After this practical you will be able to:
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:
You can make the following assumptions about the reader:
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.
Please indicate your preferred topic using this Doodle poll. Here, the day of the month (1-9) corresponds to the nine topics:
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.
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.
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)
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)
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)
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)
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)
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)
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)
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)
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)
You should submit PDF files via the Fire system before 23:59 on Thursday 15 December 2016.