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 (5 minute) presentation on one of the methods investigated.

For most topics, a good report will include consideration of two or three research articles related to that topic.

In the case of "Protein folding on a lattice", the problem addressed is a toy problem that is easy to understand and describe, so this is potentially an easier topic to investigate than many of the others. This will be taken into account when grading, so I recommend those aiming for a high grade to consider another topic. A very good report on this topic is possible, but should contain discussion and comparison of more than three research articles.

Selecting a topic

Please indicate your preferred topic using this Doodle poll. Here, the day of the month (1-9) corresponds to the nine topics:

1. Structural alignment
2. Side chain modelling
3. Channels
4. Protein folding on a lattice
5. Fold recognition
6. Multi-resolution modelling
7. Mesostates
8. Co-evolution
9. Protein design

The time of day (1-3) corresponds to slots for that topic.

Take your time and look at several papers before choosing your topic.

If your preferred topic is not available, please send me an e-mail message. I'd like as many people as possible to have their first choice, and I'll consider opening extra slots for some topics to allow this, possibly identifying additional articles for that topic.

Later on you need to decide which paper you will present. I suggest that those in "slot 1" for a topic have first choice, those in "slot 2" can have second choice, and so on. You can indicate your choice as a comment in the Doodle poll.

Presentations will be on 12 and 14 December 2016. If you need to present before 12 December, please send me an e-mail message.

Suggested topics

All of the papers listed here should be accessible from within the Chalmers (and probably also GU) network. If you have difficulty accessing any of them, please let me know.

1. 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)

2. 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)

• 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)

3. 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)

4. 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)

• 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)

• 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)

• 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)

5. 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)

6. 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)

7. Mesostates

• Gong, H., Fleming, P.J. and Rose, G.D. (2005) Building native protein conformation from highly approximate backbone torsion angles. Proc. Natl. Acad. Sci. U.S.A., 102, 16227-16232 (doi:10.1073/pnas.0508415102)

• Chellapa, G.D. and Rose, G.D. (2012) Reducing the dimensionality of the protein-folding search problem. Protein Science, 21, 1231-1240 (doi:10.1002/pro.2106)

8. Co-evolution

• Marks, D.S., Colwell, L.J., Sheridan, R., Hopf, T.A., Pagnani, A., Zecchina, R. and Sander C. (2011) Protein 3D structure computed from evolutionary sequence variation. PLoS One 6, e28766 (doi:10.1371/journal.pone.0028766)

• Hopf, T.A., Schärfe, C.P., Rodrigues, J.P., Green, A.G., Kohlbacher, O., Sander, C., Bonvin, A.M. and Marks, D.S. (2014) Sequence co-evolution gives 3D contacts and structures of protein complexes. Elife, 3, eLife 2014;3:e03430 (doi:10.7554/eLife.03430)

• Horner, D.S., Pirovano, W. and Pesole, G. (2008) Correlated substitution analysis and the prediction of amino acid structural contacts. Briefings in bioinformatics, 9, 46-56 (doi:10.1093/bib/bbm052)

9. Protein design

• Kuhlman, B., Dantas, G., Ireton, G.C., Varani, G., Stoddard, B.L. and Baker, D. (2003) Design of a novel globular protein fold with atomic-level accuracy. Science, 302, 1364-1368 (doi:10.1126/science.1089427)

• Traoré, S., Allouche, D., André, I., de Givry, S., Katsirelos, G., Schiex, T. and Barbe, S. (2013) A new framework for computational protein design through cost function network optimization. Bioinformatics, 29, 2129-2136 (doi:10.1093/bioinformatics/btt374)

• Parmeggiani, F., Huang, P.S., Vorobiev, S., Xiao, R., Park, K., Caprari S., Su, M., Jayaraman, S., Mao, L., Janjua, H., Montelione, G.T., Hunt, J. and Baker, D. (2014) A general computational approach for repeat protein design. J. Mol. Biol. (doi:10.1016/j.jmb.2014.11.005)

What to submit

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

You should submit PDF files via the Fire system before 23:59 on Thursday 15 December 2016.