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Li T, Hendrix E, He Y. Simple and Effective Conformational Sampling Strategy for Intrinsically Disordered Proteins Using the UNRES Web Server. J Phys Chem B 2023; 127:2177-2186. [PMID: 36827446 DOI: 10.1021/acs.jpcb.2c08945] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Intrinsically disordered proteins (IDPs) contain more charged amino acids than folded proteins, resulting in a lack of hydrophobic core(s) and a tendency to adopt rapidly interconverting structures rather than well-defined structures. The structural heterogeneity of IDPs, encoded by the amino acid sequence, is closely related to their unique roles in biological pathways, which require them to interact with different binding partners. Recently, Robustelli and co-workers have demonstrated that a balanced all-atom force field can be used to sample heterogeneous structures of disordered proteins ( Proc. Natl. Acad. Sci. U.S.A. 2018, 115, E4758-E4766). However, such a solution requires extensive computational resources, such as Anton supercomputers. Here, we propose a simple and effective solution to sample the conformational space of IDPs using a publicly available web server, namely, the UNited-RESidue (UNRES) web server. Our proposed solution requires no investment in computational resources and no prior knowledge of UNRES. UNRES Replica Exchange Molecular Dynamics (REMD) simulations were carried out on a set of eight disordered proteins at temperatures spanning from 270 to 430 K. Utilizing the latest UNRES force field designed for structured proteins, with proper selections of temperatures, we were able to produce comparable results to all-atom force fields as reported in work done by Robustelli and co-workers. In addition, NMR observables and the radius of gyration calculated from UNRES ensembles were directly compared with the experimental data to further evaluate the accuracy of the UNRES model at all temperatures. Our results suggest that carrying out the UNRES simulations at optimal temperatures using the UNRES web server can be a good alternative to sample heterogeneous structures of IDPs.
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Affiliation(s)
- Tongtong Li
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Emily Hendrix
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States
| | - Yi He
- Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131, United States.,Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico 87131, United States
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2
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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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3
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de Brevern AG. Analysis of Protein Disorder Predictions in the Light of a Protein Structural Alphabet. Biomolecules 2020; 10:biom10071080. [PMID: 32698546 PMCID: PMC7408373 DOI: 10.3390/biom10071080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 07/14/2020] [Accepted: 07/18/2020] [Indexed: 12/30/2022] Open
Abstract
Intrinsically-disordered protein (IDP) characterization was an amazing change of paradigm in our classical sequence-structure-function theory. Moreover, IDPs are over-represented in major disease pathways and are now often targeted using small molecules for therapeutic purposes. This has had created a complex continuum from order-that encompasses rigid and flexible regions-to disorder regions; the latter being not accessible through classical crystallographic methodologies. In X-ray structures, the notion of order is dictated by access to resolved atom positions, providing rigidity and flexibility information with low and high experimental B-factors, while disorder is associated with the missing (non-resolved) residues. Nonetheless, some rigid regions can be found in disorder regions. Using ensembles of IDPs, their local conformations were analyzed in the light of a structural alphabet. An entropy index derived from this structural alphabet allowed us to propose a continuum of states from rigidity to flexibility and finally disorder. In this study, the analysis was extended to comparing these results to disorder predictions, underlying a limited correlation, and so opening new ideas to characterize and predict disorder.
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Affiliation(s)
- Alexandre G de Brevern
- INSERM, UMR_S 1134, DSIMB, Univ Paris, INTS, Laboratoire d'Excellence GR-Ex, 75015 Paris, France
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Akhila MV, Narwani TJ, Floch A, Maljković M, Bisoo S, Shinada NK, Kranjc A, Gelly JC, Srinivasan N, Mitić N, de Brevern AG. A structural entropy index to analyse local conformations in intrinsically disordered proteins. J Struct Biol 2020; 210:107464. [DOI: 10.1016/j.jsb.2020.107464] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 01/06/2020] [Accepted: 01/15/2020] [Indexed: 10/25/2022]
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5
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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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6
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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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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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Milles S, Salvi N, Blackledge M, Jensen MR. Characterization of intrinsically disordered proteins and their dynamic complexes: From in vitro to cell-like environments. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2018; 109:79-100. [PMID: 30527137 DOI: 10.1016/j.pnmrs.2018.07.001] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2018] [Revised: 07/16/2018] [Accepted: 07/16/2018] [Indexed: 05/08/2023]
Abstract
Over the last two decades, it has become increasingly clear that a large fraction of the human proteome is intrinsically disordered or contains disordered segments of significant length. These intrinsically disordered proteins (IDPs) play important regulatory roles throughout biology, underlining the importance of understanding their conformational behavior and interaction mechanisms at the molecular level. Here we review recent progress in the NMR characterization of the structure and dynamics of IDPs in various functional states and environments. We describe the complementarity of different NMR parameters for quantifying the conformational propensities of IDPs in their isolated and phosphorylated states, and we discuss the challenges associated with obtaining structural models of dynamic protein-protein complexes involving IDPs. In addition, we review recent progress in understanding the conformational behavior of IDPs in cell-like environments such as in the presence of crowding agents, in membrane-less organelles and in the complex environment of the human cell.
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Affiliation(s)
- Sigrid Milles
- Univ. Grenoble Alpes, CNRS, CEA, IBS, F-38000 Grenoble, France
| | - Nicola Salvi
- Univ. Grenoble Alpes, CNRS, CEA, IBS, F-38000 Grenoble, France
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Bhattacharya S, Xu L, Thompson D. Revisiting the earliest signatures of amyloidogenesis: Roadmaps emerging from computational modeling and experiment. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2018. [DOI: 10.1002/wcms.1359] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Shayon Bhattacharya
- Department of Physics, Bernal InstituteUniversity of LimerickLimerickIreland
| | - Liang Xu
- Department of Physics, Bernal InstituteUniversity of LimerickLimerickIreland
| | - Damien Thompson
- Department of Physics, Bernal InstituteUniversity of LimerickLimerickIreland
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10
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Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments. COMPUTATION 2018. [DOI: 10.3390/computation6010015] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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11
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Ravera E, Sgheri L, Parigi G, Luchinat C. A critical assessment of methods to recover information from averaged data. Phys Chem Chem Phys 2017; 18:5686-701. [PMID: 26565805 DOI: 10.1039/c5cp04077a] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Conformational heterogeneity is key to the function of many biomacromolecules, but only a few groups have tried to characterize it until recently. Now, thanks to the increased throughput of experimental data and the increased computational power, the problem of the characterization of protein structural variability has become more and more popular. Several groups have devoted their efforts in trying to create quantitative, reliable and accurate protocols for extracting such information from averaged data. We analyze here different approaches, discussing strengths and weaknesses of each. All approaches can roughly be clustered into two groups: those satisfying the maximum entropy principle and those recovering ensembles composed of a restricted number of molecular conformations. In the first case, the solution focuses on the features that are common to all the infinite solutions satisfying the experimental data; in the second case, the reconstructed ensemble shows the conformational regions where a large probability can be placed. The upper limits for conformational probabilities (MaxOcc) can also be calculated. We also give an overview of the mainstream experimental observables, with considerations on the assumptions underlying their usage.
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Affiliation(s)
- Enrico Ravera
- Center for Magnetic Resonance (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Via L. Sacconi 6, 50019, Sesto Fiorentino, Italy.
| | - Luca Sgheri
- Istituto per le Applicazioni del Calcolo, Sezione di Firenze, CNR, Via Madonna del Piano 10, 50019 Sesto Fiorentino, Italy
| | - Giacomo Parigi
- Center for Magnetic Resonance (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Via L. Sacconi 6, 50019, Sesto Fiorentino, Italy.
| | - Claudio Luchinat
- Center for Magnetic Resonance (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Via L. Sacconi 6, 50019, Sesto Fiorentino, Italy.
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12
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Hummer G, Köfinger J. Bayesian ensemble refinement by replica simulations and reweighting. J Chem Phys 2016; 143:243150. [PMID: 26723635 DOI: 10.1063/1.4937786] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.
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Affiliation(s)
- Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
| | - Jürgen Köfinger
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue Str. 3, 60438 Frankfurt am Main, Germany
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Carlon A, Ravera E, Andrałojć W, Parigi G, Murshudov GN, Luchinat C. How to tackle protein structural data from solution and solid state: An integrated approach. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2016; 92-93:54-70. [PMID: 26952192 DOI: 10.1016/j.pnmrs.2016.01.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Revised: 01/13/2016] [Accepted: 01/13/2016] [Indexed: 05/17/2023]
Abstract
Long-range NMR restraints, such as diamagnetic residual dipolar couplings and paramagnetic data, can be used to determine 3D structures of macromolecules. They are also used to monitor, and potentially to improve, the accuracy of a macromolecular structure in solution by validating or "correcting" a crystal model. Since crystal structures suffer from crystal packing forces they may not be accurate models for the macromolecular structures in solution. However, the presence of real differences should be tested for by simultaneous refinement of the structure using both crystal and solution NMR data. To achieve this, the program REFMAC5 from CCP4 was modified to allow the simultaneous use of X-ray crystallographic and paramagnetic NMR data and/or diamagnetic residual dipolar couplings. Inconsistencies between crystal structures and solution NMR data, if any, may be due either to structural rearrangements occurring on passing from the solution to solid state, or to a greater degree of conformational heterogeneity in solution with respect to the crystal. In the case of multidomain proteins, paramagnetic restraints can provide the correct mutual orientations and positions of domains in solution, as well as information on the conformational variability experienced by the macromolecule.
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Affiliation(s)
- Azzurra Carlon
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Italy(1).
| | - Enrico Ravera
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Italy(1).
| | - Witold Andrałojć
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Italy(1).
| | - Giacomo Parigi
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Italy(1).
| | - Garib N Murshudov
- MRC Laboratory for Molecular Biology, Francis Crick Ave, Cambridge CB2 0QH, UK.
| | - Claudio Luchinat
- Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, Italy(1).
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14
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Olsson S, Ekonomiuk D, Sgrignani J, Cavalli A. Molecular Dynamics of Biomolecules through Direct Analysis of Dipolar Couplings. J Am Chem Soc 2015; 137:6270-8. [PMID: 25895902 DOI: 10.1021/jacs.5b01289] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Residual dipolar couplings (RDCs) are important probes in structural biology, but their analysis is often complicated by the determination of an alignment tensor or its associated assumptions. We here apply the maximum entropy principle to derive a tensor-free formalism which allows for direct, dynamic analysis of RDCs and holds the classic tensor formalism as a special case. Specifically, the framework enables us to robustly analyze data regardless of whether a clear separation of internal and overall dynamics is possible. Such a separation is often difficult in the core subjects of current structural biology, which include multidomain and intrinsically disordered proteins as well as nucleic acids. We demonstrate the method is tractable and self-consistent and generalizes to data sets comprised of observations from multiple different alignment conditions.
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Affiliation(s)
- Simon Olsson
- †Institute for Research in Biomedicine, Via Vincenzo Vela 6, CH-6500 Bellinzona, Switzerland.,‡Laboratory of Physical Chemistry, Eidgenössische Technische Hochschule Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland
| | - Dariusz Ekonomiuk
- †Institute for Research in Biomedicine, Via Vincenzo Vela 6, CH-6500 Bellinzona, Switzerland
| | - Jacopo Sgrignani
- †Institute for Research in Biomedicine, Via Vincenzo Vela 6, CH-6500 Bellinzona, Switzerland
| | - Andrea Cavalli
- †Institute for Research in Biomedicine, Via Vincenzo Vela 6, CH-6500 Bellinzona, Switzerland.,§Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW United Kingdom
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