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- Andersen, Grethe, et al.
(författare)
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Quantitative measurement of the levels of melanocortin receptor subtype 1, 2, 3 and 5 and pro-opio-melanocortin peptide gene expression in subsets of human peripheral blood leucocytes
- 2005
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Ingår i: Scandinavian Journal of Immunology. - Oxford : Wiley. - 0300-9475 .- 1365-3083. ; 61:3, s. 279-284
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Tidskriftsartikel (refereegranskat)abstract
- Levels of the melanocortin receptor (MCR) 1, 2, 3 and 5 subtypes and pro-opio-melanocortin (POMC) protein mRNA were measured by the real-time quantitative reverse transcriptase polymerase chain reaction method in CD4+ T helper (Th) cells, CD8+ T cytotoxic cells, CD19+ B cells, CD56+ natural killer (NK) cells, CD14+ monocytes and CD15+ granulocytes from healthy donors. We found high levels of all of the MC1, 2, 3 and 5R subtype mRNA in Th cells and moderate levels in NK cells, monocytes and granulocytes. POMC peptide mRNA was found in all examined leucocyte subsets, but only low levels were present in granulocytes. Our findings suggest a co-ordinating role for MCR subtypes and their naturally occurring ligands in the co-operation between innate and adaptive immunity. Moreover, our findings are compatible with earlier finding of MCR-mediated tolerance induction in Th cells.
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- Strömbergsson, Helena, 1975-
(författare)
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Chemogenomics: Models of Protein-Ligand Interaction Space
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The large majority of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand recognition is thus central to drug discovery and design. Improved experimental techniques have resulted in an immense growth of drug target information. This has stimulated the development of chemogenomics and proteochemometrics (PCM) that take target information as well as ligand information into account to study the genomic effect of potential drugs. This thesis is concerned with modeling protein-ligand recognition, and the aim is to develop models that generalize to the entire protein-ligand space. To this end, protein-ligand interaction data has been extracted and manually curated from public databases, protein and ligand descriptors have been computed, and predictive models have been induced with machine-learning methods. An introduction to chemogenomics, machine learning, and PCM modeling is given in the thesis summary, which is followed by five research papers. Paper I shows that it is possible to induce interpretable models with a non-linear rule-based method, and paper II demonstrates that local descriptors of protein structure may be used to induce PCM models that cover proteins differing in sequence and fold. In paper III, such local descriptors are used to induce a PCM model on a large dataset that includes all major enzyme classes. This demonstrates that the local descriptors may be used to induce generalized models that span the entire known structural enzyme-ligand space. Paper IV describes a step towards proteome-wide PCM models, and shows that it is possible to predict high- and low-affinity complexes using a set of protein and ligand descriptors that do not require knowledge of 3D structure. Finally, paper V presents a method to visualize and compare protein-ligand chemogenomic subspaces, which may be used to predict unwanted cross-interactions of drugs with other proteins in the proteome.
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- Strömbergsson, Helena, et al.
(författare)
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Towards proteome-wide interaction models using the proteochemometrics approach
- 2010
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Ingår i: Molecular Informatics. - : Wiley. - 1868-1743 .- 1868-1751. ; 29:6-7, s. 499-508
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Tidskriftsartikel (refereegranskat)abstract
- A proteochemometrics model was induced from all interaction data in the BindingDB database, comprizing in all 7078 protein-ligand complexes with representatives from all major drug target categories. Proteins were represented by alignment-independent sequence descriptors holding information on properties such as hydrophobicity, charge, and secondary structure. Ligands were represented by commonly used QSAR descriptors. The inhibition constant (pK(i)) values of protein-ligand complexes were discretized into "high" and "low" interaction activity. Different machine-learning techniques were used to induce models relating protein and ligand properties to the interaction activity. The best was decision trees, which gave an accuracy of 80% and an area under the ROC curve of 0.81. The tree pointed to the protein and ligand properties, which are relevant for the interaction. As the approach does neither require alignments nor knowledge of protein 3D structures virtually all available protein-ligand interaction data could be utilized, thus opening a way to completely general interaction models that may span entire proteomes.
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