1
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Simpkin AJ, Caballero I, McNicholas S, Stevenson K, Jiménez E, Sánchez Rodríguez F, Fando M, Uski V, Ballard C, Chojnowski G, Lebedev A, Krissinel E, Usón I, Rigden DJ, Keegan RM. Predicted models and CCP4. Acta Crystallogr D Struct Biol 2023; 79:806-819. [PMID: 37594303 PMCID: PMC10478639 DOI: 10.1107/s2059798323006289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 07/19/2023] [Indexed: 08/19/2023] Open
Abstract
In late 2020, the results of CASP14, the 14th event in a series of competitions to assess the latest developments in computational protein structure-prediction methodology, revealed the giant leap forward that had been made by Google's Deepmind in tackling the prediction problem. The level of accuracy in their predictions was the first instance of a competitor achieving a global distance test score of better than 90 across all categories of difficulty. This achievement represents both a challenge and an opportunity for the field of experimental structural biology. For structure determination by macromolecular X-ray crystallography, access to highly accurate structure predictions is of great benefit, particularly when it comes to solving the phase problem. Here, details of new utilities and enhanced applications in the CCP4 suite, designed to allow users to exploit predicted models in determining macromolecular structures from X-ray diffraction data, are presented. The focus is mainly on applications that can be used to solve the phase problem through molecular replacement.
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Affiliation(s)
- Adam J. Simpkin
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Iracema Caballero
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona, Spain
| | - Stuart McNicholas
- York Structural Biology Laboratory, Department of Chemistry, The University of York, York YO10 5DD, United Kingdom
| | - Kyle Stevenson
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Elisabet Jiménez
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona, Spain
| | - Filomeno Sánchez Rodríguez
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
- York Structural Biology Laboratory, Department of Chemistry, The University of York, York YO10 5DD, United Kingdom
| | - Maria Fando
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Ville Uski
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Charles Ballard
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Grzegorz Chojnowski
- European Molecular Biology Laboratory, Hamburg Unit, Notkestrasse 85, 22607 Hamburg, Germany
| | - Andrey Lebedev
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Eugene Krissinel
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Isabel Usón
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona, Spain
- ICREA, Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys 23, 08003 Barcelona, Spain
| | - Daniel J. Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Ronan M. Keegan
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
- UKRI–STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
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2
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Vymětal J, Mertová K, Boušová K, Šulc J, Tripsianes K, Vondrasek J. Fusion of two unrelated protein domains in a chimera protein and its 3D prediction: Justification of the x-ray reference structures as a prediction benchmark. Proteins 2022; 90:2067-2079. [PMID: 35833233 PMCID: PMC9796088 DOI: 10.1002/prot.26398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 05/20/2022] [Accepted: 07/08/2022] [Indexed: 12/30/2022]
Abstract
Proteins are naturally formed by domains edging their functional and structural properties. A domain out of the context of an entire protein can retain its structure and to some extent also function on its own. These properties rationalize construction of artificial fusion multidomain proteins with unique combination of various functions. Information on the specific functional and structural characteristics of individual domains in the context of new artificial fusion proteins is inevitably encoded in sequential order of composing domains defining their mutual spatial positions. So the challenges in designing new proteins with new domain combinations lie dominantly in structure/function prediction and its context dependency. Despite the enormous body of publications on artificial fusion proteins, the task of their structure/function prediction is complex and nontrivial. The degree of spatial freedom facilitated by a linker between domains and their mutual orientation driven by noncovalent interactions is beyond a simple and straightforward methodology to predict their structure with reasonable accuracy. In the presented manuscript, we tested methodology using available modeling tools and computational methods. We show that the process and methodology of such prediction are not straightforward and must be done with care even when recently introduced AlphaFold II is used. We also addressed a question of benchmarking standards for prediction of multidomain protein structures-x-ray or Nuclear Magnetic Resonance experiments. On the study of six two-domain protein chimeras as well as their composing domains and their x-ray structures selected from PDB, we conclude that the major obstacle for justified prediction is inappropriate sampling of the conformational space by the explored methods. On the other hands, we can still address particular steps of the methodology and improve the process of chimera proteins prediction.
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Affiliation(s)
- Jiří Vymětal
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic
| | - Kateřina Mertová
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic,Faculty of Natural SciencesCharles UniversityPraha 2Czech Republic
| | - Kristýna Boušová
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic
| | - Josef Šulc
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic,Faculty of Natural SciencesCharles UniversityPraha 2Czech Republic
| | | | - Jiri Vondrasek
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of SciencesPrague 6Czech Republic
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3
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Wang Y, Yin C, Zhang H, Kamau PM, Dong W, Luo A, Chai L, Yang S, Lai R. Venom resistance mechanisms in centipede show tissue specificity. Curr Biol 2022; 32:3556-3563.e3. [PMID: 35863353 DOI: 10.1016/j.cub.2022.06.074] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 05/16/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022]
Abstract
Venomous animals utilize venom glands to secrete and store powerful toxins for intraspecific and/or interspecific antagonistic interactions, implying that tissue-specific resistance is essential for venom glands to anatomically separate toxins from other tissues. Here, we show the mechanism of tissue-specific resistance in centipedes (Scolopendra subspinipes mutilans), where the splice variant of the receptor repels its own toxin. Unlike the well-known resistance mechanism by mutation in a given exon, we found that the KCNQ1 channel is highly expressed in the venom gland as a unique splice variant in which the pore domain and transmembrane domain six, partially encoded by exon 6 (rather than 7 as found in other tissues), contain eleven mutated residues. Such a splice variant is sufficient to gain resistance to SsTx (a lethal toxin for giant prey capture) in the venom gland due to a partially buried binding site. Therefore, the tissue-specific KCNQ1 modification confers resistance to the toxins, establishing a safe zone in the venom-storing/secreting environment.
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Affiliation(s)
- Yunfei Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms, Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Bioactive Peptides, The National & Local Joint Engineering Center of Natural Bioactive Peptides, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Center for Non-Human Primates, Kunming Primate Research Center, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107 Yunnan, China; College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, China
| | - Chuanlin Yin
- College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, China
| | - Hao Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms, Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Bioactive Peptides, The National & Local Joint Engineering Center of Natural Bioactive Peptides, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Center for Non-Human Primates, Kunming Primate Research Center, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107 Yunnan, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peter Muiruri Kamau
- Key Laboratory of Animal Models and Human Disease Mechanisms, Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Bioactive Peptides, The National & Local Joint Engineering Center of Natural Bioactive Peptides, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Center for Non-Human Primates, Kunming Primate Research Center, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107 Yunnan, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wenqi Dong
- College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, China
| | - Anna Luo
- Key Laboratory of Animal Models and Human Disease Mechanisms, Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Bioactive Peptides, The National & Local Joint Engineering Center of Natural Bioactive Peptides, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Center for Non-Human Primates, Kunming Primate Research Center, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107 Yunnan, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Longhui Chai
- College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, China
| | - Shilong Yang
- College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, China.
| | - Ren Lai
- Key Laboratory of Animal Models and Human Disease Mechanisms, Key Laboratory of Bioactive Peptides of Yunnan Province, Engineering Laboratory of Bioactive Peptides, The National & Local Joint Engineering Center of Natural Bioactive Peptides, KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, National Resource Center for Non-Human Primates, Kunming Primate Research Center, National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107 Yunnan, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China; Sino-African Joint Research Center, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China.
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Simpkin AJ, Thomas JMH, Keegan RM, Rigden DJ. MrParse: finding homologues in the PDB and the EBI AlphaFold database for molecular replacement and more. ACTA CRYSTALLOGRAPHICA SECTION D STRUCTURAL BIOLOGY 2022; 78:553-559. [PMID: 35503204 PMCID: PMC9063843 DOI: 10.1107/s2059798322003576] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 03/29/2022] [Indexed: 11/10/2022]
Abstract
Crystallographers have an array of search-model options for structure solution by molecular replacement (MR). The well established options of homologous experimental structures and regular secondary-structure elements or motifs are increasingly supplemented by computational modelling. Such modelling may be carried out locally or may use pre-calculated predictions retrieved from databases such as the EBI AlphaFold database. MrParse is a new pipeline to help to streamline the decision process in MR by consolidating bioinformatic predictions in one place. When reflection data are provided, MrParse can rank any experimental homologues found using eLLG, which indicates the likelihood that a given search model will work in MR. Inbuilt displays of predicted secondary structure, coiled-coil and transmembrane regions further inform the choice of MR protocol. MrParse can also identify and rank homologues in the EBI AlphaFold database, a function that will also interest other structural biologists and bioinformaticians.
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5
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Barbarin-Bocahu I, Graille M. The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report. ACTA CRYSTALLOGRAPHICA SECTION D STRUCTURAL BIOLOGY 2022; 78:517-531. [PMID: 35362474 DOI: 10.1107/s2059798322002157] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 02/23/2022] [Indexed: 11/10/2022]
Abstract
The breakthrough recently made in protein structure prediction by deep-learning programs such as AlphaFold and RoseTTAFold will certainly revolutionize biology over the coming decades. The scientific community is only starting to appreciate the various applications, benefits and limitations of these protein models. Yet, after the first thrills due to this revolution, it is important to evaluate the impact of the proposed models and their overall quality to avoid the misinterpretation or overinterpretation of these models by biologists. One of the first applications of these models is in solving the `phase problem' encountered in X-ray crystallography in calculating electron-density maps from diffraction data. Indeed, the most frequently used technique to derive electron-density maps is molecular replacement. As this technique relies on knowledge of the structure of a protein that shares strong structural similarity with the studied protein, the availability of high-accuracy models is then definitely critical for successful structure solution. After the collection of a 2.45 Å resolution data set, we struggled for two years in trying to solve the crystal structure of a protein involved in the nonsense-mediated mRNA decay pathway, an mRNA quality-control pathway dedicated to the elimination of eukaryotic mRNAs harboring premature stop codons. We used different methods (isomorphous replacement, anomalous diffraction and molecular replacement) to determine this structure, but all failed until we straightforwardly succeeded thanks to both AlphaFold and RoseTTAFold models. Here, we describe how these new models helped us to solve this structure and conclude that in our case the AlphaFold model largely outcompetes the other models. We also discuss the importance of search-model generation for successful molecular replacement.
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6
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McCoy AJ, Sammito MD, Read RJ. Implications of AlphaFold2 for crystallographic phasing by molecular replacement. Acta Crystallogr D Struct Biol 2022; 78:1-13. [PMID: 34981757 PMCID: PMC8725160 DOI: 10.1107/s2059798321012122] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/13/2021] [Indexed: 12/11/2022] Open
Abstract
The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. Here, the data from CASP14 are used to explore the prospects for changes in phasing methods, and in particular to explore the prospects for molecular-replacement phasing using in silico models.
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Affiliation(s)
- Airlie J. McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Massimo D. Sammito
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
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7
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Simpkin AJ, Rodríguez FS, Mesdaghi S, Kryshtafovych A, Rigden DJ. Evaluation of model refinement in CASP14. Proteins 2021; 89:1852-1869. [PMID: 34288138 PMCID: PMC8616799 DOI: 10.1002/prot.26185] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/19/2021] [Accepted: 07/11/2021] [Indexed: 12/15/2022]
Abstract
We report here an assessment of the model refinement category of the 14th round of Critical Assessment of Structure Prediction (CASP14). As before, predictors submitted up to five ranked refinements, along with associated residue-level error estimates, for targets that had a wide range of starting quality. The ability of groups to accurately rank their submissions and to predict coordinate error varied widely. Overall, only four groups out-performed a "naïve predictor" corresponding to the resubmission of the starting model. Among the top groups, there are interesting differences of approach and in the spread of improvements seen: some methods are more conservative, others more adventurous. Some targets were "double-barreled" for which predictors were offered a high-quality AlphaFold 2 (AF2)-derived prediction alongside another of lower quality. The AF2-derived models were largely unimprovable, many of their apparent errors being found to reside at domain and, especially, crystal lattice contacts. Refinement is shown to have a mixed impact overall on structure-based function annotation methods to predict nucleic acid binding, spot catalytic sites, and dock protein structures.
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Affiliation(s)
- Adam J. Simpkin
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Filomeno Sánchez Rodríguez
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
- Life Science, Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE, England
| | - Shahram Mesdaghi
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | | | - Daniel J. Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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8
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Millán C, Keegan RM, Pereira J, Sammito MD, Simpkin AJ, McCoy AJ, Lupas AN, Hartmann MD, Rigden DJ, Read RJ. Assessing the utility of CASP14 models for molecular replacement. Proteins 2021; 89:1752-1769. [PMID: 34387010 PMCID: PMC8881082 DOI: 10.1002/prot.26214] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/20/2021] [Accepted: 07/27/2021] [Indexed: 11/21/2022]
Abstract
The assessment of CASP models for utility in molecular replacement is a measure of their use in a valuable real‐world application. In CASP7, the metric for molecular replacement assessment involved full likelihood‐based molecular replacement searches; however, this restricted the assessable targets to crystal structures with only one copy of the target in the asymmetric unit, and to those where the search found the correct pose. In CASP10, full molecular replacement searches were replaced by likelihood‐based rigid‐body refinement of models superimposed on the target using the LGA algorithm, with the metric being the refined log‐likelihood‐gain (LLG) score. This enabled multi‐copy targets and very poor models to be evaluated, but a significant further issue remained: the requirement of diffraction data for assessment. We introduce here the relative‐expected‐LLG (reLLG), which is independent of diffraction data. This reLLG is also independent of any crystal form, and can be calculated regardless of the source of the target, be it X‐ray, NMR or cryo‐EM. We calibrate the reLLG against the LLG for targets in CASP14, showing that it is a robust measure of both model and group ranking. Like the LLG, the reLLG shows that accurate coordinate error estimates add substantial value to predicted models. We find that refinement by CASP groups can often convert an inadequate initial model into a successful MR search model. Consistent with findings from others, we show that the AlphaFold2 models are sufficiently good, and reliably so, to surpass other current model generation strategies for attempting molecular replacement phasing.
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Affiliation(s)
- Claudia Millán
- Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, United Kingdom
| | - Ronan M Keegan
- Scientific Computing Dept., Science and Technologies Facilities Council, UK Research and Innovation, Didcot, Oxfordshire, United Kingdom
| | - Joana Pereira
- Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, Tübingen, Germany
| | - Massimo D Sammito
- Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, United Kingdom
| | - Adam J Simpkin
- Institute of Systems, Molecular and Integrative Biology, Biosciences Building, Crown Street, Liverpool L69 7BE, United Kingdom
| | - Airlie J McCoy
- Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, United Kingdom
| | - Andrei N Lupas
- Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, Tübingen, Germany
| | - Marcus D Hartmann
- Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, Tübingen, Germany
| | - Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, Biosciences Building, Crown Street, Liverpool L69 7BE, United Kingdom
| | - Randy J Read
- Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, United Kingdom
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Masrati G, Landau M, Ben-Tal N, Lupas A, Kosloff M, Kosinski J. Integrative Structural Biology in the Era of Accurate Structure Prediction. J Mol Biol 2021; 433:167127. [PMID: 34224746 DOI: 10.1016/j.jmb.2021.167127] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/28/2021] [Accepted: 06/28/2021] [Indexed: 11/16/2022]
Abstract
Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy-applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure-function relationships.
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Affiliation(s)
- Gal Masrati
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Meytal Landau
- Department of Biology, Technion-Israel Institute of Technology, Haifa 3200003, Israel; European Molecular Biology Laboratory (EMBL), Hamburg 22607, Germany
| | - Nir Ben-Tal
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Andrei Lupas
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany.
| | - Mickey Kosloff
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mt. Carmel, 3498838 Haifa, Israel.
| | - Jan Kosinski
- European Molecular Biology Laboratory (EMBL), Hamburg 22607, Germany; Centre for Structural Systems Biology (CSSB), Hamburg 22607, Germany; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
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Killingbeck EE, Wilburn DB, Merrihew GE, MacCoss MJ, Swanson WJ. Proteomics support the threespine stickleback egg coat as a protective oocyte envelope. Mol Reprod Dev 2021; 88:500-515. [PMID: 34148267 PMCID: PMC8362008 DOI: 10.1002/mrd.23517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 05/20/2021] [Accepted: 05/22/2021] [Indexed: 12/20/2022]
Abstract
Ancestrally marine threespine stickleback fish (Gasterosteus aculeatus) have undergone an adaptive radiation into freshwater environments throughout the Northern Hemisphere, creating an excellent model system for studying molecular adaptation and speciation. Ecological and behavioral factors have been suggested to underlie stickleback reproductive isolation and incipient speciation, but reproductive proteins mediating gamete recognition during fertilization have so far remained unexplored. To begin to investigate the contribution of reproductive proteins to stickleback reproductive isolation, we have characterized the stickleback egg coat proteome. We find that stickleback egg coats are comprised of homologs to the zona pellucida (ZP) proteins ZP1 and ZP3, as in other teleost fish. Our molecular evolutionary analyses indicate that across teleosts, ZP3 but not ZP1 has experienced positive Darwinian selection. Mammalian ZP3 is also rapidly evolving, and surprisingly some residues under selection in stickleback and mammalian ZP3 directly align. Despite broad homology, however, we find differences between mammalian and stickleback ZP proteins with respect to glycosylation, disulfide bonding, and sites of synthesis. Taken together, the changes we observe in stickleback ZP protein architecture suggest that the egg coats of stickleback fish, and perhaps fish more generally, have evolved to fulfill a more protective functional role than their mammalian counterparts.
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Affiliation(s)
- Emily E Killingbeck
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Damien B Wilburn
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Gennifer E Merrihew
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Willie J Swanson
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
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Mulligan VK. Current directions in combining simulation-based macromolecular modeling approaches with deep learning. Expert Opin Drug Discov 2021; 16:1025-1044. [PMID: 33993816 DOI: 10.1080/17460441.2021.1918097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Introduction: Structure-guided drug discovery relies on accurate computational methods for modeling macromolecules. Simulations provide means of predicting macromolecular folds, of discovering function from structure, and of designing macromolecules to serve as drugs. Success rates are limited for any of these tasks, however. Recently, deep neural network-based methods have greatly enhanced the accuracy of predictions of protein structure from sequence, generating excitement about the potential impact of deep learning.Areas covered: This review introduces biologists to deep neural network architecture, surveys recent successes of deep learning in structure prediction, and discusses emerging deep learning-based approaches for structure-function analysis and design. Particular focus is given to the interplay between simulation-based and neural network-based approaches.Expert opinion: As deep learning grows integral to macromolecular modeling, simulation- and neural network-based approaches must grow more tightly interconnected. Modular software architecture must emerge allowing both types of tools to be combined with maximal versatility. Open sharing of code under permissive licenses will be essential. Although experiments will remain the gold standard for reliable information to guide drug discovery, we may soon see successful drug development projects based on high-accuracy predictions from algorithms that combine simulation with deep learning - the ultimate validation of this combination's power.
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Cao X, Tian P. Molecular free energy optimization on a computational graph. RSC Adv 2021; 11:12929-12937. [PMID: 35423805 PMCID: PMC8697515 DOI: 10.1039/d1ra01455b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 03/26/2021] [Indexed: 11/21/2022] Open
Abstract
Free energy is arguably the most important property of molecular systems. Despite great progress in both its efficient estimation by scoring functions/potentials and more rigorous computation based on extensive sampling, we remain far from accurately predicting and manipulating biomolecular structures and their interactions. There are fundamental limitations, including accuracy of interaction description and difficulty of sampling in high dimensional space, to be tackled. Computational graph underlies major artificial intelligence platforms and is proven to facilitate training, optimization and learning. Combining autodifferentiation, coordinates transformation and generalized solvation free energy theory, we construct a computational graph infrastructure to realize seamless integration of fully trainable local free energy landscape with end to end differentiable iterative free energy optimization. This new framework drastically improves efficiency by replacing local sampling with differentiation. Its specific implementation in protein structure refinement achieves superb efficiency and competitive accuracy when compared with state of the art all-atom mainstream methods.
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Affiliation(s)
- Xiaoyong Cao
- School of Life Sciences, Jilin University Changchun 130012 China +86 431 85155287
| | - Pu Tian
- School of Life Sciences, Jilin University Changchun 130012 China +86 431 85155287
- School of Artificial Intelligence, Jilin University Changchun 130012 China
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13
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McCoy AJ, Stockwell DH, Sammito MD, Oeffner RD, Hatti KS, Croll TI, Read RJ. Phasertng: directed acyclic graphs for crystallographic phasing. Acta Crystallogr D Struct Biol 2021; 77:1-10. [PMID: 33404520 PMCID: PMC7787104 DOI: 10.1107/s2059798320014746] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 11/06/2020] [Indexed: 12/01/2022] Open
Abstract
Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.
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Affiliation(s)
- Airlie J. McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Duncan H. Stockwell
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Massimo D. Sammito
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Robert D. Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Kaushik S. Hatti
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
- Drug Discovery Unit, Wellcome Centre for Anti-Infectives Research, School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, United Kingdom
| | - Tristan I. Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
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14
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Jin S, Miller MD, Chen M, Schafer NP, Lin X, Chen X, Phillips GN, Wolynes PG. Molecular-replacement phasing using predicted protein structures from AWSEM-Suite. IUCRJ 2020; 7:1168-1178. [PMID: 33209327 PMCID: PMC7642774 DOI: 10.1107/s2052252520013494] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/07/2020] [Indexed: 06/11/2023]
Abstract
The phase problem in X-ray crystallography arises from the fact that only the intensities, and not the phases, of the diffracting electromagnetic waves are measured directly. Molecular replacement can often estimate the relative phases of reflections starting with those derived from a template structure, which is usually a previously solved structure of a similar protein. The key factor in the success of molecular replacement is finding a good template structure. When no good solved template exists, predicted structures based partially on templates can sometimes be used to generate models for molecular replacement, thereby extending the lower bound of structural and sequence similarity required for successful structure determination. Here, the effectiveness is examined of structures predicted by a state-of-the-art prediction algorithm, the Associative memory, Water-mediated, Structure and Energy Model Suite (AWSEM-Suite), which has been shown to perform well in predicting protein structures in CASP13 when there is no significant sequence similarity to a solved protein or only very low sequence similarity to known templates. The performance of AWSEM-Suite structures in molecular replacement is discussed and the results show that AWSEM-Suite performs well in providing useful phase information, often performing better than I-TASSER-MR and the previous algorithm AWSEM-Template.
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Affiliation(s)
- Shikai Jin
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
- Department of Biosciences, Rice University, Houston, Texas, USA
| | | | - Mingchen Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
| | - Nicholas P. Schafer
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
- Department of Chemistry, Rice University, Houston, Texas, USA
| | - Xingcheng Lin
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xun Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
- Department of Chemistry, Rice University, Houston, Texas, USA
| | - George N. Phillips
- Department of Biosciences, Rice University, Houston, Texas, USA
- Department of Chemistry, Rice University, Houston, Texas, USA
| | - Peter G. Wolynes
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
- Department of Biosciences, Rice University, Houston, Texas, USA
- Department of Chemistry, Rice University, Houston, Texas, USA
- Department of Physics, Rice University, Houston, Texas, USA
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15
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Richards LS, Millán C, Miao J, Martynowycz MW, Sawaya MR, Gonen T, Borges RJ, Usón I, Rodriguez JA. Fragment-based determination of a proteinase K structure from MicroED data using ARCIMBOLDO_SHREDDER. Acta Crystallogr D Struct Biol 2020; 76:703-712. [PMID: 32744252 PMCID: PMC7397493 DOI: 10.1107/s2059798320008049] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 06/16/2020] [Indexed: 12/15/2022] Open
Abstract
Structure determination of novel biological macromolecules by X-ray crystallography can be facilitated by the use of small structural fragments, some of only a few residues in length, as effective search models for molecular replacement to overcome the phase problem. Independence from the need for a complete pre-existing model with sequence similarity to the crystallized molecule is the primary appeal of ARCIMBOLDO, a suite of programs which employs this ab initio algorithm for phase determination. Here, the use of ARCIMBOLDO is investigated to overcome the phase problem with the electron cryomicroscopy (cryoEM) method known as microcrystal electron diffraction (MicroED). The results support the use of the ARCIMBOLDO_SHREDDER pipeline to provide phasing solutions for a structure of proteinase K from 1.6 Å resolution data using model fragments derived from the structures of proteins sharing a sequence identity of as low as 20%. ARCIMBOLDO_SHREDDER identified the most accurate polyalanine fragments from a set of distantly related sequence homologues. Alternatively, such templates were extracted in spherical volumes and given internal degrees of freedom to refine towards the target structure. Both modes relied on the rotation function in Phaser to identify or refine fragment models and its translation function to place them. Model completion from the placed fragments proceeded through phase combination of partial solutions and/or density modification and main-chain autotracing using SHELXE. The combined set of fragments was sufficient to arrive at a solution that resembled that determined by conventional molecular replacement using the known target structure as a search model. This approach obviates the need for a single, complete and highly accurate search model when phasing MicroED data, and permits the evaluation of large fragment libraries for this purpose.
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Affiliation(s)
- Logan S. Richards
- Department of Chemistry and Biochemistry; UCLA–DOE Institute for Genomics and Proteomics; STROBE, NSF Science and Technology Center, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Claudia Millán
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Jennifer Miao
- Department of Chemistry and Biochemistry; UCLA–DOE Institute for Genomics and Proteomics; STROBE, NSF Science and Technology Center, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Michael W. Martynowycz
- Howard Hughes Medical Institute, University of California Los Angeles (UCLA), Los Angeles, California, USA
- Department of Biological Chemistry, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Michael R. Sawaya
- Howard Hughes Medical Institute, University of California Los Angeles (UCLA), Los Angeles, California, USA
- Department of Biological Chemistry, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA
| | - Tamir Gonen
- Howard Hughes Medical Institute, University of California Los Angeles (UCLA), Los Angeles, California, USA
- Department of Biological Chemistry, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA
- Department of Physiology, University of California Los Angeles (UCLA), Los Angeles, California, USA
| | - Rafael J. Borges
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Isabel Usón
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Barcelona Science Park, Helix Building, Baldiri Reixac 15, 08028 Barcelona, Spain
- ICREA, Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys 23, 08003 Barcelona, Spain
| | - Jose A. Rodriguez
- Department of Chemistry and Biochemistry; UCLA–DOE Institute for Genomics and Proteomics; STROBE, NSF Science and Technology Center, University of California Los Angeles (UCLA), Los Angeles, CA 90095, USA
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16
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Yang S, Wang Y, Wang L, Kamau P, Zhang H, Luo A, Lu X, Lai R. Target switch of centipede toxins for antagonistic switch. SCIENCE ADVANCES 2020; 6:eabb5734. [PMID: 32821839 PMCID: PMC7413724 DOI: 10.1126/sciadv.abb5734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 06/26/2020] [Indexed: 05/02/2023]
Abstract
Animal venoms are powerful, highly evolved chemical weapons for defense and predation. While venoms are used mainly to lethally antagonize heterospecifics (individuals of a different species), nonlethal envenomation of conspecifics (individuals of the same species) is occasionally observed. Both the venom and target specifications underlying these two forms of envenomation are still poorly understood. Here, we show a target-switching mechanism in centipede (Scolopendra subspinipes) venom. On the basis of this mechanism, a major toxin component [Ssm Spooky Toxin (SsTx)] in centipede venom inhibits the Shal channel in conspecifics but not in heterospecifics to cause short-term, recoverable, and nonlethal envenomation. This same toxin causes fatal heterospecific envenomation, for example, by switching its target to the Shaker channels in heterospecifics without inhibiting the Shaker channel of conspecific S. subspinipes individuals. These findings suggest that venom components exhibit intricate coevolution with their targets in both heterospecifics and conspecifics, which enables a single toxin to develop graded intraspecific and interspecific antagonistic interactions.
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Affiliation(s)
- Shilong Yang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of bioactive peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, China
| | - Yunfei Wang
- College of Wildlife and Protected Area, Northeast Forestry University, Harbin 150040, China
| | - Lu Wang
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, Yunnan 650091, China
| | - Peter Kamau
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of bioactive peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-African Joint Research Center, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
| | - Hao Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of bioactive peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Anna Luo
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of bioactive peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiancui Lu
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of bioactive peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ren Lai
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of bioactive peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China
- Sino-African Joint Research Center, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
- KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China
- Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, Hubei 430071, China
- Institute for Drug Discovery and Development, Chinese Academy of Sciences, Shanghai 201203, China
- Corresponding author.
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17
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Xu L, Han Y, Chen X, Aierken A, Wen H, Zheng W, Wang H, Lu X, Zhao Z, Ma C, Liang P, Yang W, Yang S, Yang F. Molecular mechanisms underlying menthol binding and activation of TRPM8 ion channel. Nat Commun 2020; 11:3790. [PMID: 32728032 PMCID: PMC7391767 DOI: 10.1038/s41467-020-17582-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 07/06/2020] [Indexed: 02/07/2023] Open
Abstract
Menthol in mints elicits coolness sensation by selectively activating TRPM8 channel. Although structures of TRPM8 were determined in the apo and liganded states, the menthol-bounded state is unresolved. To understand how menthol activates the channel, we docked menthol to the channel and systematically validated our menthol binding models with thermodynamic mutant cycle analysis. We observed that menthol uses its hydroxyl group as a hand to specifically grab with R842, and its isopropyl group as legs to stand on I846 and L843. By imaging with fluorescent unnatural amino acid, we found that menthol binding induces wide-spread conformational rearrangements within the transmembrane domains. By Φ analysis based on single-channel recordings, we observed a temporal sequence of conformational changes in the S6 bundle crossing and the selectivity filter leading to channel activation. Therefore, our study suggested a 'grab and stand' mechanism of menthol binding and how menthol activates TRPM8 at the atomic level.
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Affiliation(s)
- Lizhen Xu
- Department of Biophysics, and Kidney Disease Center of the First Affiliated Hospital, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang Province, China
| | - Yalan Han
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, 650223, Kunming, Yunnan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoying Chen
- Department of Biophysics, and Kidney Disease Center of the First Affiliated Hospital, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang Province, China
| | - Aerziguli Aierken
- Department of Biophysics, and Kidney Disease Center of the First Affiliated Hospital, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang Province, China
| | - Han Wen
- Department of Physics, State University of New York at Buffalo, Buffalo, NY, USA
| | - Wenjun Zheng
- Department of Physics, State University of New York at Buffalo, Buffalo, NY, USA
| | - Hongkun Wang
- Key Laboratory of Combined Multi-organ Transplantation, Ministry of Public Health, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, China
- Institute of Translational Medicine, Zhejiang University, Zhejiang, China
| | - Xiancui Lu
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, 650223, Kunming, Yunnan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhenye Zhao
- Department of Biophysics, and Kidney Disease Center of the First Affiliated Hospital, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang Province, China
| | - Cheng Ma
- Protein facility, School of Medicine, Zhejiang University, Zhejiang, China
| | - Ping Liang
- Key Laboratory of Combined Multi-organ Transplantation, Ministry of Public Health, The First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, China
- Institute of Translational Medicine, Zhejiang University, Zhejiang, China
| | - Wei Yang
- Department of Biophysics, and Department of Neurosurgery of the First Affiliated Hospital, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang Province, China.
| | - Shilong Yang
- College of Wildlife and Protected Area, Northeast Forestry University, 150040, Harbin, China.
| | - Fan Yang
- Department of Biophysics, and Kidney Disease Center of the First Affiliated Hospital, Zhejiang University School of Medicine, 310058, Hangzhou, Zhejiang Province, China.
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18
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Munzel L, Neumann P, Otto FB, Krick R, Metje-Sprink J, Kroppen B, Karedla N, Enderlein J, Meinecke M, Ficner R, Thumm M. Atg21 organizes Atg8 lipidation at the contact of the vacuole with the phagophore. Autophagy 2020; 17:1458-1478. [PMID: 32515645 DOI: 10.1080/15548627.2020.1766332] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Coupling of Atg8 to phosphatidylethanolamine is crucial for the expansion of the crescent-shaped phagophore during cargo engulfment. Atg21, a PtdIns3P-binding beta-propeller protein, scaffolds Atg8 and its E3-like complex Atg12-Atg5-Atg16 during lipidation. The crystal structure of Atg21, in complex with the Atg16 coiled-coil domain, showed its binding at the bottom side of the Atg21 beta-propeller. Our structure allowed detailed analyses of the complex formation of Atg21 with Atg16 and uncovered the orientation of the Atg16 coiled-coil domain with respect to the membrane. We further found that Atg21 was restricted to the phagophore edge, near the vacuole, known as the vacuole isolation membrane contact site (VICS). We identified a specialized vacuolar subdomain at the VICS, typical of organellar contact sites, where the membrane protein Vph1 was excluded, while Vac8 was concentrated. Furthermore, Vac8 was required for VICS formation. Our results support a specialized organellar contact involved in controlling phagophore elongation. Abbreviations: FCCS: fluorescence cross correlation spectroscopy; NVJ: nucleus-vacuole junction; PAS: phagophore assembly site; PE: phosphatidylethanolamine; PROPPIN: beta-propeller that binds phosphoinositides; PtdIns3P: phosphatidylinositol- 3-phosphate; VICS: vacuole isolation membrane contact site.
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Affiliation(s)
- Lena Munzel
- Department of Cellular Biochemistry, University Medicine, Goettingen, Germany
| | - Piotr Neumann
- Department of Molecular Structural Biology, Institute for Microbiology and Genetics, University of Goettingen, Goettingen, Germany
| | - Florian B Otto
- Department of Cellular Biochemistry, University Medicine, Goettingen, Germany
| | - Roswitha Krick
- Department of Cellular Biochemistry, University Medicine, Goettingen, Germany
| | - Janina Metje-Sprink
- Department of Neurobiology, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany
| | - Benjamin Kroppen
- Department of Cellular Biochemistry, University Medicine, Goettingen, Germany
| | - Narain Karedla
- Physics Department III, University of Goettingen, Goettingen, Germany
| | - Jörg Enderlein
- Physics Department III, University of Goettingen, Goettingen, Germany
| | - Michael Meinecke
- Department of Cellular Biochemistry, University Medicine, Goettingen, Germany
| | - Ralf Ficner
- Department of Molecular Structural Biology, Institute for Microbiology and Genetics, University of Goettingen, Goettingen, Germany
| | - Michael Thumm
- Department of Cellular Biochemistry, University Medicine, Goettingen, Germany
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19
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Du G, Tian Y, Yao Z, Vu S, Zheng J, Chai L, Wang K, Yang S. A specialized pore turret in the mammalian cation channel TRPV1 is responsible for distinct and species-specific heat activation thresholds. J Biol Chem 2020; 295:9641-9649. [PMID: 32461255 DOI: 10.1074/jbc.ra120.013037] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 05/20/2020] [Indexed: 11/06/2022] Open
Abstract
The transient receptor potential vanilloid 1 (TRPV1) channel is a heat-activated cation channel that plays a crucial role in ambient temperature detection and thermal homeostasis. Although several structural features of TRPV1 have been shown to be involved in heat-induced activation of the gating process, the physiological significance of only a few of these key elements has been evaluated in an evolutionary context. Here, using transient expression in HEK293 cells, electrophysiological recordings, and molecular modeling, we show that the pore turret contains both structural and functional determinants that set the heat activation thresholds of distinct TRPV1 orthologs in mammals whose body temperatures fluctuate widely. We found that TRPV1 from the bat Carollia brevicauda exhibits a lower threshold temperature of channel activation than does its human ortholog and three bat-specific amino acid substitutions located in the pore turret are sufficient to determine this threshold temperature. Furthermore, the structure of the TRPV1 pore turret appears to be of physiological and evolutionary significance for differentiating the heat-activated threshold among species-specific TRPV1 orthologs. These findings support a role for the TRPV1 pore turret in tuning the heat-activated threshold, and they suggest that its evolution was driven by adaption to specific physiological traits among mammals exposed to variable temperatures.
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Affiliation(s)
- Guangxu Du
- Department of Pharmacology, Qingdao University School of Pharmacy, Qingdao, Shandong, China
| | - Yuhua Tian
- Department of Pharmacology, Qingdao University School of Pharmacy, Qingdao, Shandong, China
| | - Zhihao Yao
- Department of Pharmacology, Qingdao University School of Pharmacy, Qingdao, Shandong, China.,Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of the Yunnan Province Kunming Institute of Zoology, Kunming, Yunnan China
| | - Simon Vu
- University of California Davis, School of Medicine, Davis, California, USA
| | - Jie Zheng
- University of California Davis, School of Medicine, Davis, California, USA
| | - Longhui Chai
- College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China
| | - KeWei Wang
- Department of Pharmacology, Qingdao University School of Pharmacy, Qingdao, Shandong, China
| | - Shilong Yang
- College of Wildlife and Protected Area, Northeast Forestry University, Harbin, China
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20
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Kim DN, Gront D, Sanbonmatsu KY. Practical Considerations for Atomistic Structure Modeling with Cryo-EM Maps. J Chem Inf Model 2020; 60:2436-2442. [PMID: 32422044 PMCID: PMC7891309 DOI: 10.1021/acs.jcim.0c00090] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We describe common approaches to atomistic structure modeling with single particle analysis derived cryo-EM maps. Several strategies for atomistic model building and atomistic model fitting methods are discussed, including selection criteria and implementation procedures. In covering basic concepts and caveats, this short perspective aims to help facilitate active discussion between scientists at different levels with diverse backgrounds.
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Affiliation(s)
- Doo Nam Kim
- Computational Biology Team, Biological Science Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, United States
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Karissa Y. Sanbonmatsu
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico, 87545, United States
- New Mexico Consortium, Los Alamos, New Mexico, 87544, United States
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21
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Borges RJ, Meindl K, Triviño J, Sammito M, Medina A, Millán C, Alcorlo M, Hermoso JA, Fontes MRDM, Usón I. SEQUENCE SLIDER: expanding polyalanine fragments for phasing with multiple side-chain hypotheses. Acta Crystallogr D Struct Biol 2020; 76:221-237. [PMID: 32133987 PMCID: PMC7057211 DOI: 10.1107/s2059798320000339] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 01/13/2020] [Indexed: 02/07/2023] Open
Abstract
Fragment-based molecular-replacement methods can solve a macromolecular structure quasi-ab initio. ARCIMBOLDO, using a common secondary-structure or tertiary-structure template or a library of folds, locates these with Phaser and reveals the rest of the structure by density modification and autotracing in SHELXE. The latter stage is challenging when dealing with diffraction data at lower resolution, low solvent content, high β-sheet composition or situations in which the initial fragments represent a low fraction of the total scattering or where their accuracy is low. SEQUENCE SLIDER aims to overcome these complications by extending the initial polyalanine fragment with side chains in a multisolution framework. Its use is illustrated on test cases and previously unknown structures. The selection and order of fragments to be extended follows the decrease in log-likelihood gain (LLG) calculated with Phaser upon the omission of each single fragment. When the starting substructure is derived from a remote homolog, sequence assignment to fragments is restricted by the original alignment. Otherwise, the secondary-structure prediction is matched to that found in fragments and traces. Sequence hypotheses are trialled in a brute-force approach through side-chain building and refinement. Scoring the refined models through their LLG in Phaser may allow discrimination of the correct sequence or filter the best partial structures for further density modification and autotracing. The default limits for the number of models to pursue are hardware dependent. In its most economic implementation, suitable for a single laptop, the main-chain trace is extended as polyserine rather than trialling models with different sequence assignments, which requires a grid or multicore machine. SEQUENCE SLIDER has been instrumental in solving two novel structures: that of MltC from 2.7 Å resolution data and that of a pneumococcal lipoprotein with 638 residues and 35% solvent content.
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Affiliation(s)
- Rafael Junqueira Borges
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixach 15, 08028 Barcelona, Spain
- Departamento de Física e Biofísica, Instituto de Biociências, Universidade Estadual Paulista (UNESP), Botucatu-SP 18618-689, Brazil
| | - Kathrin Meindl
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixach 15, 08028 Barcelona, Spain
| | - Josep Triviño
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixach 15, 08028 Barcelona, Spain
| | - Massimo Sammito
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, England
| | - Ana Medina
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixach 15, 08028 Barcelona, Spain
| | - Claudia Millán
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixach 15, 08028 Barcelona, Spain
| | - Martin Alcorlo
- Department of Crystallography and Structural Biology, Instituto de Química-Física ‘Rocasolano’, Consejo Superior de Investigaciones Científicas (CSIC), 28006 Madrid, Spain
| | - Juan A. Hermoso
- Department of Crystallography and Structural Biology, Instituto de Química-Física ‘Rocasolano’, Consejo Superior de Investigaciones Científicas (CSIC), 28006 Madrid, Spain
| | - Marcos Roberto de Mattos Fontes
- Departamento de Física e Biofísica, Instituto de Biociências, Universidade Estadual Paulista (UNESP), Botucatu-SP 18618-689, Brazil
| | - Isabel Usón
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixach 15, 08028 Barcelona, Spain
- ICREA at IBMB–CSIC, Baldiri Reixach 13-15, 08028 Barcelona, Spain
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22
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Chung J, Wu X, Lambert TJ, Lai ZW, Walther TC, Farese RV. LDAF1 and Seipin Form a Lipid Droplet Assembly Complex. Dev Cell 2019; 51:551-563.e7. [PMID: 31708432 PMCID: PMC7235935 DOI: 10.1016/j.devcel.2019.10.006] [Citation(s) in RCA: 122] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 08/12/2019] [Accepted: 10/08/2019] [Indexed: 10/25/2022]
Abstract
Lipid droplets (LDs) originate from the endoplasmic reticulum (ER) to store triacylglycerol (TG) and cholesterol esters. The ER protein seipin was shown to localize to ER-LD contacts soon after LDs form, but what determines the sites of initial LD biogenesis in the ER is unknown. Here, we identify TMEM159, now re-named lipid droplet assembly factor 1 (LDAF1), as an interaction partner of seipin. Together, LDAF1 and seipin form an ∼600 kDa oligomeric complex that copurifies with TG. LDs form at LDAF1-seipin complexes, and re-localization of LDAF1 to the plasma membrane co-recruits seipin and redirects LD formation to these sites. Once LDs form, LDAF1 dissociates from seipin and moves to the LD surface. In the absence of LDAF1, LDs form only at significantly higher cellular TG concentrations. Our data suggest that the LDAF1-seipin complex is the core protein machinery that facilitates LD biogenesis and determines the sites of their formation in the ER.
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Affiliation(s)
- Jeeyun Chung
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Xudong Wu
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Talley J Lambert
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Zon Weng Lai
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Harvard Chan Advanced Multi-omics Platform, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Tobias C Walther
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Harvard Chan Advanced Multi-omics Platform, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02124, USA; Howard Hughes Medical Institute, Boston, MA 02115, USA.
| | - Robert V Farese
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02124, USA.
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23
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Simpkin AJ, Thomas JMH, Simkovic F, Keegan RM, Rigden DJ. Molecular replacement using structure predictions from databases. Acta Crystallogr D Struct Biol 2019; 75:1051-1062. [PMID: 31793899 PMCID: PMC6889911 DOI: 10.1107/s2059798319013962] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Accepted: 10/12/2019] [Indexed: 01/19/2023] Open
Abstract
Molecular replacement (MR) is the predominant route to solution of the phase problem in macromolecular crystallography. Where the lack of a suitable homologue precludes conventional MR, one option is to predict the target structure using bioinformatics. Such modelling, in the absence of homologous templates, is called ab initio or de novo modelling. Recently, the accuracy of such models has improved significantly as a result of the availability, in many cases, of residue-contact predictions derived from evolutionary covariance analysis. Covariance-assisted ab initio models representing structurally uncharacterized Pfam families are now available on a large scale in databases, potentially representing a valuable and easily accessible supplement to the PDB as a source of search models. Here, the unconventional MR pipeline AMPLE is employed to explore the value of structure predictions in the GREMLIN and PconsFam databases. It was tested whether these deposited predictions, processed in various ways, could solve the structures of PDB entries that were subsequently deposited. The results were encouraging: nine of 27 GREMLIN cases were solved, covering target lengths of 109-355 residues and a resolution range of 1.4-2.9 Å, and with target-model shared sequence identity as low as 20%. The cluster-and-truncate approach in AMPLE proved to be essential for most successes. For the overall lower quality structure predictions in the PconsFam database, remodelling with Rosetta within the AMPLE pipeline proved to be the best approach, generating ensemble search models from single-structure deposits. Finally, it is shown that the AMPLE-obtained search models deriving from GREMLIN deposits are of sufficiently high quality to be selected by the sequence-independent MR pipeline SIMBAD. Overall, the results help to point the way towards the optimal use of the expanding databases of ab initio structure predictions.
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Affiliation(s)
- Adam J. Simpkin
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Jens M. H. Thomas
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Ronan M. Keegan
- STFC, Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, England
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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24
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Galaz-Davison P, Molina JA, Silletti S, Komives EA, Knauer SH, Artsimovitch I, Ramírez-Sarmiento CA. Differential Local Stability Governs the Metamorphic Fold Switch of Bacterial Virulence Factor RfaH. Biophys J 2019; 118:96-104. [PMID: 31810657 DOI: 10.1016/j.bpj.2019.11.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 10/21/2019] [Accepted: 11/14/2019] [Indexed: 12/15/2022] Open
Abstract
RfaH, a two-domain protein from a universally conserved NusG/Spt5 family of regulators, is required for the transcription and translation of long virulence and conjugation operons in many Gram-negative bacterial pathogens. Escherichia coli RfaH action is controlled by a unique large-scale structural rearrangement triggered by recruitment to transcription elongation complexes through a specific DNA element. Upon recruitment, the C-terminal domain of RfaH refolds from an α-hairpin, which is bound to RNA polymerase binding site within the N-terminal domain, into an unbound β-barrel that interacts with the ribosome. Although structures of the autoinhibited (α-hairpin) and active (β-barrel) states and plausible refolding pathways have been reported, how this reversible switch is encoded within RfaH sequence and structure is poorly understood. Here, we combined hydrogen-deuterium exchange measurements by mass spectrometry and nuclear magnetic resonance with molecular dynamics to evaluate the differential local stability between both RfaH folds. Deuteron incorporation reveals that the tip of the C-terminal hairpin (residues 125-145) is stably folded in the autoinhibited state (∼20% deuteron incorporation), whereas the rest of this domain is highly flexible (>40% deuteron incorporation), and its flexibility only decreases in the β-folded state. Computationally predicted ΔG agree with these results by displaying similar anisotropic stability within the tip of the α-hairpin and on neighboring N-terminal domain residues. Remarkably, the β-folded state shows comparable structural flexibility than nonmetamorphic homologs. Our findings provide information critical for understanding the metamorphic behavior of RfaH and other chameleon proteins and for devising targeted strategies to combat bacterial infections.
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Affiliation(s)
- Pablo Galaz-Davison
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - José Alejandro Molina
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Steve Silletti
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California
| | - Elizabeth A Komives
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California
| | - Stefan H Knauer
- Lehrstuhl Biopolymere, Universität Bayreuth, Bayreuth, Germany
| | - Irina Artsimovitch
- Department of Microbiology and The Center for RNA Biology, The Ohio State University, Columbus, Ohio.
| | - César A Ramírez-Sarmiento
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.
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25
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Croll TI, Sammito MD, Kryshtafovych A, Read RJ. Evaluation of template-based modeling in CASP13. Proteins 2019; 87:1113-1127. [PMID: 31407380 PMCID: PMC6851432 DOI: 10.1002/prot.25800] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 07/29/2019] [Accepted: 08/08/2019] [Indexed: 12/12/2022]
Abstract
Performance in the template‐based modeling (TBM) category of CASP13 is assessed here, using a variety of metrics. Performance of the predictor groups that participated is ranked using the primary ranking score that was developed by the assessors for CASP12. This reveals that the best results are obtained by groups that include contact predictions or inter‐residue distance predictions derived from deep multiple sequence alignments. In cases where there is a good homolog in the wwPDB (TBM‐easy category), the best results are obtained by modifying a template. However, for cases with poorer homologs (TBM‐hard), very good results can be obtained without using an explicit template, by deep learning algorithms trained on the wwPDB. Alternative metrics are introduced, to allow testing of aspects of structural models that are not addressed by traditional CASP metrics. These include comparisons to the main‐chain and side‐chain torsion angles of the target, and the utility of models for solving crystal structures by the molecular replacement method. The alternative metrics are poorly correlated with the traditional metrics, and it is proposed that modeling has reached a sufficient level of maturity that the best models should be expected to satisfy this wider range of criteria.
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Affiliation(s)
- Tristan I Croll
- Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, UK
| | - Massimo D Sammito
- Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, UK
| | | | - Randy J Read
- Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research, Cambridge, UK
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26
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Syomin BV, Ilyin YV. Virus-Like Particles as an Instrument of Vaccine Production. Mol Biol 2019; 53:323-334. [PMID: 32214478 PMCID: PMC7088979 DOI: 10.1134/s0026893319030154] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 12/19/2018] [Accepted: 12/24/2018] [Indexed: 12/13/2022]
Abstract
The paper discusses the techniques which are currently implemented for vaccine production based on virus-like particles (VLPs). The factors which determine the characteristics of VLP monomers assembly are provided in detail. Analysis of the literature demonstrates that the development of the techniques of VLP production and immobilization of target antigens on their surface have led to the development of universal platforms which make it possible for virtually any known antigen to be exposed on the particle surface in a highly concentrated form. As a result, the focus of attention has shifted from the approaches to VLP production to the development of a precise interface between the organism's immune system and the peptides inducing a strong immune response to pathogens or the organism's own pathological cells. Immunome-specified methods for vaccine design and the prospects of immunoprophylaxis are discussed. Certain examples of vaccines against viral diseases and cancers are considered.
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Affiliation(s)
- B. V. Syomin
- Institute for Statistical Studies and Economics of Knowledge (ISSEK),
National Research University Higher School of Economics, 101000 Moscow, Russia
| | - Y. V. Ilyin
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia
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27
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Luo L, Wang Y, Li B, Xu L, Kamau PM, Zheng J, Yang F, Yang S, Lai R. Molecular basis for heat desensitization of TRPV1 ion channels. Nat Commun 2019; 10:2134. [PMID: 31086183 PMCID: PMC6513986 DOI: 10.1038/s41467-019-09965-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 04/09/2019] [Indexed: 12/26/2022] Open
Abstract
The transient receptor potential vanilloid 1 (TRPV1) ion channel is a prototypical molecular sensor for noxious heat in mammals. Its role in sustained heat response remains poorly understood, because rapid heat-induced desensitization (Dh) follows tightly heat-induced activation (Ah). To understand the physiological role and structural basis of Dh, we carried out a comparative study of TRPV1 channels in mouse (mV1) and those in platypus (pV1), which naturally lacks Dh. Here we show that a temperature-sensitive interaction between the N- and C-terminal domains of mV1 but not pV1 drives a conformational rearrangement in the pore leading to Dh. We further show that knock-in mice expressing pV1 sensed heat normally but suffered scald damages in a hot environment. Our findings suggest that Dh evolved late during evolution as a protective mechanism and a delicate balance between Ah and Dh is crucial for mammals to sense and respond to noxious heat.
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Affiliation(s)
- Lei Luo
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yunfei Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Bowen Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China
| | - Lizhen Xu
- Key Laboratory of Medical Neurobiology, Department of Biophysics and Kidney Disease Center, First Affiliated Hospital, Institute of Neuroscience, National Health Commission and Chinese Academy of Medical Sciences, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
| | - Peter Muiruri Kamau
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jie Zheng
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.
| | - Fan Yang
- Key Laboratory of Medical Neurobiology, Department of Biophysics and Kidney Disease Center, First Affiliated Hospital, Institute of Neuroscience, National Health Commission and Chinese Academy of Medical Sciences, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.
| | - Shilong Yang
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.
| | - Ren Lai
- Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences/Key Laboratory of Bioactive Peptides of Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan, 650223, China.
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28
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Methods for the Refinement of Protein Structure 3D Models. Int J Mol Sci 2019; 20:ijms20092301. [PMID: 31075942 PMCID: PMC6539982 DOI: 10.3390/ijms20092301] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 04/24/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022] Open
Abstract
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.
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29
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Wang X, Huang SY. Integrating Bonded and Nonbonded Potentials in the Knowledge-Based Scoring Function for Protein Structure Prediction. J Chem Inf Model 2019; 59:3080-3090. [DOI: 10.1021/acs.jcim.9b00057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Xinxiang Wang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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30
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Stagno JR, Yu P, Dyba MA, Wang YX, Liu Y. Heavy-atom labeling of RNA by PLOR for de novo crystallographic phasing. PLoS One 2019; 14:e0215555. [PMID: 30986270 PMCID: PMC6464214 DOI: 10.1371/journal.pone.0215555] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 04/03/2019] [Indexed: 12/28/2022] Open
Abstract
Due to the paucity of known RNA structures, experimental phasing is crucial for obtaining three-dimensional structures of RNAs by X-ray crystallography. Covalent attachment of heavy atoms to RNAs is one of the most useful strategies to facilitate phase determination. However, this approach is limited by the inefficiency or inability to synthesize large RNAs (>60 nucleotides) site-specifically labeled with heavy atoms using traditional methods. Here, we applied our recently reported method, PLOR (position-selective labeling of RNA) to incorporate 5-iodouridine at specific positions in the adenine riboswitch RNA aptamer domain, which was then used for crystallization and subsequent de novo SAD phasing. PLOR is a powerful tool to improve the efficiency of obtaining RNA structures de novo by X-ray crystallography.
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Affiliation(s)
- Jason R. Stagno
- Protein-Nucleic Acid Interaction Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, United States of America
| | - Ping Yu
- Protein-Nucleic Acid Interaction Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, United States of America
| | - Marzena A. Dyba
- Basic Science Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America
| | - Yun-Xing Wang
- Protein-Nucleic Acid Interaction Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, United States of America
| | - Yu Liu
- Protein-Nucleic Acid Interaction Section, Structural Biophysics Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, Maryland, United States of America
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, People’s Republic of China
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31
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Hooper WF, Walcott BD, Wang X, Bystroff C. Fast design of arbitrary length loops in proteins using InteractiveRosetta. BMC Bioinformatics 2018; 19:337. [PMID: 30249181 PMCID: PMC6154894 DOI: 10.1186/s12859-018-2345-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 08/29/2018] [Indexed: 11/10/2022] Open
Abstract
Background With increasing interest in ab initio protein design, there is a desire to be able to fully explore the design space of insertions and deletions. Nature inserts and deletes residues to optimize energy and function, but allowing variable length indels in the context of an interactive protein design session presents challenges with regard to speed and accuracy. Results Here we present a new module (INDEL) for InteractiveRosetta which allows the user to specify a range of lengths for a desired indel, and which returns a set of low energy backbones in a matter of seconds. To make the loop search fast, loop anchor points are geometrically hashed using C α-C α and C β-C β distances, and the hash is mapped to start and end points in a pre-compiled random access file of non-redundant, protein backbone coordinates. Loops with superposable anchors are filtered for collisions and returned to InteractiveRosetta as poly-alanine for display and selective incorporation into the design template. Sidechains can then be added using RosettaDesign tools. Conclusions INDEL was able to find viable loops in 100% of 500 attempts for all lengths from 3 to 20 residues. INDEL has been applied to the task of designing a domain-swapping loop for T7-endonuclease I, changing its specificity from Holliday junctions to paranemic crossover (PX) DNA. Electronic supplementary material The online version of this article (10.1186/s12859-018-2345-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- William F Hooper
- Emmes Corporation, Rockville, Washington, MD, USA.,Department of Biology, Rensselaer Polytechnic Institute, Troy, NY, USA
| | | | - Xing Wang
- Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Christopher Bystroff
- Department of Biology, Rensselaer Polytechnic Institute, Troy, NY, USA. .,Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA.
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32
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Abstract
Chemical Shift-Rosetta (CS-Rosetta) is an automated method that employs NMR chemical shifts to model protein structures de novo. In this chapter, we introduce the terminology and central concepts of CS-Rosetta. We describe the architecture and functionality of automatic NOESY assignment (AutoNOE) and structure determination protocols (Abrelax and RASREC) within the CS-Rosetta framework. We further demonstrate how CS-Rosetta can discriminate near-native structures against a large conformational search space using restraints obtained from NMR data, and/or sequence and structure homology. We highlight how CS-Rosetta can be combined with alternative automated approaches to (i) model oligomeric systems and (ii) create NMR-based structure determination pipeline. To show its practical applicability, we emphasize on the computational requirements and performance of CS-Rosetta for protein targets of varying molecular weight and complexity. Finally, we discuss the current Python interface, which enables easy execution of protocols for rapid and accurate high-resolution structure determination.
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Affiliation(s)
- Santrupti Nerli
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, United States; Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, United States
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, United States.
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33
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Yang F, Xiao X, Lee BH, Vu S, Yang W, Yarov-Yarovoy V, Zheng J. The conformational wave in capsaicin activation of transient receptor potential vanilloid 1 ion channel. Nat Commun 2018; 9:2879. [PMID: 30038260 PMCID: PMC6056546 DOI: 10.1038/s41467-018-05339-6] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 06/19/2018] [Indexed: 01/17/2023] Open
Abstract
The capsaicin receptor TRPV1 has been intensively studied by cryo-electron microscopy and functional tests. However, though the apo and capsaicin-bound structural models are available, the dynamic process of capsaicin activation remains intangible, largely due to the lack of a capsaicin-induced open structural model and the low occupancy of the transition states. Here we report that reducing temperature toward the freezing point substantially increased channel closure events even in the presence of saturating capsaicin. We further used a combination of fluorescent unnatural amino acid (fUAA) incorporation, computational modeling, and rate-equilibrium linear free-energy relationships analysis (Φ-analysis) to derive the fully open capsaicin-bound state model, and reveal how the channel transits from the apo to the open state. We observed that capsaicin initiates a conformational wave that propagates through the S4–S5 linker towards the S6 bundle and finally reaching the selectivity filter. Our study provides a temporal mechanism for capsaicin activation of TRPV1. The capsaicin receptor TRPV1 has been structurally characterized, but the capsaicin activation dynamics remain elusive. Here authors use fluorescent unnatural amino acid incorporation, computational modeling and Φ-analysis to derive the capsaicin-bound open state model and reveal the capsaicin induced conformational changes.
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Affiliation(s)
- Fan Yang
- Department of Biophysics and Kidney Disease Center, First Affiliated Hospital, Institute of Neuroscience, National Health Commission and Chinese Academy of Medical Sciences Key Laboratory of Medical Neurobiology, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang Province, China. .,Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.
| | - Xian Xiao
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.,Institute for Basic Medical Sciences, Westlake Institute for Advanced Study, Westlake University, Shilongshan Road No. 18, Xihu District, Hangzhou, 310024, Zhejiang Province, China
| | - Bo Hyun Lee
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.,University of Washington, Department of Physiology and Biophysics, Seattle, WA, 98195, USA
| | - Simon Vu
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA
| | - Wei Yang
- Department of Biophysics and Kidney Disease Center, First Affiliated Hospital, Institute of Neuroscience, National Health Commission and Chinese Academy of Medical Sciences Key Laboratory of Medical Neurobiology, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang Province, China
| | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA
| | - Jie Zheng
- Department of Physiology and Membrane Biology, University of California, Davis, CA, 95616, USA.
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34
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Simpkin AJ, Simkovic F, Thomas JMH, Savko M, Lebedev A, Uski V, Ballard C, Wojdyr M, Wu R, Sanishvili R, Xu Y, Lisa MN, Buschiazzo A, Shepard W, Rigden DJ, Keegan RM. SIMBAD: a sequence-independent molecular-replacement pipeline. Acta Crystallogr D Struct Biol 2018; 74:595-605. [PMID: 29968670 PMCID: PMC6038384 DOI: 10.1107/s2059798318005752] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 04/12/2018] [Indexed: 12/13/2022] Open
Abstract
The conventional approach to finding structurally similar search models for use in molecular replacement (MR) is to use the sequence of the target to search against those of a set of known structures. Sequence similarity often correlates with structure similarity. Given sufficient similarity, a known structure correctly positioned in the target cell by the MR process can provide an approximation to the unknown phases of the target. An alternative approach to identifying homologous structures suitable for MR is to exploit the measured data directly, comparing the lattice parameters or the experimentally derived structure-factor amplitudes with those of known structures. Here, SIMBAD, a new sequence-independent MR pipeline which implements these approaches, is presented. SIMBAD can identify cases of contaminant crystallization and other mishaps such as mistaken identity (swapped crystallization trays), as well as solving unsequenced targets and providing a brute-force approach where sequence-dependent search-model identification may be nontrivial, for example because of conformational diversity among identifiable homologues. The program implements a three-step pipeline to efficiently identify a suitable search model in a database of known structures. The first step performs a lattice-parameter search against the entire Protein Data Bank (PDB), rapidly determining whether or not a homologue exists in the same crystal form. The second step is designed to screen the target data for the presence of a crystallized contaminant, a not uncommon occurrence in macromolecular crystallography. Solving structures with MR in such cases can remain problematic for many years, since the search models, which are assumed to be similar to the structure of interest, are not necessarily related to the structures that have actually crystallized. To cater for this eventuality, SIMBAD rapidly screens the data against a database of known contaminant structures. Where the first two steps fail to yield a solution, a final step in SIMBAD can be invoked to perform a brute-force search of a nonredundant PDB database provided by the MoRDa MR software. Through early-access usage of SIMBAD, this approach has solved novel cases that have otherwise proved difficult to solve.
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Affiliation(s)
- Adam J. Simpkin
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
- Synchrotron SOLEIL, L’Orme des Merisiers, Saint Aubin, BP 48, 91192 Gif-sur-Yvette, France
| | - Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Jens M. H. Thomas
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Martin Savko
- Synchrotron SOLEIL, L’Orme des Merisiers, Saint Aubin, BP 48, 91192 Gif-sur-Yvette, France
| | - Andrey Lebedev
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Ville Uski
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Charles Ballard
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Marcin Wojdyr
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- Global Phasing Ltd, Cambridge CB3 0AX, England
| | - Rui Wu
- Feil Family Brain and Mind Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Ruslan Sanishvili
- GM/CA@APS, The X-Ray Science Division, The Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Yibin Xu
- Division of Structural Biology, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Royal Parade, Parkville, VIC 3050, Australia
| | - María-Natalia Lisa
- Laboratory of Molecular and Structural Microbiology, Institut Pasteur de Montevideo, Mataojo 2020, 11400 Montevideo, Uruguay
| | - Alejandro Buschiazzo
- Laboratory of Molecular and Structural Microbiology, Institut Pasteur de Montevideo, Mataojo 2020, 11400 Montevideo, Uruguay
| | - William Shepard
- Synchrotron SOLEIL, L’Orme des Merisiers, Saint Aubin, BP 48, 91192 Gif-sur-Yvette, France
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Ronan M. Keegan
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
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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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Structure of the peptidoglycan polymerase RodA resolved by evolutionary coupling analysis. Nature 2018; 556:118-121. [PMID: 29590088 PMCID: PMC6035859 DOI: 10.1038/nature25985] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Accepted: 02/08/2018] [Indexed: 11/29/2022]
Abstract
The Shape, Elongation, Division, and Sporulation (“SEDS”) proteins are a large family of ubiquitous and essential transmembrane enzymes with critical roles in bacterial cell wall biology. The exact function of SEDS proteins was long enigmatic, but recent work1–3 has revealed that the prototypical SEDS family member RodA is a peptidoglycan polymerase – a role previously attributed exclusively to members of the penicillin binding protein family4. This discovery has made RodA and other SEDS proteins promising targets for the development of next-generation antibiotics. However, little is known regarding the molecular basis for SEDS activity, and no structural data are available for RodA or any homolog thereof. Here, we report the crystal structure of Thermus thermophilus RodA at a resolution of 2.9 Å, determined using evolutionary covariance-based fold prediction to enable molecular replacement. The structure reveals a novel ten-pass transmembrane fold with large extracellular loops, one of which is partially disordered. The protein contains a highly conserved cavity in the transmembrane domain, reminiscent of ligand binding sites in transmembrane receptors. Mutagenesis experiments in Bacillus subtilis and Escherichia coli show that perturbation of this cavity abolishes RodA function both in vitro and in vivo, indicating it is catalytically essential. These results provide a framework for understanding bacterial cell wall synthesis and SEDS protein function.
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Park H, Ovchinnikov S, Kim DE, DiMaio F, Baker D. Protein homology model refinement by large-scale energy optimization. Proc Natl Acad Sci U S A 2018; 115:3054-3059. [PMID: 29507254 PMCID: PMC5866580 DOI: 10.1073/pnas.1719115115] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Proteins fold to their lowest free-energy structures, and hence the most straightforward way to increase the accuracy of a partially incorrect protein structure model is to search for the lowest-energy nearby structure. This direct approach has met with little success for two reasons: first, energy function inaccuracies can lead to false energy minima, resulting in model degradation rather than improvement; and second, even with an accurate energy function, the search problem is formidable because the energy only drops considerably in the immediate vicinity of the global minimum, and there are a very large number of degrees of freedom. Here we describe a large-scale energy optimization-based refinement method that incorporates advances in both search and energy function accuracy that can substantially improve the accuracy of low-resolution homology models. The method refined low-resolution homology models into correct folds for 50 of 84 diverse protein families and generated improved models in recent blind structure prediction experiments. Analyses of the basis for these improvements reveal contributions from both the improvements in conformational sampling techniques and the energy function.
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Affiliation(s)
- Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA 98105
| | - David E Kim
- Institute for Protein Design, University of Washington, Seattle, WA 98105
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA 98105
- Institute for Protein Design, University of Washington, Seattle, WA 98105
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98105;
- Institute for Protein Design, University of Washington, Seattle, WA 98105
- Howard Hughes Medical Institute, University of Washington, Seattle, WA 98105
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Caballero I, Sammito M, Millán C, Lebedev A, Soler N, Usón I. ARCIMBOLDO on coiled coils. Acta Crystallogr D Struct Biol 2018; 74:194-204. [PMID: 29533227 PMCID: PMC5947760 DOI: 10.1107/s2059798317017582] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 12/08/2017] [Indexed: 11/10/2022] Open
Abstract
ARCIMBOLDO solves the phase problem by combining the location of small model fragments using Phaser with density modification and autotracing using SHELXE. Mainly helical structures constitute favourable cases, which can be solved using polyalanine helical fragments as search models. Nevertheless, the solution of coiled-coil structures is often complicated by their anisotropic diffraction and apparent translational noncrystallographic symmetry. Long, straight helices have internal translational symmetry and their alignment in preferential directions gives rise to systematic overlap of Patterson vectors. This situation has to be differentiated from the translational symmetry relating different monomers. ARCIMBOLDO_LITE has been run on single workstations on a test pool of 150 coiled-coil structures with 15-635 amino acids per asymmetric unit and with diffraction data resolutions of between 0.9 and 3.0 Å. The results have been used to identify and address specific issues when solving this class of structures using ARCIMBOLDO. Features from Phaser v.2.7 onwards are essential to correct anisotropy and produce translation solutions that will pass the packing filters. As the resolution becomes worse than 2.3 Å, the helix direction may be reversed in the placed fragments. Differentiation between true solutions and pseudo-solutions, in which helix fragments were correctly positioned but in a reverse orientation, was found to be problematic at resolutions worse than 2.3 Å. Therefore, after every new fragment-placement round, complete or sparse combinations of helices in alternative directions are generated and evaluated. The final solution is once again probed by helix reversal, refinement and extension. To conclude, density modification and SHELXE autotracing incorporating helical constraints is also exploited to extend the resolution limit in the case of coiled coils and to enhance the identification of correct solutions. This study resulted in a specialized mode within ARCIMBOLDO for the solution of coiled-coil structures, which overrides the resolution limit and can be invoked from the command line (keyword coiled_coil) or ARCIMBOLDO_LITE task interface in CCP4i.
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Affiliation(s)
- Iracema Caballero
- Structural Biology Unit, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Massimo Sammito
- Structural Biology Unit, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Claudia Millán
- Structural Biology Unit, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Andrey Lebedev
- CCP4, STFC Rutherford Appleton Laboratory, Research Complex at Harwell, Didcot OX11 0FA, England
| | - Nicolas Soler
- Structural Biology Unit, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixac 15, 08028 Barcelona, Spain
| | - Isabel Usón
- Structural Biology Unit, Institute of Molecular Biology of Barcelona (IBMB–CSIC), Baldiri Reixac 15, 08028 Barcelona, Spain
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain
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Keegan RM, McNicholas SJ, Thomas JMH, Simpkin AJ, Simkovic F, Uski V, Ballard CC, Winn MD, Wilson KS, Rigden DJ. Recent developments in MrBUMP: better search-model preparation, graphical interaction with search models, and solution improvement and assessment. Acta Crystallogr D Struct Biol 2018; 74:167-182. [PMID: 29533225 PMCID: PMC5947758 DOI: 10.1107/s2059798318003455] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 02/27/2018] [Indexed: 01/21/2023] Open
Abstract
Increasing sophistication in molecular-replacement (MR) software and the rapid expansion of the PDB in recent years have allowed the technique to become the dominant method for determining the phases of a target structure in macromolecular X-ray crystallography. In addition, improvements in bioinformatic techniques for finding suitable homologous structures for use as MR search models, combined with developments in refinement and model-building techniques, have pushed the applicability of MR to lower sequence identities and made weak MR solutions more amenable to refinement and improvement. MrBUMP is a CCP4 pipeline which automates all stages of the MR procedure. Its scope covers everything from the sourcing and preparation of suitable search models right through to rebuilding of the positioned search model. Recent improvements to the pipeline include the adoption of more sensitive bioinformatic tools for sourcing search models, enhanced model-preparation techniques including better ensembling of homologues, and the use of phase improvement and model building on the resulting solution. The pipeline has also been deployed as an online service through CCP4 online, which allows its users to exploit large bioinformatic databases and coarse-grained parallelism to speed up the determination of a possible solution. Finally, the molecular-graphics application CCP4mg has been combined with MrBUMP to provide an interactive visual aid to the user during the process of selecting and manipulating search models for use in MR. Here, these developments in MrBUMP are described with a case study to explore how some of the enhancements to the pipeline and to CCP4mg can help to solve a difficult case.
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Affiliation(s)
- Ronan M. Keegan
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Stuart J. McNicholas
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, England
| | - Jens M. H. Thomas
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Adam J. Simpkin
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
- Synchrotron SOLEIL, L’Orme des Merisiers, Saint Aubin, BP 48, 91192 Gif-sur-Yvette, France
| | - Felix Simkovic
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
| | - Ville Uski
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Charles C. Ballard
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Martyn D. Winn
- CCP4, Research Complex at Harwell, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
- STFC, Rutherford Appleton Laboratory, Harwell Oxford, Didcot OX11 0FA, England
| | - Keith S. Wilson
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, England
| | - Daniel J. Rigden
- Institute of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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40
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Jenkins HT. Fragon: rapid high-resolution structure determination from ideal protein fragments. Acta Crystallogr D Struct Biol 2018; 74:205-214. [PMID: 29533228 PMCID: PMC5947761 DOI: 10.1107/s2059798318002292] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 02/07/2018] [Indexed: 12/02/2022] Open
Abstract
Correctly positioning ideal protein fragments by molecular replacement presents an attractive method for obtaining preliminary phases when no template structure for molecular replacement is available. This has been exploited in several existing pipelines. This paper presents a new pipeline, named Fragon, in which fragments (ideal α-helices or β-strands) are placed using Phaser and the phases calculated from these coordinates are then improved by the density-modification methods provided by ACORN. The reliable scoring algorithm provided by ACORN identifies success. In these cases, the resulting phases are usually of sufficient quality to enable automated model building of the entire structure. Fragon was evaluated against two test sets comprising mixed α/β folds and all-β folds at resolutions between 1.0 and 1.7 Å. Success rates of 61% for the mixed α/β test set and 30% for the all-β test set were achieved. In almost 70% of successful runs, fragment placement and density modification took less than 30 min on relatively modest four-core desktop computers. In all successful runs the best set of phases enabled automated model building with ARP/wARP to complete the structure.
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Affiliation(s)
- Huw T. Jenkins
- York Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York YO10 5DD, England
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41
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Wang X, Zhang D, Huang SY. New Knowledge-Based Scoring Function with Inclusion of Backbone Conformational Entropies from Protein Structures. J Chem Inf Model 2018; 58:724-732. [DOI: 10.1021/acs.jcim.7b00601] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Xinxiang Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Di Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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42
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Vishwanath S, de Brevern AG, Srinivasan N. Same but not alike: Structure, flexibility and energetics of domains in multi-domain proteins are influenced by the presence of other domains. PLoS Comput Biol 2018; 14:e1006008. [PMID: 29432415 PMCID: PMC5825166 DOI: 10.1371/journal.pcbi.1006008] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 02/23/2018] [Accepted: 01/29/2018] [Indexed: 02/01/2023] Open
Abstract
The majority of the proteins encoded in the genomes of eukaryotes contain more than one domain. Reasons for high prevalence of multi-domain proteins in various organisms have been attributed to higher stability and functional and folding advantages over single-domain proteins. Despite these advantages, many proteins are composed of only one domain while their homologous domains are part of multi-domain proteins. In the study presented here, differences in the properties of protein domains in single-domain and multi-domain systems and their influence on functions are discussed. We studied 20 pairs of identical protein domains, which were crystallized in two forms (a) tethered to other proteins domains and (b) tethered to fewer protein domains than (a) or not tethered to any protein domain. Results suggest that tethering of domains in multi-domain proteins influences the structural, dynamic and energetic properties of the constituent protein domains. 50% of the protein domain pairs show significant structural deviations while 90% of the protein domain pairs show differences in dynamics and 12% of the residues show differences in the energetics. To gain further insights on the influence of tethering on the function of the domains, 4 pairs of homologous protein domains, where one of them is a full-length single-domain protein and the other protein domain is a part of a multi-domain protein, were studied. Analyses showed that identical and structurally equivalent functional residues show differential dynamics in homologous protein domains; though comparable dynamics between in-silico generated chimera protein and multi-domain proteins were observed. From these observations, the differences observed in the functions of homologous proteins could be attributed to the presence of tethered domain. Overall, we conclude that tethered domains in multi-domain proteins not only provide stability or folding advantages but also influence pathways resulting in differences in function or regulatory properties. High prevalence of multi-domain proteins in proteomes has been attributed to higher stability and functional and folding advantages of the multi-domain proteins. Influence of tethering of domains on the overall properties of proteins has been well studied but its influence on the properties of the constituent domains is largely unaddressed. Here, we investigate the influence of tethering of domains in multi-domain proteins on the structural, dynamics and energetics properties of the constituent domains and its implications on the functions of proteins. To this end, comparative analyses were carried out for identical protein domains crystallized in tethered and untethered forms. Also, comparative analyses of single-domain proteins and their homologous multi-domain proteins were performed. The analyses suggest that tethering influences the structural, dynamic and energetic properties of constituent protein domains. Our observations hint at regulation of protein domains by tethered domains in multi-domain systems, which may manifest at the differential function observed between single-domain and homologous multi-domain proteins.
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Grants
- IISc-DBT partnership programme
- DST, India (Mathematical Biology Initiative & J.C. Bose National Fellowship, FIST program)
- UGC, India – Centre for Advanced Studies
- Ministry of Human Resource Development
- Ministry of Research (France), University of Paris Diderot, Sorbonne Paris Cité
- National Institute for Blood Transfusion (INTS, France), Institute for Health and Medical Research (INSERM, France), Laboratory of Excellence GR-Ex
- The labex GR-Ex is funded by the program Investissements d’avenir of the French National Research Agency,
- Indo-French Centre for the Promotion of Advanced Research/CEFIPRA for a collaborative grant
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Affiliation(s)
- Sneha Vishwanath
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Alexandre G. de Brevern
- INSERM, U 1134, DSIMB, Paris, France
- Univ. Paris Diderot, Sorbonne Paris Cité, Univ de la Réunion, Univ des Antilles, UMR_S 1134, Paris, France
- Institut National de la Transfusion Sanguine (INTS), Paris, France
- Laboratoire d' Excellence GR-Ex, Paris, France
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44
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Kim DN, Sanbonmatsu KY. Tools for the cryo-EM gold rush: going from the cryo-EM map to the atomistic model. Biosci Rep 2017; 37:BSR20170072. [PMID: 28963369 PMCID: PMC5715128 DOI: 10.1042/bsr20170072] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 09/26/2017] [Accepted: 09/27/2017] [Indexed: 12/16/2022] Open
Abstract
As cryo-electron microscopy (cryo-EM) enters mainstream structural biology, the demand for fitting methods is high. Here, we review existing flexible fitting methods for cryo-EM. We discuss their importance, potential concerns and assessment strategies. We aim to give readers concrete descriptions of cryo-EM flexible fitting methods with corresponding examples.
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Affiliation(s)
- Doo Nam Kim
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, U.S.A
| | - Karissa Y Sanbonmatsu
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, U.S.A.
- New Mexico Consortium, Los Alamos, U.S.A
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45
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Hovan L, Oleinikovas V, Yalinca H, Kryshtafovych A, Saladino G, Gervasio FL. Assessment of the model refinement category in CASP12. Proteins 2017; 86 Suppl 1:152-167. [PMID: 29071750 DOI: 10.1002/prot.25409] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 10/03/2017] [Accepted: 10/24/2017] [Indexed: 01/07/2023]
Abstract
We here report on the assessment of the model refinement predictions submitted to the 12th Experiment on the Critical Assessment of Protein Structure Prediction (CASP12). This is the fifth refinement experiment since CASP8 (2008) and, as with the previous experiments, the predictors were invited to refine selected server models received in the regular (nonrefinement) stage of the CASP experiment. We assessed the submitted models using a combination of standard CASP measures. The coefficients for the linear combination of Z-scores (the CASP12 score) have been obtained by a machine learning algorithm trained on the results of visual inspection. We identified eight groups that improve both the backbone conformation and the side chain positioning for the majority of targets. Albeit the top methods adopted distinctively different approaches, their overall performance was almost indistinguishable, with each of them excelling in different scores or target subsets. What is more, there were a few novel approaches that, while doing worse than average in most cases, provided the best refinements for a few targets, showing significant latitude for further innovation in the field.
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Affiliation(s)
- Ladislav Hovan
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom
| | | | - Havva Yalinca
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom
| | | | - Giorgio Saladino
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom
| | - Francesco Luigi Gervasio
- Department of Chemistry, University College London, WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, United Kingdom
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Lin Y, Koga N, Vorobiev SM, Baker D. Cyclic oligomer design with de novo αβ-proteins. Protein Sci 2017; 26:2187-2194. [PMID: 28801928 PMCID: PMC5654858 DOI: 10.1002/pro.3270] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 07/28/2017] [Accepted: 08/10/2017] [Indexed: 12/11/2022]
Abstract
We have previously shown that monomeric globular αβ-proteins can be designed de novo with considerable control over topology, size, and shape. In this paper, we investigate the design of cyclic homo-oligomers from these starting points. We experimented with both keeping the original monomer backbones fixed during the cyclic docking and design process, and allowing the backbone of the monomer to conform to that of adjacent subunits in the homo-oligomer. The latter flexible backbone protocol generated designs with shape complementarity approaching that of native homo-oligomers, but experimental characterization showed that the fixed backbone designs were more stable and less aggregation prone. Designed C2 oligomers with β-strand backbone interactions were structurally confirmed through x-ray crystallography and small-angle X-ray scattering (SAXS). In contrast, C3-C5 designed homo-oligomers with primarily nonpolar residues at interfaces all formed a range of oligomeric states. Taken together, our results suggest that for homo-oligomers formed from globular building blocks, improved structural specificity will be better achieved using monomers with increased shape complementarity and with more polar interfaces.
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Affiliation(s)
- Yu‐Ru Lin
- Department of BiochemistryUniversity of Washington, and Howard Hughes Medical InstituteSeattleWashington 98195
| | - Nobuyasu Koga
- Research Center of Integrative Molecular SystemsInstitute for Molecular Science, National Institute of Natural Sciences (NINS)Okazaki 444‐8585Japan
- JST, PRESTOKawaguchiSaitama 332‐0012Japan
| | - Sergey M. Vorobiev
- Department of Biological ScienceNortheast Structural Genomics Consortium, Columbia UniversityNew YorkNew York
| | - David Baker
- Department of BiochemistryUniversity of Washington, and Howard Hughes Medical InstituteSeattleWashington 98195
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Leelananda SP, Lindert S. Iterative Molecular Dynamics-Rosetta Membrane Protein Structure Refinement Guided by Cryo-EM Densities. J Chem Theory Comput 2017; 13:5131-5145. [PMID: 28949136 DOI: 10.1021/acs.jctc.7b00464] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Knowing atomistic details of proteins is essential not only for the understanding of protein function but also for the development of drugs. Experimental methods such as X-ray crystallography, NMR, and cryo-electron microscopy (cryo-EM) are the preferred forms of protein structure determination and have achieved great success over the most recent decades. Computational methods may be an alternative when experimental techniques fail. However, computational methods are severely limited when it comes to predicting larger macromolecule structures with little sequence similarity to known structures. The incorporation of experimental restraints in computational methods is becoming increasingly important to more reliably predict protein structure. One such experimental input used in structure prediction and refinement is cryo-EM densities. Recent advances in cryo-EM have arguably revolutionized the field of structural biology. Our previously developed cryo-EM-guided Rosetta-MD protocol has shown great promise in the refinement of soluble protein structures. In this study, we extended cryo-EM density-guided iterative Rosetta-MD to membrane proteins. We also improved the methodology in general by picking models based on a combination of their score and fit-to-density during the Rosetta model selection. By doing so, we have been able to pick models superior to those with the previous selection based on Rosetta score only and we have been able to further improve our previously refined models of soluble proteins. The method was tested with five membrane spanning protein structures. By applying density-guided Rosetta-MD iteratively we were able to refine the predicted structures of these membrane proteins to atomic resolutions. We also showed that the resolution of the density maps determines the improvement and quality of the refined models. By incorporating high-resolution density maps (∼4 Å), we were able to more significantly improve the quality of the models than when medium-resolution maps (6.9 Å) were used. Beginning from an average starting structure root mean square deviation (RMSD) to native of 4.66 Å, our protocol was able to refine the structures to bring the average refined structure RMSD to 1.66 Å when 4 Å density maps were used. The protocol also successfully refined the HIV-1 CTD guided by an experimental 5 Å density map.
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Affiliation(s)
- Sumudu P Leelananda
- Department of Chemistry and Biochemistry, Ohio State University , Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University , Columbus, Ohio 43210, United States
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Computational design of ligand-binding membrane receptors with high selectivity. Nat Chem Biol 2017; 13:715-723. [PMID: 28459439 PMCID: PMC5478435 DOI: 10.1038/nchembio.2371] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 02/02/2017] [Indexed: 12/13/2022]
Abstract
Accurate modeling and design of protein-ligand
interactions have broad applications in cell, synthetic
biology and drug discovery but remain challenging without
experimental protein structures. Here we developed an
integrated protein homology modeling-ligand docking-protein
design approach that reconstructs protein-ligand binding
sites from homolog protein structures in the presence of
protein-bound ligand poses to capture conformational
selection and induced fit modes of ligand binding. In
structure modeling tests, we blindly predicted near-atomic
accuracy ligand conformations bound to G protein-coupled
receptors (GPCRs) that were rarely identified by traditional
approaches. We also quantitatively predicted the binding
selectivity of diverse ligands to
structurally-uncharacterized GPCRs. We then applied the
technique to design functional human dopamine receptors with
novel ligand binding selectivity. Most blindly predicted
ligand binding specificities closely agreed with
experimental validations. Our method should prove useful in
ligand discovery approaches and in reprogramming the ligand
binding profile of membrane receptors that remain difficult
to crystallize.
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Sobel JA, Krier I, Andersin T, Raghav S, Canella D, Gilardi F, Kalantzi AS, Rey G, Weger B, Gachon F, Dal Peraro M, Hernandez N, Schibler U, Deplancke B, Naef F. Transcriptional regulatory logic of the diurnal cycle in the mouse liver. PLoS Biol 2017; 15:e2001069. [PMID: 28414715 PMCID: PMC5393560 DOI: 10.1371/journal.pbio.2001069] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Accepted: 03/10/2017] [Indexed: 12/11/2022] Open
Abstract
Many organisms exhibit temporal rhythms in gene expression that propel diurnal cycles in physiology. In the liver of mammals, these rhythms are controlled by transcription-translation feedback loops of the core circadian clock and by feeding-fasting cycles. To better understand the regulatory interplay between the circadian clock and feeding rhythms, we mapped DNase I hypersensitive sites (DHSs) in the mouse liver during a diurnal cycle. The intensity of DNase I cleavages cycled at a substantial fraction of all DHSs, suggesting that DHSs harbor regulatory elements that control rhythmic transcription. Using chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq), we found that hypersensitivity cycled in phase with RNA polymerase II (Pol II) loading and H3K27ac histone marks. We then combined the DHSs with temporal Pol II profiles in wild-type (WT) and Bmal1-/- livers to computationally identify transcription factors through which the core clock and feeding-fasting cycles control diurnal rhythms in transcription. While a similar number of mRNAs accumulated rhythmically in Bmal1-/- compared to WT livers, the amplitudes in Bmal1-/- were generally lower. The residual rhythms in Bmal1-/- reflected transcriptional regulators mediating feeding-fasting responses as well as responses to rhythmic systemic signals. Finally, the analysis of DNase I cuts at nucleotide resolution showed dynamically changing footprints consistent with dynamic binding of CLOCK:BMAL1 complexes. Structural modeling suggested that these footprints are driven by a transient heterotetramer binding configuration at peak activity. Together, our temporal DNase I mappings allowed us to decipher the global regulation of diurnal transcription rhythms in the mouse liver.
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Affiliation(s)
- Jonathan Aryeh Sobel
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Irina Krier
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Teemu Andersin
- Department of Molecular Biology, University of Geneva, Geneva, Switzerland
| | - Sunil Raghav
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Donatella Canella
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Federica Gilardi
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Alexandra Styliani Kalantzi
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Guillaume Rey
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Benjamin Weger
- Department of Diabetes and Circadian Rhythms, Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Frédéric Gachon
- Department of Diabetes and Circadian Rhythms, Nestlé Institute of Health Sciences, Lausanne, Switzerland
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Matteo Dal Peraro
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Nouria Hernandez
- Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Ueli Schibler
- Department of Molecular Biology, University of Geneva, Geneva, Switzerland
| | - Bart Deplancke
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Felix Naef
- The Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Yang CH, Lin YS, Chuang LY, Chang HW. A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding. J Comput Biol 2017; 24:981-994. [PMID: 28287821 DOI: 10.1089/cmb.2016.0104] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.
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Affiliation(s)
- Cheng-Hong Yang
- 1 Department of Electronic Engineering, National Kaohsiung University of Applied Sciences , Kaohsiung, Taiwan
| | - Yu-Shiun Lin
- 1 Department of Electronic Engineering, National Kaohsiung University of Applied Sciences , Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- 2 Department of Chemical Engineering, Institute of Biotechnology and Chemical Engineering, I-Shou University , Kaohsiung, Taiwan
| | - Hsueh-Wei Chang
- 3 Institute of Medical Science and Technology, National Sun Yat-sen University , Kaohsiung, Taiwan .,4 Department of Medical Research, Kaohsiung Medical University Hospital , Kaohsiung, Taiwan .,5 Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University , Kaohsiung, Taiwan
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