Seminar 'Proteins and Disease' WS17/18

Type  Seminar (2 SWS)
ECTS 4.0
Lecturer Burkhard Rost et al.
Time Monday, 12:00 - 13:30
Room MI 01.09.034
Language English

Application / Registration

Application is organised centrally for all bioinformatics seminars. After you have been assigned to our seminar, we will distribute the topics.

Content

Topics related to the research interests of the group: protein sequence analysis, sequence based predictions, protein structure prediction and analysis; interaction networks.

Pre-meeting

The Pre-meeting will be held on Jul 25th  at 2 p.m.  in Room MI 01.09.034

The rules and hints for preparation of the seminar discussed  in the pre-meeting are also summarised in our Checklist and on these slides (updated Jul 25th).


 

Final Schedule

The order is preliminary and will be adjusted soon. All talks will talk place during the lecture period. The timing will be announced soon.

Date Topic Supervisor Student
23.10 Biological Databases Bernhofer Resch
6.11 Transmembrane Proteins/PolyPhobius Bernhofer Njah
13.11 Protein structure prediction using evolutionary couplings (EVcouplings) Schelling Scheibenreif
20.11 Protein localization prediction from evolutionary profiles Schelling Hoffmann
27.11 Protein disorder — a breakthrough invention of evolution? Heinzinger Scheibling
4.12 Mass-spectrometry-based draft of the human proteome Heinzinger Weiß
18.12 Enrichment analysis in ChIP-Seq Heinzinger Ramakrishnan
8.1 Robustness and evolvability of proteins Richter Lutz
15.1 Predicting functional effects of sequence variants Richter Santus
-changed-22.1
CRISPR/Cas
 Reeb Schröder
-changed- 29.1 Single Cell Sequencing Reeb Mayer

 

 

 

Description of Topics

 

Biological Databases

Michael Bernhofer

Huge volumes of primary data are archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories. This seminar shall give an overview of different Databases, how to access them and problems associated.

  • Arthur M. Lesk. Introduction to bioinformatics (Third Edition) Oxford University Press

 

Protein structure prediction using evolutionary couplings (EVcouplings)

Maria Schelling

The evolution of a protein sequence is constrained by the function of the protein, and interactions between residues lead to pairwise evolutionary constraints. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Yet, a major challenge is to distinguish true residue coevolution from the noisy set of observed correlations. This talk should outline the concept of correlated mutation analysis to infer evolutionary constraints. Starting from the limitations of local statistical models, it should introduce the global maximum entropy model by Marks et al., and show how this model can be used to compute protein 3D structures from sequence alone.

Literature:

 

 

Protein localization prediction from evolutionary profiles

Maria Schelling

Identification of a protein’s subcellular localization is an important step towards elucidating its function. In this seminar, a machine-learning-based methods for predicting localization in prokaryotes and eukaryotes shall be presented. The methods incorporate a hierarchical ontology of subcellular localization classes. The predictions are derived from evolutionary infromation (Loctree2/3) as well as from the powerful sequence homology-based BLAST (Loctree3).

Literature:



Protein disorder — a breakthrough invention of evolution?

Michael Heinzinger

The regions in proteins that do not adopt regular three-dimensional structures in isolation are called disordered regions. In this seminar the functional and structural aspects of disordered proteins shall be discussed. Though only one literature source is provided, the student is expected to use and refer to in his presentation to additional sources for a detailed understanding of protein disorder.

Literature:

  • Schlessinger A, Schaefer C, Vicedo E, Schmidberger M, Punta M, Rost B  (2011). Protein disorder--a breakthrough invention of evolution? Curr Opin Struct Biol. Jun;21(3):412-8 http://www.ncbi.nlm.nih.gov/pubmed/21514145
  • ...

 

Mass-spectrometry-based draft of the human proteome

Michael Heinzinger

Using mass-spectrometry, researchers from TUM have produced an almost complete inventory of the human proteome. This information is now freely available in the ProteomicsDB database, which is a joint development of TUM and software company SAP. The database includes information for example on the types, distribution, and abundance of proteins in various cells and tissues as well as in body fluids. The talk shall briefly introduce mass-spectrometry and then focus on the results of the publication below and the ProteomicsDB.

Literature:

  • Mathias Wilhelm, Judith Schlegl, Hannes Hahne, Amin Moghaddas Gholami, Marcus Lieberenz, Mikhail M. Savitski, Emanuel Ziegler, Lars Butzmann, Siegfried Gessulat, Harald Marx, Toby Mathieson, Simone Lemeer, Karsten Schnatbaum, Ulf Reimer, Holger Wenschuh, Martin Mollenhauer, Julia Slotta-Huspenina, Joos-Hendrik Boese, Marcus Bantscheff, Anja Gerstmair, Franz Faerber & Bernhard Kuster, Mass-spectrometry-based draft of the human proteome; Nature, DOI: 10.1038/nature13319
  • http://www.tum.de/en/about-tum/news/press-releases/short/article/31545/

 

Transmembrane Proteins / PolyPhobius

Michael Bernhofer

PolyPhobius uses hidden markov models (HMMs) to predict transmembrane helices in protein sequences. This talk shall introduce transmembrane proteins, HMMs and sequence-based transmembrane helix prediction at the example of PolyPhobius.

Literature:

  • Alberts
  • Bioinformatics
  • Lukas Käll, Anders Krogh and Erik Sonnhammer. An HMM posterior decoder for sequence feature prediction that includes homology information. Bioinformatics, 21 (Suppl 1):i251-i257, June 2005.
  • Bernsel, A., Viklund, H., Falk, J., Lindahl, E., Von Heijne, G., & Elofsson, A. (2008). Prediction of membrane-protein topology from first principles. Proceedings of the National Academy of Sciences, 105(20), 7177–7181. doi:10.1073/pnas.0711151105

 

Robustness and evolvability of proteins

Lothar Richter

Mutations are the catalysts of evolution. Phenotypes need to be robust against mutation in order to prevail. On the other hand, species need to be able to adapt their phenotypes to changing selection pressure. Therefore, robustness seems to be the opposite of evolvability. This topic is aimed at explaining the complex relationship between robustness and evolvability in proteins in the light of tolerating mutations at certain positions while being sensitive at others. The given literature is merely a starting point for further reading and should not be considered complete.

Literature:

  • Draghi, J.A., et al. (2010) Mutational robustness can facilitate adaptation, Nature, 463, 353-355.
  • Kowarsch, A., et al. (2010) Correlated mutations: a hallmark of phenotypic amino acid substitutions, PLoS Comput Biol, 6.
  • McLaughlin, R.N., Jr., et al. (2012) The spatial architecture of protein function and adaptation, Nature, 491, 138-142.

CRISPR/Cas

Jonas Reeb

Clustered regularly interspaced short palindromic repeat (CRISPR) technology, a microbial defense system, has been developed based on its remarkable ability to bring the endonuclease Cas9 to specific locations within complex genomes by a short RNA, to precisely edit the genome, to build toolkits for synthetic biology, and to monitor DNA in live cells. This seminar is a presentation of the underlying principles and possible applications.

  • http://www.cell.com/nucleus-CRISPR
  • Mali, P., Esvelt, K. M., & Church, G. M. (2013). Cas9 as a versatile tool for engineering biology. Nature Methods, 10(10), 957–963. doi:10.1016/j.biotechadv.2011.08.021.Secreted
  • Sander, J. D., & Joung, J. K. (2014). CRISPR-Cas systems for editing, regulating and targeting genomes. Nature Biotechnology, 32(4), 347–55. doi:10.1038/nbt.2842
  • Hsu, P. D., Lander, E. S., & Zhang, F. (2014). Development and Applications of CRISPR-Cas9 for Genome Engineering. Cell, 157(6), 1262–1278. doi:10.1016/j.cell.2014.05.010

 

Single Cell Sequencing

Jonas Reeb

Whereas genome approaches in many case are extended to meta-genome approaches also an specialization towards the opposite direction exists. Single cell sequencing acknowledge the fact of diversity in tissue and cell populations. This talk will present this new approach.

  • Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015). The Technology and Biology of Single-Cell RNA Sequencing. Molecular Cell, 58(4), 610–620. doi:10.1016/j.molcel.2015.04.005
  • Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., … Stegle, O. (2015). Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nature Biotechnology, 33(2). doi:10.1038/nbt.3102
  • Omics, S. (2015). Computational and analytical challenges in single-cell transcriptomics. Nature Publishing Group, 16(January 2014), 133–145. doi:10.1038/nrg3833

 

Predicting functional effects of sequence variants

Lothar Richter

Elucidating the effects of naturally occurring genetic variation on the wild-type cellular function is one of the major challenges in personalized medicine. This talk shall explain how variant effects can be predicted and how this can help to further our understanding of naturally occuring variation and disease. The given literature is merely a starting point for further reading and should not be considered complete.

Literature:

  • Bromberg, Y., & Rost, B. (2007). SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic acids research, 35(11), 3823-3835.
  • Cline, M. S., & Karchin, R. (2011). Using bioinformatics to predict the functional impact of SNVs. Bioinformatics, 27(4), 441-448.
  • Hecht, M., Bromberg, Y., & Rost, B. (2013). News from the protein mutability landscape. Journal of molecular biology, 425(21), 3937-3948.
  • Bromberg, Y., Kahn, P. C. & Rost, B. (2013). Neutral and weakly nonneutral sequence variants may define individuality. Proc Natl Acad Sci U S A 110, 14255-60.