1
|
Nguyen TT, Marzolf DR, Seffernick JT, Heinze S, Lindert S. Protein structure prediction using residue-resolved protection factors from hydrogen-deuterium exchange NMR. Structure 2021; 30:313-320.e3. [PMID: 34739840 DOI: 10.1016/j.str.2021.10.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/04/2021] [Accepted: 10/15/2021] [Indexed: 11/17/2022]
Abstract
Hydrogen-deuterium exchange (HDX) measured by nuclear magnetic resonance (NMR) provides structural information for proteins relating to solvent accessibility and flexibility. While this structural information is beneficial, the data cannot be used exclusively to elucidate structures. However, the structural information provided by the HDX-NMR data can be supplemented by computational methods. In previous work, we developed an algorithm in Rosetta to predict structures using qualitative HDX-NMR data (categories of exchange rate). Here we expand on the effort, and utilize quantitative protection factors (PFs) from HDX-NMR for structure prediction. From observed correlations between PFs and solvent accessibility/flexibility measures, we present a scoring function to quantify the agreement with HDX data. Using a benchmark set of 10 proteins, an average improvement of 5.13 Å in root-mean-square deviation (RMSD) is observed for cases of inaccurate Rosetta predictions. Ultimately, seven out of 10 predictions are accurate without including HDX data, and nine out of 10 are accurate when using our PF-based HDX score.
Collapse
Affiliation(s)
- Tung T Nguyen
- Department of Chemistry and Biochemistry, Denison University, Granville, OH 43023, USA
| | - Daniel R Marzolf
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 W. 18(th) Avenue, Columbus, OH 43210, USA
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 W. 18(th) Avenue, Columbus, OH 43210, USA
| | - Sten Heinze
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 W. 18(th) Avenue, Columbus, OH 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, 2114 Newman & Wolfrom Laboratory, 100 W. 18(th) Avenue, Columbus, OH 43210, USA.
| |
Collapse
|
2
|
Gaalswyk K, Liu Z, Vogel HJ, MacCallum JL. An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling. Front Mol Biosci 2021; 8:676268. [PMID: 34476238 PMCID: PMC8407082 DOI: 10.3389/fmolb.2021.676268] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 07/29/2021] [Indexed: 11/13/2022] Open
Abstract
Paramagnetic nuclear magnetic resonance (NMR) methods have emerged as powerful tools for structure determination of large, sparsely protonated proteins. However traditional applications face several challenges, including a need for large datasets to offset the sparsity of restraints, the difficulty in accounting for the conformational heterogeneity of the spin-label, and noisy experimental data. Here we propose an integrative approach to structure determination combining sparse paramagnetic NMR with physical modelling to infer approximate protein structural ensembles. We use calmodulin in complex with the smooth muscle myosin light chain kinase peptide as a model system. Despite acquiring data from samples labeled only at the backbone amide positions, we are able to produce an ensemble with an average RMSD of ∼2.8 Å from a reference X-ray crystal structure. Our approach requires only backbone chemical shifts and measurements of the paramagnetic relaxation enhancement and residual dipolar couplings that can be obtained from sparsely labeled samples.
Collapse
Affiliation(s)
- Kari Gaalswyk
- Department of Chemistry, University of Calgary, Calgary, AB, Canada
| | - Zhihong Liu
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Hans J. Vogel
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | | |
Collapse
|
3
|
Marzolf DR, Seffernick JT, Lindert S. Protein Structure Prediction from NMR Hydrogen-Deuterium Exchange Data. J Chem Theory Comput 2021; 17:2619-2629. [PMID: 33780620 DOI: 10.1021/acs.jctc.1c00077] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Amide hydrogen-deuterium exchange (HDX) has long been used to determine regional flexibility and binding sites in proteins; however, the data are too sparse for full structural characterization. Experiments that measure HDX rates, such as HDX-NMR, have far higher throughput compared to structure determination via X-ray crystallography, cryo-EM, or a full suite of NMR experiments. Data from HDX-NMR experiments encode information on the protein structure, making HDX a prime candidate to be supplemented by computational algorithms for protein structure prediction. We have developed a methodology to incorporate HDX-NMR data into ab initio protein structure prediction using the Rosetta software framework to predict structures based on experimental agreement. To demonstrate the efficacy of our algorithm, we examined 38 proteins with HDX-NMR data available, comparing the predicted model with and without the incorporation of HDX data into scoring. The root-mean-square deviation (rmsd, a measure of the average atomic distance between superimposed models) of the predicted model improved by 1.42 Å on average after incorporating the HDX-NMR data into scoring. The average rmsd improvement for the proteins where the selected model rmsd changed after incorporating HDX data was 3.63 Å, including one improvement of more than 11 Å and seven proteins improving by greater than 4 Å, with 12/15 proteins improving overall. Additionally, for independent verification, two proteins that were not part of the original benchmark were scored including HDX data, with a dramatic improvement of the selected model rmsd of nearly 9 Å for one of the proteins. Moreover, we have developed a confidence metric allowing us to successfully identify near-native models in the absence of a native structure. Improvement in model selection with a strong confidence measure demonstrates that protein structure prediction with HDX-NMR is a powerful tool which can be performed with minimal additional computational strain and expense.
Collapse
Affiliation(s)
- Daniel R Marzolf
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Justin T Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| |
Collapse
|
4
|
Geng H, Chen F, Ye J, Jiang F. Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins. Comput Struct Biotechnol J 2019; 17:1162-1170. [PMID: 31462972 PMCID: PMC6709365 DOI: 10.1016/j.csbj.2019.07.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/07/2019] [Accepted: 07/23/2019] [Indexed: 12/21/2022] Open
Abstract
Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.
Collapse
Affiliation(s)
- Hao Geng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fangfang Chen
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Jing Ye
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Fan Jiang
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- NanoAI Biotech Co.,Ltd., Silicon Valley Compound, Longhua District, Shenzhen 518109, China
- Corresponding author at: Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
| |
Collapse
|
5
|
Abstract
It is challenging to predict the docked conformations of two proteins. Current methods are susceptible to errors from treating proteins as rigid bodies and from an inability to compute relative Boltzmann populations of different docked conformations. Here, we show that by using the ClusPro server as a front end to generate possible protein-protein contacts, and using Modeling Employing Limited Data (MELD) accelerated molecular dynamics (MELD × MD) as a back end for atomistic simulations, we can find 16/20 native dimer structures of small proteins as those having the lowest free energy, starting from good-bound-backbone structures. We show that atomistic MD free energies can be used to identify native protein dimer structures.
Collapse
Affiliation(s)
- Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-5252, United States
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-5252, United States
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794-3600, United States
- Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, New York 11794-5250, United States
| | - Ken A. Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-5252, United States
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794-3800, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11790-3400, United States
| |
Collapse
|
6
|
Bonechi C, Donati A, Tamasi G, Pardini A, Volpi V, Leone G, Consumi M, Magnani A, Rossi C. Metal-Ligand Recognition Index Determination by NMR Proton Relaxation Study. Molecules 2019; 24:E1050. [PMID: 30884870 PMCID: PMC6472049 DOI: 10.3390/molecules24061050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 03/14/2019] [Accepted: 03/15/2019] [Indexed: 11/29/2022] Open
Abstract
In this study, we developed and validated a new proposed parameter quantifying the interaction strength between natural and/or synthetic molecules with paramagnetic metal ions. The Metal ion Recognition Index, Miri, is a quantitative parameter to describe the proton environment and to define their involvement in the inner and/or outer sphere of the paramagnetic metal ion. The method is based on the analysis of NMR proton spin-lattice relaxation rates of a specific ligand in both the diamagnetic and paramagnetic conditions. The proposed procedure is also useful to calculate the ligand proton spin-lattice relaxation rate in the paramagnetic bound conditions, which is typically very difficult to determine experimentally. Miri was used to compare the ligand proton involvement toward different paramagnetic species, in particular the Copper(II)-Piroxicam system. Copper(II)-Piroxicam complex is one of the most active anti-inflammatory and anti-arthritic species. Miri provides an opportunity to improve our knowledge of metal-ligand complexes that play a fundamental role in bioinorganic interactions.
Collapse
Affiliation(s)
- Claudia Bonechi
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy.
- Centre for Colloid and Surface Science (CSGI), University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Firenze, Italy.
| | - Alessandro Donati
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy.
- Centre for Colloid and Surface Science (CSGI), University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Firenze, Italy.
| | - Gabriella Tamasi
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy.
- Centre for Colloid and Surface Science (CSGI), University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Firenze, Italy.
| | - Alessio Pardini
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy.
| | - Vanessa Volpi
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy.
| | - Gemma Leone
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy.
- National Interuniversity Consortium of Materials Science and Technology (INSTM), Via G. Giusti 9, 50121 Firenze, Italy.
| | - Marco Consumi
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy.
- National Interuniversity Consortium of Materials Science and Technology (INSTM), Via G. Giusti 9, 50121 Firenze, Italy.
| | - Agnese Magnani
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy.
- National Interuniversity Consortium of Materials Science and Technology (INSTM), Via G. Giusti 9, 50121 Firenze, Italy.
| | - Claudio Rossi
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy.
- Centre for Colloid and Surface Science (CSGI), University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino, Firenze, Italy.
- Operative Unit, University of Siena, Campo Verde, Calabria, 53100 Siena, Italy.
| |
Collapse
|
7
|
Perez A, Sittel F, Stock G, Dill K. MELD-Path Efficiently Computes Conformational Transitions, Including Multiple and Diverse Paths. J Chem Theory Comput 2018; 14:2109-2116. [PMID: 29547695 DOI: 10.1021/acs.jctc.7b01294] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The molecular actions of proteins occur along reaction coordinates. Current computer methods have limited ability to explore them. We describe a fast protocol called MELD-path that (1) efficiently samples relevant conformational states via MELD, an accelerator of Molecular Dynamics (MD), (2) seeds multiple short MD trajectories from MELD states, and then (3) constructs Markov State Models (MSM) that give the routes and kinetics. We tested the method against extensive (multi μs) MD simulations of the right-handed- to left-handed-helix transition of a 9-mer peptide of AIB, the symmetry of which allows us to establish convergence. MELD-path finds all the metastable states, their correct relative populations, and the full ensemble of routes, not just a single assumed route. For this transition, we find a very broad route structure. MELD-path is highly parallelizable and efficient, yielding the full route map in a few days of computation. We believe MELD-path could be a general and rapid way to explore mechanistic processes in biomolecules on the computer.
Collapse
Affiliation(s)
- Alberto Perez
- Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 1179 4, United States
| | - Florian Sittel
- Biomolecular Dynamics, Institute of Physics , Albert Ludwigs University , 79104 Freiburg , Germany
| | - Gerhard Stock
- Biomolecular Dynamics, Institute of Physics , Albert Ludwigs University , 79104 Freiburg , Germany
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology , Stony Brook University , Stony Brook , New York 1179 4, United States
| |
Collapse
|
8
|
Current Solution NMR Techniques for Structure-Function Studies of Proteins and RNA Molecules. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1105:43-58. [DOI: 10.1007/978-981-13-2200-6_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|