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aCSM: Noise-free graph signatures for large-scale receptor-based ligand prediction

Douglas E. V. Pires, Raquel C. de Melo-Minardi, Carlos H. da Silveira, Frederico F. Campos, Wagner Meira Jr..

Abstract


Motivation: Receptor-ligand interactions are a central phenomenon in most biological systems. They are characterized by molecular recognition, a complex process that is driven mainly by the physicochemical and structural properties of both the receptor and the ligand. Understanding and predicting these interactions are a major step toward protein ligand prediction, target identification, lead discovery and drug design.

Results: We propose a novel graph-based binding pocket signature called aCSM, which is efficient and effective in handling large- scale protein ligand prediction tasks. We compare our results with those described in the literature and demonstrate that our algorithm overcomes the competitors techniques. Finally, we predict novel ligands for proteins from Trypanossoma cruzi, the parasite responsible for Chagas Disease, and validate them in silico via a docking protocol, showing the applicability of the method in suggesting ligands for pockets in a real-world scenario.


Bioinformatics, 2013.

Supplementary material (data sets + code - 128MB):

Data sets only (128MB):

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