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Yang Y, Ahmad E, Premkumar V, Liu A, Ashikur Rahman SM, Nikolovska‐Coleska Z. Structural studies of intrinsically disordered MLL-fusion protein AF9 in complex with peptidomimetic inhibitors. Protein Sci 2024; 33:e5019. [PMID: 38747396 PMCID: PMC11094776 DOI: 10.1002/pro.5019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 04/23/2024] [Accepted: 04/28/2024] [Indexed: 05/19/2024]
Abstract
AF9 (MLLT3) and its paralog ENL(MLLT1) are members of the YEATS family of proteins with important role in transcriptional and epigenetic regulatory complexes. These proteins are two common MLL fusion partners in MLL-rearranged leukemias. The oncofusion proteins MLL-AF9/ENL recruit multiple binding partners, including the histone methyltransferase DOT1L, leading to aberrant transcriptional activation and enhancing the expression of a characteristic set of genes that drive leukemogenesis. The interaction between AF9 and DOT1L is mediated by an intrinsically disordered C-terminal ANC1 homology domain (AHD) in AF9, which undergoes folding upon binding of DOT1L and other partner proteins. We have recently reported peptidomimetics that disrupt the recruitment of DOT1L by AF9 and ENL, providing a proof-of-concept for targeting AHD and assessing its druggability. Intrinsically disordered proteins, such as AF9 AHD, are difficult to study and characterize experimentally on a structural level. In this study, we present a successful protein engineering strategy to facilitate structural investigation of the intrinsically disordered AF9 AHD domain in complex with peptidomimetic inhibitors by using maltose binding protein (MBP) as a crystallization chaperone connected with linkers of varying flexibility and length. The strategic incorporation of disulfide bonds provided diffraction-quality crystals of the two disulfide-bridged MBP-AF9 AHD fusion proteins in complex with the peptidomimetics. These successfully determined first series of 2.1-2.6 Å crystal complex structures provide high-resolution insights into the interactions between AHD and its inhibitors, shedding light on the role of AHD in recruiting various binding partner proteins. We show that the overall complex structures closely resemble the reported NMR structure of AF9 AHD/DOT1L with notable difference in the conformation of the β-hairpin region, stabilized through conserved hydrogen bonds network. These first series of AF9 AHD/peptidomimetics complex structures are providing insights of the protein-inhibitor interactions and will facilitate further development of novel inhibitors targeting the AF9/ENL AHD domain.
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Affiliation(s)
- Yuting Yang
- Department of PathologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Ejaz Ahmad
- Department of PathologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Vidhya Premkumar
- Department of PathologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Alicen Liu
- Department of PathologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - S. M. Ashikur Rahman
- Department of PathologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
| | - Zaneta Nikolovska‐Coleska
- Department of PathologyUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
- Rogel Cancer CenterUniversity of Michigan Medical SchoolAnn ArborMichiganUSA
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Nguyen NP, Ersoy I, Gotberg J, Bunyak F, White TA. DRPnet: automated particle picking in cryo-electron micrographs using deep regression. BMC Bioinformatics 2021; 22:55. [PMID: 33557750 PMCID: PMC7869254 DOI: 10.1186/s12859-020-03948-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 12/22/2020] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND Identification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts. RESULTS We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution. CONCLUSION DRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.
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Affiliation(s)
- Nguyen Phuoc Nguyen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Ilker Ersoy
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO USA
| | - Jacob Gotberg
- Research Computing Support Services, University of Missouri, Columbia, MO USA
| | - Filiz Bunyak
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO USA
| | - Tommi A. White
- Department of Biochemistry, University of Missouri, Columbia, MO USA
- Electron Microscopy Core, University of Missouri, Columbia, MO USA
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Kaniyala Melanthota S, Banik S, Chakraborty I, Pallen S, Gopal D, Chakrabarti S, Mazumder N. Elucidating the microscopic and computational techniques to study the structure and pathology of SARS-CoVs. Microsc Res Tech 2020; 83:1623-1638. [PMID: 32770582 PMCID: PMC7436590 DOI: 10.1002/jemt.23551] [Citation(s) in RCA: 9] [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: 03/28/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 12/11/2022]
Abstract
Severe Acute Respiratory Syndrome Coronaviruses (SARS‐CoVs), causative of major outbreaks in the past two decades, has claimed many lives all over the world. The virus effectively spreads through saliva aerosols or nasal discharge from an infected person. Currently, no specific vaccines or treatments exist for coronavirus; however, several attempts are being made to develop possible treatments. Hence, it is important to study the viral structure and life cycle to understand its functionality, activity, and infectious nature. Further, such studies can aid in the development of vaccinations against this virus. Microscopy plays an important role in examining the structure and topology of the virus as well as pathogenesis in infected host cells. This review deals with different microscopy techniques including electron microscopy, atomic force microscopy, fluorescence microscopy as well as computational methods to elucidate various prospects of this life‐threatening virus. Structural analysis of SARS‐CoVs aids in understanding its nature, activity, and pathophysiology Revealing the surface morphology of SARS‐CoVs using scanning electron microscope and atomic force microscopy Computational methods help to understand the structure of SARS‐CoVs and their interactions with various inhibitors
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Affiliation(s)
- Sindhoora Kaniyala Melanthota
- Department of Biophysics, Manipal School of Life SciencesManipal Academy of Higher EducationManipalKarnataka576104India
| | - Soumyabrata Banik
- Department of Biophysics, Manipal School of Life SciencesManipal Academy of Higher EducationManipalKarnataka576104India
| | - Ishita Chakraborty
- Department of Biophysics, Manipal School of Life SciencesManipal Academy of Higher EducationManipalKarnataka576104India
| | - Sparsha Pallen
- Department of Bioinformatics, Manipal School of Life SciencesManipal Academy of Higher EducationManipalKarnataka576104India
| | - Dharshini Gopal
- Department of Bioinformatics, Manipal School of Life SciencesManipal Academy of Higher EducationManipalKarnataka576104India
| | - Shweta Chakrabarti
- Department of Bioinformatics, Manipal School of Life SciencesManipal Academy of Higher EducationManipalKarnataka576104India
| | - Nirmal Mazumder
- Department of Biophysics, Manipal School of Life SciencesManipal Academy of Higher EducationManipalKarnataka576104India
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Ravelli RBG, Nijpels FJT, Henderikx RJM, Weissenberger G, Thewessem S, Gijsbers A, Beulen BWAMM, López-Iglesias C, Peters PJ. Cryo-EM structures from sub-nl volumes using pin-printing and jet vitrification. Nat Commun 2020; 11:2563. [PMID: 32444637 PMCID: PMC7244535 DOI: 10.1038/s41467-020-16392-5] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 04/17/2020] [Indexed: 01/17/2023] Open
Abstract
The increasing demand for cryo-electron microscopy (cryo-EM) reveals drawbacks in current sample preparation protocols, such as sample waste and lack of reproducibility. Here, we present several technical developments that provide efficient sample preparation for cryo-EM studies. Pin printing substantially reduces sample waste by depositing only a sub-nanoliter volume of sample on the carrier surface. Sample evaporation is mitigated by dewpoint control feedback loops. The deposited sample is vitrified by jets of cryogen followed by submersion into a cryogen bath. Because the cryogen jets cool the sample from the center, premounted autogrids can be used and loaded directly into automated cryo-EMs. We integrated these steps into a single device, named VitroJet. The device’s performance was validated by resolving four standard proteins (apoferritin, GroEL, worm hemoglobin, beta-galactosidase) to ~3 Å resolution using a 200-kV electron microscope. The VitroJet offers a promising solution for improved automated sample preparation in cryo-EM studies. There is a need to further improve the automation of cryo-EM sample preparation to make it more easily accessible for non-specialists, reduce sample waste and increase reproducibility. Here, the authors present VitroJet, a single device, where sub-nl volumes of samples are deposited by pin printing thus eliminating the need for sample blotting, which is followed by jet vitrification, and they show that high-resolution structures can be obtained using four standard proteins.
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Affiliation(s)
- Raimond B G Ravelli
- The Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, Netherlands.
| | - Frank J T Nijpels
- The Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, Netherlands.,CryoSol-World, Maastricht, Netherlands
| | - Rene J M Henderikx
- The Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, Netherlands.,CryoSol-World, Maastricht, Netherlands
| | - Giulia Weissenberger
- The Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, Netherlands.,CryoSol-World, Maastricht, Netherlands
| | - Sanne Thewessem
- Instrument Development, Engineering and Evaluation (IDEE), Maastricht University, Maastricht, Netherlands
| | - Abril Gijsbers
- The Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, Netherlands
| | - Bart W A M M Beulen
- CryoSol-World, Maastricht, Netherlands.,Instrument Development, Engineering and Evaluation (IDEE), Maastricht University, Maastricht, Netherlands
| | - Carmen López-Iglesias
- The Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, Netherlands
| | - Peter J Peters
- The Maastricht Multimodal Molecular Imaging Institute (M4i), Division of Nanoscopy, Maastricht University, Maastricht, Netherlands. .,CryoSol-World, Maastricht, Netherlands.
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Narasimhan S, Folkers GE, Baldus M. When Small becomes Too Big: Expanding the Use of In‐Cell Solid‐State NMR Spectroscopy. Chempluschem 2020; 85:760-768. [DOI: 10.1002/cplu.202000167] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 03/31/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Siddarth Narasimhan
- NMR Spectroscopy Research Group Bijvoet Center for Biomolecular ResearchUtrecht University Padualaan 8 3584 CH Utrecht (The Netherlands
| | - Gert E. Folkers
- NMR Spectroscopy Research Group Bijvoet Center for Biomolecular ResearchUtrecht University Padualaan 8 3584 CH Utrecht (The Netherlands
| | - Marc Baldus
- NMR Spectroscopy Research Group Bijvoet Center for Biomolecular ResearchUtrecht University Padualaan 8 3584 CH Utrecht (The Netherlands
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6
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Targeting Tumors Using Peptides. Molecules 2020; 25:molecules25040808. [PMID: 32069856 PMCID: PMC7070747 DOI: 10.3390/molecules25040808] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Revised: 02/07/2020] [Accepted: 02/10/2020] [Indexed: 12/16/2022] Open
Abstract
To penetrate solid tumors, low molecular weight (Mw < 10 KDa) compounds have an edge over antibodies: their higher penetration because of their small size. Because of the dense stroma and high interstitial fluid pressure of solid tumors, the penetration of higher Mw compounds is unfavored and being small thus becomes an advantage. This review covers a wide range of peptidic ligands—linear, cyclic, macrocyclic and cyclotidic peptides—to target tumors: We describe the main tools to identify peptides experimentally, such as phage display, and the possible chemical modifications to enhance the properties of the identified peptides. We also review in silico identification of peptides and the most salient non-peptidic ligands in clinical stages. We later focus the attention on the current validated ligands available to target different tumor compartments: blood vessels, extracelullar matrix, and tumor associated macrophages. The clinical advances and failures of these ligands and their therapeutic conjugates will be discussed. We aim to present the reader with the state-of-the-art in targeting tumors, by using low Mw molecules, and the tools to identify new ligands.
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Fejer SN. Minimalistic coarse-grained modeling of viral capsid assembly. COMPUTATIONAL APPROACHES FOR UNDERSTANDING DYNAMICAL SYSTEMS: PROTEIN FOLDING AND ASSEMBLY 2020; 170:405-434. [DOI: 10.1016/bs.pmbts.2019.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Lawson CL, Berman HM, Chiu W. Evolving data standards for cryo-EM structures. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2020; 7:014701. [PMID: 32002441 PMCID: PMC6980868 DOI: 10.1063/1.5138589] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 01/07/2020] [Indexed: 05/04/2023]
Abstract
Electron cryo-microscopy (cryo-EM) is increasingly being used to determine 3D structures of a broad spectrum of biological specimens from molecules to cells. Anticipating this progress in the early 2000s, an international collaboration of scientists with expertise in both cryo-EM and structure data archiving was established (EMDataResource, previously known as EMDataBank). The major goals of the collaboration have been twofold: to develop the necessary infrastructure for archiving cryo-EM-derived density maps and models, and to promote development of cryo-EM structure validation standards. We describe how cryo-EM data archiving and validation have been developed and jointly coordinated for the Electron Microscopy Data Bank and Protein Data Bank archives over the past two decades, as well as the impact of evolving technology on data standards. Just as for X-ray crystallography and nuclear magnetic resonance, engaging the scientific community via workshops and challenging activities has played a central role in developing recommendations and requirements for the cryo-EM structure data archives.
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Affiliation(s)
- Catherine L. Lawson
- Institute for Quantitative Biomedicine and Research Collaboratory for Structural Bioinformatics, Rutgers, The State University of New Jersey, Piscataway, New Jersey 08854, USA
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9
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Berman HM, Adams PD, Bonvin AA, Burley SK, Carragher B, Chiu W, DiMaio F, Ferrin TE, Gabanyi MJ, Goddard TD, Griffin PR, Haas J, Hanke CA, Hoch JC, Hummer G, Kurisu G, Lawson CL, Leitner A, Markley JL, Meiler J, Montelione GT, Phillips GN, Prisner T, Rappsilber J, Schriemer DC, Schwede T, Seidel CAM, Strutzenberg TS, Svergun DI, Tajkhorshid E, Trewhella J, Vallat B, Velankar S, Vuister GW, Webb B, Westbrook JD, White KL, Sali A. Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures. Structure 2019; 27:1745-1759. [PMID: 31780431 DOI: 10.1016/j.str.2019.11.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 10/31/2019] [Accepted: 11/06/2019] [Indexed: 12/23/2022]
Abstract
Structures of biomolecular systems are increasingly computed by integrative modeling. In this approach, a structural model is constructed by combining information from multiple sources, including varied experimental methods and prior models. In 2019, a Workshop was held as a Biophysical Society Satellite Meeting to assess progress and discuss further requirements for archiving integrative structures. The primary goal of the Workshop was to build consensus for addressing the challenges involved in creating common data standards, building methods for federated data exchange, and developing mechanisms for validating integrative structures. The summary of the Workshop and the recommendations that emerged are presented here.
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Affiliation(s)
- Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA.
| | - Paul D Adams
- Physical Biosciences Division, Lawrence Berkeley Laboratory, Berkeley, CA 94720-8235, USA; Department of Bioengineering, University of California-Berkeley, Berkeley, CA 94720, USA
| | - Alexandre A Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences and San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA
| | - Bridget Carragher
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Wah Chiu
- Department of Bioengineering, Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305-5447, USA; SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
| | - Frank DiMaio
- Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Thomas E Ferrin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Margaret J Gabanyi
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Thomas D Goddard
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Juergen Haas
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Christian A Hanke
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Jeffrey C Hoch
- Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT 06030, USA
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, 60438 Frankfurt am Main, Germany; Institute for Biophysics, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Genji Kurisu
- Protein Data Bank Japan (PDBj), Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
| | - Catherine L Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Alexander Leitner
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
| | - John L Markley
- BioMagResBank (BMRB), Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN 37221, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytech Institute, Troy, NY 12180, USA
| | - George N Phillips
- BioSciences at Rice and Department of Chemistry, Rice University, Houston, TX 77251, USA
| | - Thomas Prisner
- Institute of Physical and Theoretical Chemistry and Center of Biomolecular Magnetic Resonance, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
| | - Juri Rappsilber
- Wellcome Trust Centre for Cell Biology, Edinburgh EH9 3JR, Scotland
| | - David C Schriemer
- Department of Biochemistry & Molecular Biology, Robson DNA Science Centre, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Torsten Schwede
- Swiss Institute of Bioinformatics and Biozentrum, University of Basel, 4056 Basel, Switzerland
| | - Claus A M Seidel
- Molecular Physical Chemistry, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | | | - Dmitri I Svergun
- European Molecular Biology Laboratory (EMBL), Hamburg Outstation, Notkestrasse 85, 22607 Hamburg, Germany
| | - Emad Tajkhorshid
- Department of Biochemistry, NIH Center for Macromolecular Modeling and Bioinformatics, Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia; Department of Chemistry, University of Utah, Salt Lake City, UT 84112, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire CB10 1SD, UK
| | - Geerten W Vuister
- Department of Molecular and Cell Biology, Leicester Institute of Structural and Chemical Biology, University of Leicester, Leicester LE1 9HN, UK
| | - Benjamin Webb
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kate L White
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Bridge Institute, Michelson Center, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrej Sali
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA 94158, USA.
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Khatib F, Desfosses A, Koepnick B, Flatten J, Popović Z, Baker D, Cooper S, Gutsche I, Horowitz S. Building de novo cryo-electron microscopy structures collaboratively with citizen scientists. PLoS Biol 2019; 17:e3000472. [PMID: 31714936 PMCID: PMC6850521 DOI: 10.1371/journal.pbio.3000472] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
With the rapid improvement of cryo-electron microscopy (cryo-EM) resolution, new computational tools are needed to assist and improve upon atomic model building and refinement options. This communication demonstrates that microscopists can now collaborate with the players of the computer game Foldit to generate high-quality de novo structural models. This development could greatly speed the generation of excellent cryo-EM structures when used in addition to current methods.
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Affiliation(s)
- Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, Massachusetts, United States of America
- * E-mail: (FB); (SH)
| | - Ambroise Desfosses
- Institut de Biologie Structurale, University Grenoble Alpes, CEA, CNRS, Grenoble, France
| | | | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Jeff Flatten
- Center for Game Science, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
| | - Zoran Popović
- Center for Game Science, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, Washington, United States of America
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America
| | - Irina Gutsche
- Institut de Biologie Structurale, University Grenoble Alpes, CEA, CNRS, Grenoble, France
| | - Scott Horowitz
- Department of Chemistry and Biochemistry and the Knoebel Institute for Healthy Aging, University of Denver, Denver, Colorado, United States of America
- * E-mail: (FB); (SH)
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11
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Saotome M, Horikoshi N, Urano K, Kujirai T, Yuzurihara H, Kurumizaka H, Kagawa W. Structure determination of the nucleosome core particle by selenium SAD phasing. ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY 2019; 75:930-936. [PMID: 31588924 DOI: 10.1107/s2059798319012713] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Accepted: 09/12/2019] [Indexed: 11/10/2022]
Abstract
The eukaryotic genome is compacted inside the nucleus of the cell in the form called chromatin. The fundamental unit of chromatin is the nucleosome, which contains four types of histones (H3, H4, H2A and H2B) and approximately 150 base pairs of DNA wrapped around the histone complex. The structure of the nucleosome is highly conserved across several eukaryotic species, and molecular replacement has been the primary phasing method used to solve nucleosome structures by X-ray crystallography. However, there is currently no simple, widely applicable experimental phasing method for the nucleosome. In the present study, it is demonstrated that selenomethionine-incorporated histones H3, H2A and H2B can be reconstituted into nucleosomes and crystallized for structural determination. Unexpectedly, it was found that the nucleosome can be phased with a relatively small number of Se atoms. The structures of nucleosome core particles containing 12 and 16 Se atoms were solved by SAD phasing at 2.5 and 2.4 Å resolution, respectively. The present study demonstrates a simple method for determining nucleosome structures by experimental phasing, which may be particularly useful for noncanonical structures that cannot be solved by molecular replacement.
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Affiliation(s)
- Mika Saotome
- Department of Chemistry, Graduate School of Science and Engineering, Meisei University, 2-1-1 Hodokubo, Hino-shi, Tokyo 191-8506, Japan
| | - Naoki Horikoshi
- Laboratory of Structural Biology, Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
| | - Kazuki Urano
- Department of Chemistry, Graduate School of Science and Engineering, Meisei University, 2-1-1 Hodokubo, Hino-shi, Tokyo 191-8506, Japan
| | - Tomoya Kujirai
- Laboratory of Structural Biology, Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
| | - Hidetaka Yuzurihara
- Department of Chemistry, Graduate School of Science and Engineering, Meisei University, 2-1-1 Hodokubo, Hino-shi, Tokyo 191-8506, Japan
| | - Hitoshi Kurumizaka
- Laboratory of Structural Biology, Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
| | - Wataru Kagawa
- Department of Chemistry, Graduate School of Science and Engineering, Meisei University, 2-1-1 Hodokubo, Hino-shi, Tokyo 191-8506, Japan
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Chen L, Baker B, Santos E, Sheep M, Daftarian D. A Visualization Tool for Cryo-EM Protein Validation with an Unsupervised Machine Learning Model in Chimera Platform. MEDICINES (BASEL, SWITZERLAND) 2019; 6:E86. [PMID: 31390767 PMCID: PMC6789601 DOI: 10.3390/medicines6030086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 07/31/2019] [Accepted: 08/02/2019] [Indexed: 11/22/2022]
Abstract
Background: Cryo-electron microscopy (cryo-EM) has become a major technique for protein structure determination. However, due to the low quality of cryo-EM density maps, many protein structures derived from cryo-EM contain outliers introduced during the modeling process. The current protein model validation system lacks identification features for cryo-EM proteins making it not enough to identify outliers in cryo-EM proteins. Methods: This study introduces an efficient unsupervised outlier detection model for validating protein models built from cryo-EM technique. The current model uses a high-resolution X-ray dataset (<1.5 Å) as the reference dataset. The distal block distance, side-chain length, phi, psi, and first chi angle of the residues in the reference dataset are collected and saved as a database of the histogram-based outlier score (HBOS). The HBOS value of the residues in target cryo-EM proteins can be read from this HBOS database. Results: Protein residues with a HBOS value greater than ten are labeled as outliers by default. Four datasets containing proteins derived from cryo-EM density maps were tested with this probabilistic anomaly detection model. Conclusions: According to the proposed model, a visualization assistant tool was designed for Chimera, a protein visualization platform.
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Affiliation(s)
- Lin Chen
- Department of Computer Science, Valdosta State University, Valdosta, GA 31693, USA.
| | - Brandon Baker
- Department of Natural Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
| | - Eduardo Santos
- Department of Natural Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
| | - Michell Sheep
- Department of Mathematics & Computer Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
| | - Darius Daftarian
- Department of Mathematics & Computer Science, Elizabeth City State University, Elizabeth City, NC 27909, USA
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Notredame C. Editorial: NAR Genomics and Bioinformatics: a new journal for reproducible genomics in the Big Data era. NAR Genom Bioinform 2019; 1:e1. [PMID: 33575548 PMCID: PMC7671086 DOI: 10.1093/nargab/lqz001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Cedric Notredame
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona 08003, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
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