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Turzo SMBA, Seffernick JT, Lyskov S, Lindert S. Predicting ion mobility collision cross sections using projection approximation with ROSIE-PARCS webserver. Brief Bioinform 2023; 24:bbad308. [PMID: 37609950 PMCID: PMC10516336 DOI: 10.1093/bib/bbad308] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/03/2023] [Accepted: 08/08/2023] [Indexed: 08/24/2023] Open
Abstract
Ion mobility coupled to mass spectrometry informs on the shape and size of protein structures in the form of a collision cross section (CCSIM). Although there are several computational methods for predicting CCSIM based on protein structures, including our previously developed projection approximation using rough circular shapes (PARCS), the process usually requires prior experience with the command-line interface. To overcome this challenge, here we present a web application on the Rosetta Online Server that Includes Everyone (ROSIE) webserver to predict CCSIM from protein structure using projection approximation with PARCS. In this web interface, the user is only required to provide one or more PDB files as input. Results from our case studies suggest that CCSIM predictions (with ROSIE-PARCS) are highly accurate with an average error of 6.12%. Furthermore, the absolute difference between CCSIM and CCSPARCS can help in distinguishing accurate from inaccurate AlphaFold2 protein structure predictions. ROSIE-PARCS is designed with a user-friendly interface, is available publicly and is free to use. The ROSIE-PARCS web interface is supported by all major web browsers and can be accessed via this link (https://rosie.graylab.jhu.edu).
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Affiliation(s)
- S M Bargeen Alam Turzo
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, USA
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH 43210, USA
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2
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Koehler Leman J, Künze G. Recent Advances in NMR Protein Structure Prediction with ROSETTA. Int J Mol Sci 2023; 24:ijms24097835. [PMID: 37175539 PMCID: PMC10178863 DOI: 10.3390/ijms24097835] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/15/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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3
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Developments in rapid hydrogen-deuterium exchange methods. Essays Biochem 2023; 67:165-174. [PMID: 36636941 DOI: 10.1042/ebc20220174] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 01/14/2023]
Abstract
Biological macromolecules, such as proteins, nucleic acids, and carbohydrates, contain heteroatom-bonded hydrogens that undergo exchange with solvent hydrogens on timescales ranging from microseconds to hours. In hydrogen-deuterium exchange mass spectrometry (HDX-MS), this exchange process is used to extract information about biomolecular structure and dynamics. This minireview focuses on millisecond timescale HDX-MS measurements, which, while less common than 'conventional' timescale (seconds to hours) HDX-MS, provide a unique window into weakly structured species, weak (or fast cycling) binding interactions, and subtle shifts in conformational dynamics. This includes intrinsically disordered proteins and regions (IDPs/IDRs) that are associated with cancer and amyloidotic neurodegenerative disease. For nucleic acids and carbohydrates, structures such as isomers, stems, and loops, can be elucidated and overall structural rigidity can be assessed. We will provide a brief overview of technical developments in rapid HDX followed by highlights of various applications, emphasising the importance of broadening the HDX timescale to improve throughput and to capture a wider range of function-relevant dynamic and structural shifts.
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Drake ZC, Seffernick JT, Lindert S. Protein complex prediction using Rosetta, AlphaFold, and mass spectrometry covalent labeling. Nat Commun 2022; 13:7846. [PMID: 36543826 PMCID: PMC9772387 DOI: 10.1038/s41467-022-35593-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
Covalent labeling (CL) in combination with mass spectrometry can be used as an analytical tool to study and determine structural properties of protein-protein complexes. However, data from these experiments is sparse and does not unambiguously elucidate protein structure. Thus, computational algorithms are needed to deduce structure from the CL data. In this work, we present a hybrid method that combines models of protein complex subunits generated with AlphaFold with differential CL data via a CL-guided protein-protein docking in Rosetta. In a benchmark set, the RMSD (root-mean-square deviation) of the best-scoring models was below 3.6 Å for 5/5 complexes with inclusion of CL data, whereas the same quality was only achieved for 1/5 complexes without CL data. This study suggests that our integrated approach can successfully use data obtained from CL experiments to distinguish between nativelike and non-nativelike models.
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Affiliation(s)
- Zachary C Drake
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, US
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, US
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, 43210, US.
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5
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Turzo SMBA, Seffernick JT, Rolland AD, Donor MT, Heinze S, Prell JS, Wysocki VH, Lindert S. Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction. Nat Commun 2022; 13:4377. [PMID: 35902583 PMCID: PMC9334640 DOI: 10.1038/s41467-022-32075-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
Ion mobility (IM) mass spectrometry provides structural information about protein shape and size in the form of an orientationally-averaged collision cross-section (CCSIM). While IM data have been used with various computational methods, they have not yet been utilized to predict monomeric protein structure from sequence. Here, we show that IM data can significantly improve protein structure determination using the modelling suite Rosetta. We develop the Rosetta Projection Approximation using Rough Circular Shapes (PARCS) algorithm that allows for fast and accurate prediction of CCSIM from structure. Following successful testing of the PARCS algorithm, we use an integrative modelling approach to utilize IM data for protein structure prediction. Additionally, we propose a confidence metric that identifies near native models in the absence of a known structure. The results of this study demonstrate the ability of IM data to consistently improve protein structure prediction. Collision cross sections (CCS) from ion mobility mass spectrometry provide information about protein shape and size. Here, the authors develop an algorithm to predict CCS and integrate experimental ion mobility data into Rosetta-based molecular modelling to predict protein structures from sequence.
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Affiliation(s)
- S M Bargeen Alam Turzo
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - Amber D Rolland
- Department of Chemistry and Biochemistry and Materials Science Institute, University of Oregon, Eugene, OR, 97403, USA
| | - Micah T Donor
- Department of Chemistry and Biochemistry and Materials Science Institute, University of Oregon, Eugene, OR, 97403, USA
| | - Sten Heinze
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - James S Prell
- Department of Chemistry and Biochemistry and Materials Science Institute, University of Oregon, Eugene, OR, 97403, USA
| | - Vicki H Wysocki
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, OH, 43210, USA.
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Biehn SE, Picarello DM, Pan X, Vachet RW, Lindert S. Accounting for Neighboring Residue Hydrophobicity in Diethylpyrocarbonate Labeling Mass Spectrometry Improves Rosetta Protein Structure Prediction. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:584-591. [PMID: 35147431 PMCID: PMC8988852 DOI: 10.1021/jasms.1c00373] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Covalent labeling mass spectrometry allows for protein structure elucidation via covalent modification and identification of exposed residues. Diethylpyrocarbonate (DEPC) is a commonly used covalent labeling reagent that provides insight into structure through the labeling of lysine, histidine, serine, threonine, and tyrosine residues. We recently implemented a Rosetta algorithm that used binary DEPC labeling data to improve protein structure prediction efforts. In this work, we improved on our modeling efforts by accounting for the level of hydrophobicity of neighboring residues in the microenvironment of serine, threonine, and tyrosine residues to obtain a more accurate estimate of the hydrophobic neighbor count. This was incorporated into Rosetta functionality, along with considerations for solvent-exposed histidine and lysine residues. Overall, our new Rosetta score term successfully identified best scoring models with less than 2 Å root-mean-squared deviations (RMSDs) for five of the seven benchmark proteins tested. We additionally developed a confidence metric to measure prediction success for situations in which a native structure is unavailable.
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Affiliation(s)
- Sarah E Biehn
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Danielle M Picarello
- Department of Bioengineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
- Rosetta Commons Research Experience for Undergraduates, Rosetta Commons, https://www.rosettacommons.org/about/intern
| | - Xiao Pan
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts 01003, United States
| | - Richard W Vachet
- Department of Chemistry, University of Massachusetts, Amherst, Amherst, Massachusetts 01003, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
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Devaurs D, Antunes DA, Borysik AJ. Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:215-237. [PMID: 35077179 DOI: 10.1021/jasms.1c00328] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.
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Affiliation(s)
- Didier Devaurs
- MRC Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, U.K
| | - Dinler A Antunes
- Department of Biology and Biochemistry, University of Houston, Houston, Texas 77005, United States
| | - Antoni J Borysik
- Department of Chemistry, King's College London, London SE1 1DB, U.K
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