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Usón I, Sheldrick GM. Modes and model building in SHELXE. Acta Crystallogr D Struct Biol 2024; 80:4-15. [PMID: 38088896 PMCID: PMC10833347 DOI: 10.1107/s2059798323010082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 11/21/2023] [Indexed: 01/12/2024] Open
Abstract
Density modification is a standard step to provide a route for routine structure solution by any experimental phasing method, with single-wavelength or multi-wavelength anomalous diffraction being the most popular methods, as well as to extend fragments or incomplete models into a full solution. The effect of density modification on the starting maps from either source is illustrated in the case of SHELXE. The different modes in which the program can run are reviewed; these include less well known uses such as reading external phase values and weights or phase distributions encoded in Hendrickson-Lattman coefficients. Typically in SHELXE, initial phases are calculated from experimental data, from a partial model or map, or from a combination of both sources. The initial phase set is improved and extended by density modification and, if the resolution of the data and the type of structure permits, polyalanine tracing. As a feature to systematically eliminate model bias from phases derived from predicted models, the trace can be set to exclude the area occupied by the starting model. The trace now includes an extension into the gamma position or hydrophobic and aromatic side chains if a sequence is provided, which is performed in every tracing cycle. Once a correlation coefficient of over 30% between the structure factors calculated from such a trace and the native data indicates that the structure has been solved, the sequence is docked in all model-building cycles and side chains are fitted if the map supports it. The extensions to the tracing algorithm brought in to provide a complete model are discussed. The improvement in phasing performance is assessed using a set of tests.
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Affiliation(s)
- Isabel Usón
- ICREA, Institució Catalana de Recerca i Estudis Avançats, Passeig Lluís Companys, 23, Barcelona, E-08003, Spain
- Crystallographic Methods, Institute of Molecular Biology of Barcelona (IBMB-CSIC), Barcelona Science Park, Helix Building, Baldiri Reixach, 15, Barcelona, 08028, Spain
| | - George M. Sheldrick
- Department of Structural Chemistry, Georg-August Universität Göttingen, Tammannstrasse 4, 37077 Göttingen, Germany
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Tryptophan depletion results in tryptophan-to-phenylalanine substitutants. Nature 2022; 603:721-727. [PMID: 35264796 PMCID: PMC8942854 DOI: 10.1038/s41586-022-04499-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 02/01/2022] [Indexed: 12/16/2022]
Abstract
Activated T cells secrete interferon-γ, which triggers intracellular tryptophan shortage by upregulating the indoleamine 2,3-dioxygenase 1 (IDO1) enzyme1-4. Here we show that despite tryptophan depletion, in-frame protein synthesis continues across tryptophan codons. We identified tryptophan-to-phenylalanine codon reassignment (W>F) as the major event facilitating this process, and pinpointed tryptophanyl-tRNA synthetase (WARS1) as its source. We call these W>F peptides 'substitutants' to distinguish them from genetically encoded mutants. Using large-scale proteomics analyses, we demonstrate W>F substitutants to be highly abundant in multiple cancer types. W>F substitutants were enriched in tumours relative to matching adjacent normal tissues, and were associated with increased IDO1 expression, oncogenic signalling and the tumour-immune microenvironment. Functionally, W>F substitutants can impair protein activity, but also expand the landscape of antigens presented at the cell surface to activate T cell responses. Thus, substitutants are generated by an alternative decoding mechanism with potential effects on gene function and tumour immunoreactivity.
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Waman VP, Orengo C, Kleywegt GJ, Lesk AM. Three-dimensional Structure Databases of Biological Macromolecules. Methods Mol Biol 2022; 2449:43-91. [PMID: 35507259 DOI: 10.1007/978-1-0716-2095-3_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Databases of three-dimensional structures of proteins (and their associated molecules) provide: (a) Curated repositories of coordinates of experimentally determined structures, including extensive metadata; for instance information about provenance, details about data collection and interpretation, and validation of results. (b) Information-retrieval tools to allow searching to identify entries of interest and provide access to them. (c) Links among databases, especially to databases of amino-acid and genetic sequences, and of protein function; and links to software for analysis of amino-acid sequence and protein structure, and for structure prediction. (d) Collections of predicted three-dimensional structures of proteins. These will become more and more important after the breakthrough in structure prediction achieved by AlphaFold2. The single global archive of experimentally determined biomacromolecular structures is the Protein Data Bank (PDB). It is managed by wwPDB, a consortium of five partner institutions: the Protein Data Bank in Europe (PDBe), the Research Collaboratory for Structural Bioinformatics (RCSB), the Protein Data Bank Japan (PDBj), the BioMagResBank (BMRB), and the Electron Microscopy Data Bank (EMDB). In addition to jointly managing the PDB repository, the individual wwPDB partners offer many tools for analysis of protein and nucleic acid structures and their complexes, including providing computer-graphic representations. Their collective and individual websites serve as hubs of the community of structural biologists, offering newsletters, reports from Task Forces, training courses, and "helpdesks," as well as links to external software.Many specialized projects are based on the information contained in the PDB. Especially important are SCOP, CATH, and ECOD, which present classifications of protein domains.
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Affiliation(s)
- Vaishali P Waman
- Institute of Structural and Molecular Biology, University College London, London, UK
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, London, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Arthur M Lesk
- Department of Biochemistry and Molecular Biology and Center for Computational Biology and Bioinformatics, The Pennsylvania State University, University Park, PA, USA.
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Rademaker D, van Dijk J, Titulaer W, Lange J, Vriend G, Xue L. The Future of Protein Secondary Structure Prediction Was Invented by Oleg Ptitsyn. Biomolecules 2020; 10:biom10060910. [PMID: 32560074 PMCID: PMC7355469 DOI: 10.3390/biom10060910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 06/02/2020] [Indexed: 01/15/2023] Open
Abstract
When Oleg Ptitsyn and his group published the first secondary structure prediction for a protein sequence, they started a research field that is still active today. Oleg Ptitsyn combined fundamental rules of physics with human understanding of protein structures. Most followers in this field, however, use machine learning methods and aim at the highest (average) percentage correctly predicted residues in a set of proteins that were not used to train the prediction method. We show that one single method is unlikely to predict the secondary structure of all protein sequences, with the exception, perhaps, of future deep learning methods based on very large neural networks, and we suggest that some concepts pioneered by Oleg Ptitsyn and his group in the 70s of the previous century likely are today’s best way forward in the protein secondary structure prediction field.
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Affiliation(s)
- Daniel Rademaker
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, 6525 GA Nijmegen, The Netherlands; (D.R.); (J.v.D.); (W.T.); (G.V.)
| | - Jarek van Dijk
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, 6525 GA Nijmegen, The Netherlands; (D.R.); (J.v.D.); (W.T.); (G.V.)
| | - Willem Titulaer
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, 6525 GA Nijmegen, The Netherlands; (D.R.); (J.v.D.); (W.T.); (G.V.)
| | | | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, 6525 GA Nijmegen, The Netherlands; (D.R.); (J.v.D.); (W.T.); (G.V.)
- Baco Institute of Protein Science (BIPS), Mindoro 5201, Philippines
| | - Li Xue
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, 6525 GA Nijmegen, The Netherlands; (D.R.); (J.v.D.); (W.T.); (G.V.)
- Correspondence:
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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Lange J, Baakman C, Pistorius A, Krieger E, Hooft R, Joosten RP, Vriend G. Facilities that make the PDB data collection more powerful. Protein Sci 2019; 29:330-344. [PMID: 31724231 PMCID: PMC6933850 DOI: 10.1002/pro.3788] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 11/11/2019] [Indexed: 01/13/2023]
Abstract
We describe a series of databases and tools that directly or indirectly support biomedical research on macromolecules, with focus on their applicability in protein structure bioinformatics research. DSSP, that determines secondary structures of proteins, has been updated to work well with extremely large structures in multiple formats. The PDBREPORT database that lists anomalies in protein structures has been remade to remove many small problems. These reports are now available as PDF-formatted files with a computer-readable summary. The VASE software has been added to analyze and visualize HSSP multiple sequence alignments for protein structures. The Lists collection of databases has been extended with a series of databases, most noticeably with a database that gives each protein structure a grade for usefulness in protein structure bioinformatics projects. The PDB-REDO collection of reanalyzed and re-refined protein structures that were solved by X-ray crystallography has been improved by dealing better with sugar residues and with hydrogen bonds, and adding many missing surface loops. All academic software underlying these protein structure bioinformatics applications and databases are now publicly accessible, either directly from the authors or from the GitHub software repository.
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Affiliation(s)
- Joanna Lange
- Bio-Prodict, Nijmegen, The Netherlands.,Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, Nijmegen, The Netherlands
| | - Coos Baakman
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, Nijmegen, The Netherlands
| | - Arthur Pistorius
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, Nijmegen, The Netherlands
| | - Elmar Krieger
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, Nijmegen, The Netherlands
| | - Rob Hooft
- Department of Computer Science, Dutch Techcentre for Life Sciences (DTL), Amsterdam, The Netherlands.,Department of Computer Science, Vrije Universiteit Amsterdam (VU), Amsterdam, The Netherlands
| | - Robbie P Joosten
- Biochemistry department, Netherlands Cancer Institute (NKI), Amsterdam, The Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc, Nijmegen, The Netherlands.,Baco Institute of Protein Science (BIPS), Mindoro, Philippines
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