ORCID Profile
0000-0001-5190-100X
Current Organisation
Universidade Federal de Minas Gerais
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Oxford University Press (OUP)
Date: 24-04-2015
DOI: 10.1093/BIOINFORMATICS/BTV223
Abstract: Summary: PDBest (PDB Enhanced Structures Toolkit) is a user-friendly, freely available platform for acquiring, manipulating and normalizing protein structures in a high-throughput and seamless fashion. With an intuitive graphical interface it allows users with no programming background to download and manipulate their files. The platform also exports protocols, enabling users to easily share PDB searching and filtering criteria, enhancing analysis reproducibility. Availability and implementation: PDBest installation packages are freely available for several platforms at www.pdbest.dcc.ufmg.br Contact: wellisson@dcc.ufmg.br, dpires@dcc.ufmg.br, raquelcm@dcc.ufmg.br Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Oxford University Press (OUP)
Date: 09-12-2201
DOI: 10.1093/BIOINFORMATICS/BTR680
Abstract: Motivation: Protein–protein interfaces contain important information about molecular recognition. The discovery of conserved patterns is essential for understanding how substrates and inhibitors are bound and for predicting molecular binding. When an inhibitor binds to different enzymes (e.g. dissimilar sequences, structures or mechanisms what we call cross-inhibition), identification of invariants is a difficult task for which traditional methods may fail. Results: To clarify how cross-inhibition happens, we model the problem, propose and evaluate a methodology called HydroPaCe to detect conserved patterns. Interfaces are modeled as graphs of atomic apolar interactions and hydrophobic patches are computed and summarized by centroids (HP-centroids), and their conservation is detected. Despite sequence and structure dissimilarity, our method achieves an appropriate level of abstraction to obtain invariant properties in cross-inhibition. We show ex les in which HP-centroids successfully predicted enzymes that could be inhibited by the studied inhibitors according to BRENDA database. Availability: www.dcc.ufmg.br/~raquelcm/hydropace Contact: valdetemg@ufmg.br raquelcm@dcc.ufmg.br santoro@icb.ufmg.br Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 12-2011
Publisher: Oxford University Press (OUP)
Date: 08-02-2013
DOI: 10.1093/BIOINFORMATICS/BTT058
Abstract: Motivation: Receptor-ligand interactions are a central phenomenon in most biological systems. They are characterized by molecular recognition, a complex process mainly driven by physicochemical and structural properties of both receptor and ligand. Understanding and predicting these interactions are major steps towards protein ligand prediction, target identification, lead discovery and drug design. Results: We propose a novel graph-based–binding pocket signature called aCSM, which proved to be 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 competitor’s techniques. Finally, we predict novel ligands for proteins from Trypanosoma 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. Availability and implementation: Datasets and the source code are available at www.dcc.ufmg.br/∼dpires/acsm. Contact: dpires@dcc.ufmg.br or raquelcm@dcc.ufmg.br Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Springer Science and Business Media LLC
Date: 17-11-2013
Abstract: Millions of protein database entries are not assigned reliable functions, preventing the full understanding of chemical ersity in living organisms. Here, we describe an integrated strategy for the discovery of various enzymatic activities catalyzed within protein families of unknown or little known function. This approach relies on the definition of a generic reaction conserved within the family, high-throughput enzymatic screening on representatives, structural and modeling investigations and analysis of genomic and metabolic context. As a proof of principle, we investigated the DUF849 Pfam family and unearthed 14 potential new enzymatic activities, leading to the designation of these proteins as β-keto acid cleavage enzymes. We propose an in vivo role for four enzymatic activities and suggest key residues for guiding further functional annotation. Our results show that the functional ersity within a family may be largely underestimated. The extension of this strategy to other families will improve our knowledge of the enzymatic landscape.
No related grants have been discovered for Raquel C. de Melo-Minardi.