Abstract
The purpose of this work is to demonstrate that it is possible to cluster contact maps for pairs of alpha helices such that each of the clusters corresponds to a group of pairs of alpha helices with similar properties. The property of the configuration of helix pairs that was chosen for study is the packing attribute. The contact maps are compared to one another using a novel contact map comparison scheme based upon the locations of contacts in the contact maps. A k-nearest neighbours technique is used to perform the clustering, and the cosine between vectors corresponding to contact map regions was the distance metric. The clustering of contact maps to determine whether maps corresponding to similar packing values are placed into the same clusters yielded promising results.
This research has supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the PRECARN Institute for Robotics and Intelligent Systems (IRIS).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Caprara, A., Lancia, G.: Structural alignment of large-size proteins via Lagrangian relaxation. In: RECOMB ’02: Proceedings of the sixth annual international conference on Computational biology, pp. 100–108. ACM Press, New York (2002)
Chothia, C., Levitt, M., Richardson, D.: Helix to helix packing in proteins. Journal of Molecular Biology 145, 215–250 (1981)
Ertöz, L., Steinbach, M., Kumar, V.: A new shared nearest neighbor clustering algorithm and its applications. In: Proceedings of the Workshop on Clustering High Dimensional Data and its Applications, Second SIAM International Conference on Data Mining (2002)
Fraser, R., Glasgow, J.: Introducing Hippy: A visualization tool for understanding the α-helix pair interface. In: Proceedings of the 2006 International Conference on Bioinformatics and Computational Biology (BIOCOMP) (2006)
Fariselli, P., Olmea, O., Valencia, A., Casadio, R.: Prediction of contact maps with neural networks and correlated mutations. Protein Engineering 14(11), 835–843 (2001)
Fraser, R.: A Tale of Two Helices: A study of alpha helix pair conformations in three-dimensional space. Master’s thesis, Queen’s University (2006)
Jaccard, P.: Nouvelles recherches sur la distribution florale. Bulletin de la Société Vaudoise des Sciences Naturelles 44, 223–270 (1908)
Jarvis, R.A., Patrick, E.A.: Clustering using a similarity measure based on shared near neighbors. IEEE Transactions on Computers 22, 1025–1034 (1973)
Kuo, T.: A computational approach to contact map similarity. Master’s thesis, Queen’s University (2005)
Pollastri, G., Baldi, P.: Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners. Bioinformatics 18(Supplement 1), S62–S70 (2002)
Punta, M., Rost, B.: PROFcon: novel prediction of long-range contacts. Bioinformatics 21(13), 2960–2968 (2005)
Vendruscolo, M., Kussell, E., Domany, E.: Recovery of protein structure from contact maps. Folding and Design 2(5), 295–306 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Fraser, R., Glasgow, J. (2007). A Demonstration of Clustering in Protein Contact Maps for Alpha Helix Pairs. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71618-1_84
Download citation
DOI: https://doi.org/10.1007/978-3-540-71618-1_84
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71589-4
Online ISBN: 978-3-540-71618-1
eBook Packages: Computer ScienceComputer Science (R0)