Hauptseminar SS 2011

Protein-Ligand Networks

Dr. Andrea Schafferhans

Proteins and ligands build a network of interacting molecules. With high-throuput methods it now becomes possible to map and analyse this interaction network and to exploit the results of such analyses. This talk should give an introduction to protein-ligand interaction mapping and explain one or more examples of exploiting the results.

Literature:

• Keiser,M.J. et al. (2009) Predicting new molecular targets for known drugs. Nature, 462.http://www.nature.com/doifinder/10.1038/nature08506
• Keiser,M.J. et al. (2007) Relating protein pharmacology by ligand chemistry. Nature Biotechnology, 25, 197-206.http://www.ncbi.nlm.nih.gov/pubmed/17287757
• Adams,J.C. et al. (2009) A mapping of drug space from the viewpoint of small molecule metabolism. PLoS Computational Biology, 5, e1000474.http://www.ncbi.nlm.nih.gov/pubmed/19701464

Protein design and engineering

Dr. Marc Offman

Proteins are central to most biological processes and their spectrum of functions is seemingly endless. Given that proteins are found in almost any living forms and each organism had to adapt to evolutionary pressure over million of years, a large number of different proteins have evolved. Some of these proteins could potentially be used as drugs, others need to be adapted (engineered), and for some purposes new proteins need to be designed. In protein engineering/design either known proteins are adapted in order to meet certain criteria such as increased stability, function, activity and recognition, or novel protein folds are created. Given the fact that proteins are large, complicated molecules with a huge number of degrees of freedom, protein engineering seems to be an unsolvable task. Nevertheless, methods are under constant development and show some success, as engineered proteins can already be used as therapeutics and as tools for cell biology.

Reference

• Lippow, A. M. and Tidor, B. (2007). Progress in computational protein design. Curr Opin Biotechnol, vol. 18 (4) pp. 305-311.
  http://www.ncbi.nlm.nih.gov/pubmed/17644370

• Kuhlman, B. et al. (2003). Design of a novel globular protein fold with atomic-level accuracy. Science, vol 302 (5649) pp. 1364-1368.
  http://www.sciencemag.org/content/302/5649/1364.full.pdf

• Rothlisberger D, Khersonsky O, Wollacott AM, et al. (2008). Kemp elimination catalysts by computational enzyme design. Nature. Vol 453(7192)pp. 190-195.
  http://www.ncbi.nlm.nih.gov/pubmed/18354394?dopt=AbstractPlus

• Jiang L, Althoff EA, Clemente FR, et al. (2008). De novo computational design of retro-aldol enzymes. Science, vol 319(5868), pp. 1387-1391.
  http://www.ncbi.nlm.nih.gov/pubmed/18323453?dopt=AbstractPlus

   

   

   

Network-based Protein Complex Prediction

Tobias Hamp

Having started as the grand goal of many experimental and computational efforts, protein-protein interaction networks are increasingly being viewed as only building blocks in the establishment of a more sophisticated model of the cellular machinery. A major step in this process is the addition of spatial and temporal information, for example by the detection of protein complexes. This seminar is supposed to give an introduction to the whole field and present a state-of-the-art method to computationally predict protein complexes by using protein interaction networks.

Literature:

• Pieroni, E., et al. (2008), Protein networking: insights into global functional organization of proteomes. Proteomics, 8: 799–816. http://www.ncbi.nlm.nih.gov/pubmed/18297653
• Qi et al. (2008), Protein complex identification by supervised graph local clustering. Bioinformatics, 24(13), i250-i268. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2718642/
• Qiu J, Noble WS. (2008) Predicting Co-Complexed Protein Pairs from Heterogeneous Data. PLoS Comput Biol 4(4). http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2275314/

Prediction of functional effects of non-synonymous SNPs

Dr. Shaila Roessle

Single Nucleotide Polymorphisms (SNPs) represent a very large portion of all genetic variations. SNPs found in the coding regions of genes are often non-synonymous, changing a single amino acid in the encoded protein sequence. SNPs are either "neutral" in the sense that the resulting point-mutated protein is not functionally discernible from the wild-type, or they are "non-neutral" in that the mutant and wild-type differ in function. The ability to identify non-neutral substitutions in an ocean of SNPs could significantly aid targeting disease causing detrimental mutations, as well as SNPs that increase the fitness of particular phenotypes.

There are methods based on physical and comparative considerations that estimate the impact of an amino acid replacement on the three-dimensional structure and function of the protein.

Literature:

• A method and server for predicting damaging missense mutations: Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. Nat Methods 7(4):248-249 (2010)
• SNAP: predict effect of non-synonymous polymorphisms on function. ?Yana Bromberg and Burkhard Rost ?Nucleic Acids Research, 2007, Vol. 35, No. 11 3823-3835 (PubMed)

Disease Networks

Dr. Arthur Dong

Molecular studies of diseases have traditionally focused on single genes (so called monogenic diseases). However, most common diseases are surprisingly complex, involving the interplay of multiple genes and proteins. The increasing availability of genome-scale data and the rise of systems biology ushered in a new era of network-based disease studies.

Literature:

• The human disease network.PNAS 2007 May 22;104(21):8685-90

   

• Network-based classification of breast cancer metastasis. Mol Syst Biol. 3:140 (2007)

Computational Gene Prediction

Dr. Edda Kloppmann

Identifying the relatively small stretches of DNA that are biologically functional is the first step in the analysis of genome sequences. Of interest are so-called non-coding RNA genes and regulatory sequences. However the main objective is often the characterization of protein-coding genes. This seminar should give an introduction to computational gene prediction in complete genomes. The talk should focus on eukaryotic genomes, especially the human genome.

Literature:

• C. Burge and S. Karlin. Prediction of Complete Gene Structures in Human Genomic DNA. J. Mol. Biol. (1997) 268: 78-94

• I. Korf. Gene finding in novel genomes. BMC Bioinformatics (2004) 5:59

Amino acid substitution matrices - PAM and BLOSUM

Dipl.-Bioinf. Christian Schaefer

Amino acid scoring matrices are at the heart of today's molecular biology and bioinformatics. One of their most important area of application is to be found in the wide field of biological sequence alignment. In this seminar two popular concepts should be presented and discussed: PAM and BLOSUM.

Literature:

• D. Gusfield: Algorithms on strings, trees and sequences. Chpts. 15.7-10. Cambridge university press.

• M.O. Dayhoff et al. A model of evolutionay change in proteins. Atlas of Protein Science and Structure, 5:345-52, 1978
• S. Henikoff et al. Automated assembly of protein blocks for database searching. Nucl. Acids Res., 19:6565-72, 1991
• S. Henikoff et al. Amino acid substitution matrices from protein blocks. Proc. Natl. Academy Science, 89:10,915-19, 1992

Automated prediction method assessment practices in bioinformatics

Present an overview of the current state of continuous automated performance assessment solutions in bioinformatics.

Assorted references:

• Bujnicki, J. M.; Elofsson, A.; Fischer, D.; Rychlewski, L. LiveBench-1: continuous benchmarking of protein structure prediction servers. Journal: Protein Sci 2001 Volume: 10 Number: 2 Pages: 352--61 /and similar/
• Eyrich, V. A.; Marti-Renom, M. A.; Przybylski, D.; Madhusudhan, M. S.; Fiser, A.; Pazos, F.; Valencia, A.; Sali, A.; Rost, B. EVA: continuous automatic evaluation of protein structure prediction servers. Bioinformatics 2001 Volume: 17 Number: 12 Pages: 1242-3
• and recent publications you find

Predicting regions with no regular structure (NORS)

Esmeralda Vicedo

One common definiton of regions of “disorder” in proteins is that they do not adopt a regular three-dimensional (3D)structure in   isolation  on their own. These disordered regions are in contrast to regions that are well structured or “ordered”. Notably, there is a great variety of “flavors” of disorder: some adopt a unique regular 3D secondary structure only upon binding; others, for example loops, remain irregular; some proteins are almost entirely disordered and others have only short disordered region. Numerous computational methods exist that predict disorder based on a variety of concepts. One of these methods, Norsnet has been developed in our group. Norsnet uses a neural network to predict disordered regions of the “loopy” type (unstructured loops), important regions for network complexity.

Literature:

• Schlessinger A, Liu J. & Rost B. (2007) ;Natively Unstructured Loops Differ from Other Loops; PLoS Comput Biol 3(7) - Jinfeng Liu, Hepan Tan & Burkhard Rost; Loopy proteins appear conserved in evolution;J Mol Biol, 2002, 332(1): 53-64 (http://www.rostlab.org/papers/2002_nors/paper.pdf )
• Radlvojac P., Iakoucheva L.M., Oldfield C.J., Obradovic Z., Uversky V.N. and Dunker A.K.(2007) Intrinsic disorder and functional proteomics, Biophzs J. 92(5) 1439-56.
• Radivojac P, Iakoucheva LM, Oldfield CJ, Obradovic Z, Uversky VN, Dunker AK; Intrinsic disorder and functional proteomics; Biophys J. 2007 Mar 1; 92(5):1429-46