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The course introduces structural bioinformatics – a multifields discipline which utilizes biomolecular structural data obtained by a range of experimental methods – X-Ray, NMR and electron microscopy. High numbers of structures in structural databases (ca. 130 000 structures in total) allow the application of computational and statistical methods to extract principles which control the folding process. These principles are used in structure prediction methods or as a basis for structural modelling in combination with physico-chemical rules. Students will get knowledge of basic principles used in structural bioinformatics, statistical methods, and available tools for structural analysis and prediction.
Last update: Svozil Daniel (26.01.2018)
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Students will know: Basic principles of structure classification as well as physico-chemical principles determining spatial structures of proteins, DNA and RNA. Priciples and algorithms used for prediction of physico-chemical properties of biomolecules based on primary sequence knowledge. Background and principles of algorithms used for secondary structure prediction of proteins and RNA, coparison of their accuracy. Practical application of methods for tertiary structure prediction of biomolecules including homology modelling, threading, and ab initio methods. Orientation in primary resources of data (structural databases) as well as in knowledge-based databases. Description of intermolecular complexes, their statistical foundation and computational methods for intermolecular docking. Last update: Svozil Daniel (26.01.2018)
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The exam consists of written and oral parts. Last update: Svozil Daniel (26.01.2018)
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R: Sokol A., Ab initio predikce struktury membránových proteinů, PřF UK, Bc. thesis, 2016, https://is.cuni.cz/webapps/zzp/detail/143778/ R: Filippi M., Predikce sekundární struktury proteinu pomocí hlubokých neuronových sítí, MFF UK, dipl. thesis, 2017, https://dspace.cuni.cz/handle/20.500.11956/90584 R: Havrila M., Struktura a dynamika RNA, dipl. thesis, MU Brno, 2012, https://is.muni.cz/th/bj1ru/ R: Klímová M., Predikce sekundární struktury RNA sekvencí, dipl. thesis, VUT Brno, 2015, https://www.vutbr.cz/studenti/zav-prace?zp_id=84425 R: Jenny Gu, Phylip Bourne: Structural Bioinformatics, Wiley-Blackwell 2009 A: Thomas Hamelryck, Kanti Mardia, Jesper Ferkinghoff-Borg: Bayesian Methods in Structural Bioinformatics (Statistics for Biology and Health), Springer 2012 A: Stephen Neidle: Principles of Nucleic Acid Structure, Elsevier 2008 A: Andrew D. Bates, Anthony Maxwell, M. Zvelebil, J. Baum: Understanding Bioinformatics, Oxford University Press 2007 A: Christina Marshall: Structural Bioinformatics Handbook, Syrawood Publishing House 2016 Last update: Svozil Daniel (04.11.2018)
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1. Proteins - basic characteristics, structural building blocks 2. Protein Databases - structure and knowledge based 3. Prediction of biomolecular properties based on their sequences 4. Secondary structure prediction of proteins, utilization and methods 5. Protein folding – principles, experimental methods, protein code 6. Tertiary structure prediction of proteins - homology modelling 7. Tertiary sructure prediction of proteins - threading 8. Tertiary structure prediction of proteins – ab initio methods 9. Methods used for prediction and analysis of protein protein interactions 10. Proteins - a case study 11. DNA structure and its quantitative description 12. Basic characteristics of RNA structural motifs 13. Structural databases of nucleic acids 14. Nucleic acids – a case study Last update: Svozil Daniel (26.01.2018)
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Online course materials at http://bioinfo.uochb.cas.cz/teaching.html Training courses: https://www.ebi.ac.uk/training/events/2017/structural-bioinformatics-1 SIB training portal: https://edu.isb-sib.ch Bioinformatics tutorials: http://beckerinfo.net/bioinformatics/bioinformatics-tutorials-2/ Last update: Svozil Daniel (26.01.2018)
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Molecular Biology/Genetics, Biochemistry, Physical Chemistry, Molecular modelling Last update: Svozil Daniel (26.01.2018)
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| Teaching methods | ||||
| Activity | Credits | Hours | ||
| Účast na přednáškách | 1 | 28 | ||
| Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 1 | 28 | ||
| Příprava na zkoušku a její absolvování | 1 | 28 | ||
| 3 / 3 | 84 / 84 | |||