| 1. |
- Freyhult, Eva, et al.
(författare)
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Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling
- 2005
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 6, s. 50-
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Tidskriftsartikel (refereegranskat)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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| 2. |
- Kontijevskis, Aleksejs, et al.
(författare)
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A look inside HIV resistance through retroviral protease interaction maps
- 2007
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 3:3, s. 424-435
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Tidskriftsartikel (refereegranskat)abstract
- Retroviruses affect a large number of species, from fish and birds to mammals and humans, with global socioeconomic negative impacts. Here the authors report and experimentally validate a novel approach for the analysis of the molecular networks that are involved in the recognition of substrates by retroviral proteases. Using multivariate analysis of the sequence-based physiochemical descriptions of 61 retroviral proteases comprising wild-type proteases, natural mutants, and drug-resistant forms of proteases from nine different viral species in relation to their ability to cleave 299 substrates, the authors mapped the physicochemical properties and cross-dependencies of the amino acids of the proteases and their substrates, which revealed a complex molecular interaction network of substrate recognition and cleavage. The approach allowed a detailed analysis of the molecular-chemical mechanisms involved in substrate cleavage by retroviral proteases.
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| 3. |
- 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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| 4. |
- Lapins, Maris, et al.
(författare)
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Proteochemometric modeling of HIV protease susceptibility
- 2008
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Ingår i: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 9, s. 181-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUNDA major obstacle in treatment of HIV is the ability of the virus to mutate rapidly into drug-resistant variants. A method for predicting the susceptibility of mutated HIV strains to antiviral agents would provide substantial clinical benefit as well as facilitate the development of new candidate drugs. Therefore, we used proteochemometrics to model the susceptibility of HIV to protease inhibitors in current use, utilizing descriptions of the physico-chemical properties of mutated HIV proteases and 3D structural property descriptions for the protease inhibitors. The descriptions were correlated to the susceptibility data of 828 unique HIV protease variants for seven protease inhibitors in current use; the data set comprised 4792 protease-inhibitor combinations.RESULTSThe model provided excellent predictability (R2 = 0.92, Q2 = 0.87) and identified general and specific features of drug resistance. The model's predictive ability was verified by external prediction in which the susceptibilities to each one of the seven inhibitors were omitted from the data set, one inhibitor at a time, and the data for the six remaining compounds were used to create new models. This analysis showed that the over all predictive ability for the omitted inhibitors was Q2 inhibitors = 0.72.CONCLUSIONOur results show that a proteochemometric approach can provide generalized susceptibility predictions for new inhibitors. Our proteochemometric model can directly analyze inhibitor-protease interactions and facilitate treatment selection based on viral genotype. The model is available for public use, and is located at HIV Drug Research Centre.
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| 7. |
- 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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| 8. |
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| 9. |
- 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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