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  A fast topological analysis algorithm for large-scale similarity evaluations of ligands and binding pockets

ElGamacy, M., & Van Meervelt, L. (2015). A fast topological analysis algorithm for large-scale similarity evaluations of ligands and binding pockets. Journal of Cheminformatics, 7: 42. doi:10.1186/s13321-015-0091-5.

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 Creators:
ElGamacy, M1, Author           
Van Meervelt, L, Author
Affiliations:
1Department Protein Evolution, Max Planck Institute for Developmental Biology, Max Planck Society, ou_3375791              

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 Abstract:

Motivation: With the rapid increase of the structural data of biomolecular complexes, novel structural analysis methods have to be devised with high-throughput capacity to handle immense data input and to construct massive networks at the minimal computational cost. Moreover, novel methods should be capable of handling a broad range of molecular structural sizes and chemical natures, cognisant of the conformational and electrostatic bases of molecular recognition, and sufficiently accurate to enable contextually relevant biological inferences.

Results: A novel molecular topology comparison method was developed and tested. The method was tested for both ligand and binding pocket similarity analyses and a PDB-wide ligand topological similarity map was computed.

Conclusion: The unprecedentedly wide scope of ligand definition and large-scale topological similarity mapping can provide very robust tools, of performance unmatched by the present alignment-based methods. The method remarkably shows potential for application for scaffold hopping purposes. It also opens new frontiers in the areas of ligand-mediated protein connectivity, ligand-based molecular phylogeny, target fishing, and off-target predictions. Graphical abstract:A novel molecular topology comparison method based on a combined shape distribution and charge binning scheme is presented.

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 Dates: 2015-08
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1186/s13321-015-0091-5
PMID: 26561508
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Title: Journal of Cheminformatics
Source Genre: Journal
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Publ. Info: BioMed Central
Pages: 14 Volume / Issue: 7 Sequence Number: 42 Start / End Page: - Identifier: ISSN: 1758-2946
CoNE: https://pure.mpg.de/cone/journals/resource/1758-2946