RESUMO
We present the nuclear magnetic resonance spectroscopy (NMR) solution structure of the 5'-terminal stem loop 5_SL1 (SL1) of the SARS-CoV-2 genome. SL1 contains two A-form helical elements and two regions with non-canonical structure, namely an apical pyrimidine-rich loop and an asymmetric internal loop with one and two nucleotides at the 5'- and 3'-terminal part of the sequence, respectively. The conformational ensemble representing the averaged solution structure of SL1 was validated using NMR residual dipolar coupling (RDC) and small-angle X-ray scattering (SAXS) data. We show that the internal loop is the major binding site for fragments of low molecular weight. This internal loop of SL1 can be stabilized by an A12-C28 interaction that promotes the transient formation of an A+â¢C base pair. As a consequence, the pKa of the internal loop adenosine A12 is shifted to 5.8, compared to a pKa of 3.63 of free adenosine. Furthermore, applying a recently developed pH-differential mutational profiling (PD-MaP) approach, we not only recapitulated our NMR findings of SL1 but also unveiled multiple sites potentially sensitive to pH across the 5'-UTR of SARS-CoV-2.
Assuntos
Conformação de Ácido Nucleico , RNA Viral , SARS-CoV-2 , SARS-CoV-2/genética , SARS-CoV-2/química , SARS-CoV-2/metabolismo , RNA Viral/química , RNA Viral/genética , RNA Viral/metabolismo , Concentração de Íons de Hidrogênio , Humanos , Espalhamento a Baixo Ângulo , COVID-19/virologia , COVID-19/genética , Espectroscopia de Ressonância Magnética , Difração de Raios X , Sítios de Ligação , Genoma Viral , Pareamento de Bases , Regiões 5' não Traduzidas , Modelos MolecularesRESUMO
Both experimental and theoretical structure determinations of RNAs have remained challenging due to the intrinsic dynamics of RNAs. We report here an integrated nuclear magnetic resonance/molecular dynamics (NMR/MD) structure determination approach to describe the dynamic structure of the CUUG tetraloop. We show that the tetraloop undergoes substantial dynamics, leading to averaging of the experimental data. These dynamics are particularly linked to the temperature-dependent presence of a hydrogen bond within the tetraloop. Interpreting the NMR data by a single structure represents the low-temperature structure well but fails to capture all conformational states occurring at a higher temperature. We integrate MD simulations, starting from structures of CUUG tetraloops within the Protein Data Bank, with an extensive set of NMR data, and provide a structural ensemble that describes the dynamic nature of the tetraloop and the experimental NMR data well. We thus show that one of the most stable and frequently found RNA tetraloops displays substantial dynamics, warranting such an integrated structural approach.
Assuntos
Simulação de Dinâmica Molecular , RNA , RNA/química , Conformação de Ácido Nucleico , Espectroscopia de Ressonância Magnética , TemperaturaRESUMO
Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.
Assuntos
Hipoxantina Fosforribosiltransferase , Síndrome de Lesch-Nyhan , Humanos , Estudos Prospectivos , Hipoxantina Fosforribosiltransferase/genética , Hipoxantina Fosforribosiltransferase/metabolismo , Proteínas/genética , Mutação de Sentido IncorretoRESUMO
RNA viruses have evolved elaborate strategies to protect their genomes, including 5' capping. However, until now no RNA 5' cap has been identified for hepatitis C virus1,2 (HCV), which causes chronic infection, liver cirrhosis and cancer3. Here we demonstrate that the cellular metabolite flavin adenine dinucleotide (FAD) is used as a non-canonical initiating nucleotide by the viral RNA-dependent RNA polymerase, resulting in a 5'-FAD cap on the HCV RNA. The HCV FAD-capping frequency is around 75%, which is the highest observed for any RNA metabolite cap across all kingdoms of life4-8. FAD capping is conserved among HCV isolates for the replication-intermediate negative strand and partially for the positive strand. It is also observed in vivo on HCV RNA isolated from patient samples and from the liver and serum of a human liver chimeric mouse model. Furthermore, we show that 5'-FAD capping protects RNA from RIG-I mediated innate immune recognition but does not stabilize the HCV RNA. These results establish capping with cellular metabolites as a novel viral RNA-capping strategy, which could be used by other viruses and affect anti-viral treatment outcomes and persistence of infection.
Assuntos
Flavina-Adenina Dinucleotídeo , Hepacivirus , Capuzes de RNA , RNA Viral , Animais , Humanos , Camundongos , Quimera/virologia , Flavina-Adenina Dinucleotídeo/metabolismo , Hepacivirus/genética , Hepacivirus/imunologia , Hepatite C/virologia , Reconhecimento da Imunidade Inata , Fígado/virologia , Estabilidade de RNA , RNA Viral/química , RNA Viral/genética , RNA Viral/imunologia , RNA Viral/metabolismo , RNA Polimerase Dependente de RNA/metabolismo , Replicação Viral/genética , Capuzes de RNA/metabolismoRESUMO
BACKGROUND: The application of Machine Learning (ML) to genetic individual-level data represents a foreseeable advancement for the field, which is still in its infancy. Here, we aimed to evaluate the feasibility and accuracy of an ML-based model for disease risk prediction applied to Primary Biliary Cholangitis (PBC). METHODS: Genome-wide significant variants identified in subjects of European ancestry in the recently released second international meta-analysis of GWAS in PBC were used as input data. Quality-checked, individual genomic data from two Italian cohorts were used. The ML included the following steps: import of genotype and phenotype data, genetic variant selection, supervised classification of PBC by genotype, generation of "if-then" rules for disease prediction by logic learning machine (LLM), and model validation in a different cohort. RESULTS: The training cohort included 1345 individuals: 444 were PBC cases and 901 were healthy controls. After pre-processing, 41,899 variants entered the analysis. Several configurations of parameters related to feature selection were simulated. The best LLM model reached an Accuracy of 71.7%, a Matthews correlation coefficient of 0.29, a Youden's value of 0.21, a Sensitivity of 0.28, a Specificity of 0.93, a Positive Predictive Value of 0.66, and a Negative Predictive Value of 0.72. Thirty-eight rules were generated. The rule with the highest covering (19.14) included the following genes: RIN3, KANSL1, TIMMDC1, TNPO3. The validation cohort included 834 individuals: 255 cases and 579 controls. By applying the ruleset derived in the training cohort, the Area under the Curve of the model was 0.73. CONCLUSIONS: This study represents the first illustration of an ML model applied to common variants associated with PBC. Our approach is computationally feasible, leverages individual-level data to generate intelligible rules, and can be used for disease prediction in at-risk individuals.
RESUMO
We describe the conformational ensemble of the single-stranded r(UCAAUC) oligonucleotide obtained using extensive molecular dynamics (MD) simulations and Rosetta's FARFAR2 algorithm. The conformations observed in MD consist of A-form-like structures and variations thereof. These structures are not present in the pool generated using FARFAR2. By comparing with available nuclear magnetic resonance (NMR) measurements, we show that the presence of both A-form-like and other extended conformations is necessary to quantitatively explain experimental data. To further validate our results, we measure solution X-ray scattering (SAXS) data on the RNA hexamer and find that simulations result in more compact structures than observed from these experiments. The integration of simulations with NMR via a maximum entropy approach shows that small modifications to the MD ensemble lead to an improved description of the conformational ensemble. Nevertheless, we identify persisting discrepancies in matching experimental SAXS data.
Assuntos
Simulação de Dinâmica Molecular , RNA , Espectroscopia de Ressonância Magnética , Oligonucleotídeos , Conformação Proteica , Espalhamento a Baixo Ângulo , Difração de Raios XRESUMO
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic. Besides virus intrinsic characteristics, the host genetic makeup is predicted to account for the extreme clinical heterogeneity of the disease, which is characterized, among other manifestations, by a derangement of hemostasis associated with thromboembolic events. To date, large-scale studies confirmed that genetic predisposition plays a role in COVID-19 severity, pinpointing several susceptibility genes, often characterized by immunologic functions. With these premises, we performed an association study of common variants in 32 hemostatic genes with COVID-19 severity. We investigated 49,845 single-nucleotide polymorphism in a cohort of 332 Italian severe COVID-19 patients and 1668 controls from the general population. The study was conducted engaging a class of students attending the second year of the MEDTEC school (a six-year program, held in collaboration between Humanitas University and the Politecnico of Milan, allowing students to gain an MD in Medicine and a Bachelor's Degree in Biomedical Engineering). Thanks to their willingness to participate in the fight against the pandemic, we evidenced several suggestive hits (p < 0.001), involving the PROC, MTHFR, MTR, ADAMTS13, and THBS2 genes (top signal in PROC: chr2:127192625:G:A, OR = 2.23, 95%CI = 1.50-3.34, p = 8.77 × 10-5). The top signals in PROC, MTHFR, MTR, ADAMTS13 were instrumental for the construction of a polygenic risk score, whose distribution was significantly different between cases and controls (p = 1.62 × 10-8 for difference in median levels). Finally, a meta-analysis performed using data from the Regeneron database confirmed the contribution of the MTHFR variant chr1:11753033:G:A to the predisposition to severe COVID-19 (pooled OR = 1.21, 95%CI = 1.09-1.33, p = 4.34 × 10-14 in the weighted analysis).
RESUMO
The 5' untranslated region (UTR) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome is a conserved, functional and structured genomic region consisting of several RNA stem-loop elements. While the secondary structure of such elements has been determined experimentally, their three-dimensional structures are not known yet. Here, we predict structure and dynamics of five RNA stem loops in the 5'-UTR of SARS-CoV-2 by extensive atomistic molecular dynamics simulations, more than 0.5 ms of aggregate simulation time, in combination with enhanced sampling techniques. We compare simulations with available experimental data, describe the resulting conformational ensembles, and identify the presence of specific structural rearrangements in apical and internal loops that may be functionally relevant. Our atomic-detailed structural predictions reveal a rich dynamics in these RNA molecules, could help the experimental characterization of these systems, and provide putative three-dimensional models for structure-based drug design studies.
Assuntos
COVID-19/virologia , RNA Viral/química , SARS-CoV-2/genética , Regiões 5' não Traduzidas , Sequência de Bases , Genoma Viral , Humanos , Simulação de Dinâmica Molecular , Estrutura Molecular , Conformação de Ácido Nucleico , RNA Viral/genética , SARS-CoV-2/químicaRESUMO
Nanodiscs are membrane mimetics that consist of a protein belt surrounding a lipid bilayer, and are broadly used for characterization of membrane proteins. Here, we investigate the structure, dynamics and biophysical properties of two small nanodiscs, MSP1D1ΔH5 and ΔH4H5. We combine our SAXS and SANS experiments with molecular dynamics simulations and previously obtained NMR and EPR data to derive and validate a conformational ensemble that represents the structure and dynamics of the nanodisc. We find that it displays conformational heterogeneity with various elliptical shapes, and with substantial differences in lipid ordering in the centre and rim of the discs. Together, our results reconcile previous apparently conflicting observations about the shape of nanodiscs, and pave the way for future integrative studies of larger complex systems such as membrane proteins embedded in nanodiscs.
Assuntos
Espectroscopia de Ressonância Magnética , Nanoestruturas/ultraestrutura , Difração de Nêutrons , Espalhamento a Baixo Ângulo , Difração de Raios X , Bicamadas Lipídicas/química , Proteínas de Membrana/química , Simulação de Dinâmica MolecularRESUMO
We provide an atomic-level description of the structure and dynamics of the UUCG RNA stem-loop by combining molecular dynamics simulations with experimental data. The integration of simulations with exact nuclear Overhauser enhancements data allowed us to characterize two distinct states of this molecule. The most stable conformation corresponds to the consensus three-dimensional structure. The second state is characterized by the absence of the peculiar non-Watson-Crick interactions in the loop region. By using machine learning techniques we identify a set of experimental measurements that are most sensitive to the presence of non-native states. We find that although our MD ensemble, as well as the consensus UUCG tetraloop structures, are in good agreement with experiments, there are remaining discrepancies. Together, our results show that (i) the MD simulation overstabilize a non-native loop conformation, (ii) eNOE data support its presence with a population of ≈10% and (iii) the structural interpretation of experimental data for dynamic RNAs is highly complex, even for a simple model system such as the UUCG tetraloop.
Assuntos
Espectroscopia de Ressonância Magnética , Simulação de Dinâmica Molecular , Movimento , Conformação de Ácido Nucleico , Sequência de Bases , Teorema de Bayes , Conjuntos de Dados como Assunto , Entropia , RNA/químicaRESUMO
Many proteins contain multiple folded domains separated by flexible linkers, and the ability to describe the structure and conformational heterogeneity of such flexible systems pushes the limits of structural biology. Using the three-domain protein TIA-1 as an example, we here combine coarse-grained molecular dynamics simulations with previously measured small-angle scattering data to study the conformation of TIA-1 in solution. We show that while the coarse-grained potential (Martini) in itself leads to too compact conformations, increasing the strength of protein-water interactions results in ensembles that are in very good agreement with experiments. We show how these ensembles can be refined further using a Bayesian/Maximum Entropy approach, and examine the robustness to errors in the energy function. In particular we find that as long as the initial simulation is relatively good, reweighting against experiments is very robust. We also study the relative information in X-ray and neutron scattering experiments and find that refining against the SAXS experiments leads to improvement in the SANS data. Our results suggest a general strategy for studying the conformation of multi-domain proteins in solution that combines coarse-grained simulations with small-angle X-ray scattering data that are generally most easy to obtain. These results may in turn be used to design further small-angle neutron scattering experiments that exploit contrast variation through 1H/2H isotope substitutions.
Assuntos
Simulação de Dinâmica Molecular , Proteínas , Espalhamento a Baixo Ângulo , Difração de Raios X , Algoritmos , Biologia Computacional , Nêutrons , Conformação Proteica , Domínios Proteicos , Proteínas/análise , Proteínas/químicaRESUMO
Molecular simulations and biophysical experiments can be used to provide independent and complementary insights into the molecular origin of biological processes. A particularly useful strategy is to use molecular simulations as a modeling tool to interpret experimental measurements, and to use experimental data to refine our biophysical models. Thus, explicit integration and synergy between molecular simulations and experiments is fundamental for furthering our understanding of biological processes. This is especially true in the case where discrepancies between measured and simulated observables emerge. In this chapter, we provide an overview of some of the core ideas behind methods that were developed to improve the consistency between experimental information and numerical predictions. We distinguish between situations where experiments are used to refine our understanding and models of specific systems, and situations where experiments are used more generally to refine transferable models. We discuss different philosophies and attempt to unify them in a single framework. Until now, such integration between experiments and simulations have mostly been applied to equilibrium data, and we discuss more recent developments aimed to analyze time-dependent or time-resolved data.
Assuntos
Simulação de Dinâmica Molecular , Teorema de Bayes , Entropia , Cinética , Proteínas/química , RNA/química , Fatores de TempoRESUMO
We describe a Bayesian/Maximum entropy (BME) procedure and software to construct a conformational ensemble of a biomolecular system by integrating molecular simulations and experimental data. First, an initial conformational ensemble is constructed using, for example, Molecular Dynamics or Monte Carlo simulations. Due to potential inaccuracies in the model and finite sampling effects, properties predicted from simulations may not agree with experimental data. In BME we use the experimental data to refine the simulation so that the new conformational ensemble has the following properties: (1) the calculated averages are close to the experimental values taking uncertainty into account and (2) it maximizes the relative Shannon entropy with respect to the original simulation ensemble. The output of this procedure is a set of optimized weights that can be used to calculate other properties and distributions of these. Here, we provide a practical guide on how to obtain and use such weights, how to choose adjustable parameters and discuss shortcomings of the method.
Assuntos
Teorema de Bayes , Entropia , Simulação de Acoplamento Molecular/métodos , Algoritmos , Simulação de Dinâmica Molecular , Método de Monte Carlo , Conformação Proteica , SoftwareRESUMO
Empirical force fields for biomolecular systems are usually derived from quantum chemistry calculations and validated against experimental data. We here show how it is possible to refine the full dihedral-angle potential of the Amber RNA force field by using solution NMR data as well as stability of known structural motifs. The procedure can be used to mix multiple systems and heterogeneous experimental information and crucially depends on a regularization term chosen with a cross-validation procedure. By fitting corrections to the dihedral angles on the order of less than 1 kJ/mol per angle, it is possible to increase the stability of difficult-to-fold RNA tetraloops by more than 1 order of magnitude.
Assuntos
Conformação de Ácido Nucleico , RNA/química , Algoritmos , Espectroscopia de Ressonância Magnética/métodos , Simulação de Dinâmica Molecular , Reprodutibilidade dos TestesRESUMO
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.
Assuntos
Biologia Computacional/métodos , Conformação de Ácido Nucleico , RNA/ultraestrutura , Software , Pareamento de Bases/genética , Bases de Dados de Proteínas , Modelos MolecularesRESUMO
A fundamental challenge in biological research is achieving an atomic-level description and mechanistic understanding of the function of biomolecules. Techniques for biomolecular simulations have undergone substantial developments, and their accuracy and scope have expanded considerably. Progress has been made through an increasingly tight integration of experiments and simulations, with experiments being used to refine simulations and simulations used to interpret experiments. Here we review the underpinnings of this progress, including methods for more efficient conformational sampling, accuracy of the physical models used, and theoretical approaches to integrate experiments and simulations. These developments are enabling detailed studies of complex biomolecular assemblies.
Assuntos
Fenômenos Biofísicos , Biofísica/métodos , Simulação de Dinâmica Molecular , Modelos Teóricos , Conformação MolecularRESUMO
RNA molecules are key players in numerous cellular processes and are characterized by a complex relationship between structure, dynamics, and function. Despite their apparent simplicity, RNA oligonucleotides are very flexible molecules, and understanding their internal dynamics is particularly challenging using experimental data alone. We show how to reconstruct the conformational ensemble of four RNA tetranucleotides by combining atomistic molecular dynamics simulations with nuclear magnetic resonance spectroscopy data. The goal is achieved by reweighting simulations using a maximum entropy/Bayesian approach. In this way, we overcome problems of current simulation methods, as well as in interpreting ensemble- and time-averaged experimental data. We determine the populations of different conformational states by considering several nuclear magnetic resonance parameters and point toward properties that are not captured by state-of-the-art molecular force fields. Although our approach is applied on a set of model systems, it is fully general and may be used to study the conformational dynamics of flexible biomolecules and to detect inaccuracies in molecular dynamics force fields.
Assuntos
Simulação de Dinâmica Molecular , Ressonância Magnética Nuclear Biomolecular , Conformação de Ácido Nucleico , Oligorribonucleotídeos/química , Algoritmos , Reprodutibilidade dos TestesRESUMO
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.
Assuntos
Simulação de Dinâmica Molecular , Conformação de Ácido Nucleico , RNA/química , Catálise , Simulação por Computador , DNA/químicaRESUMO
Coarse-grained models can be of great help to address the problem of structure prediction in nucleic acids. On one hand they can make the prediction more efficient, while on the other hand they can also help to identify the essential degrees of freedom and interactions for the description of a number of structures. With the aim to provide an all-atom representation in an explicit solvent to the predictions of our SPlit and conQueR (SPQR) coarse-grained model of RNA, we recently introduced a backmapping procedure which enforces the predicted structure into an atomistic one by means of steered molecular dynamics. These simulations minimize the ERMSD, a particular metric which deals exclusively with the relative arrangement of nucleobases, between the atomistic representation and the target structure. In this paper, we explore the effects of this approach on the resulting interaction networks and backbone conformations by applying it on a set of fragments using as a target their native structure. We find that the geometry of the target structures can be reliably recovered, with limitations in the regions with unpaired bases such as bulges. In addition, we observe that the folding pathway can also change depending on the parameters used in the definition of the ERMSD and the use of other metrics such as the RMSD.