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- Kontijevskis, Aleksejs, et al.
(författare)
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Proteochemometric analysis of small cyclic peptides' interaction with wild-type and chimeric melanocortin receptors
- 2007
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Ingår i: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 69:1, s. 83-96
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Tidskriftsartikel (refereegranskat)abstract
- The melanocortin (MC) system confines unique G-protein coupled receptor pathways, which include the MC1-5 receptors and their endogenous agonists and antagonists, the MCs and the agouti and agouti-related proteins. The MC4 receptor is an important target for development of drugs for treatment of obesity and cachexia. While natural MC peptides are selective for the MC1 receptor, some cyclic pentapeptides, such as the HS-129 peptide, show high selectivity for the MC4 receptor. Here we gained insight into the mechanisms for its recognition by MC receptors. To this end we correlated the interaction data of four HS peptide analogues with four wild-type and 14 multiple chimeric MC receptors to the binary and physicochemical descriptions of the studied entities by use of partial least squares regression, which resulted in highly valid proteochemometric models. Analysis of the models revealed that the recognition sites of the HS peptides are different from the earlier proteochemometrically mapped linear MSH peptides' recognitions sites, although they overlap partially. The analysis also revealed important amino acids that explain the selectivity of the HS-129 peptide for the MC4 receptor.
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| 2. |
- Lapinsh, Maris, et al.
(författare)
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Improved approach for proteochemometrics modeling : application to organic compound--amine G protein-coupled receptor interactions
- 2005
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 21:23, s. 4289-4296
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Tidskriftsartikel (refereegranskat)abstract
- MOTIVATION: Proteochemometrics is a novel technology for the analysis of interactions of series of proteins with series of ligands. We have here customized it for analysis of large datasets and evaluated it for the modeling of the interaction of psychoactive organic amines with all the five known families of amine G protein-coupled receptors (GPCRs). RESULTS: The model exploited data for the binding of 22 compounds to 31 amine GPCRs, correlating chemical descriptions and cross-descriptions of compounds and receptors to binding affinity using a novel strategy. A highly valid model (q2 = 0.76) was obtained which was further validated by external predictions using data for 10 other entirely independent compounds, yielding the high q2ext = 0.67. Interpretation of the model reveals molecular interactions that govern psychoactive organic amines overall affinity for amine GPCRs, as well as their selectivity for particular amine GPCRs. The new modeling procedure allows us to obtain fully interpretable proteochemometrics models using essentially unlimited number of ligand and protein descriptors.
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| 3. |
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| 4. |
- Lapinsh, Maris, et al.
(författare)
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Proteochemometric mapping of the interaction of organic compounds with melanocortin receptor subtypes
- 2005
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Ingår i: Molecular Pharmacology. - : American Society for Pharmacology & Experimental Therapeutics (ASPET). - 0026-895X .- 1521-0111. ; 67:1, s. 50-59
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Tidskriftsartikel (refereegranskat)abstract
- Proteochemometrics was applied in the analysis of the binding of organic compounds to wild-type and chimeric melanocortin receptors. Thirteen chimeric melanocortin receptors were designed based on statistical molecular design; each chimera contained parts from three of the MC(1,3-5) receptors. The binding affinities of 18 compounds were determined for these chimeric melanocortin receptors and the four wild-type melanocortin receptors. The data for 14 of these compounds were correlated to the physicochemical and structural descriptors of compounds, binary descriptors of receptor sequences, and cross-terms derived from ligand and receptor descriptors to obtain a proteochemometric model (correlation was performed using partial least-squares projections to latent structures; PLS). A well fitted mathematical model (R(2) = 0.92) with high predictive ability (Q(2) = 0.79) was obtained. In a further validation of the model, the predictive ability for ligands (Q(2)lig = 0.68) and receptors (Q(2)rec = 0.76) was estimated. The model was moreover validated by external prediction by using the data for the four additional compounds that had not at all been included in the proteochemometric model; the analysis yielded a Q(2)ext = 0.73. An interpretation of the results using PLS coefficients revealed the influence of particular properties of organic compounds on their affinity to melanocortin receptors. Three-dimensional models of melanocortin receptors were also created, and physicochemical properties of the amino acids inside the receptors' transmembrane cavity were correlated to the PLS modeling results. The importance of particular amino acids for selective binding of organic compounds was estimated and used to outline the ligand recognition site in the melanocortin receptors.
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| 6. |
- Lapinsh, Maris, et al.
(författare)
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Proteochemometric modeling reveals the interaction site for Trp9 modified alpha-MSH peptides in melanocortin receptors
- 2007
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Ingår i: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 67:3, s. 653-660
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Tidskriftsartikel (refereegranskat)abstract
- The interactions of α-MSH peptides with melanocortin receptors (MCRs) were located by proteochemometric modeling. Nine α-MSH peptide analogues were constructed by exchanging the Trp9 residue in the α-MSH core with the natural or artificial amino acids Arg, Asp, Cys, Gly, Leu, Nal, d-Nal, Pro, or d-Trp. The nine peptides created, and α-MSH itself, were evaluated for their interactions with the 4 wild-type MC1,3-5Rs and 15 multichimeric MCRs, each of the latter being constructed from three sequence segments, each taken from a different wild-type MC1,3-5R. The segments of the chimeric MCRs were selected according to the principles of statistical molecular design and were arranged so as to divide the receptors into five parts. By this approach, a set of 19 maximally diverse MC receptor proteins was obtained for which the interaction activity with the 10 peptides were measured by radioligand binding thus creating data for 190 ligand-protein pairs, which were subsequently analyzed by use of proteochemometric modeling. In proteochemometrics, the structural or physicochemical properties of both interaction partners, which represent the complementarity of the interacting entities, are used to create multivariate mathematical descriptions. (Here, physicochemical property descriptors of the receptors' and peptides' amino acids were used). A valid, highly predictive (Q2 = 0.74) and easily interpretable model was then obtained. The model was further validated by its ability to correctly predicting the affinity of α-MSH for new point and cassette-mutated MC4/MC1RS, and it was then used to identify the receptor residues that are important for affording the high affinity and selectivity of α-MSH for the MC1R. It was revealed that these residues are located in several quite distant parts of the receptors' transmembrane cavity and must therefore cause their influence at various stages of the dynamic ligand-binding process, such as by affecting the conformation of the ligand at the vicinity of the receptor and taking part in the path of the ligand's entry into its binding pocket. Our study can be used as a template how to create high resolution proteochemometric models when there are a limited number of natural proteins and ligands available.
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| 9. |
- Prusis, Peteris, et al.
(författare)
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Prediction of indirect interactions in proteins
- 2006
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 7, s. 167-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Both direct and indirect interactions determine molecular recognition of ligands by proteins. Indirect interactions can be defined as effects on recognition controlled from distant sites in the proteins, e.g. by changes in protein conformation and mobility, whereas direct interactions occur in close proximity of the protein's amino acids and the ligand. Molecular recognition is traditionally studied using three-dimensional methods, but with such techniques it is difficult to predict the effects caused by mutational changes of amino acids located far away from the ligand-binding site. We recently developed an approach, proteochemometrics, to the study of molecular recognition that models the chemical effects involved in the recognition of ligands by proteins using statistical sampling and mathematical modelling. RESULTS: A proteochemometric model was built, based on a statistically designed protein library's (melanocortin receptors') interaction with three peptides and used to predict which amino acids and sequence fragments that are involved in direct and indirect ligand interactions. The model predictions were confirmed by directed mutagenesis. The predicted presumed direct interactions were in good agreement with previous three-dimensional studies of ligand recognition. However, in addition the model could also correctly predict the location of indirect effects on ligand recognition arising from distant sites in the receptors, something that three-dimensional modelling could not afford. CONCLUSION: We demonstrate experimentally that proteochemometric modelling can be used with high accuracy to predict the site of origin of direct and indirect effects on ligand recognitions by proteins.
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