the tridimensional structure of ribonucleic acid molecules is fundamental for understanding their function in the cell. A common method to investigate RNA structures of large molecules is the use of chemical probes such as SHAPE ( 2 ′ -hydroxyl acylation analyzed by primer extension) reagents, DMS (dimethyl sulfate) and CMCT (1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfate), the reaction of which is dependent on the local structural properties of each nucleotide. In order to understand the interplay between local flexibility, sugar pucker, canonical pairing and chemical reactivity of the probes, we performed all-atom molecular dynamics simulations on a set of RNA molecules for which both tridimensional structure and chemical probing data are available and we analyzed the correlations between geometrical parameters and the chemical reactivity. Our study confirms that SHAPE reactivity is guided by the local flexibility of the different chemical moieties but suggests that a combination of multiple parameters is needed to better understand the implications of the reactivity at the molecular level. This is also the case for DMS and CMCT for which the reactivity appears to be more complex than commonly accepted. [ABSTRACT FROM AUTHOR]

RNA molecules can easily adopt alternative structures in response to different environmental conditions. As a result, a molecule's energy landscape is rough and can exhibit a multitude of deep basins. In the absence of a high-resolution structure, small-angle X-ray scattering data (SAXS) can narrow down the conformational space available to the molecule and be used in conjunction with physical modeling to obtain high-resolution putative structures to be further tested by experiments. Because of the low resolution of these data, it is natural to implement the integration of SAXS data into simulations using a coarse-grained representation of the molecule, allowing for much wider searches and faster evaluation of SAXS theoretical intensity curves than with atomistic models. We present here the theoretical framework and the implementation of a simulation approach based on our coarse-grained model HiRE-RNA combined with SAXS evaluations "on-the-fly" leading the simulation toward conformations agreeing with the scattering data, starting from partially folded structures as the ones that can easily be obtained from secondary structure prediction-based tools. We show on three benchmark systems how our approach can successfully achieve high-resolution structures with remarkable similarity with the native structure recovering not only the overall shape, as imposed by SAXS data, but also the details of initially missing base pairs.

The recent advances in computational abilities, such as the enormous speed-ups provided by GPU computing, allow for large scale computational studies of RNA molecules at an atomic level of detail. As RNA molecules are known to adopt multiple conformations with comparable energies, but different two-dimensional structures, all-atom models are necessary to better describe the structural ensembles for RNA molecules. This point is important because different conformations can exhibit different functions, and their regulation or mis-regulation is linked to a number of diseases. Problematically, the energy barriers between different conformational ensembles are high, resulting in long time scales for interensemble transitions. The computational potential energy landscape framework was designed to overcome this problem of broken ergodicity by use of geometry optimization. Here, we describe the algorithms used in the energy landscape explorations with the OPTIM and PATHSAMPLE programs, and how they are used in biomolecular simulations. We present a recent case study of the 5'-hairpin of RNA 7SK to illustrate how the method can be applied to interpret experimental results, and to obtain a detailed description of molecular properties.

Inspired by the recent success of scientific-discovery games for predicting protein tertiary and RNA secondary structures, we have developed an open software for coarse-grained RNA folding simulations, guided by human intuition. To determine the extent to which interactive simulations can accurately predict 3D RNA structures of increasing complexity and lengths (four RNAs with 22-47 nucleotides), an interactive experiment was conducted with 141 participants who had very little knowledge of nucleic acids systems and computer simulations, and had received only a brief description of the important forces stabilizing RNA structures. Their structures and full trajectories have been analyzed statistically and compared to standard replica exchange molecular dynamics simulations. Our analyses show that participants gain easily chemical intelligence to fold simple and nontrivial topologies, with little computer time, and this result opens the door for the use of human-guided simulations to RNA folding. Our experiment shows that interactive simulations have better chances of success when the user widely explores the conformational space. Interestingly, providing on-the-fly feedback of the root mean square deviation with respect to the experimental structure did not improve the quality of the proposed models., (Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

G-quadruplexes are the most important noncanonical DNA architectures. Many quadruplex-forming sequences, including the human telomeric sequence d(GGGTTA) n , have been investigated due to their implications in cancer and other diseases, and because of their potential in DNA-based nanotechnology. Despite the availability of atomistic structural studies of folded G-quadruplexes, their folding pathways remain mysterious, and mutually contradictory models of folding coexist in the literature. Recent experiments convincingly demonstrated that G-quadruplex folding often takes days to reach thermodynamic equilibrium. Based on atomistic simulations of diverse classes of intermediates in G-quadruplex folding, we have suggested that the folding is an extremely multipathway process combining a kinetic partitioning mechanism with conformational diffusion. However, complete G-quadruplex folding is far beyond the time scale of atomistic simulations. Here we use high-resolution coarse-grained simulations to investigate potential unfolding intermediates, whose structural dynamics are then further explored with all-atom simulations. This multiscale approach indicates how various pathways are interconnected in a complex network. Spontaneous conversions between different folds are observed. We demonstrate the inability of simple order parameters, such as radius of gyration or the number of native H-bonds, to describe the folding landscape of the G-quadruplexes. Our study also provides information relevant to further development of the coarse-grained force field.

HiRE-RNA is a coarse-grained model for RNA structure prediction and the dynamical study of RNA folding. Using a reduced set of particles and detailed interactions accounting for base-pairing and stacking, we show that noncanonical and multiple base interactions are necessary to capture the full physical behavior of complex RNAs. In this paper, we give a full account of the model and present results on the folding, stability, and free energy surfaces of 16 systems with 12 to 76 nucleotides of increasingly complex architectures, ranging from monomers to dimers, using a total of 850 μs of simulation time.

RNA molecules are essential cellular machines performing a wide variety of functions for which a specific three-dimensional structure is required. Over the last several years, the experimental determination of RNA structures through x-ray crystallography and NMR seems to have reached a plateau in the number of structures resolved each year, but as more and more RNA sequences are being discovered, the need for structure prediction tools to complement experimental data is strong. Theoretical approaches to RNA folding have been developed since the late nineties, when the first algorithms for secondary structure prediction appeared. Over the last 10 years a number of prediction methods for 3D structures have been developed, first based on bioinformatics and data-mining, and more recently based on a coarse-grained physical representation of the systems. In this review we are going to present the challenges of RNA structure prediction and the main ideas behind bioinformatic approaches and physics-based approaches. We will focus on the description of the more recent physics-based phenomenological models and on how they are built to include the specificity of the interactions of RNA bases, whose role is critical in folding. Through examples from different models, we will point out the strengths of physics-based approaches, which are able not only to predict equilibrium structures, but also to investigate dynamical and thermodynamical behavior, and the open challenges to include more key interactions ruling RNA folding.

The simulation of amyloid fibril formation is impossible if one takes into account all chemical details of the amino acids and their detailed interactions with the solvent. We investigate the folding and aggregation of two model peptides using the optimized potential for efficient structure prediction (OPEP) coarse-grained model and replica exchange molecular dynamics (REMD) simulations coupled with either the Langevin or the Berendsen thermostat. For both the monomer of blocked penta-alanine and the trimer of the 25-35 fragment of the Alzheimer's amyloid β protein, we find little variations in the equilibrium structures and heat capacity curves using the two thermostats. Despite this high similarity, we detect significant differences in the populations of the dominant conformations at low temperatures, whereas the configurational distributions remain the same in proximity of the melting temperature. Aβ25-35 trimers at 300 K have an averaged β-sheet content of 12% and are primarily characterized by fully disordered peptides or a small curved two-stranded β-sheet stabilized by a disordered peptide. In addition, OPEP molecular dynamics simulations of Aβ25-35 hexamers at 300 K with a small curved six-stranded antiparallel β-sheet do not show any extension of the β-sheet content. These data support the idea that the mechanism of Aβ25-35 amyloid formation does not result from a high fraction of extended β-sheet-rich trimers and hexamers.

Current analyses of protein sequence/structure relationships have focused on expected similarity relationships for structurally similar proteins. To survey and explore the basis of these relationships, we present a general sequence/structure map that covers all combinations of similarity/dissimilarity relationships and provide novel energetic analyses of these relationships. To aid our analysis, we divide protein relationships into four categories: expected/unexpected similarity (S and S(?)) and expected/unexpected dissimilarity (D and D(?)) relationships. In the expected similarity region S, we show that trends in the sequence/structure relation can be derived based on the requirement of protein stability and the energetics of sequence and structural changes. Specifically, we derive a formula relating sequence and structural deviations to a parameter characterizing protein stiffness; the formula fits the data reasonably well. We suggest that the absence of data in region S(?) (high structural but low sequence similarity) is due to unfavorable energetics. In contrast to region S, region D(?) (high sequence but low structural similarity) is well-represented by proteins that can accommodate large structural changes. Our analyses indicate that there are several categories of similarity relationships and that protein energetics provide a basis for understanding these relationships.