A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)

. 2020 ; 9 () : . [epub] 20200422

Jazyk angličtina Země Anglie, Velká Británie Médium electronic-ecollection

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid32566135

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

Bijvoet Center Faculty of Science Chemistry Utrecht University Utrecht 3584CH The Netherlands

Bioinformatics center Department of Biology University of Copenhagen Copenhagen DK 2200 Denmark

Department of Biology University of Rome Tor Vergata Rome 1 00133 Italy

Department of Oncology Lausanne University Swiss Institute of Bioinformatics Lausanne Switzerland

Department of Structural Biology Weizmann Institute of Science Rehovot 76100 Israel

Dept Computer Science Center for Integrative Bioinformatics VU Vrije Universiteit Amsterdam 1081 HV The Netherlands

ELIXIR Hub ELIXIR Hinxton UK

European Molecular Biology Laboratory European Bioinformatics Institute Hinxton CB10 1SD UK

FBMM Biocenter Oulu University of Oulu Oulu Finland

Institute for Research in Biomedicine The Barcelona Institute of Science and Technology Barcelona 08028 Spain

Institute of Biotechnology of the Czech Academy of Sciences Vestec CZ 25250 Czech Republic

Instituto de Tecnologia Química e Biológica Antonio Xavier Universidade Nova de Lisboa Lisbon Portugal

Luxembourg Centre for Systems Biomedicine University of Luxembourg Belvaux L 4367 Luxembourg

Membrane Bioinformatics Research Group Institute of Enzymology Budapest H 1117 Hungary

Netherlands Cancer Institute and Oncode Institute Utrecht The Netherlands

Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Hinxton CB10 1SD UK

Ressource Parisienne en Bioinformatique Structurale Université de Paris Paris F 75205 France

Science for Life Laboratory Stockholm University Solna S 17121 Sweden

Structural and Molecular Biology Department University College London UK

Structural Bioinformatics Laboratory Åbo Akademi University Turku FI 20500 Finland

VIB VUB Center for Structural Biology Brussels Belgium

ZBH Center for Bioinformatics Universität Hamburg Hamburg D 20146 Germany

Zobrazit více v PubMed

wwPDB consortium: Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 2019;47(D1):D520–D528. 10.1093/nar/gky949 PubMed DOI PMC

Laskowski RA, MacArthur MW, Moss DS, et al. : PROCHECK: a program to check the stereochemical quality of protein structures. J App Cryst. 1993;26:283–291. 10.1107/S0021889892009944 DOI

Vaguine AA, Richelle J, Wodak SJ: SFCHECK: a unified set of procedures for evaluating the quality of macromolecular structure-factor data and their agreement with the atomic model. Acta Crystallogr D Biol Crystallogr. 1999;55(Pt 1):191–205. 10.1107/S0907444998006684 PubMed DOI

Todd AE, Orengo CA, Thornton JM: Evolution of function in protein superfamilies, from a structural perspective. J Mol Biol. 2001;307(4):1113–43. 10.1006/jmbi.2001.4513 PubMed DOI

Murzin AG, Brenner SE, Hubbard T, et al. : SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol. 1995;247(4):536–40. 10.1016/S0022-2836(05)80134-2 PubMed DOI

Orengo CA, Michie AD, Jones S, et al. : CATH--a hierarchic classification of protein domain structures. Structure. 1997;5(8):1093–108. 10.1016/s0969-2126(97)00260-8 PubMed DOI

Sali A, Blundell TL: Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol. 1993;234(3):779–815. 10.1006/jmbi.1993.1626 PubMed DOI

Peitsch MC: ProMod and Swiss-Model: Internet-based tools for automated comparative protein modelling. Biochem Soc Trans. 1996;24(1):274–9. 10.1042/bst0240274 PubMed DOI

Jones DT, Taylor WR, Thornton JM: A new approach to protein fold recognition. Nature. 1992;358(6381):86–9. 10.1038/358086a0 PubMed DOI

Janin J, Bonvin AM: Protein-protein interactions. Curr Opin Struct Biol. 2013;23(6):859–61. 10.1016/j.sbi.2013.10.003 PubMed DOI

Lensink MF, Méndez R, Wodak SJ: Docking and scoring protein complexes: CAPRI 3rd Edition. Proteins. 2007;69(4):704–18. 10.1002/prot.21804 PubMed DOI

Wodak SJ, Janin J: Structural basis of macromolecular recognition. Adv Protein Chem. 2002;61:9–73. 10.1016/s0065-3233(02)61001-0 PubMed DOI

Rodrigues JP, Bonvin AM: Integrative computational modeling of protein interactions. FEBS J. 2014;281(8):1988–2003. 10.1111/febs.12771 PubMed DOI

Miao Z, Westhof E: RNA Structure: Advances and Assessment of 3D Structure Prediction. Annu Rev Biophys. 2017;46:483–503. 10.1146/annurev-biophys-070816-034125 PubMed DOI

Lorenz R, Bernhart SH, Höner zu Siederdissen C, et al. : ViennaRNA Package 2.0. Algorithms Mol Biol. 2011;6:26. 10.1186/1748-7188-6-26 PubMed DOI PMC

Cruz JA, Blanchet MF, Boniecki M, et al. : RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. RNA. 2012;18(4):610–25. 10.1261/rna.031054.111 PubMed DOI PMC

Miao Z, Adamiak RW, Blanchet MF, et al. : RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures. RNA. 2015;21(6):1066–84. 10.1261/rna.049502.114 PubMed DOI PMC

Miao Z, Adamiak RW, Antczak M, et al. : RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme. RNA. 2017;23(5):655–672. 10.1261/rna.060368.116 PubMed DOI PMC

Śledź P, Caflisch A: Protein structure-based drug design: from docking to molecular dynamics. Curr Opin Struct Biol. 2018;48:93–102. 10.1016/j.sbi.2017.10.010 PubMed DOI

Gioia D, Bertazzo M, Recanatini M, et al. : Dynamic Docking: A Paradigm Shift in Computational Drug Discovery. Molecules. 2017;22(11): pii: E2029. 10.3390/molecules22112029 PubMed DOI PMC

Rachman MM, Barril X, Hubbard RE: Predicting how drug molecules bind to their protein targets. Curr Opin Pharmacol. 2018;42:34–39. 10.1016/j.coph.2018.07.001 PubMed DOI

Van Gunsteren WF, Berendsen HJ: Molecular dynamics: perspective for complex systems. Biochem Soc Trans. 1982;10(5):301–5. 10.1042/bst0100301 PubMed DOI

Vreede J, Juraszek J, Bolhuis PG: Predicting the reaction coordinates of millisecond light-induced conformational changes in photoactive yellow protein. Proc Natl Acad Sci U S A. 2010;107(6):2397–402. 10.1073/pnas.0908754107 PubMed DOI PMC

Chodera JD, Noé F: Markov state models of biomolecular conformational dynamics. Curr Opin Struct Biol. 2014;25:135–44. 10.1016/j.sbi.2014.04.002 PubMed DOI PMC

Chothia C, Lesk AM: Canonical structures for the hypervariable regions of immunoglobulins. J Mol Biol. 1987;196(4):901–17. 10.1016/0022-2836(87)90412-8 PubMed DOI

Chothia C, Lesk AM, Levitt M, et al. : The predicted structure of immunoglobulin D1.3 and its comparison with the crystal structure. Science. 1986;233(4765):755–8. 10.1126/science.3090684 PubMed DOI

Moult J, Fidelis K, Kryshtafovych A, et al. : Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins. 2018;86(Suppl 1):7–15. 10.1002/prot.25415 PubMed DOI PMC

Haas J, Barbato A, Behringer D, et al. : Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12. Proteins. 2018;86(Suppl 1):387–398. 10.1002/prot.25431 PubMed DOI PMC

Lensink MF, Wodak SJ: Docking, scoring, and affinity prediction in CAPRI. Proteins. 2013;81(12):2082–95. 10.1002/prot.24428 PubMed DOI

Lensink MF, Velankar S, Wodak SJ: Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition. Proteins. 2017;85(3):359–377. 10.1002/prot.25215 PubMed DOI

Waterhouse A, Bertoni M, Bienert S, et al. : SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46(W1):W296–W303. 10.1093/nar/gky427 PubMed DOI PMC

Kelley LA, Mezulis S, Yates CM, et al. : The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015;10(6):845–58. 10.1038/nprot.2015.053 PubMed DOI PMC

McGuffin LJ, Street SA, Bryson K, et al. : The Genomic Threading Database: a comprehensive resource for structural annotations of the genomes from key organisms. Nucleic Acids Res. 2004;32(Database issue):D196–9. 10.1093/nar/gkh043 PubMed DOI PMC

Shi J, Blundell TL, Mizuguchi K: FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. J Mol Biol. 2001;310(1):243–57. 10.1006/jmbi.2001.4762 PubMed DOI

Pandurangan AP, Stahlhacke J, Oates ME, et al. : The SUPERFAMILY 2.0 database: a significant proteome update and a new webserver. Nucleic Acids Res. 2019;47(D1):D490–D494. 10.1093/nar/gky1130 PubMed DOI PMC

Lewis TE, Sillitoe I, Dawson N, et al. : Gene3D: Extensive prediction of globular domains in proteins. Nucleic Acids Res. 2018;46(D1):D435–D439. 10.1093/nar/gkx1069 PubMed DOI PMC

Mir S, Alhroub Y, Anyango S, et al. : PDBe: towards reusable data delivery infrastructure at protein data bank in Europe. Nucleic Acids Res. 2018;46(D1):D486–D492. 10.1093/nar/gkx1070 PubMed DOI PMC

Mitchell AL, Attwood TK, Babbitt PC, et al. : InterPro in 2019: improving coverage, classification and access to protein sequence annotations. Nucleic Acids Res. 2019;47(D1):D351–D360. 10.1093/nar/gky1100 PubMed DOI PMC

Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. : The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3: 160018. 10.1038/sdata.2016.18 PubMed DOI PMC

Lewis TE, Sillitoe I, Andreeva A, et al. : Genome3D: exploiting structure to help users understand their sequences. Nucleic Acids Res. 2015;43(Database issue):D382–6. 10.1093/nar/gku973 PubMed DOI PMC

Pieper U, Webb BM, Dong GQ, et al. : ModBase, a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res. 2014;42(Database issue):D336–46. 10.1093/nar/gkt1144 PubMed DOI PMC

Ovchinnikov S, Park H, Kim DE, et al. : Protein structure prediction using Rosetta in CASP12. Proteins. 2018;86(Suppl 1):113–121. 10.1002/prot.25390 PubMed DOI PMC

Hwang H, Vreven T, Janin J, et al. : Protein-protein docking benchmark version 4.0. Proteins. 2010;78(15):3111–4. 10.1002/prot.22830 PubMed DOI PMC

Pérez-Cano L, Jiménez-García B, Fernández-Recio J: A protein-RNA docking benchmark (II): extended set from experimental and homology modeling data. Proteins. 2012;80(7):1872–82. 10.1002/prot.24075 PubMed DOI

Vreven T, Moal IH, Vangone A, et al. : Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2. J Mol Biol. 2015;427(19):3031–41. 10.1016/j.jmb.2015.07.016 PubMed DOI PMC

Kastritis PL, Moal IH, Hwang H, et al. : A structure-based benchmark for protein-protein binding affinity. Protein Sci. 2011;20(3):482–91. 10.1002/pro.580 PubMed DOI PMC

Xue LC, Rodrigues JP, Kastritis PL, et al. : PRODIGY: a web server for predicting the binding affinity of protein-protein complexes. Bioinformatics. 2016;32(23):3676–3678. 10.1093/bioinformatics/btw514 PubMed DOI

Capella S, Iglesia D, Haas J, et al. : Lessons Learned: Recommendations for Establishing Critical Periodic Scientific Benchmarking. BioRxiv. 2017. 10.1101/181677 DOI

Lensink MF, Wodak SJ: Score_set: a CAPRI benchmark for scoring protein complexes. Proteins. 2014;82(11):3163–9. 10.1002/prot.24678 PubMed DOI

Bertoni M, Aloy P: DynBench3D, a Web-Resource to Dynamically Generate Benchmark Sets of Large Heteromeric Protein Complexes. J Mol Biol. 2018;430(21):4431–4438. 10.1016/j.jmb.2018.09.011 PubMed DOI

Bohnuud T, Luo L, Wodak SJ, et al. : A benchmark testing ground for integrating homology modeling and protein docking. Proteins. 2017;85(1):10–16. 10.1002/prot.25063 PubMed DOI PMC

Prathipati P, Mizuguchi K: Integration of Ligand and Structure Based Approaches for CSAR-2014. J Chem Inf Model. 2016;56(6):974–87. 10.1021/acs.jcim.5b00477 PubMed DOI

Gaieb Z, Liu S, Gathiaka S, et al. : D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies. J Comput Aided Mol Des. 2018;32(1):1–20. 10.1007/s10822-017-0088-4 PubMed DOI PMC

Gaulton A, Hersey A, Nowotka M, et al. : The ChEMBL database in 2017. Nucleic Acids Res. 2017;45(D1):D945–D954. 10.1093/nar/gkw1074 PubMed DOI PMC

https://pubchem.ncbi.nlm.nih.gov/.

Norambuena T, Cares JF, Capriotti E, et al. : WebRASP: a server for computing energy scores to assess the accuracy and stability of RNA 3D structures. Bioinformatics. 2013;29(20):2649–2650. 10.1093/bioinformatics/btt441 PubMed DOI PMC

Flores SC, Sherman MA, Bruns CM, et al. : Fast flexible modeling of RNA structure using internal coordinates. IEEE/ACM Trans Comput Biol Bioinform. 2011;8(5):1247–57. 10.1109/TCBB.2010.104 PubMed DOI PMC

Schneider B, Boǽíková P, Necasova I, et al. : A DNA structural alphabet provides new insight into DNA flexibility. Acta Crystallogr D Struct Biol. 2018;74(Pt 1):52–64. 10.1107/S2059798318000050 PubMed DOI PMC

Černý J, Božíková P, Schneider B: DNATCO: assignment of DNA conformers at dnatco.org. Nucleic Acids Res. 2016;44(W1):W287–W287. 10.1093/nar/gkw381 PubMed DOI PMC

de Beauchene IC, de Vries SJ, Zacharias M: Fragment-based modelling of single stranded RNA bound to RNA recognition motif containing proteins. Nucleic Acids Res. 2016;44(10):4565–80. 10.1093/nar/gkw328 PubMed DOI PMC

Boniecki MJ, Lach G, Dawson WK, et al. : SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res. 2016;44(7):e63. 10.1093/nar/gkv1479 PubMed DOI PMC

Popenda M, Szachniuk M, Antczak M, et al. : Automated 3D structure composition for large RNAs. Nucleic Acids Res. 2012;40(14):e112. 10.1093/nar/gks339 PubMed DOI PMC

Cheng CY, Chou FC, Das R: Modeling complex RNA tertiary folds with Rosetta. Methods Enzymol. 2015;553:35–64. 10.1016/bs.mie.2014.10.051 PubMed DOI

Mattei E, Pietrosanto M, Ferrè F, et al. : Web-Beagle: a web server for the alignment of RNA secondary structures. Nucleic Acids Res. 2015;43(W1):W493–7. 10.1093/nar/gkv489 PubMed DOI PMC

Murshudov GN, Skubák P, Lebede AA, et al. : REFMAC5 for the refinement of macromolecular crystal structures. Acta Crystallogr D Biol Crystallogr. 2011;67(Pt 4):355–367. 10.1107/S0907444911001314 PubMed DOI PMC

Emsley P, Lohkamp B, Scott WG, et al. : Features and development of Coot. Acta Crystallogr D Biol Crystallogr. 2010;66(Pt 4):486–501. 10.1107/S0907444910007493 PubMed DOI PMC

Adams PD, Afonine PV, Bunkóczi G, et al. : PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr D Biol Crystallogr. 2010;66(Pt 2):213–221. 10.1107/S0907444909052925 PubMed DOI PMC

Joosten RP, Long F, Murshudov GN, et al. : The PDB_REDO server for macromolecular structure model optimization. IUCrJ. 2014;1(Pt 4):213–20. 10.1107/S2052252514009324 PubMed DOI PMC

Seibel PN, Krüger J, Hartmeier S, et al. : XML schemas for common bioinformatic data types and their application in workflow systems. BMC Bioinformatics. 2006;7:490. 10.1186/1471-2105-7-490 PubMed DOI PMC

Daina A, Blatter MC, Baillie Gerritsen V, et al. : Drug Design Workshop: A Web-Based Educational Tool To Introduce Computer-Aided Drug Design to the General Public. J Chem Educ. 2017;94(3):335–344. 10.1021/acs.jchemed.6b00596 DOI

Najít záznam

Citační ukazatele

Nahrávání dat ...

    Možnosti archivace