A short survey on protein blocks
- PMID: 21731588
- PMCID: PMC3124139
- DOI: 10.1007/s12551-010-0036-1
A short survey on protein blocks
Abstract
Protein structures are classically described in terms of secondary structures. Even if the regular secondary structures have relevant physical meaning, their recognition from atomic coordinates has some important limitations such as uncertainties in the assignment of boundaries of helical and β-strand regions. Further, on an average about 50% of all residues are assigned to an irregular state, i.e., the coil. Thus different research teams have focused on abstracting conformation of protein backbone in the localized short stretches. Using different geometric measures, local stretches in protein structures are clustered in a chosen number of states. A prototype representative of the local structures in each cluster is generally defined. These libraries of local structures prototypes are named as "structural alphabets". We have developed a structural alphabet, named Protein Blocks, not only to approximate the protein structure, but also to predict them from sequence. Since its development, we and other teams have explored numerous new research fields using this structural alphabet. We review here some of the most interesting applications.
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References
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- Agarwal G, Dinesh D, Srinivasan N and de Brevern AG (2010) Characterization of conformational patterns in active and inactive forms of kinases using Protein Blocks approach. In: Maulik U, Bandyopadhyay S, Wang J (eds) Computational Intelligence and Pattern Analysis in Biological Informatics. Wiley, in press
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- Benros C, Hazout S, de Brevern AG (2002) Extension of a local backbone description using a structural alphabet. "Hybrid Protein Model": a new clustering approach for 3D local structures. In: International Workshop on Bioinformatics ISMIS. Lyon, pp 36-45
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- Benros C, de Brevern AG, Hazout S (2003) Hybrid Protein Model (HPM): A method for building a library of overlapping local structural prototypes. Sensitivity study and improvements of the training. In: IEEE Workshop on Neural Networks for Signal Processing. IEEE Int Work 1:53–72