Kaczor A.A.,University of Eastern Finland |
Kaczor A.A.,Medical University of Lublin |
Selent J.,Research Unit on Biomedical Informatics GRIB |
Poso A.,University of Eastern Finland
Methods in Cell Biology | Year: 2013
Classical structure-based drug design techniques using G-protein-coupled receptors (GPCRs) as targets focus nearly exclusively on binding at the orthosteric site of a single receptor. Dimerization and oligomerization of GPCRs, proposed almost 30 years ago, have, however, crucial relevance for drug design. Targeting these complexes selectively or designing small molecules that affect receptor-receptor interactions might provide new opportunities for novel drug discovery. In order to study the mechanisms and dynamics that rule GPCRs oligomerization, it is essential to understand the dynamic process of receptor-receptor association and to identify regions that are suitable for selective drug binding, which may be determined with experimental methods such as Förster resonance energy transfer (FRET) or Bioluminescence resonance energy transfer (BRET) and computational sequence- and structure-based approaches. The aim of this chapter is to provide a comprehensive description of the structure-based molecular modeling methods for studying GPCR dimerization, that is, protein-protein docking, molecular dynamics, normal mode analysis, and electrostatics studies. © 2013 Elsevier Inc.
Thomas P.E.,Fraunhofer Institute for Algorithms and Scientific Computing |
Thomas P.E.,Humboldt University of Berlin |
Klinger R.,Fraunhofer Institute for Algorithms and Scientific Computing |
Furlong L.I.,Research Unit on Biomedical Informatics GRIB |
And 3 more authors.
BMC Bioinformatics | Year: 2011
Background: Most information on genomic variations and their associations with phenotypes are covered exclusively in scientific publications rather than in structured databases. These texts commonly describe variations using natural language; database identifiers are seldom mentioned. This complicates the retrieval of variations, associated articles, as well as information extraction, e. g. the search for biological implications. To overcome these challenges, procedures to map textual mentions of variations to database identifiers need to be developed.Results: This article describes a workflow for normalization of variation mentions, i.e. the association of them to unique database identifiers. Common pitfalls in the interpretation of single nucleotide polymorphism (SNP) mentions are highlighted and discussed. The developed normalization procedure achieves a precision of 98.1 % and a recall of 67.5% for unambiguous association of variation mentions with dbSNP identifiers on a text corpus based on 296 MEDLINE abstracts containing 527 mentions of SNPs.The annotated corpus is freely available at http://www.scai.fraunhofer.de/snp-normalization-corpus.html.Conclusions: Comparable approaches usually focus on variations mentioned on the protein sequence and neglect problems for other SNP mentions. The results presented here indicate that normalizing SNPs described on DNA level is more difficult than the normalization of SNPs described on protein level. The challenges associated with normalization are exemplified with ambiguities and errors, which occur in this corpus. © 2011 Thomas et al; licensee BioMed Central Ltd.
Pinto M.,Research Unit on Biomedical Informatics GRIB |
Blasi D.,Drug Discovery Platform PDD |
Nieto J.,Ramon Llull University |
Arsequell G.,CSIC - Institute of Advanced Chemistry of Catalonia |
And 4 more authors.
Amyloid | Year: 2011
A computational analysis was performed on a selected group of 13 TTR-ligand crystallographic complexes in order to deduce information useful for drug design and discovery. The results obtained can be summarized as follows: (1) the binding site of TTR is a large and very flexible cavity, which is composed of three regions with different chemical features; (2) ligands bind to TTR in forward or reverse modes depending on the conformation adopted by the serine and threonine residues located at the end of the cavity; (3) no relationship could be found between the binding mode of the ligands and their TTR fibrillogenesis inhibitory activity; (4) regardless of the structure, chemical properties or binding mode of the ligand to TTR, there is always a contribution of residues Lys15, Leu17, Ala108, Leu110, Ser117 and Thr119 to ligand binding and finally, (5) the most active compounds are characterised by the presence of at least one halogen atom in the HBP1/HBP1' or HBP3/HBP3' pockets.© 2011 Informa UK, Ltd.
Kaczor A.A.,University of Eastern Finland |
Kaczor A.A.,Research Unit on Biomedical Informatics GRIB |
Kaczor A.A.,University of Regensburg |
Guixa-Gonzalez R.,Research Unit on Biomedical Informatics GRIB |
And 5 more authors.
Molecular Informatics | Year: 2015
In order to apply structure-based drug design techniques to GPCR complexes, it is essential to model their 3D structure. For this purpose, a multi-component protocol was derived based on protein-protein docking which generates populations of dimers compatible with membrane integration, considering all reasonable interfaces. At the next stage, we applied a scoring procedure based on up to eleven different parameters including shape or electrostatics complementarity. Two methods of consensus scoring were performed: (i) average scores of 100 best scored dimers with respect to each interface, and (ii) frequencies of interfaces among 100 best scored dimers. In general, our multi-component protocol gives correct indications for dimer interfaces that have been observed in X-ray crystal structures of GPCR dimers (opsin dimer, chemokine CXCR4 and CCR5 dimers, κ opioid receptor dimer, β1 adrenergic receptor dimer and smoothened receptor dimer) but also suggests alternative dimerization interfaces. Interestingly, at times these alternative interfaces are scored higher than the experimentally observed ones suggesting them to be also relevant in the life cycle of studied GPCR dimers. Further results indicate that GPCR dimer and higher-order oligomer formation may involve transmembrane helices (TMs) TM1-TM2-TM7, TM3-TM4-TM5 or TM4-TM5-TM6 but not TM1-TM2-TM3 or TM2-TM3-TM4 which is in general agreement with available experimental and computational data. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.