Practical 4

Review of a topic in structural bioinformatics

Aims

• To give practice in reading and summarising a research articles.

Objectives

After this practical you will be able to:

• describe bioinformatics problems and computational approaches to solving them;
• summarise problems and methods described in research articles;
• critically discuss different methods that address the same task.

Introduction

In this exercise you will investigate a topic within structural bioinformatics. You will summarise the problem and computational solutions to that problem.

After reading your document the reader should:

• understand the computational problem that is being addressed;
• understand what needs to be done to implement a solution to the problem;
• recognise advantages and/or disadvantages of the methods investigated;
• recognise similarities and/or differences in the problems addressed and the solutions presented in research articles;
• recognise that computing science students (particularly those with some experience with algorithms) have the knowledge and skills that are needed to address this problem and implement solutions.

You can make the following assumptions about the reader:

• the reader's knowledge and background in computing science is similar to your own;
• the reader is aware that proteins consist of chains of amino acid residues (with 20 commonly occurring side chain types), and that each amino acid residue has a central carbon atom called the alpha-carbon.

You should use a clear and straightforward writing style. You may refer to other research articles in your report.

In addition to a report on the topic investigated, you should prepare a short (10 minute) presentation on one of the methods investigated.

Suggested topics

Structural alignment

• Taylor W.R. and Orengo C.A. (1989) Protein structure alignment. J. Mol. Biol., 208, 1-22 (doi:10.1016/0022-2836(89)90084-3)

• Holm, L. and Sander, C. (1996) Mapping the Protein Universe. Science, 273, 595-602 (local copy)

• Zhu, J. and Weng, Z. (2005) FAST: A novel protein structure alignment algorithm. Proteins: Structure, Function, and Bioinformatics, 58, 618-627 (doi:10.1002/prot.20331)

Side chain modelling

• Desmet, J., de Meyer, M., Hazes, B. and Lasters, I. (1992) The dead-end elimination theorem and its use in protein side-chain positioning. Nature, 356, 539-542 (doi:10.1038/356539a0)

• Swain, M.T. and Kemp, G.J.L. (2001) A CLP approach to the protein side-chain placement problem. In Walsh, T. (ed.) Principles and Practice of Constraint Programming - CP2001, Lecture Notes in Computer Science (vol. 2239), Springer-Verlag, Berlin, pp 479-493 (doi:10.1007/3-540-45578-7_33)

• Canutescu, A.A., Shelenkov, A.A. and Dunbrack, R.L. (2003) A graph-theory algorithm for rapid protein side-chain prediction. Protein Science, 11, 2001-2014 (doi:10.1110/ps.03154503)

• (Added 2013-12-02) Lee, C. and Subbiah, S. (1991) Prediction of protein side-chain conformation by packing optimization. J. Mol. Biol., 217, 373-388 (doi:10.1016/0022-2836(91)90550-P)

Channels

• Smart, O.S., Goodfellow, J.M. and Wallace, B.A. (1993) The Pore Dimensions of Gramicidin A. Biophys. J., 65, 2455-2460 (doi:10.1016/S0006-3495(93)81293-1)

• Tilton, R.F. Jr, Singh, U.C., Weiner, S.J., Connolly, M.L., Kuntz, I.D. Jr, Kollman, P.A., Max, N. and Case, D.A. (1986) Computational Studies of the Interaction of Myoglobin and Xenon. J. Mol. Biol., 192, 443-456 [in particular Sections 2(b-d)] (doi:10.1016/0022-2836(86)90374-8)

• Petrek, M., Kosinova, P., Koca, J. and Otyepke, M. (2007) MOLE: A Voronoi Diagram-Based Explorer of Molecular Channels, Pores, and Tunnels. Structure, 15, 1357-1363 (doi:10.1016/j.str.2007.10.007)

• Yaffe, E., Fishelovitch, D., Wolfson, H.J., Halperin, D. and Nussinov R. (2008) MolAxis:Efficient and accurate identification of channels in macromolecules. Proteins: Structure, Function and Bioinformatics, 73, 72-86 (doi:10.1002/prot.22052)

Protein folding on a lattice

• Lau, K.F. and Dill, K.A. (1989) A Lattice Statistical Mechanics Model of the Conformational and Sequence Spaces of Proteins. Macromolecules, 22, 3986-3997 (doi:10.1021/ma00200a030)

• Unger, R. and Moult, J. (1993) Genetic Algorithms for Protein Folding Simulations. J. Mol. Biol., 231, 75-81 (doi:10.1006/jmbi.1993.1258)

• Shmygelska, A. and Hoos, H.H. (2005) An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinformatics, 6, 30 (doi:10.1186/1471-2105-6-30)

• Hockenmaier, J., Joshi, A.K. and Dill, K.A. (2007) Routes are trees: The parsing perspective on protein folding. Proteins: Structure, Function, and Bioinformatics, 66, 1-15 (doi:10.1002/prot.21195)

• (Added 2013-12-02) Sali, A., Shakhnovich, E. and Karplus, M. (1994) Kinetics of Protein Folding: A Lattice Model Study of the Requirements for Folding to the Native State. J. Mol. Biol., 235, 1614-1638 (doi:10.1006/jmbi.1994.1110)

• (Added 2013-12-02) Hinds, D.A. and Levitt, M. (1992) A lattice model for protein structure prediction at low resolution. Proc. Natl. Acad. Sci. U.S.A., 89, 2536-2540 (Journal web site)

• (Added 2013-12-02) Kolinski, A. and Skolnick, J. (1994) Monte carlo simulations of protein folding. I. Lattice model and interaction scheme. Proteins: Structure, Function, and Bioinformatics, 18, 338-352 (doi:10.1002/prot.340180405)

Fold recognition

• Bowie, J.U., Lüthy, R. and Eisenberg, D. (1991) A Method to Identify Protein Sequences That Fold into a Known Three-Dimensional Structure. Science, 253, 164-170 (JSTOR)

• Sippl, M.J. and Weitckus, S. (1992) Detection of Native-Like Models for Amino Acid Sequences of Unknown Three-Dimensional Structure in a Data Base of Known Protein Conformations. Proteins: Structure, Function and Genetics, 13, 258-271 (doi:10.1002/prot.340130308)

• Jones, D.T., Taylor, W.R. and Thornton, J.M. (1992) A new approach to protein fold recognition. Nature, 358, 86-89 (doi:10.1038/358086a0)

Multi-resolution modelling

• Wu, X., Milne, J.L.S., Borgnia, M.J., Rostapshov, A.V., Subramaniam, S. and Brooks, B.R. (2003) A core-weighted fitting method for docking atomic structures into low-resolution maps: Application to cryo-electron microscopy. J. Struct. Biol., 141, 63-76 (doi:10.1016/S1047-8477(02)00570-1)

• Ceulemans, H. and Russell, R.B. (2004) Fast Fitting of Atomic Structures to Low-resolution Electron Density Maps by Surface Overlap Maximization. J. Mol. Biol., 338, 783-793 (doi:10.1016/j.jmb.2004.02.066)

• Zhang, S., Vasishtan, D., Xu, M., Topf, M. and Alber, F. (2010) A fast mathematical programming procedure for simultaneous fitting of assembly components into cryoEM density maps. Bioinformatics, 26, 1261-1268 (doi:10.1093/bioinformatics/btq201)

What to submit

• report on the topic investigated;
• presentation slides on the topic investigated;

You should submit a PDF files via the Fire system before 23:59 on Thursday 12 December 2013.