1
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Piomponi V, Krepl M, Sponer J, Bussi G. Molecular Simulations to Investigate the Impact of N6-Methylation in RNA Recognition: Improving Accuracy and Precision of Binding Free Energy Prediction. J Phys Chem B 2024. [PMID: 39240243 DOI: 10.1021/acs.jpcb.4c03397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
N6-Methyladenosine (m6A) is a prevalent RNA post-transcriptional modification that plays crucial roles in RNA stability, structural dynamics, and interactions with proteins. The YT521-B (YTH) family of proteins, which are notable m6A readers, functions through its highly conserved YTH domain. Recent structural investigations and molecular dynamics (MD) simulations have shed light on the mechanism of recognition of m6A by the YTHDC1 protein. Despite advancements, using MD to predict the stabilization induced by m6A on the free energy of binding between RNA and YTH proteins remains challenging due to inaccuracy of the employed force field and limited sampling. For instance, simulations often fail to sufficiently capture the hydration dynamics of the binding pocket. This study addresses these challenges through an innovative methodology that integrates metadynamics, alchemical simulations, and force-field refinement. Importantly, our research identifies hydration of the binding pocket as giving only a minor contribution to the binding free energy and emphasizes the critical importance of precisely tuning force-field parameters to experimental data. By employing a fitting strategy built on alchemical calculations, we refine the m6A partial charge parameters, thereby enabling the simultaneous reproduction of N6 methylation on both the protein binding free energy and the thermodynamic stability of nine RNA duplexes. Our findings underscore the sensitivity of binding free energies to partial charges, highlighting the necessity for thorough parametrization and validation against experimental observations across a range of structural contexts.
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Affiliation(s)
- Valerio Piomponi
- Scuola Internazionale Superiore di Studi Avanzati, SISSA, Via Bonomea 265, Trieste 34136, Italy
- Area Science Park, località Padriciano, 99, Trieste 34149, Italy
| | - Miroslav Krepl
- Institute of Biophysics of the Czech Academy of Sciences, Kralovopolská 135, Brno 612 00, Czech Republic
| | - Jiri Sponer
- Institute of Biophysics of the Czech Academy of Sciences, Kralovopolská 135, Brno 612 00, Czech Republic
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, SISSA, Via Bonomea 265, Trieste 34136, Italy
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2
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Muscat S, Martino G, Manigrasso J, Marcia M, De Vivo M. On the Power and Challenges of Atomistic Molecular Dynamics to Investigate RNA Molecules. J Chem Theory Comput 2024. [PMID: 39150960 DOI: 10.1021/acs.jctc.4c00773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/18/2024]
Abstract
RNA molecules play a vital role in biological processes within the cell, with significant implications for science and medicine. Notably, the biological functions exerted by specific RNA molecules are often linked to the RNA conformational ensemble. However, the experimental characterization of such three-dimensional RNA structures is challenged by the structural heterogeneity of RNA and by its multiple dynamic interactions with binding partners such as small molecules, proteins, and metal ions. Consequently, our current understanding of the structure-function relationship of RNA molecules is still limited. In this context, we highlight molecular dynamics (MD) simulations as a powerful tool to complement experimental efforts on RNAs. Despite the recognized limitations of current force fields for RNA MD simulations, examining the dynamics of selected RNAs has provided valuable functional insights into their structures.
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Affiliation(s)
- Stefano Muscat
- Laboratory of Molecular Modelling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Gianfranco Martino
- Laboratory of Molecular Modelling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Jacopo Manigrasso
- Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, 431 50 Mölndal, Sweden
| | - Marco Marcia
- European Molecular Biology Laboratory Grenoble, 71 Avenue des Martyrs, 38042 Grenoble, France
- Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, 751 23 Uppsala, Sweden
- Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Marco De Vivo
- Laboratory of Molecular Modelling and Drug Discovery, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
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3
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Latham AP, Tempkin JOB, Otsuka S, Zhang W, Ellenberg J, Sali A. Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.06.606842. [PMID: 39149317 PMCID: PMC11326192 DOI: 10.1101/2024.08.06.606842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.
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Affiliation(s)
- Andrew P Latham
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jeremy O B Tempkin
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Shotaro Otsuka
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Wanlu Zhang
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Jan Ellenberg
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA
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4
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Włodarski T, Streit JO, Mitropoulou A, Cabrita LD, Vendruscolo M, Christodoulou J. Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method. Sci Rep 2024; 14:18149. [PMID: 39103467 PMCID: PMC11300795 DOI: 10.1038/s41598-024-68468-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 07/24/2024] [Indexed: 08/07/2024] Open
Abstract
Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for the determination of structures of complex biological molecules. The accurate characterisation of the dynamics of such systems, however, remains a challenge. To address this problem, we introduce cryoENsemble, a method that applies Bayesian reweighting to conformational ensembles derived from molecular dynamics simulations to improve their agreement with cryo-EM data, thus enabling the extraction of dynamics information. We illustrate the use of cryoENsemble to determine the dynamics of the ribosome-bound state of the co-translational chaperone trigger factor (TF). We also show that cryoENsemble can assist with the interpretation of low-resolution, noisy or unaccounted regions of cryo-EM maps. Notably, we are able to link an unaccounted part of the cryo-EM map to the presence of another protein (methionine aminopeptidase, or MetAP), rather than to the dynamics of TF, and model its TF-bound state. Based on these results, we anticipate that cryoENsemble will find use for challenging heterogeneous cryo-EM maps for biomolecular systems encompassing dynamic components.
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Affiliation(s)
- Tomasz Włodarski
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK.
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, 02-106, Warsaw, Poland.
| | - Julian O Streit
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Alkistis Mitropoulou
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Lisa D Cabrita
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - John Christodoulou
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
- Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK
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5
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Gilardoni I, Fröhlking T, Bussi G. Boosting Ensemble Refinement with Transferable Force-Field Corrections: Synergistic Optimization for Molecular Simulations. J Phys Chem Lett 2024; 15:1204-1210. [PMID: 38272001 DOI: 10.1021/acs.jpclett.3c03423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
A novel method combining the force-field fitting approach and ensemble refinement by the maximum entropy principle is presented. Its formulation allows us to continuously interpolate between these two methods, which can thus be interpreted as two limiting cases. A cross-validation procedure enables us to correctly assess the relative weight of both of them, distinguishing scenarios in which the combined approach is meaningful from those in which either ensemble refinement or force-field fitting separately prevails. The efficacy of their combination is examined for a realistic case study of RNA oligomers. Within the new scheme, molecular dynamics simulations are integrated with experimental data provided by nuclear magnetic resonance measures. We show that force-field corrections are in general superior when applied to the appropriate force-field terms but are automatically discarded by the method when applied to inappropriate force-field terms.
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Affiliation(s)
- Ivan Gilardoni
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, 34136 Trieste, Italy
| | - Thorben Fröhlking
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, 34136 Trieste, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, 34136 Trieste, Italy
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6
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Ballabio F, Paissoni C, Bollati M, de Rosa M, Capelli R, Camilloni C. Accurate and Efficient SAXS/SANS Implementation Including Solvation Layer Effects Suitable for Molecular Simulations. J Chem Theory Comput 2023; 19:8401-8413. [PMID: 37923304 PMCID: PMC10687869 DOI: 10.1021/acs.jctc.3c00864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/11/2023] [Accepted: 10/24/2023] [Indexed: 11/07/2023]
Abstract
Small-angle X-ray and neutron scattering (SAXS/SANS) provide valuable insights into the structure and dynamics of biomolecules in solution, complementing a wide range of structural techniques, including molecular dynamics simulations. As contrast-based methods, they are sensitive not only to structural properties but also to solvent-solute interactions. Their use in molecular dynamics simulations requires a forward model that should be as fast and accurate as possible. In this work, we demonstrate the feasibility of calculating SAXS and SANS intensities using a coarse-grained representation consisting of one bead per amino acid and three beads per nucleic acid, with form factors that can be corrected on the fly to account for solvation effects at no additional computational cost. By coupling this forward model with molecular dynamics simulations restrained with SAS data, it is possible to determine conformational ensembles or refine the structure and dynamics of proteins and nucleic acids in agreement with the experimental results. To assess the robustness of this approach, we applied it to gelsolin, for which we acquired SAXS data on its closed state, and to a UP1-microRNA complex, for which we used previously collected measurements. Our hybrid-resolution small-angle scattering (hySAS) implementation, being distributed in PLUMED, can be used with atomistic and coarse-grained simulations using diverse restraining strategies.
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Affiliation(s)
- Federico Ballabio
- Dipartimento
di Bioscienze, Università degli Studi
di Milano, via Celoria 26, 20133 Milano, Italy
| | - Cristina Paissoni
- Dipartimento
di Bioscienze, Università degli Studi
di Milano, via Celoria 26, 20133 Milano, Italy
| | - Michela Bollati
- Dipartimento
di Bioscienze, Università degli Studi
di Milano, via Celoria 26, 20133 Milano, Italy
- Istituto
di Biofisica, Consiglio Nazionale delle
Ricerche (IBF-CNR), via
Alfonso Corti 12, 20133 Milano, Italy
| | - Matteo de Rosa
- Dipartimento
di Bioscienze, Università degli Studi
di Milano, via Celoria 26, 20133 Milano, Italy
- Istituto
di Biofisica, Consiglio Nazionale delle
Ricerche (IBF-CNR), via
Alfonso Corti 12, 20133 Milano, Italy
| | - Riccardo Capelli
- Dipartimento
di Bioscienze, Università degli Studi
di Milano, via Celoria 26, 20133 Milano, Italy
| | - Carlo Camilloni
- Dipartimento
di Bioscienze, Università degli Studi
di Milano, via Celoria 26, 20133 Milano, Italy
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7
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Shukla VK, Heller GT, Hansen DF. Biomolecular NMR spectroscopy in the era of artificial intelligence. Structure 2023; 31:1360-1374. [PMID: 37848030 DOI: 10.1016/j.str.2023.09.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/15/2023] [Accepted: 09/21/2023] [Indexed: 10/19/2023]
Abstract
Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts.
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Affiliation(s)
- Vaibhav Kumar Shukla
- Department of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK
| | - Gabriella T Heller
- Department of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK.
| | - D Flemming Hansen
- Department of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, UK.
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8
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Gama Lima Costa R, Fushman D. Reweighting methods for elucidation of conformation ensembles of proteins. Curr Opin Struct Biol 2022; 77:102470. [PMID: 36183447 PMCID: PMC9771963 DOI: 10.1016/j.sbi.2022.102470] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/24/2022] [Accepted: 08/28/2022] [Indexed: 12/24/2022]
Abstract
Proteins are inherently dynamic macromolecules that exist in equilibrium among multiple conformational states, and motions of protein backbone and side chains are fundamental to biological function. The ability to characterize the conformational landscape is particularly important for intrinsically disordered proteins, multidomain proteins, and weakly bound complexes, where single-structure representations are inadequate. As the focus of structural biology shifts from relatively rigid macromolecules toward larger and more complex systems and molecular assemblies, there is a need for structural approaches that can paint a more realistic picture of such conformationally heterogeneous systems. Here, we review reweighting methods for elucidation of structural ensembles based on experimental data, with the focus on applications to multidomain proteins.
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Affiliation(s)
- Raquel Gama Lima Costa
- Chemical Physics Program, Institute for Physical Sciences and Technology, University of Maryland, College Park, 20742, MD, USA.
| | - David Fushman
- Chemical Physics Program, Institute for Physical Sciences and Technology, University of Maryland, College Park, 20742, MD, USA; Department of Chemistry and Biochemistry, Center for Biomolecular Structure and Organization, University of Maryland, College Park, 20742, MD, USA.
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9
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Zhu J, Salvatella X, Robustelli P. Small molecules targeting the disordered transactivation domain of the androgen receptor induce the formation of collapsed helical states. Nat Commun 2022; 13:6390. [PMID: 36302916 PMCID: PMC9613762 DOI: 10.1038/s41467-022-34077-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 10/13/2022] [Indexed: 12/25/2022] Open
Abstract
Intrinsically disordered proteins, which do not adopt well-defined structures under physiological conditions, are implicated in many human diseases. Small molecules that target the disordered transactivation domain of the androgen receptor have entered human trials for the treatment of castration-resistant prostate cancer (CRPC), but no structural or mechanistic rationale exists to explain their inhibition mechanisms or relative potencies. Here, we utilize all-atom molecular dynamics computer simulations to elucidate atomically detailed binding mechanisms of the compounds EPI-002 and EPI-7170 to the androgen receptor. Our simulations reveal that both compounds bind at the interface of two transiently helical regions and induce the formation of partially folded collapsed helical states. We find that EPI-7170 binds androgen receptor more tightly than EPI-002 and we identify a network of intermolecular interactions that drives higher affinity binding. Our results suggest strategies for developing more potent androgen receptor inhibitors and general strategies for disordered protein drug design.
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Affiliation(s)
- Jiaqi Zhu
- grid.254880.30000 0001 2179 2404Dartmouth College, Department of Chemistry, Hanover, NH 03755 USA
| | - Xavier Salvatella
- grid.473715.30000 0004 6475 7299Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028 Barcelona, Spain ,grid.425902.80000 0000 9601 989XICREA, Passeig Lluís Companys 23, 0810 Barcelona, Spain
| | - Paul Robustelli
- grid.254880.30000 0001 2179 2404Dartmouth College, Department of Chemistry, Hanover, NH 03755 USA
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10
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Characterisation of HOIP RBR E3 ligase conformational dynamics using integrative modelling. Sci Rep 2022; 12:15201. [PMID: 36076045 PMCID: PMC9458678 DOI: 10.1038/s41598-022-18890-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/22/2022] [Indexed: 11/29/2022] Open
Abstract
Multidomain proteins composed of individual domains connected by flexible linkers pose a challenge for structural studies due to their intrinsic conformational dynamics. Integrated modelling approaches provide a means to characterise protein flexibility by combining experimental measurements with molecular simulations. In this study, we characterise the conformational dynamics of the catalytic RBR domain of the E3 ubiquitin ligase HOIP, which regulates immune and inflammatory signalling pathways. Specifically, we combine small angle X-ray scattering experiments and molecular dynamics simulations to generate weighted conformational ensembles of the HOIP RBR domain using two different approaches based on maximum parsimony and maximum entropy principles. Both methods provide optimised ensembles that are instrumental in rationalising observed differences between SAXS-based solution studies and available crystal structures and highlight the importance of interdomain linker flexibility.
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11
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Piomponi V, Fröhlking T, Bernetti M, Bussi G. Molecular Simulations Matching Denaturation Experiments for N 6-Methyladenosine. ACS CENTRAL SCIENCE 2022; 8:1218-1228. [PMID: 36032773 PMCID: PMC9413829 DOI: 10.1021/acscentsci.2c00565] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Indexed: 06/15/2023]
Abstract
Post-transcriptional modifications are crucial for RNA function and can affect its structure and dynamics. Force-field-based classical molecular dynamics simulations are a fundamental tool to characterize biomolecular dynamics, and their application to RNA is flourishing. Here, we show that the set of force-field parameters for N6-methyladenosine (m6A) developed for the commonly used AMBER force field does not reproduce duplex denaturation experiments and, specifically, cannot be used to describe both paired and unpaired states. Then, we use reweighting techniques to derive new parameters matching available experimental data. The resulting force field can be used to properly describe paired and unpaired m6A in both syn and anti conformation, which thus opens the way to the use of molecular simulations to investigate the effects of N6 methylations on RNA structural dynamics.
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12
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Bergonzo C, Grishaev A, Bottaro S. Conformational heterogeneity of UCAAUC RNA oligonucleotide from molecular dynamics simulations, SAXS, and NMR experiments. RNA (NEW YORK, N.Y.) 2022; 28:937-946. [PMID: 35483823 PMCID: PMC9202585 DOI: 10.1261/rna.078888.121] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 03/17/2022] [Indexed: 06/14/2023]
Abstract
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.
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Affiliation(s)
- Christina Bergonzo
- National Institute of Standards and Technology and Institute for Bioscience and Biotechnology Research, Rockville, Maryland 20850, USA
| | - Alexander Grishaev
- National Institute of Standards and Technology and Institute for Bioscience and Biotechnology Research, Rockville, Maryland 20850, USA
| | - Sandro Bottaro
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, DK-2200 Copenhagen N, Denmark
- Department of Biomedical Sciences, Humanitas University, 20090 Pieve Emanuele, Italy
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13
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Löhr T, Kohlhoff K, Heller GT, Camilloni C, Vendruscolo M. A Small Molecule Stabilizes the Disordered Native State of the Alzheimer's Aβ Peptide. ACS Chem Neurosci 2022; 13:1738-1745. [PMID: 35649268 PMCID: PMC9204762 DOI: 10.1021/acschemneuro.2c00116] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 05/04/2022] [Indexed: 11/30/2022] Open
Abstract
The stabilization of native states of proteins is a powerful drug discovery strategy. It is still unclear, however, whether this approach can be applied to intrinsically disordered proteins. Here, we report a small molecule that stabilizes the native state of the Aβ42 peptide, an intrinsically disordered protein fragment associated with Alzheimer's disease. We show that this stabilization takes place by a disordered binding mechanism, in which both the small molecule and the Aβ42 peptide remain disordered. This disordered binding mechanism involves enthalpically favorable local π-stacking interactions coupled with entropically advantageous global effects. These results indicate that small molecules can stabilize disordered proteins in their native states through transient non-specific interactions that provide enthalpic gain while simultaneously increasing the conformational entropy of the proteins.
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Affiliation(s)
- Thomas Löhr
- Department
of Chemistry, University of Cambridge, CB2 1EW Cambridge, UK
| | - Kai Kohlhoff
- Google
Research, Mountain
View, California 94043, United States
| | - Gabriella T. Heller
- Department
of Chemistry, University of Cambridge, CB2 1EW Cambridge, UK
- Department
of Structural and Molecular Biology, University
College London, WC1E 6BT London, UK
| | - Carlo Camilloni
- Dipartimento
di Bioscienze, Università degli Studi
di Milano, 20133 Milano, Italy
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14
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Fröhlking T, Mlýnský V, Janeček M, Kührová P, Krepl M, Banáš P, Šponer J, Bussi G. Automatic Learning of Hydrogen-Bond Fixes in the AMBER RNA Force Field. J Chem Theory Comput 2022; 18:4490-4502. [PMID: 35699952 PMCID: PMC9281393 DOI: 10.1021/acs.jctc.2c00200] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
![]()
The
capability of
current force fields to reproduce RNA structural
dynamics is limited. Several methods have been developed to take advantage
of experimental data in order to enforce agreement with experiments.
Here, we extend an existing framework which allows arbitrarily chosen
force-field correction terms to be fitted by quantification of the
discrepancy between observables back-calculated from simulation and
corresponding experiments. We apply a robust regularization protocol
to avoid overfitting and additionally introduce and compare a number
of different regularization strategies, namely, L1, L2, Kish size,
relative Kish size, and relative entropy penalties. The training set
includes a GACC tetramer as well as more challenging systems, namely,
gcGAGAgc and gcUUCGgc RNA tetraloops. Specific intramolecular hydrogen
bonds in the AMBER RNA force field are corrected with automatically
determined parameters that we call gHBfixopt. A validation
involving a separate simulation of a system present in the training
set (gcUUCGgc) and new systems not seen during training (CAAU and
UUUU tetramers) displays improvements regarding the native population
of the tetraloop as well as good agreement with NMR experiments for
tetramers when using the new parameters. Then, we simulate folded
RNAs (a kink–turn and L1 stalk rRNA) including hydrogen bond
types not sufficiently present in the training set. This allows a
final modification of the parameter set which is named gHBfix21 and
is suggested to be applicable to a wider range of RNA systems.
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Affiliation(s)
- Thorben Fröhlking
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, Trieste 34136, Italy
| | - Vojtěch Mlýnský
- Institute of Biophysics of the Czech Academy of Sciences, Kralovopolska 135, Brno 612 65, Czech Republic
| | - Michal Janeček
- Department of Physical Chemistry, Faculty of Science, Palacky University, tr. 17 listopadu 12, Olomouc 771 46, Czech Republic
| | - Petra Kührová
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacky University Olomouc, Slechtitelu 27, 779 00 Olomouc, Czech Republic
| | - Miroslav Krepl
- Institute of Biophysics of the Czech Academy of Sciences, Kralovopolska 135, Brno 612 65, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacky University Olomouc, Slechtitelu 27, 779 00 Olomouc, Czech Republic
| | - Pavel Banáš
- Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacky University Olomouc, Slechtitelu 27, 779 00 Olomouc, Czech Republic
| | - Jiří Šponer
- Institute of Biophysics of the Czech Academy of Sciences, Kralovopolska 135, Brno 612 65, Czech Republic.,Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute (CATRIN), Palacky University Olomouc, Slechtitelu 27, 779 00 Olomouc, Czech Republic
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 265, Trieste 34136, Italy
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15
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Barrett R, Ansari M, Ghoshal G, White AD. Simulation-based inference with approximately correct parameters via maximum entropy. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac6286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Inferring the input parameters of simulators from observations is a crucial challenge with applications from epidemiology to molecular dynamics. Here we show a simple approach in the regime of sparse data and approximately correct models, which is common when trying to use an existing model to infer latent variables with observed data. This approach is based on the principle of maximum entropy (MaxEnt) and provably makes the smallest change in the latent joint distribution to fit new data. This method requires no likelihood or model derivatives and its fit is insensitive to prior strength, removing the need to balance observed data fit with prior belief. The method requires the ansatz that data is fit in expectation, which is true in some settings and may be reasonable in all settings with few data points. The method is based on sample reweighting, so its asymptotic run time is independent of prior distribution dimension. We demonstrate this MaxEnt approach and compare with other likelihood-free inference methods across three systems: a point particle moving in a gravitational field, a compartmental model of epidemic spread and molecular dynamics simulation of a protein.
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16
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Kulkarni P, Leite VBP, Roy S, Bhattacharyya S, Mohanty A, Achuthan S, Singh D, Appadurai R, Rangarajan G, Weninger K, Orban J, Srivastava A, Jolly MK, Onuchic JN, Uversky VN, Salgia R. Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma. BIOPHYSICS REVIEWS 2022; 3:011306. [PMID: 38505224 PMCID: PMC10903413 DOI: 10.1063/5.0080512] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 02/17/2022] [Indexed: 03/21/2024]
Abstract
Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.
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Affiliation(s)
- Prakash Kulkarni
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California 91010, USA
| | - Vitor B. P. Leite
- Departamento de Física, Instituto de Biociências, Letras e Ciências Exatas, Universidade Estadual Paulista (UNESP), São José do Rio Preto, São Paulo 15054-000, Brazil
| | - Susmita Roy
- Department of Chemical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal 741246, India
| | - Supriyo Bhattacharyya
- Translational Bioinformatics, Center for Informatics, Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, California 91010, USA
| | - Atish Mohanty
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California 91010, USA
| | - Srisairam Achuthan
- Center for Informatics, Division of Research Informatics, City of Hope National Medical Center, Duarte, California 91010, USA
| | - Divyoj Singh
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Rajeswari Appadurai
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Govindan Rangarajan
- Department of Mathematics, Indian Institute of Science, Bangalore 560012, India
| | - Keith Weninger
- Department of Physics, North Carolina State University, Raleigh, North Carolina 27695, USA
| | | | - Anand Srivastava
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | - Mohit Kumar Jolly
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Jose N. Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005-1892, USA
| | | | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California 91010, USA
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17
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Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins. Biochem Soc Trans 2022; 50:541-554. [PMID: 35129612 DOI: 10.1042/bst20210499] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/13/2022] [Accepted: 01/17/2022] [Indexed: 12/29/2022]
Abstract
Intrinsically disordered proteins (IDPs) and multidomain proteins with flexible linkers show a high level of structural heterogeneity and are best described by ensembles consisting of multiple conformations with associated thermodynamic weights. Determining conformational ensembles usually involves the integration of biophysical experiments and computational models. In this review, we discuss current approaches to determine conformational ensembles of IDPs and multidomain proteins, including the choice of biophysical experiments, computational models used to sample protein conformations, models to calculate experimental observables from protein structure, and methods to refine ensembles against experimental data. We also provide examples of recent applications of integrative conformational ensemble determination to study IDPs and multidomain proteins and suggest future directions for research in the field.
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18
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Latham AP, Zhang B. Unifying coarse-grained force fields for folded and disordered proteins. Curr Opin Struct Biol 2022; 72:63-70. [PMID: 34536913 PMCID: PMC9057422 DOI: 10.1016/j.sbi.2021.08.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 08/08/2021] [Accepted: 08/17/2021] [Indexed: 12/22/2022]
Abstract
Liquid-liquid phase separation drives the formation of biological condensates that play essential roles in transcriptional regulation and signal sensing. Computational modeling could provide high-resolution structural characterizations of these condensates and help uncover physicochemical interactions that dictate their stability. However, many protein molecules involved in phase separation often contain multiple ordered domains connected with flexible, structureless linkers. Simulating such proteins necessitates force fields with consistent accuracy for both folded and disordered proteins. We provide a critical review of existing coarse-grained force fields for disordered proteins and highlight the challenges in their application to folded proteins. After discussing existing algorithms for force field parameterization, we propose an optimization strategy that should lead to computer models with improved transferability across protein types.
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Affiliation(s)
- Andrew P Latham
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
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19
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Hou XN, Tochio H. Characterizing conformational ensembles of multi-domain proteins using anisotropic paramagnetic NMR restraints. Biophys Rev 2022; 14:55-66. [PMID: 35340613 PMCID: PMC8921464 DOI: 10.1007/s12551-021-00916-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/16/2021] [Indexed: 01/13/2023] Open
Abstract
It has been over two decades since paramagnetic NMR started to form part of the essential techniques for structural analysis of proteins under physiological conditions. Paramagnetic NMR has significantly expanded our understanding of the inherent flexibility of proteins, in particular, those that are formed by combinations of two or more domains. Here, we present a brief overview of techniques to characterize conformational ensembles of such multi-domain proteins using paramagnetic NMR restraints produced through anisotropic metals, with a focus on the basics of anisotropic paramagnetic effects, the general procedures of conformational ensemble reconstruction, and some representative reweighting approaches.
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Affiliation(s)
- Xue-Ni Hou
- Department of Biophysics, Graduate School of Science, Kyoto University, Sakyo-ku, Kyoto, 606-8502 Japan
| | - Hidehito Tochio
- Department of Biophysics, Graduate School of Science, Kyoto University, Sakyo-ku, Kyoto, 606-8502 Japan
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20
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Topf M, Rosta E, Bowman GR, Bonomi M. Editorial: Experiments and Simulations: A Pas de Deux to Unravel Biological Function. Front Mol Biosci 2021; 8:799406. [PMID: 34912853 PMCID: PMC8667856 DOI: 10.3389/fmolb.2021.799406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 11/01/2021] [Indexed: 11/29/2022] Open
Affiliation(s)
- Maya Topf
- Center for Structural Systems Biology (CSSB), Leibniz-Institut für Experimentelle Virologie (HPI) and Universitätsklinikum Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Edina Rosta
- Department of Chemistry, King's College London, London, United Kingdom.,Department of Physics and Astronomy, University College London, London, United Kingdom
| | - Gregory R Bowman
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St Louis, MO, United States
| | - Massimiliano Bonomi
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, Institut Pasteur, Paris, France.,USR3756 Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Paris, France
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21
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Zhang K, Frank AT. Probabilistic Modeling of RNA Ensembles Using NMR Chemical Shifts. J Phys Chem B 2021; 125:9970-9978. [PMID: 34449236 DOI: 10.1021/acs.jpcb.1c05651] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
NMR-derived chemical shifts are structural fingerprints that are sensitive to the underlying conformational distributions of molecules. Thus, chemical shift data are now routinely used to infer the dynamical or conformational ensembles of peptides and proteins. However, for RNAs, techniques for inferring their conformational ensembles from chemical shift data have received less attention. Here, we used chemical shift data and the Bayesian/maximum entropy (BME) approach to model the secondary structure ensembles of several single-stranded RNAs. Inspection of the resulting ensembles indicates that the secondary structure of the highest weighted (most probable) conformer in the ensemble typically resembled the known NMR structure. Furthermore, using apo chemical shifts measured for the HIV-1 TAR RNA, we found that our framework reproduces the expected structure yet predicts the existence of a previously unobserved base pair, which we speculate may be sampled transiently. We expect that the chemical shift-based BME (CS-BME) framework we describe here should find utility as a general strategy for modeling RNA ensembles using chemical shift data.
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Affiliation(s)
- Kexin Zhang
- Chemistry Department, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
| | - Aaron T Frank
- Biophysics Program, University of Michigan, 930 North University Avenue, Ann Arbor, Michigan 48109, United States
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22
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Alston JJ, Soranno A, Holehouse AS. Integrating single-molecule spectroscopy and simulations for the study of intrinsically disordered proteins. Methods 2021; 193:116-135. [PMID: 33831596 PMCID: PMC8713295 DOI: 10.1016/j.ymeth.2021.03.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/25/2021] [Accepted: 03/31/2021] [Indexed: 12/21/2022] Open
Abstract
Over the last two decades, intrinsically disordered proteins and protein regions (IDRs) have emerged from a niche corner of biophysics to be recognized as essential drivers of cellular function. Various techniques have provided fundamental insight into the function and dysfunction of IDRs. Among these techniques, single-molecule fluorescence spectroscopy and molecular simulations have played a major role in shaping our modern understanding of the sequence-encoded conformational behavior of disordered proteins. While both techniques are frequently used in isolation, when combined they offer synergistic and complementary information that can help uncover complex molecular details. Here we offer an overview of single-molecule fluorescence spectroscopy and molecular simulations in the context of studying disordered proteins. We discuss the various means in which simulations and single-molecule spectroscopy can be integrated, and consider a number of studies in which this integration has uncovered biological and biophysical mechanisms.
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Affiliation(s)
- Jhullian J Alston
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis 63110, MO, USA; Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis 63130, MO, USA
| | - Andrea Soranno
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis 63110, MO, USA; Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis 63130, MO, USA.
| | - Alex S Holehouse
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis 63110, MO, USA; Center for Science and Engineering of Living Systems (CSELS), Washington University in St. Louis, St. Louis 63130, MO, USA.
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23
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Bernetti M, Hall KB, Bussi G. Reweighting of molecular simulations with explicit-solvent SAXS restraints elucidates ion-dependent RNA ensembles. Nucleic Acids Res 2021; 49:e84. [PMID: 34107023 PMCID: PMC8373061 DOI: 10.1093/nar/gkab459] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/07/2021] [Accepted: 05/16/2021] [Indexed: 01/03/2023] Open
Abstract
Small-angle X-ray scattering (SAXS) experiments are increasingly used to probe RNA structure. A number of forward models that relate measured SAXS intensities and structural features, and that are suitable to model either explicit-solvent effects or solute dynamics, have been proposed in the past years. Here, we introduce an approach that integrates atomistic molecular dynamics simulations and SAXS experiments to reconstruct RNA structural ensembles while simultaneously accounting for both RNA conformational dynamics and explicit-solvent effects. Our protocol exploits SAXS pure-solute forward models and enhanced sampling methods to sample an heterogenous ensemble of structures, with no information towards the experiments provided on-the-fly. The generated structural ensemble is then reweighted through the maximum entropy principle so as to match reference SAXS experimental data at multiple ionic conditions. Importantly, accurate explicit-solvent forward models are used at this reweighting stage. We apply this framework to the GTPase-associated center, a relevant RNA molecule involved in protein translation, in order to elucidate its ion-dependent conformational ensembles. We show that (a) both solvent and dynamics are crucial to reproduce experimental SAXS data and (b) the resulting dynamical ensembles contain an ion-dependent fraction of extended structures.
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Affiliation(s)
- Mattia Bernetti
- Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, Trieste 34136, Italy
| | - Kathleen B Hall
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, Trieste 34136, Italy
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24
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Quaglia F, Lazar T, Hatos A, Tompa P, Piovesan D, Tosatto SCE. Exploring Curated Conformational Ensembles of Intrinsically Disordered Proteins in the Protein Ensemble Database. Curr Protoc 2021; 1:e192. [PMID: 34252246 DOI: 10.1002/cpz1.192] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The Protein Ensemble Database (PED; https://proteinensemble.org/) is the major repository of conformational ensembles of intrinsically disordered proteins (IDPs). Conformational ensembles of IDPs are primarily provided by their authors or occasionally collected from literature, and are subsequently deposited in PED along with the corresponding structured, manually curated metadata. The modeling of conformational ensembles usually relies on experimental data from small-angle X-ray scattering (SAXS), fluorescence resonance energy transfer (FRET), NMR spectroscopy, and molecular dynamics (MD) simulations, or a combination of these techniques. The growing number of scientific studies based on these data, along with the astounding and swift progress in the field of protein intrinsic disorder, has required a significant update and upgrade of PED, first published in 2014. To this end, the database was entirely renewed in 2020 and now has a dedicated team of biocurators providing manually curated descriptions of the methods and conditions applied to generate the conformational ensembles and for checking consistency of the data. Here, we present a detailed description on how to explore PED with its protein pages and experimental pages, and how to interpret entries of conformational ensembles. We describe how to efficiently search conformational ensembles deposited in PED by means of its web interface and API. We demonstrate how to make sense of the PED protein page and its associated experimental entry pages with reference to the yeast Sic1 use case. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Performing a search in PED Support Protocol 1: Programmatic access with the PED API Basic Protocol 2: Interpreting the protein page and the experimental entry page-the Sic1 use case Support Protocol 2: Downloading options Support Protocol 3: Understanding the validation report-the Sic1 use case Basic Protocol 3: Submitting new conformational ensembles to PED Basic Protocol 4: Providing feedback in PED.
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Affiliation(s)
- Federica Quaglia
- Department of Biomedical Sciences, University of Padova, Padova, Italy.,Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy
| | - Tamas Lazar
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.,VIB-VUB Center for Structural Biology, Brussels, Belgium
| | - András Hatos
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Peter Tompa
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.,VIB-VUB Center for Structural Biology, Brussels, Belgium.,Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padova, Padova, Italy
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25
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Clerc I, Sagar A, Barducci A, Sibille N, Bernadó P, Cortés J. The diversity of molecular interactions involving intrinsically disordered proteins: A molecular modeling perspective. Comput Struct Biotechnol J 2021; 19:3817-3828. [PMID: 34285781 PMCID: PMC8273358 DOI: 10.1016/j.csbj.2021.06.031] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/17/2021] [Accepted: 06/21/2021] [Indexed: 01/15/2023] Open
Abstract
Intrinsically Disordered Proteins and Regions (IDPs/IDRs) are key components of a multitude of biological processes. Conformational malleability enables IDPs/IDRs to perform very specialized functions that cannot be accomplished by globular proteins. The functional role for most of these proteins is related to the recognition of other biomolecules to regulate biological processes or as a part of signaling pathways. Depending on the extent of disorder, the number of interacting sites and the type of partner, very different architectures for the resulting assemblies are possible. More recently, molecular condensates with liquid-like properties composed of multiple copies of IDPs and nucleic acids have been proven to regulate key processes in eukaryotic cells. The structural and kinetic details of disordered biomolecular complexes are difficult to unveil experimentally due to their inherent conformational heterogeneity. Computational approaches, alone or in combination with experimental data, have emerged as unavoidable tools to understand the functional mechanisms of this elusive type of assemblies. The level of description used, all-atom or coarse-grained, strongly depends on the size of the molecular systems and on the timescale of the investigated mechanism. In this mini-review, we describe the most relevant architectures found for molecular interactions involving IDPs/IDRs and the computational strategies applied for their investigation.
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Affiliation(s)
- Ilinka Clerc
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
| | - Amin Sagar
- Centre de Biochimie Structurale, INSERM, CNRS, Université de Montpellier, France
| | - Alessandro Barducci
- Centre de Biochimie Structurale, INSERM, CNRS, Université de Montpellier, France
| | - Nathalie Sibille
- Centre de Biochimie Structurale, INSERM, CNRS, Université de Montpellier, France
| | - Pau Bernadó
- Centre de Biochimie Structurale, INSERM, CNRS, Université de Montpellier, France
| | - Juan Cortés
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
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26
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Latham AP, Zhang B. Consistent Force Field Captures Homologue-Resolved HP1 Phase Separation. J Chem Theory Comput 2021; 17:3134-3144. [PMID: 33826337 PMCID: PMC8119372 DOI: 10.1021/acs.jctc.0c01220] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Many proteins have been shown to function via liquid-liquid phase separation. Computational modeling could offer much needed structural details of protein condensates and reveal the set of molecular interactions that dictate their stability. However, the presence of both ordered and disordered domains in these proteins places a high demand on the model accuracy. Here, we present an algorithm to derive a coarse-grained force field, MOFF, which can model both ordered and disordered proteins with consistent accuracy. It combines maximum entropy biasing, least-squares fitting, and basic principles of energy landscape theory to ensure that MOFF recreates experimental radii of gyration while predicting the folded structures for globular proteins with lower energy. The theta temperature determined from MOFF separates ordered and disordered proteins at 300 K and exhibits a strikingly linear relationship with amino acid sequence composition. We further applied MOFF to study the phase behavior of HP1, an essential protein for post-translational modification and spatial organization of chromatin. The force field successfully resolved the structural difference of two HP1 homologues despite their high sequence similarity. We carried out large-scale simulations with hundreds of proteins to determine the critical temperature of phase separation and uncover multivalent interactions that stabilize higher-order assemblies. In all, our work makes significant methodological strides to connect theories of ordered and disordered proteins and provides a powerful tool for studying liquid-liquid phase separation with near-atomistic details.
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Affiliation(s)
- Andrew P Latham
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Bin Zhang
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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27
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Lazar T, Martínez-Pérez E, Quaglia F, Hatos A, Chemes L, Iserte JA, Méndez NA, Garrone NA, Saldaño T, Marchetti J, Rueda A, Bernadó P, Blackledge M, Cordeiro TN, Fagerberg E, Forman-Kay JD, Fornasari M, Gibson TJ, Gomes GNW, Gradinaru C, Head-Gordon T, Jensen MR, Lemke E, Longhi S, Marino-Buslje C, Minervini G, Mittag T, Monzon A, Pappu RV, Parisi G, Ricard-Blum S, Ruff KM, Salladini E, Skepö M, Svergun D, Vallet S, Varadi M, Tompa P, Tosatto SCE, Piovesan D. PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins. Nucleic Acids Res 2021; 49:D404-D411. [PMID: 33305318 PMCID: PMC7778965 DOI: 10.1093/nar/gkaa1021] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/13/2020] [Accepted: 12/08/2020] [Indexed: 12/21/2022] Open
Abstract
The Protein Ensemble Database (PED) (https://proteinensemble.org), which holds structural ensembles of intrinsically disordered proteins (IDPs), has been significantly updated and upgraded since its last release in 2016. The new version, PED 4.0, has been completely redesigned and reimplemented with cutting-edge technology and now holds about six times more data (162 versus 24 entries and 242 versus 60 structural ensembles) and a broader representation of state of the art ensemble generation methods than the previous version. The database has a completely renewed graphical interface with an interactive feature viewer for region-based annotations, and provides a series of descriptors of the qualitative and quantitative properties of the ensembles. High quality of the data is guaranteed by a new submission process, which combines both automatic and manual evaluation steps. A team of biocurators integrate structured metadata describing the ensemble generation methodology, experimental constraints and conditions. A new search engine allows the user to build advanced queries and search all entry fields including cross-references to IDP-related resources such as DisProt, MobiDB, BMRB and SASBDB. We expect that the renewed PED will be useful for researchers interested in the atomic-level understanding of IDP function, and promote the rational, structure-based design of IDP-targeting drugs.
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Affiliation(s)
- Tamas Lazar
- VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology, Brussels 1050, Belgium
- Structural Biology Brussels, Bioengineering Sciences Department, Vrije Universiteit Brussel, Brussels 1050, Belgium
| | - Elizabeth Martínez-Pérez
- Bioinformatics Unit, Fundación Instituto Leloir, Buenos Aires, C1405BWE, Argentina
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Federica Quaglia
- Dept. of Biomedical Sciences, University of Padua, Padova 35131, Italy
| | - András Hatos
- Dept. of Biomedical Sciences, University of Padua, Padova 35131, Italy
| | - Lucía B Chemes
- Instituto de Investigaciones Biotecnológicas “Dr. Rodolfo A. Ugalde’’, IIB-UNSAM, IIBIO-CONICET, Universidad Nacional de SanMartín, CP1650 San Martín, Buenos Aires, Argentina
| | - Javier A Iserte
- Bioinformatics Unit, Fundación Instituto Leloir, Buenos Aires, C1405BWE, Argentina
| | - Nicolás A Méndez
- Instituto de Investigaciones Biotecnológicas “Dr. Rodolfo A. Ugalde’’, IIB-UNSAM, IIBIO-CONICET, Universidad Nacional de SanMartín, CP1650 San Martín, Buenos Aires, Argentina
| | - Nicolás A Garrone
- Instituto de Investigaciones Biotecnológicas “Dr. Rodolfo A. Ugalde’’, IIB-UNSAM, IIBIO-CONICET, Universidad Nacional de SanMartín, CP1650 San Martín, Buenos Aires, Argentina
| | - Tadeo E Saldaño
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Julia Marchetti
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Ana Julia Velez Rueda
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Pau Bernadó
- Centre de Biochimie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier 34090, France
| | | | - Tiago N Cordeiro
- Centre de Biochimie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier 34090, France
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, Oeiras 2780-157, Portugal
| | - Eric Fagerberg
- Theoretical Chemistry, Lund University, Lund, POB 124, SE-221 00, Sweden
| | - Julie D Forman-Kay
- Molecular Medicine Program, Hospital for Sick Children, Toronto, M5G 1X8, Ontario, Canada
- Department of Biochemistry, University of Toronto, Toronto, M5S 1A8, Ontario, Canada
| | - Maria S Fornasari
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Toby J Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Gregory-Neal W Gomes
- Department of Physics, University of Toronto, Toronto, M5S 1A7, Ontario, Canada
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, L5L 1C6, Ontario, Canada
| | - Claudiu C Gradinaru
- Department of Physics, University of Toronto, Toronto, M5S 1A7, Ontario, Canada
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, L5L 1C6, Ontario, Canada
| | - Teresa Head-Gordon
- Departments of Chemistry, Bioengineering, Chemical and Biomolecular Engineering University of California, Berkeley, CA 94720, USA
| | | | - Edward A Lemke
- Biocentre, Johannes Gutenberg-University Mainz, Mainz 55128, Germany
- Institute of Molecular Biology, Mainz 55128, Germany
| | - Sonia Longhi
- Aix-Marseille University, CNRS, Architecture et Fonction des Macromolécules Biologiques (AFMB), Marseille 13288, France
| | | | | | - Tanja Mittag
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | | | - Rohit V Pappu
- Department of Biomedical Engineering, Center for Science & Engineering of Living Systems (CSELS), Washington University in St. Louis, MO 63130, USA
| | - Gustavo Parisi
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Sylvie Ricard-Blum
- Univ Lyon, University Claude Bernard Lyon 1, CNRS, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, Villeurbanne, 69629 Lyon Cedex 07, France
| | - Kiersten M Ruff
- Department of Biomedical Engineering, Center for Science & Engineering of Living Systems (CSELS), Washington University in St. Louis, MO 63130, USA
| | - Edoardo Salladini
- Aix-Marseille University, CNRS, Architecture et Fonction des Macromolécules Biologiques (AFMB), Marseille 13288, France
| | - Marie Skepö
- Theoretical Chemistry, Lund University, Lund, POB 124, SE-221 00, Sweden
- LINXS - Lund Institute of Advanced Neutron and X-ray Science, Lund 223 70, Sweden
| | - Dmitri Svergun
- European Molecular Biology Laboratory, Hamburg Unit, Hamburg 22607, Germany
| | - Sylvain D Vallet
- Univ Lyon, University Claude Bernard Lyon 1, CNRS, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, Villeurbanne, 69629 Lyon Cedex 07, France
| | - Mihaly Varadi
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Peter Tompa
- To whom correspondence should be addressed. Tel +32 473 785386;
| | - Silvio C E Tosatto
- Correspondence may also be addressed to Silvio C. E. Tosatto. Tel: +39 049 827 6269;
| | - Damiano Piovesan
- Dept. of Biomedical Sciences, University of Padua, Padova 35131, Italy
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28
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Kauffmann C, Zawadzka‐Kazimierczuk A, Kontaxis G, Konrat R. Using Cross-Correlated Spin Relaxation to Characterize Backbone Dihedral Angle Distributions of Flexible Protein Segments. Chemphyschem 2021; 22:18-28. [PMID: 33119214 PMCID: PMC7839595 DOI: 10.1002/cphc.202000789] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/28/2020] [Indexed: 01/11/2023]
Abstract
Crucial to the function of proteins is their existence as conformational ensembles sampling numerous and structurally diverse substates. Despite this widely accepted notion there is still a high demand for meaningful and reliable approaches to characterize protein ensembles in solution. As it is usually conducted in solution, NMR spectroscopy offers unique possibilities to address this challenge. Particularly, cross-correlated relaxation (CCR) effects have long been established to encode both protein structure and dynamics in a compelling manner. However, this wealth of information often limits their use in practice as structure and dynamics might prove difficult to disentangle. Using a modern Maximum Entropy (MaxEnt) reweighting approach to interpret CCR rates of Ubiquitin, we demonstrate that these uncertainties do not necessarily impair resolving CCR-encoded structural information. Instead, a suitable balance between complementary CCR experiments and prior information is found to be the most crucial factor in mapping backbone dihedral angle distributions. Experimental and systematic deviations such as oversimplified dynamics appear to be of minor importance. Using Ubiquitin as an example, we demonstrate that CCR rates are capable of characterizing rigid and flexible residues alike, indicating their unharnessed potential in studying disordered proteins.
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Affiliation(s)
- Clemens Kauffmann
- Department of Structural and Computational BiologyMax Perutz LaboratoriesUniversity of ViennaVienna Biocenter Campus 5A-1030ViennaAustria
| | - Anna Zawadzka‐Kazimierczuk
- Biological and Chemical Research CentreFaculty of ChemistryUniversity of WarsawŻwirki i Wigury 10102-089WarsawPoland
| | - Georg Kontaxis
- Department of Structural and Computational BiologyMax Perutz LaboratoriesUniversity of ViennaVienna Biocenter Campus 5A-1030ViennaAustria
| | - Robert Konrat
- Department of Structural and Computational BiologyMax Perutz LaboratoriesUniversity of ViennaVienna Biocenter Campus 5A-1030ViennaAustria
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29
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Medeiros Selegato D, Bracco C, Giannelli C, Parigi G, Luchinat C, Sgheri L, Ravera E. Comparison of Different Reweighting Approaches for the Calculation of Conformational Variability of Macromolecules from Molecular Simulations. Chemphyschem 2020; 22:127-138. [DOI: 10.1002/cphc.202000714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/14/2020] [Indexed: 11/07/2022]
Affiliation(s)
- Denise Medeiros Selegato
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
- Present address: Fundación MEDINA, Centro de Excelentia en Investigación de Medicamentos Innovadores and Andalucía MSD España Granada Spain
| | - Cesare Bracco
- Dipartimento di Matematica e Informatica “U. Dini” Università degli Studi di Firenze Viale Morgagni 67/a 50134 Florence Italy
| | - Carlotta Giannelli
- Dipartimento di Matematica e Informatica “U. Dini” Università degli Studi di Firenze Viale Morgagni 67/a 50134 Florence Italy
| | - Giacomo Parigi
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
| | - Luca Sgheri
- Istituto per le Applicazioni del Calcolo (CNR) sede di Firenze via Madonna del Piano 10 50019 Sesto Fiorentino Italy
| | - Enrico Ravera
- Magnetic Resonance Center (CERM) and Interuniversity Consortium for Magnetic Resonance of Metallo Proteins (CIRMMP) Via L. Sacconi 6 50019 Sesto Fiorentino Italy
- Dipartimento di Chimica “Ugo Schiff” Università degli Studi di Firenze Via della Lastruccia 3 50019 Sesto Fiorentino Italy
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30
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Bernetti M, Bertazzo M, Masetti M. Data-Driven Molecular Dynamics: A Multifaceted Challenge. Pharmaceuticals (Basel) 2020; 13:E253. [PMID: 32961909 PMCID: PMC7557855 DOI: 10.3390/ph13090253] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 12/18/2022] Open
Abstract
The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.
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Affiliation(s)
- Mattia Bernetti
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), via Bonomea 265, I-34136 Trieste, Italy;
| | - Martina Bertazzo
- Computational Sciences, Istituto Italiano di Tecnologia, via Morego 30, I-16163 Genova, Italy;
| | - Matteo Masetti
- Department of Pharmacy and Biotechnology, Alma Mater Studiorum—Università di Bologna, via Belmeloro 6, I-40126 Bologna, Italy
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31
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Fröhlking T, Bernetti M, Calonaci N, Bussi G. Toward empirical force fields that match experimental observables. J Chem Phys 2020; 152:230902. [PMID: 32571067 DOI: 10.1063/5.0011346] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Biomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics simulations on larger systems achieving ergodic sampling is paving the way to directly using such simulations along with solution experiments obtained on macromolecular systems. Recently, a number of methods have been introduced to automatize this approach. Here, we review these methods, highlight their relationship with machine learning methods, and discuss the open challenges in the field.
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Affiliation(s)
- Thorben Fröhlking
- Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, Trieste 34136, Italy
| | - Mattia Bernetti
- Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, Trieste 34136, Italy
| | - Nicola Calonaci
- Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, Trieste 34136, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati, Via Bonomea 265, Trieste 34136, Italy
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32
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Kauffmann C, Kazimierczuk K, Schwarz TC, Konrat R, Zawadzka-Kazimierczuk A. A novel high-dimensional NMR experiment for resolving protein backbone dihedral angle ambiguities. JOURNAL OF BIOMOLECULAR NMR 2020; 74:257-265. [PMID: 32239382 PMCID: PMC7211790 DOI: 10.1007/s10858-020-00308-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 03/12/2020] [Indexed: 05/07/2023]
Abstract
Intrinsically disordered proteins (IDPs) are challenging established structural biology perception and urge a reassessment of the conventional understanding of the subtle interplay between protein structure and dynamics. Due to their importance in eukaryotic life and central role in protein interaction networks, IDP research is a fascinating and highly relevant research area in which NMR spectroscopy is destined to be a key player. The flexible nature of IDPs, as a result of the sampling of a vast conformational space, however, poses a tremendous scientific challenge, both technically and theoretically. Pronounced signal averaging results in narrow signal dispersion and requires higher dimensionality NMR techniques. Moreover, a fundamental problem in the structural characterization of IDPs is the definition of the conformational ensemble sampled by the polypeptide chain in solution, where often the interpretation relies on the concept of 'residual structure' or 'conformational preference'. An important source of structural information is information-rich NMR experiments that probe protein backbone dihedral angles in a unique manner. Cross-correlated relaxation experiments have proven to fulfil this task as they provide unique information about protein backbones, particularly in IDPs. Here we present a novel cross-correlation experiment that utilizes non-uniform sampling detection schemes to resolve protein backbone dihedral ambiguities in IDPs. The sensitivity of this novel technique is illustrated with an application to the prototypical IDP [Formula: see text]-Synculein for which unexpected deviations from random-coil-like behaviour could be observed.
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Affiliation(s)
- Clemens Kauffmann
- Max Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Vienna Biocenter Campus 5, 1030, Vienna, Austria
| | | | - Thomas C Schwarz
- Max Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Vienna Biocenter Campus 5, 1030, Vienna, Austria
| | - Robert Konrat
- Max Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Vienna Biocenter Campus 5, 1030, Vienna, Austria.
| | - Anna Zawadzka-Kazimierczuk
- Max Perutz Laboratories, Department of Structural and Computational Biology, University of Vienna, Vienna Biocenter Campus 5, 1030, Vienna, Austria.
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Żwirki i Wigury 101, 02-089, Warsaw, Poland.
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33
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Reißer S, Zucchelli S, Gustincich S, Bussi G. Conformational ensembles of an RNA hairpin using molecular dynamics and sparse NMR data. Nucleic Acids Res 2020; 48:1164-1174. [PMID: 31889193 PMCID: PMC7026608 DOI: 10.1093/nar/gkz1184] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 12/05/2019] [Accepted: 12/09/2019] [Indexed: 01/12/2023] Open
Abstract
Solution nuclear magnetic resonance (NMR) experiments allow RNA dynamics to be determined in an aqueous environment. However, when a limited number of peaks are assigned, it is difficult to obtain structural information. We here show a protocol based on the combination of experimental data (Nuclear Overhauser Effect, NOE) and molecular dynamics simulations with enhanced sampling methods. This protocol allows to (a) obtain a maximum entropy ensemble compatible with NMR restraints and (b) obtain a minimal set of metastable conformations compatible with the experimental data (maximum parsimony). The method is applied to a hairpin of 29 nt from an inverted SINEB2, which is part of the SINEUP family and has been shown to enhance protein translation. A clustering procedure is introduced where the annotation of base-base interactions and glycosidic bond angles is used as a metric. By reweighting the contributions of the clusters, minimal sets of four conformations could be found which are compatible with the experimental data. A motif search on the structural database showed that some identified low-population states are present in experimental structures of other RNA transcripts. The introduced method can be applied to characterize RNA dynamics in systems where a limited amount of NMR information is available.
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Affiliation(s)
- Sabine Reißer
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy
| | - Silvia Zucchelli
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy
- Department of Health Sciences, Center for Autoimmune and Allergic Diseases (CAAD) and Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Piemonte Orientale, Novara, Italy
| | - Stefano Gustincich
- Central RNA Laboratory and Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia (IIT), 16163 Genova, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Via Bonomea 265, 34136 Trieste, Italy
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34
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Bradshaw RT, Marinelli F, Faraldo-Gómez JD, Forrest LR. Interpretation of HDX Data by Maximum-Entropy Reweighting of Simulated Structural Ensembles. Biophys J 2020; 118:1649-1664. [PMID: 32105651 PMCID: PMC7136279 DOI: 10.1016/j.bpj.2020.02.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/28/2020] [Accepted: 02/05/2020] [Indexed: 01/12/2023] Open
Abstract
Hydrogen-deuterium exchange combined with mass spectrometry (HDX-MS) is a widely applied biophysical technique that probes the structure and dynamics of biomolecules without the need for site-directed modifications or bio-orthogonal labels. The mechanistic interpretation of HDX data, however, is often qualitative and subjective, owing to a lack of quantitative methods to rigorously translate observed deuteration levels into atomistic structural information. To help address this problem, we have developed a methodology to generate structural ensembles that faithfully reproduce HDX-MS measurements. In this approach, an ensemble of protein conformations is first generated, typically using molecular dynamics simulations. A maximum-entropy bias is then applied post hoc to the resulting ensemble such that averaged peptide-deuteration levels, as predicted by an empirical model, agree with target values within a given level of uncertainty. We evaluate this approach, referred to as HDX ensemble reweighting (HDXer), for artificial target data reflecting the two major conformational states of a binding protein. We demonstrate that the information provided by HDX-MS experiments and by the model of exchange are sufficient to recover correctly weighted structural ensembles from simulations, even when the relevant conformations are rarely observed. Degrading the information content of the target data—e.g., by reducing sequence coverage, by averaging exchange levels over longer peptide segments, or by incorporating different sources of uncertainty—reduces the structural accuracy of the reweighted ensemble but still allows for useful insights into the distinctive structural features reflected by the target data. Finally, we describe a quantitative metric to rank candidate structural ensembles according to their correspondence with target data and illustrate the use of HDXer to describe changes in the conformational ensemble of the membrane protein LeuT. In summary, HDXer is designed to facilitate objective structural interpretations of HDX-MS data and to inform experimental approaches and further developments of theoretical exchange models.
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Affiliation(s)
- Richard T Bradshaw
- Computational Structural Biology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland
| | - Fabrizio Marinelli
- Theoretical Molecular Biophysics Unit, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
| | - José D Faraldo-Gómez
- Theoretical Molecular Biophysics Unit, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
| | - Lucy R Forrest
- Computational Structural Biology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland.
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35
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Abstract
Functions of intrinsically disordered proteins do not require structure. Such structure-independent functionality has melted away the classic rigid "lock and key" representation of structure-function relationships in proteins, opening a new page in protein science, where molten keys operate on melted locks and where conformational flexibility and intrinsic disorder, structural plasticity and extreme malleability, multifunctionality and binding promiscuity represent a new-fangled reality. Analysis and understanding of this new reality require novel tools, and some of the techniques elaborated for the examination of intrinsically disordered protein functions are outlined in this review.
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Affiliation(s)
- Vladimir N. Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer’s Research Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, 33620, USA
- Laboratory of New Methods in Biology, Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, Russian Federation
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36
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Geraets JA, Pothula KR, Schröder GF. Integrating cryo-EM and NMR data. Curr Opin Struct Biol 2020; 61:173-181. [PMID: 32028106 DOI: 10.1016/j.sbi.2020.01.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/13/2020] [Accepted: 01/14/2020] [Indexed: 01/06/2023]
Abstract
Single-particle cryo-electron microscopy (cryo-EM) is increasingly used as a technique to determine the atomic structure of challenging biological systems. Recent advances in microscope engineering, electron detection, and image processing have allowed the structural determination of bigger and more flexible targets than possible with the complementary techniques X-ray crystallography and NMR spectroscopy. However, there exist many biological targets for which atomic resolution cannot be currently achieved with cryo-EM, making unambiguous determination of the protein structure impossible. Although determining the structure of large biological systems using solely NMR is often difficult, highly complementary experimental atomic-level data for each molecule can be derived from the spectra, and used in combination with cryo-EM data. We review here strategies with which both techniques can be synergistically combined, in order to reach detail and understanding unattainable by each technique acting alone; and the types of biological systems for which such an approach would be desirable.
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Affiliation(s)
- James A Geraets
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and JuStruct, Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Karunakar R Pothula
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and JuStruct, Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Gunnar F Schröder
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry) and JuStruct, Jülich Center for Structural Biology, Forschungszentrum Jülich, 52425 Jülich, Germany; Physics Department, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany.
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37
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Orioli S, Larsen AH, Bottaro S, Lindorff-Larsen K. How to learn from inconsistencies: Integrating molecular simulations with experimental data. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 170:123-176. [PMID: 32145944 DOI: 10.1016/bs.pmbts.2019.12.006] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
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.
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Affiliation(s)
- Simone Orioli
- Structural Biology and NMR Laboratory & Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark; Structural Biophysics, Niels Bohr Institute, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Andreas Haahr Larsen
- Structural Biology and NMR Laboratory & Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark; Structural Biophysics, Niels Bohr Institute, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Sandro Bottaro
- Structural Biology and NMR Laboratory & Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark; Atomistic Simulations Laboratory, Istituto Italiano di Tecnologia, Genova, Italy
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory & Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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38
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Smith CA, Mazur A, Rout AK, Becker S, Lee D, de Groot BL, Griesinger C. Enhancing NMR derived ensembles with kinetics on multiple timescales. JOURNAL OF BIOMOLECULAR NMR 2020; 74:27-43. [PMID: 31838619 PMCID: PMC7015964 DOI: 10.1007/s10858-019-00288-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 11/11/2019] [Indexed: 05/14/2023]
Abstract
Nuclear magnetic resonance (NMR) has the unique advantage of elucidating the structure and dynamics of biomolecules in solution at physiological temperatures, where they are in constant movement on timescales from picoseconds to milliseconds. Such motions have been shown to be critical for enzyme catalysis, allosteric regulation, and molecular recognition. With NMR being particularly sensitive to these timescales, detailed information about the kinetics can be acquired. However, nearly all methods of NMR-based biomolecular structure determination neglect kinetics, which introduces a large approximation to the underlying physics, limiting both structural resolution and the ability to accurately determine molecular flexibility. Here we present the Kinetic Ensemble approach that uses a hierarchy of interconversion rates between a set of ensemble members to rigorously calculate Nuclear Overhauser Effect (NOE) intensities. It can be used to simultaneously refine both temporal and structural coordinates. By generalizing ideas from the extended model free approach, the method can analyze the amplitudes and kinetics of motions anywhere along the backbone or side chains. Furthermore, analysis of a large set of crystal structures suggests that NOE data contains a surprising amount of high-resolution information that is better modeled using our approach. The Kinetic Ensemble approach provides the means to unify numerous types of experiments under a single quantitative framework and more fully characterize and exploit kinetically distinct protein states. While we apply the approach here to the protein ubiquitin and cross validate it with previously derived datasets, the approach can be applied to any protein for which NOE data is available.
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Affiliation(s)
- Colin A Smith
- Department for Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
- Department for NMR-Based Structural Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
- Department of Chemistry, Wesleyan University, Middletown, USA.
| | - Adam Mazur
- Department for NMR-Based Structural Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
- Biozentrum, University of Basel, Basel, Switzerland
| | - Ashok K Rout
- Department for NMR-Based Structural Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Stefan Becker
- Department for NMR-Based Structural Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Donghan Lee
- Department for NMR-Based Structural Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
- James Graham Brown Cancer Center, University of Louisville, Louisville, USA.
| | - Bert L de Groot
- Department for Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
| | - Christian Griesinger
- Department for NMR-Based Structural Biology, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
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39
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Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach. Methods Mol Biol 2020; 2112:219-240. [PMID: 32006288 DOI: 10.1007/978-1-0716-0270-6_15] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
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.
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40
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Abstract
Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.
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41
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Hermann MR, Hub JS. SAXS-Restrained Ensemble Simulations of Intrinsically Disordered Proteins with Commitment to the Principle of Maximum Entropy. J Chem Theory Comput 2019; 15:5103-5115. [DOI: 10.1021/acs.jctc.9b00338] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Markus R. Hermann
- Institute for Microbiology and Genetics, Georg-August-Universität Göttingen, 37077 Göttingen, Germany
| | - Jochen S. Hub
- Theoretical Physics and Center for Biophysics, Saarland University, Campus E2 6, 66123 Saarbrücken, Germany
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42
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Köfinger J, Stelzl LS, Reuter K, Allande C, Reichel K, Hummer G. Efficient Ensemble Refinement by Reweighting. J Chem Theory Comput 2019; 15:3390-3401. [PMID: 30939006 PMCID: PMC6727217 DOI: 10.1021/acs.jctc.8b01231] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Indexed: 01/24/2023]
Abstract
Ensemble refinement produces structural ensembles of flexible and dynamic biomolecules by integrating experimental data and molecular simulations. Here we present two efficient numerical methods to solve the computationally challenging maximum-entropy problem arising from a Bayesian formulation of ensemble refinement. Recasting the resulting constrained weight optimization problem into an unconstrained form enables the use of gradient-based algorithms. In two complementary formulations that differ in their dimensionality, we optimize either the log-weights directly or the generalized forces appearing in the explicit analytical form of the solution. We first demonstrate the robustness, accuracy, and efficiency of the two methods using synthetic data. We then use NMR J-couplings to reweight an all-atom molecular dynamics simulation ensemble of the disordered peptide Ala-5 simulated with the AMBER99SB*-ildn-q force field. After reweighting, we find a consistent increase in the population of the polyproline-II conformations and a decrease of α-helical-like conformations. Ensemble refinement makes it possible to infer detailed structural models for biomolecules exhibiting significant dynamics, such as intrinsically disordered proteins, by combining input from experiment and simulation in a balanced manner.
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Affiliation(s)
- Jürgen Köfinger
- Department
of Theoretical Biophysics, Max Planck Institute
of Biophysics, Max-von-Laue-Straße
3, 60438 Frankfurt
am Main, Germany
| | - Lukas S. Stelzl
- Department
of Theoretical Biophysics, Max Planck Institute
of Biophysics, Max-von-Laue-Straße
3, 60438 Frankfurt
am Main, Germany
| | - Klaus Reuter
- Max Planck Computing and
Data Facility, Gießenbachstr. 2, 85748 Garching, Germany
| | - César Allande
- Max Planck Computing and
Data Facility, Gießenbachstr. 2, 85748 Garching, Germany
| | - Katrin Reichel
- Department
of Theoretical Biophysics, Max Planck Institute
of Biophysics, Max-von-Laue-Straße
3, 60438 Frankfurt
am Main, Germany
| | - Gerhard Hummer
- Department
of Theoretical Biophysics, Max Planck Institute
of Biophysics, Max-von-Laue-Straße
3, 60438 Frankfurt
am Main, Germany
- Institute for Biophysics, Goethe University, 60438 Frankfurt
am Main, Germany
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43
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Cesari A, Bottaro S, Lindorff-Larsen K, Banáš P, Šponer J, Bussi G. Fitting Corrections to an RNA Force Field Using Experimental Data. J Chem Theory Comput 2019; 15:3425-3431. [DOI: 10.1021/acs.jctc.9b00206] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Andrea Cesari
- Scuola Internazionale
Superiore di Studi Avanzati (SISSA), via Bonomea 265, 34136 Trieste, Italy
| | - Sandro Bottaro
- Structural Biology and NMR Laboratory and Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory and Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Pavel Banáš
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacký University, tř. 17 listopadu 12, 771 46, Olomouc, Czech Republic
| | - Jiří Šponer
- Regional Centre of Advanced Technologies and Materials, Department of Physical Chemistry, Faculty of Science, Palacký University, tř. 17 listopadu 12, 771 46, Olomouc, Czech Republic
- Institute of Biophysics
of the Czech Academy of Sciences, Kralovopolska 135, Brno 612 65, Czech Republic
| | - Giovanni Bussi
- Scuola Internazionale
Superiore di Studi Avanzati (SISSA), via Bonomea 265, 34136 Trieste, Italy
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44
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Escobedo A, Topal B, Kunze MBA, Aranda J, Chiesa G, Mungianu D, Bernardo-Seisdedos G, Eftekharzadeh B, Gairí M, Pierattelli R, Felli IC, Diercks T, Millet O, García J, Orozco M, Crehuet R, Lindorff-Larsen K, Salvatella X. Side chain to main chain hydrogen bonds stabilize a polyglutamine helix in a transcription factor. Nat Commun 2019; 10:2034. [PMID: 31048691 PMCID: PMC6497633 DOI: 10.1038/s41467-019-09923-2] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 04/09/2019] [Indexed: 01/18/2023] Open
Abstract
Polyglutamine (polyQ) tracts are regions of low sequence complexity frequently found in transcription factors. Tract length often correlates with transcriptional activity and expansion beyond specific thresholds in certain human proteins is the cause of polyQ disorders. To study the structural basis of the association between tract length, transcriptional activity and disease, we addressed how the conformation of the polyQ tract of the androgen receptor, associated with spinobulbar muscular atrophy (SBMA), depends on its length. Here we report that this sequence folds into a helical structure stabilized by unconventional hydrogen bonds between glutamine side chains and main chain carbonyl groups, and that its helicity directly correlates with tract length. These unusual hydrogen bonds are bifurcate with the conventional hydrogen bonds stabilizing α-helices. Our findings suggest a plausible rationale for the association between polyQ tract length and androgen receptor transcriptional activity and have implications for establishing the mechanistic basis of SBMA. Polyglutamine (polyQ) tracts are low-complexity regions and their expansion is linked to certain neurodegenerative diseases. Here the authors combine experimental and computational approaches to find that the length of the androgen receptor polyQ tract correlates with its helicity and show that the polyQ helical structure is stabilized by hydrogen bonds between the Gln side chains and main chain carbonyl groups.
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Affiliation(s)
- Albert Escobedo
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain.,Joint BSC-IRB Research Programme in Computational Biology, Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Busra Topal
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain.,Joint BSC-IRB Research Programme in Computational Biology, Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Micha B A Kunze
- Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Juan Aranda
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain.,Joint BSC-IRB Research Programme in Computational Biology, Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Giulio Chiesa
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain.,Joint BSC-IRB Research Programme in Computational Biology, Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Daniele Mungianu
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain.,Joint BSC-IRB Research Programme in Computational Biology, Baldiri Reixac 10, 08028, Barcelona, Spain
| | | | - Bahareh Eftekharzadeh
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain.,Joint BSC-IRB Research Programme in Computational Biology, Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Margarida Gairí
- NMR Facility, Scientific and Technological Centers University of Barcelona (CCiTUB), Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Roberta Pierattelli
- CERM and Department of Chemistry "Ugo Schiff", University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, 50019, Florence, Italy
| | - Isabella C Felli
- CERM and Department of Chemistry "Ugo Schiff", University of Florence, Via Luigi Sacconi 6, Sesto Fiorentino, 50019, Florence, Italy
| | - Tammo Diercks
- CIC bioGUNE, Bizkaia Science and Technology Park bld 801A, 48160, Derio, Bizkaia, Spain
| | - Oscar Millet
- CIC bioGUNE, Bizkaia Science and Technology Park bld 801A, 48160, Derio, Bizkaia, Spain
| | - Jesús García
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain.,Joint BSC-IRB Research Programme in Computational Biology, Baldiri Reixac 10, 08028, Barcelona, Spain.,Department of Biochemistry and Biomedicine, University of Barcelona, Avinguda Diagonal 645, 08028, Barcelona, Spain
| | - Ramon Crehuet
- Institute for Advanced Chemistry of Catalonia (IQAC-CSIC), Jordi Girona 18-26, 08034, Barcelona, Spain.
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory, Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, 2200, Copenhagen, Denmark.
| | - Xavier Salvatella
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, 08028, Barcelona, Spain. .,Joint BSC-IRB Research Programme in Computational Biology, Baldiri Reixac 10, 08028, Barcelona, Spain. .,ICREA, Passeig Lluís Companys 23, 08010, Barcelona, Spain.
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Abstract
All-atom, classical force fields for protein molecular dynamics (MD) simulations currently occupy a sweet spot in the universe of computational models, sufficiently detailed to be of predictive value in many cases, yet also simple enough that some biologically relevant time scales (microseconds or more) can now be sampled via specialized hardware or enhanced sampling methods. However, due to their long evolutionary history, there is now a myriad of force field branches in current use, which can make it hard for those entering the simulation field to know which would be the best set of parameters for a given application. In this chapter, I try to give an overview of the historical motivation for the different force fields available, suggestions for how to determine the most appropriate model and what to do if the results are in conflict with experimental evidence.
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Affiliation(s)
- Robert B Best
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
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