1
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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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2
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Das NR, Chaudhury KN, Pal D. Improved NMR-data-compliant protein structure modeling captures context-dependent variations and expands the scope of functional inference. Proteins 2023; 91:412-435. [PMID: 36287124 DOI: 10.1002/prot.26439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 09/12/2022] [Accepted: 10/20/2022] [Indexed: 11/13/2022]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy can reveal conformational states of a protein in physiological conditions. However, sparsely available NMR data for a protein with large degrees of freedom can introduce structural artifacts in the built models. Currently used state-of-the-art methods deriving protein structure and conformation from NMR deploy molecular dynamics (MD) coupled with simulated annealing for building models. We provide an alternate graph-based modeling approach, where we first build substructures from NMR-derived distance-geometry constraints combined in one shot to form the core structure. The remaining molecule with inadequate data is modeled using a hybrid approach respecting the observed distance-geometry constraints. One-shot structure building is rarely undertaken for large and sparse data systems, but our data-driven bottom-up approach makes this uniquely feasible by suitable partitioning of the problem. A detailed comparison of select models with state-of-art methods reveals differences in the secondary structure regions wherein the correctness of our models is confirmed by NMR data. Benchmarking of 106 protein-folds covering 38-282 length structures shows minimal experimental-constraint violations while conforming to other structure quality parameters such as the proper folding, steric clash, and torsion angle violation based on Ramachandran plot criteria. Comparative MD studies using select protein models from a state-of-art method and ours under identical experimental parameters reveal distinct conformational dynamics that could be attributed to protein structure-function. Our work is thus useful in building enhanced NMR-evidence-based models that encapsulate the contextual secondary and tertiary structure variations present during the experimentation and expand the scope of functional inference.
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Affiliation(s)
- Niladri R Das
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, India.,Department of Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Kunal N Chaudhury
- Department of Electrical Engineering, Indian Institute of Science, Bangalore, India
| | - Debnath Pal
- Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
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3
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Hu K, Lee W, Montelione GT, Sgourakis NG, Vögeli B. Editorial: Computational approaches for interpreting experimental data and understanding protein structure, dynamics and function relationships. Front Mol Biosci 2022; 9:1018149. [PMID: 36262477 PMCID: PMC9576191 DOI: 10.3389/fmolb.2022.1018149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Kaifeng Hu
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Woonghee Lee
- Department of Chemistry, University of Colorado Denver, Denver, CO, United States
- *Correspondence: Woonghee Lee,
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Nikolaos G. Sgourakis
- Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, and Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Beat Vögeli
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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4
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Tejero R, Huang YJ, Ramelot TA, Montelione GT. AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures. Front Mol Biosci 2022; 9:877000. [PMID: 35769913 PMCID: PMC9234698 DOI: 10.3389/fmolb.2022.877000] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.
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Affiliation(s)
- Roberto Tejero
- Departamento de Química Física, Universidad de Valencia, Valencia, Spain
| | - Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Theresa A. Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
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5
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Mondal A, Perez A. Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets. Front Mol Biosci 2021; 8:774394. [PMID: 34912846 PMCID: PMC8667806 DOI: 10.3389/fmolb.2021.774394] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 10/25/2021] [Indexed: 11/29/2022] Open
Abstract
Sparsely labeled NMR samples provide opportunities to study larger biomolecular assemblies than is traditionally done by NMR. This requires new computational tools that can handle the sparsity and ambiguity in the NMR datasets. The MELD (modeling employing limited data) Bayesian approach was assessed to be the best performing in predicting structures from sparsely labeled NMR data in the 13th edition of the Critical Assessment of Structure Prediction (CASP) event—and limitations of the methodology were also noted. In this report, we evaluate the nature and difficulty in modeling unassigned sparsely labeled NMR datasets and report on an improved methodological pipeline leading to higher-accuracy predictions. We benchmark our methodology against the NMR datasets provided by CASP 13.
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Affiliation(s)
- Arup Mondal
- The Quantum Theory Project, Department of Chemistry, University of Florida, Gainesville, FL, United States
| | - Alberto Perez
- The Quantum Theory Project, Department of Chemistry, University of Florida, Gainesville, FL, United States
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6
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De novo protein design by deep network hallucination. Nature 2021; 600:547-552. [PMID: 34853475 DOI: 10.1038/s41586-021-04184-w] [Citation(s) in RCA: 220] [Impact Index Per Article: 73.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 10/21/2021] [Indexed: 12/25/2022]
Abstract
There has been considerable recent progress in protein structure prediction using deep neural networks to predict inter-residue distances from amino acid sequences1-3. Here we investigate whether the information captured by such networks is sufficiently rich to generate new folded proteins with sequences unrelated to those of the naturally occurring proteins used in training the models. We generate random amino acid sequences, and input them into the trRosetta structure prediction network to predict starting residue-residue distance maps, which, as expected, are quite featureless. We then carry out Monte Carlo sampling in amino acid sequence space, optimizing the contrast (Kullback-Leibler divergence) between the inter-residue distance distributions predicted by the network and background distributions averaged over all proteins. Optimization from different random starting points resulted in novel proteins spanning a wide range of sequences and predicted structures. We obtained synthetic genes encoding 129 of the network-'hallucinated' sequences, and expressed and purified the proteins in Escherichia coli; 27 of the proteins yielded monodisperse species with circular dichroism spectra consistent with the hallucinated structures. We determined the three-dimensional structures of three of the hallucinated proteins, two by X-ray crystallography and one by NMR, and these closely matched the hallucinated models. Thus, deep networks trained to predict native protein structures from their sequences can be inverted to design new proteins, and such networks and methods should contribute alongside traditional physics-based models to the de novo design of proteins with new functions.
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7
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Huang YJ, Zhang N, Bersch B, Fidelis K, Inouye M, Ishida Y, Kryshtafovych A, Kobayashi N, Kuroda Y, Liu G, LiWang A, Swapna GVT, Wu N, Yamazaki T, Montelione GT. Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2. Proteins 2021; 89:1959-1976. [PMID: 34559429 PMCID: PMC8616817 DOI: 10.1002/prot.26246] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/09/2021] [Accepted: 09/14/2021] [Indexed: 12/26/2022]
Abstract
NMR studies can provide unique information about protein conformations in solution. In CASP14, three reference structures provided by solution NMR methods were available (T1027, T1029, and T1055), as well as a fourth data set of NMR‐derived contacts for an integral membrane protein (T1088). For the three targets with NMR‐based structures, the best prediction results ranged from very good (GDT_TS = 0.90, for T1055) to poor (GDT_TS = 0.47, for T1029). We explored the basis of these results by comparing all CASP14 prediction models against experimental NMR data. For T1027, NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction methods. The analysis of T1029 motivated exploration of a novel method of “inverse structure determination,” in which an AlphaFold2 model was used to guide NMR data analysis. NMR data provided to CASP predictor groups for target T1088, a 238‐residue integral membrane porin, was also used to assess several NMR‐assisted prediction methods. Most groups involved in this exercise generated similar beta‐barrel models, with good agreement with the experimental data. However, as was also observed in CASP13, some pure prediction groups that did not use any NMR data generated models for T1088 that better fit the NMR data than the models generated using these experimental data. These results demonstrate the remarkable power of modern methods to predict structures of proteins with accuracies rivaling solution NMR structures, and that it is now possible to reliably use prediction models to guide and complement experimental NMR data analysis.
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Affiliation(s)
- Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Ning Zhang
- Department of Chemistry and Biochemistry, University of California, Merced, California, USA
| | - Beate Bersch
- Biomolecular NMR Spectroscopy Group, Institut de Biologie Structurale, UMD-5075, CNRS-CEA-UJF, Grenoble, France
| | | | - Masayori Inouye
- Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.,Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | - Yojiro Ishida
- Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.,Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | | | - Naohiro Kobayashi
- NMR Science and Development Division, RSC, RIKEN, Yokohama, Kanagawa, Japan
| | - Yutaka Kuroda
- Department of Biotechnology and Life Science, Graduate School of Engineering, Tokyo University of Agriculture and Technology (TUAT), Tokyo, Japan
| | - Gaohua Liu
- Nexomics Biosciences, Inc., Rocky Hill, New Jersey, USA
| | - Andy LiWang
- Department of Chemistry and Biochemistry, University of California, Merced, California, USA.,Center for Cellular and Biomolecular Machines and Health Sciences Research Institute, University of California, Merced, California, USA
| | - G V T Swapna
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA
| | - Nan Wu
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Toshio Yamazaki
- NMR Science and Development Division, RSC, RIKEN, Yokohama, Kanagawa, Japan
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA
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8
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Koga N, Koga R, Liu G, Castellanos J, Montelione GT, Baker D. Role of backbone strain in de novo design of complex α/β protein structures. Nat Commun 2021; 12:3921. [PMID: 34168113 PMCID: PMC8225619 DOI: 10.1038/s41467-021-24050-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 05/28/2021] [Indexed: 12/24/2022] Open
Abstract
We previously elucidated principles for designing ideal proteins with completely consistent local and non-local interactions which have enabled the design of a wide range of new αβ-proteins with four or fewer β-strands. The principles relate local backbone structures to supersecondary-structure packing arrangements of α-helices and β-strands. Here, we test the generality of the principles by employing them to design larger proteins with five- and six- stranded β-sheets flanked by α-helices. The initial designs were monomeric in solution with high thermal stability, and the nuclear magnetic resonance (NMR) structure of one was close to the design model, but for two others the order of strands in the β-sheet was swapped. Investigation into the origins of this strand swapping suggested that the global structures of the design models were more strained than the NMR structures. We incorporated explicit consideration of global backbone strain into the design methodology, and succeeded in designing proteins with the intended unswapped strand arrangements. These results illustrate the value of experimental structure determination in guiding improvement of de novo design, and the importance of consistency between local, supersecondary, and global tertiary interactions in determining protein topology. The augmented set of principles should inform the design of larger functional proteins.
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Affiliation(s)
- Nobuyasu Koga
- University of Washington, Department of Biochemistry and Howard Hughes Medical Institute, Seattle, Washington, WA, USA. .,Research Center of Integrative Molecular Systems, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, Aichi, Japan. .,Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Aichi, Japan. .,SOKENDAI, The Graduate University for Advanced Studies, Hayama, Kanagawa, Japan.
| | - Rie Koga
- University of Washington, Department of Biochemistry and Howard Hughes Medical Institute, Seattle, Washington, WA, USA.,Protein Design Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Aichi, Japan
| | - Gaohua Liu
- Nexomics Biosciences, Rocky Hill, NJ, USA
| | - Javier Castellanos
- University of Washington, Department of Biochemistry and Howard Hughes Medical Institute, Seattle, Washington, WA, USA
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, and Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, NY, USA.
| | - David Baker
- University of Washington, Department of Biochemistry and Howard Hughes Medical Institute, Seattle, Washington, WA, USA.
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9
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Aiyer S, Swapna GVT, Ma LC, Liu G, Hao J, Chalmers G, Jacobs BC, Montelione GT, Roth MJ. A common binding motif in the ET domain of BRD3 forms polymorphic structural interfaces with host and viral proteins. Structure 2021; 29:886-898.e6. [PMID: 33592170 DOI: 10.1016/j.str.2021.01.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/22/2020] [Accepted: 01/21/2021] [Indexed: 12/23/2022]
Abstract
The extraterminal (ET) domain of BRD3 is conserved among BET proteins (BRD2, BRD3, BRD4), interacting with multiple host and viral protein-protein networks. Solution NMR structures of complexes formed between the BRD3 ET domain and either the 79-residue murine leukemia virus integrase (IN) C-terminal domain (IN329-408) or its 22-residue IN tail peptide (IN386-407) alone reveal similar intermolecular three-stranded β-sheet formations. 15N relaxation studies reveal a 10-residue linker region (IN379-388) tethering the SH3 domain (IN329-378) to the ET-binding motif (IN389-405):ET complex. This linker has restricted flexibility, affecting its potential range of orientations in the IN:nucleosome complex. The complex of the ET-binding peptide of the host NSD3 protein (NSD3148-184) and the BRD3 ET domain includes a similar three-stranded β-sheet interaction, but the orientation of the β hairpin is flipped compared with the two IN:ET complexes. These studies expand our understanding of molecular recognition polymorphism in complexes of ET-binding motifs with viral and host proteins.
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Affiliation(s)
- Sriram Aiyer
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - G V T Swapna
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Li-Chung Ma
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Gaohua Liu
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Jingzhou Hao
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Gordon Chalmers
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Brian C Jacobs
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Gaetano T Montelione
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Monica J Roth
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA.
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10
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REDCRAFT: A computational platform using residual dipolar coupling NMR data for determining structures of perdeuterated proteins in solution. PLoS Comput Biol 2021; 17:e1008060. [PMID: 33524015 PMCID: PMC7877757 DOI: 10.1371/journal.pcbi.1008060] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 02/11/2021] [Accepted: 01/05/2021] [Indexed: 01/10/2023] Open
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is one of the three primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by solution NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination from Residual Dipolar Couplings (RDCs) is relatively less well developed. Here, we describe the new features of the protein structure modeling program REDCRAFT and focus on the new Adaptive Decimation (AD) feature. The AD plays a critical role in improving the robustness of REDCRAFT to missing or noisy data, while allowing structure determination of larger proteins from less data. In this report we demonstrate the successful application of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data, and of larger proteins (145 to 573 residues) using simulated RDC data. In both cases, REDCRAFT uses only RDC data that can be collected from perdeuterated proteins. Finally, we compare the accuracy of structure determination from RDCs alone with traditional NOE-based methods for the structurally novel PF.2048.1 protein. The RDC-based structure of PF.2048.1 exhibited 1.0 Å BB-RMSD with respect to a high-quality NOE-based structure. Although optimal strategies would include using RDC data together with chemical shift, NOE, and other NMR data, these studies provide proof-of-principle for robust structure determination of largely-perdeuterated proteins from RDC data alone using REDCRAFT. Residual Dipolar Couplings have the potential to improve the accuracy and reduce the time needed to characterize protein structures. In addition, RDC data have been demonstrated to concurrently elucidate structure of proteins, provide assignment of resonances, and characterize the internal dynamics of proteins. Given all the advantages associated with the study of proteins from RDC data, based on the statistics provided by the Protein Databank (PDB), surprisingly only 124 proteins (out of nearly 150,000 proteins) have utilized RDCs as part of their structure determination. Even a smaller subset of these proteins (approximately 7) have utilized RDCs as the primary source of data for structure determination. One key factor in the use of RDCs is the challenging computational and analytical aspects of this source of data. In this report, we demonstrate the success of the REDCRAFT software package in structure determination of proteins using RDC data that can be collected from small and large proteins in a routine fashion. REDCRAFT accomplishes the challenging task of structure determination from RDCs by introducing a unique search and optimization technique that is both robust and computationally tractable. Structure determination from routinely collectable RDC data using REDCRAFT can complement existing methods to provide faster and more accurate studies of larger and more complex protein structures by NMR spectroscopy in solution state.
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11
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Allain F, Mareuil F, Ménager H, Nilges M, Bardiaux B. ARIAweb: a server for automated NMR structure calculation. Nucleic Acids Res 2020; 48:W41-W47. [PMID: 32383755 PMCID: PMC7319541 DOI: 10.1093/nar/gkaa362] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/14/2020] [Accepted: 04/28/2020] [Indexed: 11/13/2022] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a method of choice to study the dynamics and determine the atomic structure of macromolecules in solution. The standalone program ARIA (Ambiguous Restraints for Iterative Assignment) for automated assignment of nuclear Overhauser enhancement (NOE) data and structure calculation is well established in the NMR community. To ultimately provide a perfectly transparent and easy to use service, we designed an online user interface to ARIA with additional functionalities. Data conversion, structure calculation setup and execution, followed by interactive visualization of the generated 3D structures are all integrated in ARIAweb and freely accessible at https://ariaweb.pasteur.fr.
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Affiliation(s)
- Fabrice Allain
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, Institut Pasteur, Paris, 75015, France
| | - Fabien Mareuil
- Bioinformatics and Biostatistics Hub, Department of Computational Biology, CNRS USR 3756, Institut Pasteur, Paris, 75015, France
| | - Hervé Ménager
- Bioinformatics and Biostatistics Hub, Department of Computational Biology, CNRS USR 3756, Institut Pasteur, Paris, 75015, France
| | - Michael Nilges
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, Institut Pasteur, Paris, 75015, France
| | - Benjamin Bardiaux
- Structural Bioinformatics Unit, Department of Structural Biology and Chemistry, CNRS UMR 3528, Institut Pasteur, Paris, 75015, France
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12
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Sala D, Huang YJ, Cole CA, Snyder DA, Liu G, Ishida Y, Swapna GVT, Brock KP, Sander C, Fidelis K, Kryshtafovych A, Inouye M, Tejero R, Valafar H, Rosato A, Montelione GT. Protein structure prediction assisted with sparse NMR data in CASP13. Proteins 2019; 87:1315-1332. [PMID: 31603581 DOI: 10.1002/prot.25837] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 01/05/2023]
Abstract
CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.
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Affiliation(s)
- Davide Sala
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York
| | - Casey A Cole
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - David A Snyder
- Department of Chemistry, College of Science and Health, William Paterson University, Wayne, New Jersey
| | - Gaohua Liu
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Nexomics Biosciences, Bordentown, New Jersey
| | - Yojiro Ishida
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - G V T Swapna
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts.,cBio Center, Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | | | - Masayori Inouye
- Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Roberto Tejero
- Departamento de Quimica Fisica, Universidad de Valencia, Valencia, Spain
| | - Homayoun Valafar
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - Antonio Rosato
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
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13
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Robertson JC, Nassar R, Liu C, Brini E, Dill KA, Perez A. NMR-assisted protein structure prediction with MELDxMD. Proteins 2019; 87:1333-1340. [PMID: 31350773 DOI: 10.1002/prot.25788] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 06/27/2019] [Accepted: 07/19/2019] [Indexed: 12/19/2022]
Abstract
We describe the performance of MELD-accelerated molecular dynamics (MELDxMD) in determining protein structures in the NMR-data-assisted category in CASP13. Seeded from web server predictions, MELDxMD was found best in the NMR category, over 17 targets, outperforming the next-best groups by a factor of ~4 in z-score. MELDxMD gives ensembles, not single structures; succeeds on a 326-mer, near the current upper limit for NMR structures; and predicts structures that match experimental residual dipolar couplings even though the only NMR-derived data used in the simulations was NOE-based ambiguous atom-atom contacts and backbone dihedrals. MELD can use noisy and ambiguous experimental information to reduce the MD search space. We believe MELDxMD is a promising method for determining protein structures from NMR data.
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Affiliation(s)
- James C Robertson
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Roy Nassar
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York.,Department of Chemistry, Stony Brook University, Stony Brook, New York
| | - Cong Liu
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York.,Department of Chemistry, Stony Brook University, Stony Brook, New York
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York.,Department of Chemistry, Stony Brook University, Stony Brook, New York.,Department of Physics & Astronomy, Stony Brook University, Stony Brook, New York
| | - Alberto Perez
- Department of Chemistry, University of Florida, Gainesville, Florida
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14
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Kuenze G, Meiler J. Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13. Proteins 2019; 87:1341-1350. [PMID: 31292988 DOI: 10.1002/prot.25769] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 05/25/2019] [Accepted: 07/06/2019] [Indexed: 12/30/2022]
Abstract
Computational methods that produce accurate protein structure models from limited experimental data, for example, from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a blind test to explore the capabilities and limitations of current modeling techniques in leveraging NMR data which had high sparsity, ambiguity, and error rate for protein structure prediction. We describe our approach to predict the structure of these proteins leveraging the Rosetta software suite. Protein structure models were predicted de novo using a two-stage protocol. First, low-resolution models were generated with the Rosetta de novo method guided by nonambiguous nuclear Overhauser effect (NOE) contacts and residual dipolar coupling (RDC) restraints. Second, iterative model hybridization and fragment insertion with the Rosetta comparative modeling method was used to refine and regularize models guided by all ambiguous and nonambiguous NOE contacts and RDCs. Nine out of 16 of the Rosetta de novo models had the correct fold (global distance test total score > 45) and in three cases high-resolution models were achieved (root-mean-square deviation < 3.5 å). We also show that a meta-approach applying iterative Rosetta + NMR refinement on server-predicted models which employed non-NMR-contacts and structural templates leads to substantial improvement in model quality. Integrating these data-assisted refinement strategies with innovative non-data-assisted approaches which became possible in CASP13 such as high precision contact prediction will in the near future enable structure determination for large proteins that are outside of the realm of conventional NMR.
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Affiliation(s)
- Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee
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15
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Abstract
Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have been very successful for data collection, annotation, and processing, but for the most part have harnessed human pattern recognition skills rather than human creativity. An exception is the game EteRNA4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson-Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein folding game Foldit5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure with an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top scoring solutions, and subsequent game improvement, Foldit players can now, starting from an extended polypeptide chain, generate a diversity of protein structures and sequences which encode them in silico. 146 Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed in E. coli with good solubility and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds—including a new fold not observed in natural proteins. High resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge contributing to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges, such as the protein design problem.
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16
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Rosario-Cruz Z, Eletsky A, Daigham NS, Al-Tameemi H, Swapna GVT, Kahn PC, Szyperski T, Montelione GT, Boyd JM. The copBL operon protects Staphylococcus aureus from copper toxicity: CopL is an extracellular membrane-associated copper-binding protein. J Biol Chem 2019; 294:4027-4044. [PMID: 30655293 PMCID: PMC6422080 DOI: 10.1074/jbc.ra118.004723] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 01/08/2019] [Indexed: 12/22/2022] Open
Abstract
As complications associated with antibiotic resistance have intensified, copper (Cu) is attracting attention as an antimicrobial agent. Recent studies have shown that copper surfaces decrease microbial burden, and host macrophages use Cu to increase bacterial killing. Not surprisingly, microbes have evolved mechanisms to tightly control intracellular Cu pools and protect against Cu toxicity. Here, we identified two genes (copB and copL) encoded within the Staphylococcus aureus arginine-catabolic mobile element (ACME) that we hypothesized function in Cu homeostasis. Supporting this hypothesis, mutational inactivation of copB or copL increased copper sensitivity. We found that copBL are co-transcribed and that their transcription is increased during copper stress and in a strain in which csoR, encoding a Cu-responsive transcriptional repressor, was mutated. Moreover, copB displayed genetic synergy with copA, suggesting that CopB functions in Cu export. We further observed that CopL functions independently of CopB or CopA in Cu toxicity protection and that CopL from the S. aureus clone USA300 is a membrane-bound and surface-exposed lipoprotein that binds up to four Cu+ ions. Solution NMR structures of the homologous Bacillus subtilis CopL, together with phylogenetic analysis and chemical-shift perturbation experiments, identified conserved residues potentially involved in Cu+ coordination. The solution NMR structure also revealed a novel Cu-binding architecture. Of note, a CopL variant with defective Cu+ binding did not protect against Cu toxicity in vivo Taken together, these findings indicate that the ACME-encoded CopB and CopL proteins are additional factors utilized by the highly successful S. aureus USA300 clone to suppress copper toxicity.
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Affiliation(s)
- Zuelay Rosario-Cruz
- From the Department of Biochemistry and Microbiology, Rutgers, the State University of New Jersey, New Brunswick, New Jersey 08901
| | - Alexander Eletsky
- the Department of Chemistry, State University of New York at Buffalo and Northeast Structural Genomics Consortium, Buffalo, New York 14260, and
| | - Nourhan S Daigham
- the Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854
| | - Hassan Al-Tameemi
- From the Department of Biochemistry and Microbiology, Rutgers, the State University of New Jersey, New Brunswick, New Jersey 08901
| | - G V T Swapna
- the Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854
| | - Peter C Kahn
- From the Department of Biochemistry and Microbiology, Rutgers, the State University of New Jersey, New Brunswick, New Jersey 08901
| | - Thomas Szyperski
- the Department of Chemistry, State University of New York at Buffalo and Northeast Structural Genomics Consortium, Buffalo, New York 14260, and
| | - Gaetano T Montelione
- the Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854,
- the Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, the State University of New Jersey, Piscataway, New Jersey 08854
| | - Jeffrey M Boyd
- From the Department of Biochemistry and Microbiology, Rutgers, the State University of New Jersey, New Brunswick, New Jersey 08901,
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17
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Vila JA, Arnautova YA. 13C Chemical Shifts in Proteins: A Rich Source of Encoded Structural Information. SPRINGER SERIES ON BIO- AND NEUROSYSTEMS 2019. [PMCID: PMC7123919 DOI: 10.1007/978-3-319-95843-9_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Despite the formidable progress in Nuclear Magnetic Resonance (NMR) spectroscopy, quality assessment of NMR-derived structures remains as an important problem. Thus, validation of protein structures is essential for the spectroscopists, since it could enable them to detect structural flaws and potentially guide their efforts in further refinement. Moreover, availability of accurate and efficient validation tools would help molecular biologists and computational chemists to evaluate quality of available experimental structures and to select a protein model which is the most suitable for a given scientific problem. The 13Cα nuclei are ubiquitous in proteins, moreover, their shieldings are easily obtainable from NMR experiments and represent a rich source of encoded structural information that makes 13Cα chemical shifts an attractive candidate for use in computational methods aimed at determination and validation of protein structures. In this chapter, the basis of a novel methodology of computing, at the quantum chemical level of theory, the 13Cα shielding for the amino acid residues in proteins is described. We also identify and examine the main factors affecting the 13Cα-shielding computation. Finally, we illustrate how the information encoded in the 13C chemical shifts can be used for a number of applications, viz., from protein structure prediction of both α-helical and β-sheet conformations, to determination of the fraction of the tautomeric forms of the imidazole ring of histidine in proteins as a function of pH or to accurate detection of structural flaws, at a residue-level, in NMR-determined protein models.
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18
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Huang YJ, Brock KP, Ishida Y, Swapna GVT, Inouye M, Marks DS, Sander C, Montelione GT. Combining Evolutionary Covariance and NMR Data for Protein Structure Determination. Methods Enzymol 2018; 614:363-392. [PMID: 30611430 PMCID: PMC6640129 DOI: 10.1016/bs.mie.2018.11.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.
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Affiliation(s)
- Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Yojiro Ishida
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Gurla V T Swapna
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Masayori Inouye
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School and cBio Center, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States.
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19
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Nerli S, McShan AC, Sgourakis NG. Chemical shift-based methods in NMR structure determination. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2018; 106-107:1-25. [PMID: 31047599 PMCID: PMC6788782 DOI: 10.1016/j.pnmrs.2018.03.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/09/2018] [Accepted: 03/09/2018] [Indexed: 05/08/2023]
Abstract
Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.
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Affiliation(s)
- Santrupti Nerli
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States; Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Andrew C McShan
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States.
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20
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Gibbs AC, Steele R, Liu G, Tounge BA, Montelione GT. Inhibitor Bound Dengue NS2B-NS3pro Reveals Multiple Dynamic Binding Modes. Biochemistry 2018; 57:1591-1602. [PMID: 29447443 DOI: 10.1021/acs.biochem.7b01127] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Dengue virus poses a significant global health threat as the source of increasingly deleterious dengue fever, dengue hemorrhagic fever, and dengue shock syndrome. As no specific antiviral treatment exists for dengue infection, considerable effort is being applied to discover therapies and drugs for maintenance and prevention of these afflictions. The virus is primarily transmitted by mosquitoes, and infection occurs following viral endocytosis by host cells. Upon entering the cell, viral RNA is translated into a large multisubunit polyprotein which is post-translationally cleaved into mature, structural and nonstructural (NS) proteins. The viral genome encodes the enzyme to carry out cleavage of the large polyprotein, specifically the NS2B-NS3pro cofactor-protease complex-a target of high interest for drug design. One class of recently discovered NS2B-NS3pro inhibitors is the substrate-based trifluoromethyl ketone containing peptides. These compounds interact covalently with the active site Ser135 via a hemiketal adduct. A detailed picture of the intermolecular protease/inhibitor interactions of the hemiketal adduct is crucial for rational drug design. We demonstrate, through the use of protein- and ligand-detected solution-state 19F and 1H NMR methods, an unanticipated multibinding mode behavior of a representative of this class of inhibitors to dengue NS2B-NS3pro. Our results illustrate the highly dynamic nature of both the covalently bound ligand and protease protein structure, and the need to consider these dynamics when designing future inhibitors in this class.
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Affiliation(s)
- Alan C Gibbs
- Janssen Research and Development LLC , Welsh & McKean Roads , Spring House , Pennsylvania 19477 , United States
| | - Ruth Steele
- Janssen Research and Development LLC , Welsh & McKean Roads , Spring House , Pennsylvania 19477 , United States
| | - Gaohua Liu
- Nexomics Biosciences, Inc. , 1200 Florence Columbus Road , Bordentown , New Jersey 08505 , United States
| | - Brett A Tounge
- Janssen Research and Development LLC , Welsh & McKean Roads , Spring House , Pennsylvania 19477 , United States
| | - Gaetano T Montelione
- Nexomics Biosciences, Inc. , 1200 Florence Columbus Road , Bordentown , New Jersey 08505 , United States
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21
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Huang YJ, Brock KP, Sander C, Marks DS, Montelione GT. A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1105:153-169. [PMID: 30617828 DOI: 10.1007/978-981-13-2200-6_10] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
While 3D structure determination of small (<15 kDa) proteins by solution NMR is largely automated and routine, structural analysis of larger proteins is more challenging. An emerging hybrid strategy for modeling protein structures combines sparse NMR data that can be obtained for larger proteins with sequence co-variation data, called evolutionary couplings (ECs), obtained from multiple sequence alignments of protein families. This hybrid "EC-NMR" method can be used to accurately model larger (15-60 kDa) proteins, and more rapidly determine structures of smaller (5-15 kDa) proteins using only backbone NMR data. The resulting structures have accuracies relative to reference structures comparable to those obtained with full backbone and sidechain NMR resonance assignments. The requirement that evolutionary couplings (ECs) are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, potentially also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.
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Affiliation(s)
- Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Kelly P Brock
- cBio Center, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- cBio Center, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
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22
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Hoch JC. Beyond Fourier. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2017; 283:117-123. [PMID: 28385517 PMCID: PMC5730986 DOI: 10.1016/j.jmr.2017.03.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2017] [Revised: 03/23/2017] [Accepted: 03/25/2017] [Indexed: 06/07/2023]
Abstract
Non-Fourier methods of spectrum analysis are gaining traction in NMR spectroscopy, driven by their utility for processing nonuniformly sampled data. These methods afford new opportunities for optimizing experiment time, resolution, and sensitivity of multidimensional NMR experiments, but they also pose significant challenges not encountered with the discrete Fourier transform. A brief history of non-Fourier methods in NMR serves to place different approaches in context. Non-Fourier methods reflect broader trends in the growing importance of computation in NMR, and offer insights for future software development.
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Affiliation(s)
- Jeffrey C Hoch
- Department of Molecular Biology and Biophysics, UConn Health, 263 Farmington Ave., Farmington, CT 06030-3305, USA.
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23
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Sugiki T, Kobayashi N, Fujiwara T. Modern Technologies of Solution Nuclear Magnetic Resonance Spectroscopy for Three-dimensional Structure Determination of Proteins Open Avenues for Life Scientists. Comput Struct Biotechnol J 2017; 15:328-339. [PMID: 28487762 PMCID: PMC5408130 DOI: 10.1016/j.csbj.2017.04.001] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Revised: 03/31/2017] [Accepted: 04/03/2017] [Indexed: 02/07/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique for structural studies of chemical compounds and biomolecules such as DNA and proteins. Since the NMR signal sensitively reflects the chemical environment and the dynamics of a nuclear spin, NMR experiments provide a wealth of structural and dynamic information about the molecule of interest at atomic resolution. In general, structural biology studies using NMR spectroscopy still require a reasonable understanding of the theory behind the technique and experience on how to recorded NMR data. Owing to the remarkable progress in the past decade, we can easily access suitable and popular analytical resources for NMR structure determination of proteins with high accuracy. Here, we describe the practical aspects, workflow and key points of modern NMR techniques used for solution structure determination of proteins. This review should aid NMR specialists aiming to develop new methods that accelerate the structure determination process, and open avenues for non-specialist and life scientists interested in using NMR spectroscopy to solve protein structures.
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Affiliation(s)
- Toshihiko Sugiki
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Naohiro Kobayashi
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Toshimichi Fujiwara
- Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
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24
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NMR-based automated protein structure determination. Arch Biochem Biophys 2017; 628:24-32. [PMID: 28263718 DOI: 10.1016/j.abb.2017.02.011] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 02/18/2017] [Accepted: 02/28/2017] [Indexed: 11/21/2022]
Abstract
NMR spectra analysis for protein structure determination can now in many cases be performed by automated computational methods. This overview of the computational methods for NMR protein structure analysis presents recent automated methods for signal identification in multidimensional NMR spectra, sequence-specific resonance assignment, collection of conformational restraints, and structure calculation, as implemented in the CYANA software package. These algorithms are sufficiently reliable and integrated into one software package to enable the fully automated structure determination of proteins starting from NMR spectra without manual interventions or corrections at intermediate steps, with an accuracy of 1-2 Å backbone RMSD in comparison with manually solved reference structures.
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25
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Catazaro J, Periago J, Shortridge MD, Worley B, Kirchner A, Powers R, Griep MA. Identification of a Ligand-Binding Site on the Staphylococcus aureus DnaG Primase C-Terminal Domain. Biochemistry 2017; 56:932-943. [PMID: 28125218 PMCID: PMC6476306 DOI: 10.1021/acs.biochem.6b01273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The interface between the DnaG primase C-terminal domain (CTD) and the N-terminal domain of DnaB helicase is essential for bacterial DNA replication because it allows coordinated priming of DNA synthesis at the replication fork while the DNA is being unwound. Because these two proteins are conserved in all bacteria and distinct from those in eukaryotes, their interface is an attractive antibiotic target. To learn more about this interface, we determined the solution structure and dynamics of the DnaG primase CTD from Staphylococcus aureus, a medically important bacterial species. Comparison with the known primase CTD structures shows there are two biologically relevant conformations, an open conformation that likely binds to DnaB helicase and a closed conformation that does not. The S. aureus primase CTD is in the closed conformation, but nuclear magnetic resonance (NMR) dynamic studies indicate there is considerable movement in the linker between the two subdomains and that N564 is the most dynamic residue within the linker. A high-throughput NMR ligand affinity screen identified potential binding compounds, among which were acycloguanosine and myricetin. Although the affinity for these compounds and adenosine was in the millimolar range, all three bind to a common pocket that is present only on the closed conformation of the CTD. This binding pocket is at the opposite end of helices 6 and 7 from N564, the key hinge residue. The identification of this binding pocket should allow the development of stronger-binding ligands that can prevent formation of the CTD open conformation that binds to DnaB helicase.
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Affiliation(s)
| | | | | | - Bradley Worley
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304
| | - Andrew Kirchner
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304
| | - Mark A. Griep
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304
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26
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Structural/Functional Properties of Human NFU1, an Intermediate [4Fe-4S] Carrier in Human Mitochondrial Iron-Sulfur Cluster Biogenesis. Structure 2016; 24:2080-2091. [PMID: 27818104 DOI: 10.1016/j.str.2016.08.020] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 08/03/2016] [Accepted: 10/05/2016] [Indexed: 01/26/2023]
Abstract
Human mitochondrial NFU1 functions in the maturation of iron-sulfur proteins, and NFU1 deficiency is associated with a fatal mitochondrial disease. We determined three-dimensional structures of the N- and C-terminal domains of human NFU1 by nuclear magnetic resonance spectroscopy and used these structures along with small-angle X-ray scattering (SAXS) data to derive structural models for full-length monomeric apo-NFU1, dimeric apo-NFU1 (an artifact of intermolecular disulfide bond formation), and holo-NFUI (the [4Fe-4S] cluster-containing form of the protein). Apo-NFU1 contains two cysteine residues in its C-terminal domain, and two apo-NFU1 subunits coordinate one [4Fe-4S] cluster to form a cluster-linked dimer. Holo-NFU1 consists of a complex of three of these dimers as shown by molecular weight estimates from SAXS and size-exclusion chromatography. The SAXS-derived structural model indicates that one N-terminal region from each of the three dimers forms a tripartite interface. The activity of the holo-NFU1 preparation was verified by demonstrating its ability to activate apo-aconitase.
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27
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Korzhnev DM, Neculai D, Dhe-Paganon S, Arrowsmith CH, Bezsonova I. Solution NMR structure of the HLTF HIRAN domain: a conserved module in SWI2/SNF2 DNA damage tolerance proteins. JOURNAL OF BIOMOLECULAR NMR 2016; 66:209-219. [PMID: 27771863 DOI: 10.1007/s10858-016-0070-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Accepted: 10/17/2016] [Indexed: 06/06/2023]
Abstract
HLTF is a SWI2/SNF2-family ATP-dependent chromatin remodeling enzyme that acts in the error-free branch of DNA damage tolerance (DDT), a cellular mechanism that enables replication of damaged DNA while leaving damage repair for a later time. Human HLTF and a closely related protein SHPRH, as well as their yeast homologue Rad5, are multi-functional enzymes that share E3 ubiquitin-ligase activity required for activation of the error-free DDT. HLTF and Rad5 also function as ATP-dependent dsDNA translocases and possess replication fork reversal activities. Thus, they can convert Y-shaped replication forks into X-shaped Holliday junction structures that allow error-free replication over DNA lesions. The fork reversal activity of HLTF is dependent on 3'-ssDNA-end binding activity of its N-terminal HIRAN domain. Here we present the solution NMR structure of the human HLTF HIRAN domain, an OB-like fold module found in organisms from bacteria (as a stand-alone domain) to plants, fungi and metazoan (in combination with SWI2/SNF2 helicase-like domain). The obtained structure of free HLTF HIRAN is similar to recently reported structures of its DNA bound form, while the NMR analysis also reveals that the DNA binding site of the free domain exhibits conformational heterogeneity. Sequence comparison of N-terminal regions of HLTF, SHPRH and Rad5 aided by knowledge of the HLTF HIRAN structure suggests that the SHPRH N-terminus also includes an uncharacterized structured module, exhibiting weak sequence similarity with HIRAN regions of HLTF and Rad5, and potentially playing a similar functional role.
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Affiliation(s)
- Dmitry M Korzhnev
- Department of Molecular Biology and Biophysics, University of Connecticut Health, Farmington, CT, 06030, USA
| | - Dante Neculai
- School of Medicine, Zhejiang University, Hangzhou, China
| | - Sirano Dhe-Paganon
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Cheryl H Arrowsmith
- Structural Genomics Consortium, University of Toronto, Toronto, ON, M5G 1L7, Canada
- Department of Medical Biophysics, Princess Margaret Cancer Centre, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Irina Bezsonova
- Department of Molecular Biology and Biophysics, University of Connecticut Health, Farmington, CT, 06030, USA.
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28
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Mechanistic insights into mode of action of rice allene oxide synthase on hydroxyperoxides: An intermediate step in herbivory-induced jasmonate pathway. Comput Biol Chem 2016; 64:227-236. [PMID: 27471161 DOI: 10.1016/j.compbiolchem.2016.07.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Revised: 06/08/2016] [Accepted: 07/01/2016] [Indexed: 11/22/2022]
Abstract
Various types of oxygenated fatty acids termed 'oxylipins' are involved in plant response to herbivory. Oxylipins like jasmonic acid (JA) and green leafy volatiles (GLVs) are formed by the action of enzymes like allene oxide synthase (AOS) and hydroxyperoxide lyase (HPL) respectively. In this study, we focus on AOS of Oryza sativa sb. Japonica, that interact with 9- and 13- hydroxyperoxides to produce intermediates of jasmonate pathway and compare it with rice HPL that yields GLVs. We attempt to elucidate the interaction pattern by computational docking protocols keeping the Arabidopsis AOS system as the reference model system. Both 9-hydroxyperoxide and 13-hydroxyperoxide fit into the active site of AOS completely with Phe347, Phe92, Ile463, Val345, and Asn278 being the common interacting residues. Phe347 and Phe92 were mutated with Leucine and docked again with the hydroxyperoxides. The Phe347→Leu347 mutant showed a different mode of action than AOS-hydroxyperoxide complex with Trp413 in direct bonding with the OOH group of 9-hydroxyperoxide. The loss of Lys88-OOH interaction in 13-hydroxyperoxide and loss-of-interaction of Leu347 indicated the importance of Phe347 residue in hydroxyperoxide catalysis. The second mutant Phe92→Leu92 also shows a very different interaction pattern with 13-hydroxyperoxide but not with 9-hydroxyperoxide.Therefore, it can be concluded that Phe347 is more crucial for AOS functionality than Phe92. The aromatic ring of a Phenylalanine residue is important for catalysis and its mutation affects the binding of the two ligands. Another important residue is Asn278 which is an important part of the AOS catalytic site for maintaining stability and can be compared with the Arabidopsis AOS residue Asn321. Lastly, the interaction of HPL with these two derivatives involves Leu363 residue instead of Phe347 and thus, validating the importance of Phe→Leu substitution to be the reason of different modes of action that result in completely different products from same substrates.
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29
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Basanta B, Chan KK, Barth P, King T, Sosnick TR, Hinshaw JR, Liu G, Everett JK, Xiao R, Montelione GT, Baker D. Introduction of a polar core into the de novo designed protein Top7. Protein Sci 2016; 25:1299-307. [PMID: 26873166 PMCID: PMC4918430 DOI: 10.1002/pro.2899] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Revised: 02/04/2016] [Accepted: 02/08/2016] [Indexed: 01/26/2023]
Abstract
Design of polar interactions is a current challenge for protein design. The de novo designed protein Top7, like almost all designed proteins, has an entirely nonpolar core. Here we describe the replacing of a sizable fraction (5 residues) of this core with a designed polar hydrogen bond network. The polar core design is expressed at high levels in E. coli, has a folding free energy of 10 kcal/mol, and retains the multiphasic folding kinetics of the original Top7. The NMR structure of the design shows that conformations of three of the five residues, and the designed hydrogen bonds between them, are very close to those in the design model. The remaining two residues, which are more solvent exposed, sample a wide range of conformations in the NMR ensemble. These results show that hydrogen bond networks can be designed in protein cores, but also highlight challenges that need to be overcome when there is competition with solvent.
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Affiliation(s)
- Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, Washington, 98195
- Institute for Protein Design, University of Washington, Seattle, Washington, 98195
- Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, Washington, 98195, USA
| | - Kui K Chan
- Enzyme Engineering, EnzymeWorks, California, 92121
| | - Patrick Barth
- Structural and Computational Biology and Molecular Biophysics Graduate Program, Baylor College of Medicine, Houston, Texas 77030
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas, 77030
- Department of Pharmacology Baylor College of Medicine, Houston, Texas, 77030
| | - Tiffany King
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, 60637
| | - Tobin R Sosnick
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, Illinois, 60637
- Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois, 60637
| | - James R Hinshaw
- Department of Chemistry, University of Chicago, Chicago, Illinois, 60637
| | - Gaohua Liu
- Department of Molecular Biology and Biochemistry, Center of Advanced Biotechnology and Medicine, The State University of New Jersey, Piscataway, New Jersey, 08854
- Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854
| | - John K Everett
- Department of Molecular Biology and Biochemistry, Center of Advanced Biotechnology and Medicine, The State University of New Jersey, Piscataway, New Jersey, 08854
- Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854
| | - Rong Xiao
- Department of Molecular Biology and Biochemistry, Center of Advanced Biotechnology and Medicine, The State University of New Jersey, Piscataway, New Jersey, 08854
- Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854
| | - Gaetano T Montelione
- Department of Molecular Biology and Biochemistry, Center of Advanced Biotechnology and Medicine, The State University of New Jersey, Piscataway, New Jersey, 08854
- Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, New Jersey, 08854
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington, 98195
- Institute for Protein Design, University of Washington, Seattle, Washington, 98195
- Howard Hughes Medical Institute, University of Washington, Seattle, Washington, 98195
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30
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Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction: Progress and new directions in round XI. Proteins 2016; 84 Suppl 1:4-14. [PMID: 27171127 DOI: 10.1002/prot.25064] [Citation(s) in RCA: 149] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 04/29/2016] [Accepted: 05/08/2016] [Indexed: 12/15/2022]
Abstract
Modeling of protein structure from amino acid sequence now plays a major role in structural biology. Here we report new developments and progress from the CASP11 community experiment, assessing the state of the art in structure modeling. Notable points include the following: (1) New methods for predicting three dimensional contacts resulted in a few spectacular template free models in this CASP, whereas models based on sequence homology to proteins with experimental structure continue to be the most accurate. (2) Refinement of initial protein models, primarily using molecular dynamics related approaches, has now advanced to the point where the best methods can consistently (though slightly) improve nearly all models. (3) The use of relatively sparse NMR constraints dramatically improves the accuracy of models, and another type of sparse data, chemical crosslinking, introduced in this CASP, also shows promise for producing better models. (4) A new emphasis on modeling protein complexes, in collaboration with CAPRI, has produced interesting results, but also shows the need for more focus on this area. (5) Methods for estimating the accuracy of models have advanced to the point where they are of considerable practical use. (6) A first assessment demonstrates that models can sometimes successfully address biological questions that motivate experimental structure determination. (7) There is continuing progress in accuracy of modeling regions of structure not directly available by comparative modeling, while there is marginal or no progress in some other areas. Proteins 2016; 84(Suppl 1):4-14. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- John Moult
- Institute for Bioscience and Biotechnology Research and Department of Cell Biology and Molecular Genetics, University of Maryland, Rockville, Maryland, 20850.
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, Davis, California, 95616
| | | | - Torsten Schwede
- Biozentrum & SIB Swiss Institute of Bioinformatics, University of Basel, Basel, Switzerland
| | - Anna Tramontano
- Department of Physics and Istituto Pasteur - Fondazione Cenci Bolognetti, Sapienza University of Rome, Rome, Italy
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31
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Everett JK, Tejero R, Murthy SBK, Acton TB, Aramini JM, Baran MC, Benach J, Cort JR, Eletsky A, Forouhar F, Guan R, Kuzin AP, Lee HW, Liu G, Mani R, Mao B, Mills JL, Montelione AF, Pederson K, Powers R, Ramelot T, Rossi P, Seetharaman J, Snyder D, Swapna GVT, Vorobiev SM, Wu Y, Xiao R, Yang Y, Arrowsmith CH, Hunt JF, Kennedy MA, Prestegard JH, Szyperski T, Tong L, Montelione GT. A community resource of experimental data for NMR / X-ray crystal structure pairs. Protein Sci 2015; 25:30-45. [PMID: 26293815 DOI: 10.1002/pro.2774] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 08/17/2015] [Indexed: 12/11/2022]
Abstract
We have developed an online NMR / X-ray Structure Pair Data Repository. The NIGMS Protein Structure Initiative (PSI) has provided many valuable reagents, 3D structures, and technologies for structural biology. The Northeast Structural Genomics Consortium was one of several PSI centers. NESG used both X-ray crystallography and NMR spectroscopy for protein structure determination. A key goal of the PSI was to provide experimental structures for at least one representative of each of hundreds of targeted protein domain families. In some cases, structures for identical (or nearly identical) constructs were determined by both NMR and X-ray crystallography. NMR spectroscopy and X-ray diffraction data for 41 of these "NMR / X-ray" structure pairs determined using conventional triple-resonance NMR methods with extensive sidechain resonance assignments have been organized in an online NMR / X-ray Structure Pair Data Repository. In addition, several NMR data sets for perdeuterated, methyl-protonated protein samples are included in this repository. As an example of the utility of this repository, these data were used to revisit questions about the precision and accuracy of protein NMR structures first outlined by Levy and coworkers several years ago (Andrec et al., Proteins 2007;69:449-465). These results demonstrate that the agreement between NMR and X-ray crystal structures is improved using modern methods of protein NMR spectroscopy. The NMR / X-ray Structure Pair Data Repository will provide a valuable resource for new computational NMR methods development.
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Affiliation(s)
- John K Everett
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Roberto Tejero
- Departamento De Química Física, Universidad De Valencia, Valencia, Spain
| | - Sarath B K Murthy
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Thomas B Acton
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - James M Aramini
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Michael C Baran
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Jordi Benach
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - John R Cort
- Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
| | - Alexander Eletsky
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, New York, 14260, USA
| | - Farhad Forouhar
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Rongjin Guan
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Alexandre P Kuzin
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Hsiau-Wei Lee
- Complex Carbohydrate Research Center and Northeast Structural Genomics Consortium, University of Georgia, Athens, Georgia, 30602, USA
| | - Gaohua Liu
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Rajeswari Mani
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Binchen Mao
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Jeffrey L Mills
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, New York, 14260, USA
| | - Alexander F Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Kari Pederson
- Complex Carbohydrate Research Center and Northeast Structural Genomics Consortium, University of Georgia, Athens, Georgia, 30602, USA
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, Nebraska, 68588, USA
| | - Theresa Ramelot
- Department of Chemistry and Biochemistry, Northeast Structural Genomics Consortium, Miami University, Oxford, Ohio, 45056, USA
| | - Paolo Rossi
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Jayaraman Seetharaman
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - David Snyder
- Department of Chemistry, College of Science and Health, William Paterson University of NJ, Wayne, New Jersey, 07470, USA
| | - G V T Swapna
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Sergey M Vorobiev
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Yibing Wu
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, New York, 14260, USA
| | - Rong Xiao
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Yunhuang Yang
- Department of Chemistry and Biochemistry, Northeast Structural Genomics Consortium, Miami University, Oxford, Ohio, 45056, USA
| | - Cheryl H Arrowsmith
- Cancer Genomics & Proteomics, Department of Medical Biophysics, Ontario Cancer Institute, and Northeast Structural Genomics Consortium, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
| | - John F Hunt
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Northeast Structural Genomics Consortium, Miami University, Oxford, Ohio, 45056, USA
| | - James H Prestegard
- Complex Carbohydrate Research Center and Northeast Structural Genomics Consortium, University of Georgia, Athens, Georgia, 30602, USA
| | - Thomas Szyperski
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, New York, 14260, USA
| | - Liang Tong
- Department of Biological Sciences and Northeast Structural Genomics Consortium, Columbia University, New York, NY, 10027, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA.,Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers, the State University of New Jersey, Piscataway, New Jersey, 08854, USA
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32
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Yilmaz EM, Güntert P. NMR structure calculation for all small molecule ligands and non-standard residues from the PDB Chemical Component Dictionary. JOURNAL OF BIOMOLECULAR NMR 2015; 63:21-37. [PMID: 26123317 DOI: 10.1007/s10858-015-9959-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Accepted: 06/22/2015] [Indexed: 05/15/2023]
Abstract
An algorithm, CYLIB, is presented for converting molecular topology descriptions from the PDB Chemical Component Dictionary into CYANA residue library entries. The CYANA structure calculation algorithm uses torsion angle molecular dynamics for the efficient computation of three-dimensional structures from NMR-derived restraints. For this, the molecules have to be represented in torsion angle space with rotations around covalent single bonds as the only degrees of freedom. The molecule must be given a tree structure of torsion angles connecting rigid units composed of one or several atoms with fixed relative positions. Setting up CYANA residue library entries therefore involves, besides straightforward format conversion, the non-trivial step of defining a suitable tree structure of torsion angles, and to re-order the atoms in a way that is compatible with this tree structure. This can be done manually for small numbers of ligands but the process is time-consuming and error-prone. An automated method is necessary in order to handle the large number of different potential ligand molecules to be studied in drug design projects. Here, we present an algorithm for this purpose, and show that CYANA structure calculations can be performed with almost all small molecule ligands and non-standard amino acid residues in the PDB Chemical Component Dictionary.
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Affiliation(s)
- Emel Maden Yilmaz
- Center for Biomolecular Magnetic Resonance, Institute of Biophysical Chemistry, Goethe University Frankfurt am Main, Max-von-Laue-Str. 9, 60438, Frankfurt am Main, Germany
| | - Peter Güntert
- Center for Biomolecular Magnetic Resonance, Institute of Biophysical Chemistry, Goethe University Frankfurt am Main, Max-von-Laue-Str. 9, 60438, Frankfurt am Main, Germany.
- Laboratory of Physical Chemistry, ETH Zürich, Zurich, Switzerland.
- Graduate School of Science, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.
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33
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Didenko T, Proudfoot A, Dutta SK, Serrano P, Wüthrich K. Non-Uniform Sampling and J-UNIO Automation for Efficient Protein NMR Structure Determination. Chemistry 2015; 21:12363-9. [PMID: 26227870 PMCID: PMC4576834 DOI: 10.1002/chem.201502544] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Indexed: 11/10/2022]
Abstract
High-resolution structure determination of small proteins in solution is one of the big assets of NMR spectroscopy in structural biology. Improvements in the efficiency of NMR structure determination by advances in NMR experiments and automation of data handling therefore attracts continued interest. Here, non-uniform sampling (NUS) of 3D heteronuclear-resolved [(1)H,(1)H]-NOESY data yielded two- to three-fold savings of instrument time for structure determinations of soluble proteins. With the 152-residue protein NP_372339.1 from Staphylococcus aureus and the 71-residue protein NP_346341.1 from Streptococcus pneumonia we show that high-quality structures can be obtained with NUS NMR data, which are equally well amenable to robust automated analysis as the corresponding uniformly sampled data.
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Affiliation(s)
- Tatiana Didenko
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014
- GPCR-Network, 3430 S. Vermont Ave., TRF 105, Los Angeles, CA 90089-3301 (USA), Fax: (+1) 858-784-8014 http://gpcr.usc.edu
| | - Andrew Proudfoot
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014
| | - Samit Kumar Dutta
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014
| | - Pedro Serrano
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014
| | - Kurt Wüthrich
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA) http://www.jcsg.org. , ,
- Joint Center for Structural Genomics, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014. , ,
- GPCR-Network, 3430 S. Vermont Ave., TRF 105, Los Angeles, CA 90089-3301 (USA), Fax: (+1) 858-784-8014 http://gpcr.usc.edu. , ,
- Skaggs Institute for Chemical Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 (USA), Fax: (+1) 858-784-8014. , ,
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34
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Abstract
Three-dimensional structures of proteins in solution can be calculated on the basis of conformational restraints derived from NMR measurements. This chapter gives an overview of the computational methods for NMR protein structure analysis highlighting recent automated methods for the assignment of NMR spectra, the collection of conformational restraints, and the structure calculation.
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35
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Rosato A, Vranken W, Fogh RH, Ragan TJ, Tejero R, Pederson K, Lee HW, Prestegard JH, Yee A, Wu B, Lemak A, Houliston S, Arrowsmith CH, Kennedy M, Acton TB, Xiao R, Liu G, Montelione GT, Vuister GW. The second round of Critical Assessment of Automated Structure Determination of Proteins by NMR: CASD-NMR-2013. JOURNAL OF BIOMOLECULAR NMR 2015; 62:413-24. [PMID: 26071966 PMCID: PMC4569658 DOI: 10.1007/s10858-015-9953-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2015] [Accepted: 05/28/2015] [Indexed: 05/21/2023]
Abstract
The second round of the community-wide initiative Critical Assessment of automated Structure Determination of Proteins by NMR (CASD-NMR-2013) comprised ten blind target datasets, consisting of unprocessed spectral data, assigned chemical shift lists and unassigned NOESY peak and RDC lists, that were made available in both curated (i.e. manually refined) or un-curated (i.e. automatically generated) form. Ten structure calculation programs, using fully automated protocols only, generated a total of 164 three-dimensional structures (entries) for the ten targets, sometimes using both curated and un-curated lists to generate multiple entries for a single target. The accuracy of the entries could be established by comparing them to the corresponding manually solved structure of each target, which was not available at the time the data were provided. Across the entire data set, 71 % of all entries submitted achieved an accuracy relative to the reference NMR structure better than 1.5 Å. Methods based on NOESY peak lists achieved even better results with up to 100% of the entries within the 1.5 Å threshold for some programs. However, some methods did not converge for some targets using un-curated NOESY peak lists. Over 90% of the entries achieved an accuracy better than the more relaxed threshold of 2.5 Å that was used in the previous CASD-NMR-2010 round. Comparisons between entries generated with un-curated versus curated peaks show only marginal improvements for the latter in those cases where both calculations converged.
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Affiliation(s)
- Antonio Rosato
- Department of Chemistry and Magnetic Resonance Center, University of Florence, 50019, Sesto Fiorentino, Italy
| | - Wim Vranken
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium
- (IB)2 Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, 1050, Brussels, Belgium
| | - Rasmus H Fogh
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK
| | - Timothy J Ragan
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK
| | - Roberto Tejero
- Departamento de Química Física, Universidad de Valencia, Avda. Dr. Moliner 50, 46100, Burjassot (Valencia), Spain
| | - Kari Pederson
- Complex Carbohydrate Research Center and Northeast Structural Genomics Consortium, University of Georgia, Athens, GA, 30602, USA
| | - Hsiau-Wei Lee
- Complex Carbohydrate Research Center and Northeast Structural Genomics Consortium, University of Georgia, Athens, GA, 30602, USA
| | - James H Prestegard
- Complex Carbohydrate Research Center and Northeast Structural Genomics Consortium, University of Georgia, Athens, GA, 30602, USA
| | - Adelinda Yee
- Department of Medical Biophysics, Cancer Genomics and Proteomics, Ontario Cancer Institute, Northeast Structural Genomics Consortium, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Bin Wu
- Department of Medical Biophysics, Cancer Genomics and Proteomics, Ontario Cancer Institute, Northeast Structural Genomics Consortium, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Alexander Lemak
- Department of Medical Biophysics, Cancer Genomics and Proteomics, Ontario Cancer Institute, Northeast Structural Genomics Consortium, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Scott Houliston
- Department of Medical Biophysics, Cancer Genomics and Proteomics, Ontario Cancer Institute, Northeast Structural Genomics Consortium, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Cheryl H Arrowsmith
- Department of Medical Biophysics, Cancer Genomics and Proteomics, Ontario Cancer Institute, Northeast Structural Genomics Consortium, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Michael Kennedy
- Department of Chemistry and Biochemistry, Northeast Structural Genomics Consortium, Miami University, Oxford, OH, 45056, USA
| | - Thomas B Acton
- Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Piscataway, NJ, 08854, USA
| | - Rong Xiao
- Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Piscataway, NJ, 08854, USA
| | - Gaohua Liu
- Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Piscataway, NJ, 08854, USA
| | - Gaetano T Montelione
- Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Piscataway, NJ, 08854, USA.
| | - Geerten W Vuister
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK.
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Huang YJ, Mao B, Xu F, Montelione GT. Guiding automated NMR structure determination using a global optimization metric, the NMR DP score. JOURNAL OF BIOMOLECULAR NMR 2015; 62:439-51. [PMID: 26081575 PMCID: PMC4943320 DOI: 10.1007/s10858-015-9955-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2015] [Accepted: 06/03/2015] [Indexed: 05/07/2023]
Abstract
ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases (15)N-(1)H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta.
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Affiliation(s)
- Yuanpeng Janet Huang
- Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
| | - Binchen Mao
- Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Fei Xu
- Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Gaetano T Montelione
- Department of Molecular Biology and Biochemistry, Center for Advanced Biotechnology and Medicine, and Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
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Güntert P, Buchner L. Combined automated NOE assignment and structure calculation with CYANA. JOURNAL OF BIOMOLECULAR NMR 2015; 62:453-71. [PMID: 25801209 DOI: 10.1007/s10858-015-9924-9] [Citation(s) in RCA: 264] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Accepted: 03/17/2015] [Indexed: 05/12/2023]
Abstract
The automated assignment of NOESY cross peaks has become a fundamental technique for NMR protein structure analysis. A widely used algorithm for this purpose is implemented in the program CYANA. It has been used for a large number of structure determinations of proteins in solution but was so far not described in full detail. In this paper we present a complete description of the CYANA implementation of automated NOESY assignment, which differs extensively from its predecessor CANDID by the use of a consistent probabilistic treatment, and we discuss its performance in the second round of the critical assessment of structure determination by NMR.
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Affiliation(s)
- Peter Güntert
- Center for Biomolecular Magnetic Resonance, Institute of Biophysical Chemistry, Goethe University Frankfurt am Main, Max-von-Laue-Str. 9, 60438, Frankfurt am Main, Germany.
- Laboratory of Physical Chemistry, ETH Zürich, Zurich, Switzerland.
- Graduate School of Science, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.
| | - Lena Buchner
- Center for Biomolecular Magnetic Resonance, Institute of Biophysical Chemistry, Goethe University Frankfurt am Main, Max-von-Laue-Str. 9, 60438, Frankfurt am Main, Germany
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Ragan TJ, Fogh RH, Tejero R, Vranken W, Montelione GT, Rosato A, Vuister GW. Analysis of the structural quality of the CASD-NMR 2013 entries. JOURNAL OF BIOMOLECULAR NMR 2015; 62:527-40. [PMID: 26032236 PMCID: PMC4569653 DOI: 10.1007/s10858-015-9949-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Accepted: 05/20/2015] [Indexed: 05/30/2023]
Abstract
We performed a comprehensive structure validation of both automated and manually generated structures of the 10 targets of the CASD-NMR-2013 effort. We established that automated structure determination protocols are capable of reliably producing structures of comparable accuracy and quality to those generated by a skilled researcher, at least for small, single domain proteins such as the ten targets tested. The most robust results appear to be obtained when NOESY peak lists are used either as the primary input data or to augment chemical shift data without the need to manually filter such lists. A detailed analysis of the long-range NOE restraints generated by the different programs from the same data showed a surprisingly low degree of overlap. Additionally, we found that there was no significant correlation between the extent of the NOE restraint overlap and the accuracy of the structure. This result was surprising given the importance of NOE data in producing good quality structures. We suggest that this could be explained by the information redundancy present in NOEs between atoms contained within a fixed covalent network.
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Affiliation(s)
- Timothy J Ragan
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK
| | - Rasmus H Fogh
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK
| | - Roberto Tejero
- Departamento de Química Física, Universidad de Valencia, Avda. Dr. Moliner 50, 46100, Burjassot (Valencia), Spain
| | - Wim Vranken
- Structural Biology Brussels, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, Belgium
- (IB)2 Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Triomflaan, 1050, Brussels, Belgium
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, and Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Robert Wood Johnson Medical School, Piscataway, NJ, 08854, USA
| | - Antonio Rosato
- Magnetic Resonance Center, Department of Chemistry, University of Florence, 50019, Sesto Fiorentino, Italy
| | - Geerten W Vuister
- Department of Biochemistry, School of Biological Sciences, University of Leicester, Henry Wellcome Building, Lancaster Road, Leicester, LE1 9HN, UK.
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Tang Y, Huang YJ, Hopf TA, Sander C, Marks DS, Montelione GT. Protein structure determination by combining sparse NMR data with evolutionary couplings. Nat Methods 2015; 12:751-4. [PMID: 26121406 PMCID: PMC4521990 DOI: 10.1038/nmeth.3455] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Accepted: 05/26/2015] [Indexed: 11/13/2022]
Abstract
Accurate protein structure determination by NMR is challenging for larger proteins, for which experimental data is often incomplete and ambiguous. Fortunately, the upsurge in evolutionary sequence information and advances in maximum entropy statistical methods now provide a rich complementary source of structural constraints. We have developed a hybrid approach (EC-NMR) combining sparse NMR data with evolutionary residue-residue couplings, and demonstrate accurate structure determination for several 6 to 41 kDa proteins.
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Affiliation(s)
- Yuefeng Tang
- 1] Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA. [2] Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Yuanpeng Janet Huang
- 1] Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA. [2] Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Thomas A Hopf
- 1] Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. [2] Department of Informatics, Technische Universität München, Garching, Germany
| | - Chris Sander
- Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - Gaetano T Montelione
- 1] Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA. [2] Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA. [3] Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
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Buchner L, Güntert P. Systematic evaluation of combined automated NOE assignment and structure calculation with CYANA. JOURNAL OF BIOMOLECULAR NMR 2015; 62:81-95. [PMID: 25796507 DOI: 10.1007/s10858-015-9921-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Accepted: 03/16/2015] [Indexed: 05/07/2023]
Abstract
The automated assignment of NOESY cross peaks has become a fundamental technique for NMR protein structure analysis. A widely used algorithm for this purpose is implemented in the program CYANA. It has been used for a large number of structure determinations of proteins in solution but a systematic evaluation of its performance has not yet been reported. In this paper we systematically analyze the reliability of combined automated NOESY assignment and structure calculation with CYANA under a variety of conditions on the basis of the experimental NMR data sets of ten proteins. To evaluate the robustness of the algorithm, the original high-quality experimental data sets were modified in different ways to simulate the effect of data imperfections, i.e. incomplete or erroneous chemical shift assignments, missing NOESY cross peaks, inaccurate peak positions, inaccurate peak intensities, lower dimensionality NOESY spectra, and higher tolerances for the matching of chemical shifts and peak positions. The results show that the algorithm is remarkably robust with regard to imperfections of the NOESY peak lists and the chemical shift tolerances but susceptible to lacking or erroneous resonance assignments, in particular for nuclei that are involved in many NOESY cross peaks.
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Affiliation(s)
- Lena Buchner
- Institute of Biophysical Chemistry, Center for Biomolecular Magnetic Resonance, and Frankfurt Institute of Advanced Studies, Goethe University Frankfurt am Main, Max-von-Laue-Str. 9, 60438, Frankfurt am Main, Germany
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41
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Buchner L, Güntert P. Increased reliability of nuclear magnetic resonance protein structures by consensus structure bundles. Structure 2015; 23:425-34. [PMID: 25579816 DOI: 10.1016/j.str.2014.11.014] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 11/17/2014] [Accepted: 11/17/2014] [Indexed: 11/17/2022]
Abstract
Nuclear magnetic resonance (NMR) structures are represented by bundles of conformers calculated from different randomized initial structures using identical experimental input data. The spread among these conformers indicates the precision of the atomic coordinates. However, there is as yet no reliable measure of structural accuracy, i.e., how close NMR conformers are to the "true" structure. Instead, the precision of structure bundles is widely (mis)interpreted as a measure of structural quality. Attempts to increase precision often overestimate accuracy by tight bundles of high precision but much lower accuracy. To overcome this problem, we introduce a protocol for NMR structure determination with the software package CYANA, which produces, like the traditional method, bundles of conformers in agreement with a common set of conformational restraints but with a realistic precision that is, throughout a variety of proteins and NMR data sets, a much better estimate of structural accuracy than the precision of conventional structure bundles.
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Affiliation(s)
- Lena Buchner
- Institute of Biophysical Chemistry and Center for Biomolecular Magnetic Resonance, Goethe University Frankfurt, Max-von-Laue-Straße 9, 60438 Frankfurt am Main, Germany
| | - Peter Güntert
- Institute of Biophysical Chemistry and Center for Biomolecular Magnetic Resonance, Goethe University Frankfurt, Max-von-Laue-Straße 9, 60438 Frankfurt am Main, Germany; Frankfurt Institute of Advanced Studies, Goethe University Frankfurt, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany; Department of Chemistry, Graduate School of Science and Engineering, Tokyo Metropolitan University, 1-1 Minami-Ohsawa, Hachioji, Tokyo 192-0397, Japan.
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Wu Y, Canturk B, Jo H, Ma C, Gianti E, Klein M, Pinto LH, Lamb RA, Fiorin G, Wang J, DeGrado WF. Flipping in the pore: discovery of dual inhibitors that bind in different orientations to the wild-type versus the amantadine-resistant S31N mutant of the influenza A virus M2 proton channel. J Am Chem Soc 2014; 136:17987-95. [PMID: 25470189 PMCID: PMC4286326 DOI: 10.1021/ja508461m] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2014] [Indexed: 12/13/2022]
Abstract
Influenza virus infections lead to numerous deaths and millions of hospitalizations each year. One challenge facing anti-influenza drug development is the heterogeneity of the circulating influenza viruses, which comprise several strains with variable susceptibility to antiviral drugs. For example, the wild-type (WT) influenza A viruses, such as the seasonal H1N1, tend to be sensitive to antiviral drugs, amantadine and rimantadine, while the S31N mutant viruses, such as the pandemic 2009 H1N1 (H1N1pdm09) and seasonal H3N2, are resistant to this class of drugs. Thus, drugs targeting both WT and the S31N mutant are highly desired. We report our design of a novel class of dual inhibitors along with their ion channel blockage and antiviral activities. The potency of the most active compound 11 in inhibiting WT and the S31N mutant influenza viruses is comparable with that of amantadine in inhibiting WT influenza virus. Solution NMR studies and molecular dynamics (MD) simulations of drug-M2 interactions supported our design hypothesis: namely, the dual inhibitor binds in the WT M2 channel with an aromatic group facing down toward the C-terminus, while the same drug binds in the S31N M2 channel with its aromatic group facing up toward the N-terminus. The flip-flop mode of drug binding correlates with the structure-activity relationship (SAR) and has paved the way for the next round of rational design of broad-spectrum antiviral drugs.
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Affiliation(s)
- Yibing Wu
- Department
of Pharmaceutical Chemistry, University
of California, Mission Bay Box 3122, San Francisco, California 94158, United States
| | - Belgin Canturk
- Department
of Chemistry, University of Pennsylvania, 231 South 34th Street, Philadelphia, Pennsylvania 19104, United States
| | - Hyunil Jo
- Department
of Pharmaceutical Chemistry, University
of California, Mission Bay Box 3122, San Francisco, California 94158, United States
| | - Chunlong Ma
- Department
of Pharmacology and Toxicology and the BIO5 Institute, The University of Arizona, 1501 N. Campbell Avenue, Tucson, Arizona 85721, United States
| | - Eleonora Gianti
- Institute
for Computational and Molecular Science, Science Education and Research
Center (035-07), Temple University, 1925 North 12th Street, Philadelphia, Pennsylvania 19122, United States
| | - Michael
L. Klein
- Institute
for Computational and Molecular Science, Science Education and Research
Center (035-07), Temple University, 1925 North 12th Street, Philadelphia, Pennsylvania 19122, United States
| | - Lawrence H. Pinto
- Department
of Neurobiology, Northwestern University, 2205 Tech Drive, Evanston, Illinois 60208, United States
| | - Robert A. Lamb
- Department
of Molecular Biosciences, Northwestern University, 2205 Tech Drive, Evanston, Illinois 60208, United States
- Howard
Hughes Medical Institute, Northwestern University, Evanston, Illinois 60208, United States
| | - Giacomo Fiorin
- Institute
for Computational and Molecular Science, Science Education and Research
Center (035-07), Temple University, 1925 North 12th Street, Philadelphia, Pennsylvania 19122, United States
| | - Jun Wang
- Department
of Pharmacology and Toxicology and the BIO5 Institute, The University of Arizona, 1501 N. Campbell Avenue, Tucson, Arizona 85721, United States
| | - William F. DeGrado
- Department
of Pharmaceutical Chemistry, University
of California, Mission Bay Box 3122, San Francisco, California 94158, United States
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Tyagi C, Gupta A, Goyal S, Dhanjal JK, Grover A. Fragment based group QSAR and molecular dynamics mechanistic studies on arylthioindole derivatives targeting the α-β interfacial site of human tubulin. BMC Genomics 2014; 15 Suppl 9:S3. [PMID: 25521775 PMCID: PMC4290613 DOI: 10.1186/1471-2164-15-s9-s3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A number of microtubule disassembly blocking agents and inhibitors of tubulin polymerization have been elements of great interest in anti-cancer therapy, some of them even entering into the clinical trials. One such class of tubulin assembly inhibitors is of arylthioindole derivatives which results in effective microtubule disorganization responsible for cell apoptosis by interacting with the colchicine binding site of the β-unit of tubulin close to the interface with the α unit. We modelled the human tubulin β unit (chain D) protein and performed docking studies to elucidate the detailed binding mode of actions associated with their inhibition. The activity enhancing structural aspects were evaluated using a fragment-based Group QSAR (G-QSAR) model and was validated statistically to determine its robustness. A combinatorial library was generated keeping the arylthioindole moiety as the template and their activities were predicted. RESULTS The G-QSAR model obtained was statistically significant with r2 value of 0.85, cross validated correlation coefficient q2 value of 0.71 and pred_r2 (r2 value for test set) value of 0.89. A high F test value of 65.76 suggests robustness of the model. Screening of the combinatorial library on the basis of predicted activity values yielded two compounds HPI (predicted pIC50 = 6.042) and MSI (predicted pIC50 = 6.001) whose interactions with the D chain of modelled human tubulin protein were evaluated in detail. A toxicity evaluation resulted in MSI being less toxic in comparison to HPI. CONCLUSIONS The study provides an insight into the crucial structural requirements and the necessary chemical substitutions required for the arylthioindole moiety to exhibit enhanced inhibitory activity against human tubulin. The two reported compounds HPI and MSI showed promising anti cancer activities and thus can be considered as potent leads against cancer. The toxicity evaluation of these compounds suggests that MSI is a promising therapeutic candidate. This study provided another stepping stone in the direction of evaluating tubulin inhibition and microtubule disassembly degeneration as viable targets for development of novel therapeutics against cancer.
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Affiliation(s)
- Chetna Tyagi
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India - 110067
| | - Ankita Gupta
- Department of Biotechnology, Delhi Technological University, New Delhi, India -110042
| | - Sukriti Goyal
- Apaji Institute of Mathematics & Applied Computer Technology, Banasthali University, Tonk, Rajasthan, India - 304022
| | | | - Abhinav Grover
- School of Biotechnology, Jawaharlal Nehru University, New Delhi, India - 110067
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Pulavarti SVSRK, Huang YJ, Pederson K, Acton TB, Xiao R, Everett JK, Prestegard JH, Montelione GT, Szyperski T. Solution NMR structures of immunoglobulin-like domains 7 and 12 from obscurin-like protein 1 contribute to the structural coverage of the Human Cancer Protein Interaction Network. JOURNAL OF STRUCTURAL AND FUNCTIONAL GENOMICS 2014; 15:209-214. [PMID: 24989974 PMCID: PMC4945113 DOI: 10.1007/s10969-014-9185-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Accepted: 06/14/2014] [Indexed: 06/03/2023]
Abstract
High-quality solution NMR structures of immunoglobulin-like domains 7 and 12 from human obscurin-like protein 1 were solved. The two domains share 30% sequence identity and their structures are, as expected, rather similar. The new structures contribute to structural coverage of human cancer associated proteins. Mutations of Arg 812 in domain 7 cause the rare 3-M syndrome, and this site is located in a surface area predicted to be involved in protein-protein interactions.
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Affiliation(s)
- Surya VSRK Pulavarti
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, NY 14260, USA
| | - Yuanpeng J. Huang
- Center of Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey and Northeast Structural Genomics Consortium, Piscataway, NJ 08854, USA
| | - Kari Pederson
- Complex Carbohydrate Research Center, University of Georgia, and Northeast Structural Genomics Consortium, Athens, GA 30602, USA
| | - Thomas B. Acton
- Center of Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey and Northeast Structural Genomics Consortium, Piscataway, NJ 08854, USA
| | - Rong Xiao
- Center of Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey and Northeast Structural Genomics Consortium, Piscataway, NJ 08854, USA
| | - John K. Everett
- Center of Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey and Northeast Structural Genomics Consortium, Piscataway, NJ 08854, USA
| | - James H. Prestegard
- Complex Carbohydrate Research Center, University of Georgia, and Northeast Structural Genomics Consortium, Athens, GA 30602, USA
| | - Gaetano T. Montelione
- Center of Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey and Northeast Structural Genomics Consortium, Piscataway, NJ 08854, USA
| | - Thomas Szyperski
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, NY 14260, USA
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45
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Lin YJ, Ikeya T, Kirchner DK, Güntert P. Influence of NMR Data Completeness on Structure Determinations of Homodimeric Proteins. J CHIN CHEM SOC-TAIP 2014. [DOI: 10.1002/jccs.201400095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Structural and functional characterization of DUF1471 domains of Salmonella proteins SrfN, YdgH/SssB, and YahO. PLoS One 2014; 9:e101787. [PMID: 25010333 PMCID: PMC4092069 DOI: 10.1371/journal.pone.0101787] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2014] [Accepted: 04/07/2014] [Indexed: 11/20/2022] Open
Abstract
Bacterial species in the Enterobacteriaceae typically contain multiple paralogues of a small domain of unknown function (DUF1471) from a family of conserved proteins also known as YhcN or BhsA/McbA. Proteins containing DUF1471 may have a single or three copies of this domain. Representatives of this family have been demonstrated to play roles in several cellular processes including stress response, biofilm formation, and pathogenesis. We have conducted NMR and X-ray crystallographic studies of four DUF1471 domains from Salmonella representing three different paralogous DUF1471 subfamilies: SrfN, YahO, and SssB/YdgH (two of its three DUF1471 domains: the N-terminal domain I (residues 21–91), and the C-terminal domain III (residues 244–314)). Notably, SrfN has been shown to have a role in intracellular infection by Salmonella Typhimurium. These domains share less than 35% pairwise sequence identity. Structures of all four domains show a mixed α+β fold that is most similar to that of bacterial lipoprotein RcsF. However, all four DUF1471 sequences lack the redox sensitive cysteine residues essential for RcsF activity in a phospho-relay pathway, suggesting that DUF1471 domains perform a different function(s). SrfN forms a dimer in contrast to YahO and SssB domains I and III, which are monomers in solution. A putative binding site for oxyanions such as phosphate and sulfate was identified in SrfN, and an interaction between the SrfN dimer and sulfated polysaccharides was demonstrated, suggesting a direct role for this DUF1471 domain at the host-pathogen interface.
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Zhang Z, Porter J, Tripsianes K, Lange OF. Robust and highly accurate automatic NOESY assignment and structure determination with Rosetta. JOURNAL OF BIOMOLECULAR NMR 2014; 59:135-45. [PMID: 24845473 DOI: 10.1007/s10858-014-9832-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2014] [Accepted: 04/19/2014] [Indexed: 05/16/2023]
Abstract
We have developed a novel and robust approach for automatic and unsupervised simultaneous nuclear Overhauser effect (NOE) assignment and structure determination within the CS-Rosetta framework. Starting from unassigned peak lists and chemical shift assignments, autoNOE-Rosetta determines NOE cross-peak assignments and generates structural models. The approach tolerates incomplete and raw NOE peak lists as well as incomplete or partially incorrect chemical shift assignments, and its performance has been tested on 50 protein targets ranging from 50 to 200 residues in size. We find a significantly improved performance compared to established programs, particularly for larger proteins and for NOE data obtained on perdeuterated protein samples. X-ray crystallographic structures allowed comparison of Rosetta and conventional, PDB-deposited, NMR models in 20 of 50 test cases. The unsupervised autoNOE-Rosetta models were often of significantly higher accuracy than the corresponding expert-supervised NMR models deposited in the PDB. We also tested the method with unrefined peak lists and found that performance was nearly as good as for refined peak lists. Finally, demonstrating our method's remarkable robustness against problematic input data, we provided correct models for an incorrect PDB-deposited NMR solution structure.
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Affiliation(s)
- Zaiyong Zhang
- Department Chemie, Biomolecular NMR and Munich Center for Integrated Protein Science, Technische Universität München, Lichtenbergstrasse 4, 85747, Garching, Germany
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Solution NMR structures of homeodomains from human proteins ALX4, ZHX1, and CASP8AP2 contribute to the structural coverage of the Human Cancer Protein Interaction Network. JOURNAL OF STRUCTURAL AND FUNCTIONAL GENOMICS 2014; 15:201-7. [PMID: 24941917 DOI: 10.1007/s10969-014-9184-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Accepted: 06/10/2014] [Indexed: 10/25/2022]
Abstract
High-quality solution NMR structures of three homeodomains from human proteins ALX4, ZHX1 and CASP8AP2 were solved. These domains were chosen as targets of a biomedical theme project pursued by the Northeast Structural Genomics Consortium. This project focuses on increasing the structural coverage of human proteins associated with cancer.
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Vila JA, Arnautova YA. 13C Chemical Shifts in Proteins: A Rich Source of Encoded Structural Information. COMPUTATIONAL METHODS TO STUDY THE STRUCTURE AND DYNAMICS OF BIOMOLECULES AND BIOMOLECULAR PROCESSES 2014. [PMCID: PMC7121069 DOI: 10.1007/978-3-642-28554-7_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Despite the formidable progress in Nuclear Magnetic Resonance (NMR) spectroscopy, quality assessment of NMR-derived structures remains as an important problem. Thus, validation of protein structures is essential for the spectroscopists, since it could enable them to detect structural flaws and potentially guide their efforts in further refinement. Moreover, availability of accurate and efficient validation tools would help molecular biologists and computational chemists to evaluate quality of available experimental structures and to select a protein model which is the most suitable for a given scientific problem. The 13Cα nuclei are ubiquitous in proteins, moreover, their shieldings are easily obtainable from NMR experiments and represent a rich source of encoded structural information that makes 13Cα chemical shifts an attractive candidate for use in computational methods aimed at determination and validation of protein structures. In this chapter, the basis of a novel methodology of computing, at the quantum chemical level of theory, the 13Cα shielding for the amino acid residues in proteins is described. We also identify and examine the main factors affecting the 13Cα-shielding computation. Finally, we illustrate how the information encoded in the 13C chemical shifts can be used for a number of applications, viz., from protein structure prediction of both α-helical and β-sheet conformations, to determination of the fraction of the tautomeric forms of the imidazole ring of histidine in proteins as a function of pH or to accurate detection of structural flaws, at a residue-level, in NMR-determined protein models.
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50
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Pulavarti SVSRK, Eletsky A, Lee HW, Acton TB, Xiao R, Everett JK, Prestegard JH, Montelione GT, Szyperski T. Solution NMR structure of CD1104B from pathogenic Clostridium difficile reveals a distinct α-helical architecture and provides first structural representative of protein domain family PF14203. JOURNAL OF STRUCTURAL AND FUNCTIONAL GENOMICS 2013; 14:155-160. [PMID: 24048810 PMCID: PMC3844015 DOI: 10.1007/s10969-013-9164-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2013] [Accepted: 09/10/2013] [Indexed: 05/30/2023]
Abstract
A high-quality structure of the 68-residue protein CD1104B from Clostridium difficile strain 630 exhibits a distinct all α-helical fold. The structure presented here is the first representative of bacterial protein domain family PF14203 (currently 180 members) of unknown function (DUF4319) and reveals that the side-chains of the only two strictly conserved residues (Glu 8 and Lys 48) form a salt bridge. Moreover, these two residues are located in the vicinity of the largest surface cleft which is predicted to contribute to a surface area involved in protein-protein interactions. This, along with its coding in transposon CTn4, suggests that CD1104B (and very likely all members of Pfam 14203) functions by interacting with other proteins required for the transfer of transposons between different bacterial species.
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Affiliation(s)
- Surya VSRK Pulavarti
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, NY 14260, USA
| | - Alexander Eletsky
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, NY 14260, USA
| | - Hsiau-Wei Lee
- Complex Carbohydrate Research Center, University at Georgia, and Northeast Structural Genomics Consortium, Athens, GA 30602, USA
| | - Thomas B. Acton
- Center of Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey and Northeast Structural Genomics Consortium, Piscataway, NJ 08854, USA
| | - Rong Xiao
- Center of Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey and Northeast Structural Genomics Consortium, Piscataway, NJ 08854, USA
| | - John K. Everett
- Center of Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey and Northeast Structural Genomics Consortium, Piscataway, NJ 08854, USA
| | - James H. Prestegard
- Complex Carbohydrate Research Center, University at Georgia, and Northeast Structural Genomics Consortium, Athens, GA 30602, USA
| | - Gaetano T. Montelione
- Center of Advanced Biotechnology and Medicine and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey and Northeast Structural Genomics Consortium, Piscataway, NJ 08854, USA, Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, UMDNJ, Piscataway NJ 08854, USA
| | - Thomas Szyperski
- Department of Chemistry, The State University of New York at Buffalo, and Northeast Structural Genomics Consortium, Buffalo, NY 14260, USA
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