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- Ignatovica, Vita, et al.
(author)
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Identification and analysis of functionally important amino acids in human purinergic 12 receptor using a Saccharomyces cerevisiae expression system
- 2012
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In: The FEBS Journal. - : Wiley. - 1742-464X .- 1742-4658. ; 279:1, s. 180-191
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Journal article (peer-reviewed)abstract
- The purinergic 12 receptor (P2Y12) is a major drug target for anticoagulant therapies, but little is known about the regions involved in ligand binding and activation of this receptor. We generated four randomized P2Y12 libraries and investigated their ligand binding characteristics. P2Y12 was expressed in a Saccharomyces cerevisiae model system. Four libraries were generated with randomized amino acids at positions 181, 256, 265 and 280. Mutant variants were screened for functional activity in yeast using the natural P2Y12 ligand ADP. Activation results were investigated using quantitative structure-activity relationship (QSAR) models and ligand-receptor docking. We screened four positions in P2Y12 for functional activity by substitution with amino acids with diverse physiochemical properties. This analysis revealed that positions E181, R256 and R265 alter the functional activity of P2Y12 in a specific manner. QSAR models for E181 and R256 mutant libraries strongly supported the experimental data. All substitutions of amino acid K280 were completely inactive, highlighting the crucial role of this residue in P2Y12 function. Ligand-receptor docking revealed that K280 is likely to be a key element in the ligand-binding pocket of P2Y12. The results of this study demonstrate that positions 181, 256, 265 and 280 of P2Y12 are important for the functional integrity of the receptor. Moreover, K280 appears to be a crucial feature of the P2Y12 ligand-binding pocket. These results are important for rational design of novel antiplatelet agents.
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| 4. |
- Junaid, Muhammad, et al.
(author)
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Enzymatic Analysis of Recombinant Japanese Encephalitis Virus NS2B(H)-NS3pro Protease with Fluorogenic Model Peptide Substrates
- 2012
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 7:5, s. e36872-
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Journal article (peer-reviewed)abstract
- Background Japanese encephalitis virus (JEV), a member of the Flaviviridae family, causes around 68,000 encephalitis cases annually, of which 20–30% are fatal, while 30–50% of the recovered cases develop severe neurological sequelae. Specific antivirals for JEV would be of great importance, particularly in those cases where the infection has become persistent. Being indispensable for flaviviral replication, the NS2B-NS3 protease is a promising target for design of anti-flaviviral inhibitors. Contrary to related flaviviral proteases, the JEV NS2B-NS3 protease is structurally and mechanistically much less characterized. Here we aimed at establishing a straightforward procedure for cloning, expression, purification and biochemical characterization of JEV NS2B(H)-NS3pro protease. Methodology/Principal Findings The full-length sequence of JEV NS2B-NS3 genotype III strain JaOArS 982 was obtained as a synthetic gene. The sequence of NS2B(H)-NS3pro was generated by splicing by overlap extension PCR (SOE-PCR) and cloned into the pTrcHisA vector. Hexahistidine-tagged NS2B(H)-NS3pro, expressed in E. coli as soluble protein, was purified to >95% purity by a single-step immobilized metal affinity chromatography. SDS-PAGE and immunoblotting of the purified enzyme demonstrated NS2B(H)-NS3pro precursor and its autocleavage products, NS3pro and NS2B(H), as 36, 21, and 10 kDa bands, respectively. Kinetic parameters, Km and kcat, for fluorogenic protease model substrates, Boc-GRR-amc, Boc-LRR-amc, Ac-nKRR-amc, Bz-nKRR-amc, Pyr-RTKR-amc and Abz-(R)4SAG-nY-amide, were obtained using inner filter effect correction. The highest catalytic efficiency kcat/Km was found for Pyr-RTKR-amc (kcat/Km: 1962.96±85.0 M−1 s−1) and the lowest for Boc-LRR-amc (kcat/Km: 3.74±0.3 M−1 s−1). JEV NS3pro is inhibited by aprotinin but to a lesser extent than DEN and WNV NS3pro. Conclusions/Significance A simplified procedure for the cloning, overexpression and purification of the NS2B(H)-NS3pro was established which is generally applicable to other flaviviral proteases. Kinetic parameters obtained for a number of model substrates and inhibitors, are useful for the characterization of substrate specificity and eventually for the design of high-throughput assays aimed at antiviral inhibitor discovery.
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| 5. |
- Junaid, Muhammad, et al.
(author)
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Proteochemometric Modeling of the Susceptibility of Mutated Variants of the HIV-1 Virus to Reverse Transcriptase Inhibitors
- 2010
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 5:12, s. e14353-
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Journal article (peer-reviewed)abstract
- BackgroundReverse transcriptase is a major drug target in highly active antiretroviral therapy (HAART) against HIV, which typically comprises two nucleoside/nucleotide analog reverse transcriptase (RT) inhibitors (NRTIs) in combination with a non-nucleoside RT inhibitor or a protease inhibitor. Unfortunately, HIV is capable of escaping the therapy by mutating into drug-resistant variants. Computational models that correlate HIV drug susceptibilities to the virus genotype and to drug molecular properties might facilitate selection of improved combination treatment regimens.Methodology/Principal FindingsWe applied our earlier developed proteochemometric modeling technology to analyze HIV mutant susceptibility to the eight clinically approved NRTIs. The data set used covered 728 virus variants genotyped for 240 sequence residues of the DNA polymerase domain of the RT; 165 of these residues contained mutations; totally the data-set covered susceptibility data for 4,495 inhibitor-RT combinations. Inhibitors and RT sequences were represented numerically by 3D-structural and physicochemical property descriptors, respectively. The two sets of descriptors and their derived cross-terms were correlated to the susceptibility data by partial least-squares projections to latent structures. The model identified more than ten frequently occurring mutations, each conferring more than two-fold loss of susceptibility for one or several NRTIs. The most deleterious mutations were K65R, Q151M, M184V/I, and T215Y/F, each of them decreasing susceptibility to most of the NRTIs. The predictive ability of the model was estimated by cross-validation and by external predictions for new HIV variants; both procedures showed very high correlation between the predicted and actual susceptibility values (Q2 = 0.89 and Q2ext = 0.86). The model is available at www.hivdrc.org as a free web service for the prediction of the susceptibility to any of the clinically used NRTIs for any HIV-1 mutant variant.Conclusions/SignificanceOur results give directions how to develop approaches for selection of genome-based optimum combination therapy for patients harboring mutated HIV variants.
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| 6. |
- Lapins, Maris, et al.
(author)
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A confidence predictor for logD using conformal regression and a support-vector machine
- 2018
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In: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 10:1
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Journal article (peer-reviewed)abstract
- Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.
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| 7. |
- Lapins, Maris, et al.
(author)
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A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms
- 2013
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:6, s. e66566-
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Journal article (peer-reviewed)abstract
- A unified proteochemometric (PCM) model for the prediction of the ability of drug-like chemicals to inhibit five major drug metabolizing CYP isoforms (i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) was created and made publicly available under the Bioclipse Decision Support open source system at www.cyp450model.org. In regards to the proteochemometric modeling we represented the chemical compounds by molecular signature descriptors and the CYP-isoforms by alignment-independent description of composition and transition of amino acid properties of their protein primary sequences. The entire training dataset contained 63 391 interactions and the best PCM model was obtained using signature descriptors of height 1, 2 and 3 and inducing the model with a support vector machine. The model showed excellent predictive ability with internal AUC = 0.923 and an external AUC = 0.940, as evaluated on a large external dataset. The advantage of PCM models is their extensibility making it possible to extend our model for new CYP isoforms and polymorphic CYP forms. A key benefit of PCM is that all proteins are confined in one single model, which makes it generally more stable and predictive as compared with single target models. The inclusion of the model in Bioclipse Decision Support makes it possible to make virtual instantaneous predictions (∼100 ms per prediction) while interactively drawing or modifying chemical structures in the Bioclipse chemical structure editor.
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| 8. |
- Lapins, Maris, 1970-
(author)
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Development of Proteochemometrics—A New Approach for Analysis of Protein-Ligand Interactions
- 2006
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Doctoral thesis (other academic/artistic)abstract
- A new approach to analysis of protein-ligand interactions, termed proteochemometrics, has been developed. Contrary to traditional quantitative structure-activity relationship (QSAR) methods that aim to correlate a description of ligands to their interactions with one particular target protein, proteochemometrics considers many targets simultaneously.Proteochemometrics thus analyzes the experimentally determined protein-ligand interaction activity data by correlating the data to a complex description of all interaction partners and; in a more general case even to interaction environment and assaying conditions, as well. In this way, a proteochemometric model analyzes an “interaction space,” from which only one cross-section would be contemplated by any one QSAR model.Proteochemometric models reveal the physicochemical and structural properties that are essential for protein-ligand complementarity and determine specificity of molecular interactions. From a drug design perspective, models may find use in the design of drugs with improved selectivity and in the design of drugs for multiple targets, such as mutated proteins (e.g., drug resistant mutations of pathogens).In this thesis, a general concept for creating of proteochemometric models and approaches for validation and interpretation of models are presented. Different types of physicochemical and structural description of ligands and macromolecules are evaluated; mathematical algorithms for proteochemometric modeling, in particular for analysis of large-scale data sets, are developed. Artificial chimeric proteins constructed according to principles of statistical design are used to derive high-resolution models for small classes of proteins.The studies of this thesis use data sets comprising ligand interactions with several families of G protein-coupled receptors. The presented approach is, however, general and can be applied to study molecular recognition mechanisms of any class of drug targets.
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| 9. |
- Lapins, Maris, et al.
(author)
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Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques
- 2010
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In: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 11, s. 339-
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Journal article (peer-reviewed)abstract
- BackgroundProtein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment of these diseases. However, current inhibitors interact with a broad variety of kinases and interfere with multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that can predict inhibitor-kinase interactions from the chemical properties of the inhibitors and the kinase macromolecules might aid in design of more selective therapeutic agents, that show better efficacy and lower toxicity.ResultsWe applied proteochemometric modelling to correlate the properties of 317 wild-type and mutated kinases and 38 inhibitors (12,046 inhibitor-kinase combinations) to the respective combination's interaction dissociation constant (K-d). We compared six approaches for description of protein kinases and several linear and non-linear correlation methods. The best performing models encoded kinase sequences with amino acid physico-chemical z-scale descriptors and used support vector machines or partial least-squares projections to latent structures for the correlations. Modelling performance was estimated by double cross-validation. The best models showed high predictive ability; the squared correlation coefficient for new kinase-inhibitor pairs ranging P-2 = 0.67-0.73; for new kinases it ranged P-kin(2) = 0.65-0.70. Models could also separate interacting from non-interacting inhibitor-kinase pairs with high sensitivity and specificity; the areas under the ROC curves ranging AUC = 0.92-0.93. We also investigated the relationship between the number of protein kinases in the dataset and the modelling results. Using only 10% of all data still a valid model was obtained with P-2 = 0.47, P-kin(2) = 0.42 and AUC = 0.83.ConclusionsOur results strongly support the applicability of proteochemometrics for kinome-wide interaction modelling. Proteochemometrics might be used to speed-up identification and optimization of protein kinase targeted and multi-targeted inhibitors.
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| 10. |
- Lapins, Maris, et al.
(author)
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Proteochemometric Modeling of Drug Resistance over the Mutational Space for Multiple HIV Protease Variants and Multiple Protease Inhibitors
- 2009
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In: Journal of chemical information and modeling. - : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 49:5, s. 1202-1210
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Journal article (peer-reviewed)abstract
- The main therapeutic targets in HIV are its protease and reverse transcriptase. A major problem in treatment of HIV is the ability of the virus to develop drug resistance by accumulating mutations in its targets. Acquiring detailed understanding of the molecular mechanisms for the interactions of drugs with mutated variants of the HIV virus is mandatory to be able to design inhibitors that can evade the resistance. Here we have used proteochemometric modeling to simultaneously analyze the interactions of 21 protease inhibitors with 72 unique protease variants. Inhibition data (pK(i)) were correlated to descriptions of chemical and structural properties of the inhibitors and proteases. The proteochemometric model obtained showed excellent fit and predictive ability (R(2)=0.92, Q(2)=0.83, Q(2)(inh)=0.78) and provided quantitative assessments for the contribution of each mutation and their combinations to the decrease in inhibitor activity, both for the whole compounds series as well as for individual compounds. The model revealed the most deleterious mutations in the protease to be D30N, V32I, G48V, I50V, I54V, V82A, I84V, and L90M. The model was further used to identify molecular properties of chemical compounds that are important for their inhibition of multimutated protease variants. Our results give directions how to design novel improved inhibitors.
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