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Pro-/Seminar WS 09/10: Selected Topics from Membrane Bioinformatics

General Information

Lecturer: Prof. Dr. Volkhard Helms

Tutors: Sikander Hayat, Po-Hsien Lee, Jennifer Metzger, Nadine Schaadt, Christian Spaniol

Dates: March 29 - April 1, 2010, 02:00 pm - 06:30 pm, E2 1, room 007

Place: E2 1, room 007

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, January 11, 2010, 1:00 pm, building E2 1, room 106

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

Maximum number of participants: 17

study regulations 2006: 5 (proseminar) or 7 (seminar)



Identifying transmembrane helices


(a) give an introduction to topologies of helical membrane proteins

(b) give an introduction to Hidden Markov models (HMMs)

(c) present Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J. Mol. Biol. (2001) 305:567–580 and

(2) (a) explain experimental data set on measuring the free energy for TM helix insertion into the lipid bilayer by Hessa, T., et al. (2005) Recognition of transmembrane helices by the endoplasmic reticulum translocon. Nature 433, 377-381 and by Hessa, T., et al.  (2007) Molecular code for transmembrane-helix recognition by the Sec61 translocon. Nature 450, 1026-1030

(b) present Bernsel, A., Viklund, H., Falk, J., Lindahl, E., von Heijne, G. and Elofsson, A. (2008) Prediction of membrane-protein topology from first principles. Proc Natl Acad Sci U S A 105 (20) : 7177-7181 and

(3) (a) present Jones DT, Taylor WR, Thornton JM. (1994) A Model Recognition Approach to the Prediction of All-Helical Membrane Protein Structure and Topology. Biochem. 33: 3038-3049 and

(b) present Jones DT. (2007) Improving the accuracy of transmembrane protein topology prediction using evolutionary information. Bioinformatics. 23: 538-544.

(4) (a) give an introduction to Support Vector Machines and

(b) present Nugent T, Jones DT. (2009) Transmembrane protein topology prediction using support vector machines. BMC Bioinformatics. 10: 159


Topological features of helical membrane proteins


(5) (a) present Viklund, H., Granseth, E. and Elofsson, A. (2006) Structural classification and prediction of reentrant regions in alpha-helical transmembrane proteins: application to complete genomes. J Mol Biol 361 (3) : 591-603.


(b) present Granseth, E., Viklund, H. and Elofsson, A. (2006) ZPRED: predicting the distance to the membrane center for residues in alpha-helical membrane proteins. Bioinformatics 22 (14) : e191-6.


(6) (a) present Fuchs, A., Martin-Galiano, A.J., Kalman, M., Fleishman, S., Ben-Tal, S., and Frishman, D. (2007) Co-Evolving Residues in Membrane Proteins. Bioinformatics, 23, 3312-3319 and
(b) present Fuchs, A., Kirschner, A., Frishman D. (2009) Prediction of helix-helix contacts and interacting helices in polytopic membrane proteins using neural networks. Proteins, 74(4), 857-71.

(7) (a) present Park, Y., Hayat, S., and Helms, V. (2007) BMC Bioinformatics, 8:302. Prediction of the Burial Status of Transmembrane Residues of Helical Membrane Proteins and
(b) present Rose A, Lorenzen S, Goede A, Gruening B, Hildebrand PW (2009) RHYTHM--a server to predict the orientation of transmembrane helices in channels and membrane-coils. Nucleic Acids Res 37, No. suppl_2 W575-W580

(8) present Viklund, H. and Elofsson, A. (2008) OCTOPUS: improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics 24 (15) : 1662-1668.


Structure prediction of helical membrane proteins


(9) (a) present Barth P, Wallner B, Baker D (2009). Prediction of membrane protein structures with complex topologies using limited constraints. Proc. Natl. Acad. Sci. U.S.A. 106, 1409-14. plus the Supporting Information for this paper

(b) give some background about Rosetta program

(c) you will need some information from Bradley P, Baker D (2006) Improved beta-protein structure prediction by multilevel optimization of nonlocal strand pairings and local backbone conformation. Proteins 65:922–929.

(10) (a) present Baker ML, Ju T, Chiu W: Identification of secondary structure elements in intermediate-resolution density maps. Structure 2007, 15:7-19 and

(b) present Julio A. Kovacs, Mark Yeager,  and Ruben Abagyan, Computational Prediction of Atomic Structures of Helical Membrane Proteins Aided by EM Maps, Biophysical Journal, 93, 1950-1959

(c) give some information on the SCATD algorithm for side chain placement used in (b)


Pore identification


(11) (a) present Pellegrini-Calace M, Maiwald T, Thornton JM, 2009 PoreWalker: A Novel Tool for the Identification and Characterization of Channels in Transmembrane Proteins from Their Three-Dimensional Structure. PLoS Comput Biol 5(7): e1000440.


(b) present Po-Hsien Lee, Kuei-Ling Kuo, Pei-Ying Chu, Eric M. Liu and Jung-Hsin Lin, SLITHER: a web server for generating contiguous conformations of substrate molecules entering into deep active sites of proteins or migrating through channels in membrane transporters, Nucleic Acids Research, 2009, 37, No. suppl_2 W559-W564

(11a) (a) present  Petrek M, Otyepka M, Banás P, Kosinová P, Koca J, Damborský J. (2006) CAVER: a new tool to explore routes from protein clefts, pockets and cavities. BMC Bioinformatics 7:316.


(b) present  Petrek M, Kosinová P, Koca J, Otyepka M (2007) MOLE: a Voronoi diagram-based explorer of molecular channels, pores, and tunnels. Structure. 11:1357-63

Introduce the concepts of Voronoi Diagram and Dijkstra`s algorithm. And compare these two methods of pore identification.

Ligand binding in membrane proteins

(12) (a) introduce the basic principles of homology modelling

(b) present the major findings of Michino, M., Abola, E., Brooks, 3rd, C.L., Dixon, J.S., Moult, J. and Stevens, R.C. (2009) Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008. Nat Rev Drug Discov 8 (6) : 455-463


Beta-barrel topologies

(13) (a) give an introduction to the 3D structures of transmembrane beta barrels (TMBs) using Georg E Schulz, β-Barrel membrane proteins, Current Opinion in Structural Biology (2000) 10:443–447 and

(b) present Ou YY, Chen SA, Gromiha MM, J Comput Chem. 2010 Jan 15;31(1):217-23.

Prediction of membrane spanning segments and topology in beta-barrel membrane proteins at better accuracy.

 (14) present Arlo Randall, Jianlin Cheng, Michael Sweredoski and Pierre Baldi, TMBpro: secondary structure, b-contact and tertiary structure prediction of transmembrane b-barrel proteins. Bioinformatics 24 (2008) 513–520

(15) (a) present Ronald Jackups Jr and Jie Liang, J. Mol. Biol. (2005) 354, 979–993, Interstrand Pairing Patterns in b-Barrel Membrane Proteins: The Positive-outside Rule, Aromatic Rescue, and Strand Registration Prediction and
(b) present Hammad Naveed, Ronald Jackups, and Jie Liang, Predicting weakly stable regions, oligomerization state, and protein–protein interfaces in transmembrane domains of outer membrane proteins, Proc. Natl. Acad. Sci. USA (2009) 106, 12735-12740


Function and location prediction


(16) (a) present Gromiha MM, Yabuki Y, Suresh MX, Thangakani AM, Suwa M, Fukui K, Nucleic Acids Res. 2009 Jan;37(Database issue):D201-4. TMFunction: database for functional residues in membrane proteins. and

(b) present Gromiha MM, Yabuki Y. Functional discrimination of membrane proteins using machine learning techniques. BMC Bioinform. (2008) 9:135



(17) (a) present H. Liu, M. Wang, KC Chou, Low-frequency Fourier spectrum for predicting membrane protein types, Biochem Biophys Res Comm 336 (2005) 737-739


(b) present Liu H, Yang J, Wang M, Xue L, Chou KC, Using Fourier spectrum analysis and pseudo amino acid composition for prediction of membrane protein types, Protein Journal (2005) 24, 385-388 and

(c) present Y. Gao, S. Shao, X. Xiao, Y. Ding, Y. Huang, Z.Huang, K.C. Chou, Using pseudo amino acid composition to predict protein subcellular location: approached with Lyapunov index, Bessel function, and Chebychev filter, Amino Acids (2005) 28, 373-376


(18) Identification of novel multi-transmembrane proteins from genomic databases using quasi-periodic structural properties. by Kim J. et al. Bioinformatics, Vol 16, No.9, Pages 767-775, year: 2000


(19) Protein Classification Based on Text Document Classification Techniques.

von Betty Yee Man Cheng et al. Proteins: Structure, Function, and Bioinformatics 58:955-970, 2005


(20) (a) TransportTP: A two-phase calssification approach for membrane transporter prediction and characterization. BMC Bioinform. (2009) 10:418 and

(b) A nearest neighbor approach for automated transporter prediction and categorization from protein sequences. Bioinformatics (2008) 1129-1136.




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