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- Freyhult, Eva, et al.
(author)
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Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling
- 2005
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In: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 6, s. 50-
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Journal article (peer-reviewed)abstract
- Background Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis. Results A methodology for an unbiased evaluation of the predictive power of proteochemometric models was implemented and results from applying it to two of the largest proteochemometric data sets yet reported are presented. A double cross-validation loop procedure is used to estimate the expected performance of a given design method. The unbiased performance estimates (P2) obtained for the data sets that we consider confirm that properly designed single proteochemometric models have useful predictive power, but that a standard design based on cross validation may yield models with quite limited performance. The results also show that different commercial software packages employed for the design of proteochemometric models may yield very different and therefore misleading performance estimates. In addition, the differences in the models obtained in the double CV loop indicate that detailed chemical interpretation of a single proteochemometric model is uncertain when data sets are small. Conclusion The double CV loop employed offer unbiased performance estimates about a given proteochemometric modelling procedure, making it possible to identify cases where the proteochemometric design does not result in useful predictive models. Chemical interpretations of single proteochemometric models are uncertain and should instead be based on all the models selected in the double CV loop employed here.
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- Lapinsh, Maris, et al.
(author)
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Improved approach for proteochemometrics modeling : application to organic compound--amine G protein-coupled receptor interactions
- 2005
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 21:23, s. 4289-4296
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Journal article (peer-reviewed)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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| 6. |
- Lapinsh, Maris, et al.
(author)
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Proteochemometric mapping of the interaction of organic compounds with melanocortin receptor subtypes
- 2005
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In: Molecular Pharmacology. - : American Society for Pharmacology & Experimental Therapeutics (ASPET). - 0026-895X .- 1521-0111. ; 67:1, s. 50-59
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Journal article (peer-reviewed)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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| 8. |
- Lapinsh, Maris, et al.
(author)
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Proteochemometric modeling reveals the interaction site for Trp9 modified alpha-MSH peptides in melanocortin receptors
- 2007
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In: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 67:3, s. 653-660
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Journal article (peer-reviewed)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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