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Pro-/Seminar SS 2008: Current Topics in Bioinformatics

General Information

Lecturer: Prof. Dr. Volkhard Helms

Tutors: Mazen Ahmad, Susanne Eyrisch, Sikander Hayat, Barbara Hutter, Peter Walter

Time: Wednesday, October 15 till Friday, October 17; 01:30 pm, building B2 1, room 312

Deadline for submitting the first draft of the presentation to the tutor: Wednesday, October 1

Proseminar: Vorkenntnisse entsprechend dem 4. Studiensemester
Seminar: Vorkenntnisse entsprechend dem Umfang des Bachelorstudiums/Knowledge corresponding to the bachelor course


Preliminary discussion and placement of the topics: Monday, May 19, 4:30 pm, building C7 1, room 1.08 

Condition for certification: successful presentation, regular (≥ 75 %) participation.       

Maximum number of participants: 12

study regulations 2006: 5 (proseminar) or 7 (seminar)
study regulations 2004: 5 (proseminar) or 8 (seminar)
study regulations 2001: 9 (proseminar and seminar)




  1. DNA Sequence Analysis Tools: Alignment of protein-coding DNA sequences

    For efficient multiple alignment of protein-coding DNA sequences, transAlign makes use of translated amino acid sequences. Describe the method and apply transAlign on a set of given DNA sequences. Compare the results to those of a ClustalW DNA sequence alignment.
    Publication: Bininda-Emonds, O. (2005): "transAlign: using amino acids to faciliate the multiple alignment of protein-coding DNA sequences." BMC Bioinformatics 6:156.
    (Paper URL:
    The transAlign Perl script is available at

  2. DNA Sequence Analysis Tools: Identification of CpG islands (Proseminar talk by Carsten Ehrler)

    CpG islands are frequently found at the transcriptional start sites of vertebrate genes. Describe the approach of the CpGcluster method in comparison to traditional methods. Investigate the effect of different parameter thresholds for a set of given DNA sequences.
    Publication: Hackenberg, M. et al. (2006): "CpGcluster: a distance-based algorithm for CpG-island detection." BMC Bioinformatics 7:446
    (Paper URL:
    The application can be found at

  3. DNA Sequence Analysis Tools: Multiple aligments of long DNA sequences

    As dynamic programming is unsuited for the alignment of long DNA sequences, special algorithms have been developed for this task. Describe the approach of the LAGAN and Multi-LAGAN tools and illustrate their application on a set of given DNA sequences.
    Publication: Brudno, M, et al. (2003): "LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomc DNA." Genome Research 13:721
    (Paper URL:
    The application can be found at 

  4. Longest common subsequence
    Given two protein sequences, find out the longest common subsequence.
    - The student will be provided with the LCS algorithm based on dynamic
    programming and is expected to make a program in python that can take
    2 sequences as input and output the LCS along with the matrix generated.

    Scientific backgroud:
    LCS algorithm has been used for multiple structural aligment and has
    also been extensively used in protein threading, where an amino acid
    sequence is fitted onto a 3D structure.
    1. (main paper) RNA multiple structural alignment with longest common
    subsequences, S Bereg, M Kubica, T Wale?, B Zhu - Journal of
    Combinatorial Optimization, 2007

    2. On the approximation of protein threading, T Akutsu, S Miyano -
    Theoretical Computer Science, 1999

  5. Analysis of shortest pathways (Proseminar talk by Aaron Weimann)
    Greedy Algorithm and A* for finding out the shortest path between two cities
    - What is the shortest path between two "given" cities. The distances
    between the cities
    and the number of cities is provided beforehand.
    -Based on dynamic programming, implement A* and greedy approach in python.

    Scientific backgroud:
    Both dynamic programming and A* heuristic search algorithms for
    optimal sequence alignment are discussed and evaluated. Presented here
    are two new algorithms for optimal pairwise sequence alignment which
    outperform traditional methods on very large problem instances
    (hundreds of thousands of characters, for example). The technique
    combines the benefits of dynamic programming and A* heuristic search,
    with a minimal amount of additional overhead.
    1. A fast pruning algorithm for optimal sequence alignment, A. Davidson - Bioinformatics and Bioengineering Conference, 2001.
  6. Are conformational changes induced by ligand binding related to normal
    Your task:
    Perform a normal mode analysis with GROMACS ( on the
    unbound structures of the test systems. Test whether the conformational
    changes in the bound structures can be described by deformations along the
    normal modes.
    Test systems: beta-Lactamase (unbound: 1btl, bound: 1pzp), LFA-1 (unbound:
    1zon, bound: 1xdg), and XIAP (unbound: 1f9x, bound: 1tfq)
    - Ma, J. Usefulness and Limitations of Normal Mode Analysis in Modeling
    Dynamics of Biomolecular Complexes. Structure (2005), 13:373-380.
    - Tama F., Sanejouand Y.-H. Conformational change of proteins arising
    from normal mode calculations. Protein Engineering (2001), 14, 1-6.
  7. How druggable are ligand binding pockets? (Proseminar)
    Your task:
    Use BALLPass (will be provided) to calculate the druggability
    of the binding pockets in the refined set of the PDBBind database. Is
    there a correlation between the druggability of a pocket and the
    experimental binding data?
    - Hajduk, P. J.; Huth, J. R.; Fesik, S. W. Druggability Indices for
    Protein Targets Derived from NMR-Based Screening Data. J. Med. Chem.
    (2005), 48, 2518-2525.
    - Wang, R., Fang, X., Lu, Y., Yang, C.-Y., Wang, S. The PDBbind
    Database: Collection of binding affinities for protein-ligand complexes
    with known three-dimensional structures. J. Med. Chem. (2004), 47,
    - Wang, R., Fang, X., Lu, Y., Yang, C.-Y., Wang, S. The PDBbind
    database: Methodologies and updates. J. Med. Chem. (2005), 48, 4111-4119.
  8. The importance of protein flexibility in protein-ligand docking (Seminar talk by Jennifer Metzger)
    Your task:
    Show that accounting for protein flexibility is crucial in protein-ligand
    docking when the ligand binds to the protein surface instead of a deep
    binding pocket by using two test systems:
    streptavidin (unbound: 1swb, complexed with biotin: 1mk5) and MDM2
    (unbound: 1z1m, complexed with DIZ: 1t4e). Dock the ligands into the bound
    protein structures (re-docking), into the unbound protein structures
    (apo-docking), and into snapshots extracted from molecular dynamics
    simulations of MDM2 (will be provided) with AutoDock3 and AutoDock4
    ( Can the docking results be improved when
    defining some protein residues as flexible? (As a Seminar topic: Try to
    improve the docking results by changing the docking parameters.)
    - Eyrisch, S., Helms, V. Transient Pockets on Protein Surfaces Involved
    in Protein-Protein Interaction. J. Med. Chem. (2007), 50, 3457-3464.
    - Morris, G. M., Goodsell, et al. Automated Docking Using a Lamarckian
    Genetic Algorithm and and Empirical Binding Free Energy Function. J.
    Computational Chemistry (1998), 19, 1639-1662.
    - Huey, R., Morris, G. M., Olson, A. J. and Goodsell, D. S. A
    Semiempirical Free Energy Force Field with Charge-Based Desolvation J.
    Computational Chemistr (2007), 28, 1145-1152.
  9. Peptide segments in protein-protein interfaces (Proseminar talk by Johannes Trumm)
    Paper: Pal A, Chakrabarti P, Bahadur R , Rodier F and Janin J 2006 Peptide segments in protein-protein interfaces; J. Biosci. 32 101–111
    Understanding protein association is an important component of functional genomics. One of the features allowing a charcterization of an interaction is the number and size of interface stretches. It was found that there are differences between specific and non-specific interactions.
    - given a set of protein complexes, define the interface segments with tools such as VMD
    - do analysis (are there correlations between groups of complexes and their interfaces stretches...)
  10. A new, structurally nonredundant, diverse data set of protein-protein interfaces and its implications
    Paper: O. Keskin, C.J. Tsai, H. Wolfson, and R. Nussinov. A new, structurally nonredundant, diverse data set of protein-protein interfaces and its implications. Protein Science, 13(4):1043, 2004.
    A diverse, structurally nonredundant data set of two-chain protein-protein interfaces was compiled from RCSB. Then it was clustered into several groups. Analysis of these groups showed that structurally similar interface don't share necessarily the same function.
    - perform structural alignment for a set of interfaces with Multiprot
    - compare the interface regions with each other (common physicochemical features...)
  11. A Dissection of Specific and Non-specific Protein–Protein Interfaces
    Paper: R. Prasad Bahadur, P. Chakrabarti, F. Rodier, and J. Janin. A Dissection of Specific and Non-specific Protein–Protein Interfaces. Journal of Molecular Biology, 336(4):943–955, 2004.
    Protein complexes derived from crystallography may exhibit biological interfaces or crystal contacts. For understanding the biologically relevant protein-protein associations it is important to distinguish between these types of interactions. In this paper geometric and physical-chemical properties of both types were compared with each other.
    - for a given set of biological and non-biological interfaces calculate physicochemical features (such as aminoacid composition, interface surface...)
    - compare properties between these interface types
  12. Prediction of SH3 domain binding motifs by using computational alanine scanning (Seminar talk by Siba Ismael)
    Hou, T.J.; Chen, K.; McLaughlin, W.A.; Lu, B. Z.; Wang, W.; Computational Analysis and prediction of the binding motifs and protein interacting partners of the ABI SH3 domain. Plos Computational Biology 2006, 2 (1), 46-55.
    Massova, I.; Kollman, P. A., Computational alanine scanning to probe protein-protein interactions: A novel approach to evaluate binding free energies. Journal of the American Chemical Society 1999, 121 (36), 8133-8143.




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