Comparative Modeling of Drug Target Proteins
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ABSTRACT:
SUBMITTER: Eswar N
PROVIDER: S-EPMC7151936 | biostudies-literature | 2007 Jan
REPOSITORIES: biostudies-literature
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Project description:In the present study, comparative genome analysis between Clostridium perfringens and the human genome was carried out to identify genes that are essential for the pathogen's survival, and non-homologous to the genes of human host, that can be used as potential drug targets. The study resulted in the identification of 426 such genes. The number of these potential drug targets thus identified is significantly lower than the genome's protein coding capacity (2558 protein coding genes). The 426 genes of C. perfringens were further analyzed for overall similarities with the essential genes of 14 different bacterial species present in Database of Essential Genes (DEG). Our results show that there are only 5 essential genes of C. perfringens that exhibit similarity with 12 species of the 14 different bacterial species present in DEG database. Of these, 1 gene was similar in 12 species and 4 genes were similar in 11 species. Thus, the study opens a new avenue for the development of potential drugs against the highly pathogenic bacterium. Further, by selecting these essential genes of C. perfringens, which are common and essential for other pathogenic microbial species, a broad spectrum anti-microbial drug can be developed. As a case study, we have built a homology model of one of the potential drug targets, ABC transporter-ATP binding protein, which can be employed for in silico docking studies by suitable inhibitors.
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Project description:Chemical probes are important tools for understanding signaling systems. However, because of the huge combinatorial space of targets and potential compounds, traditional chemical screens cannot be applied systematically to find probes for all possible druggable targets. Here, we demonstrate a novel concept for overcoming this challenge by leveraging high-throughput metabolomics and overexpression to predict drug-target interactions. The metabolome profiles of yeast treated with 1280 compounds from a chemical library were collected and compared with those of inducible yeast membrane protein overexpression strains. By matching metabolome profiles, we predicted which small molecules targeted which signaling systems and recovered known interactions. Drug-target predictions were generated across the 86 genes studied, including for difficult to study membrane proteins. A subset of those predictions were tested and validated, including the novel targeting of GPR1 signaling by ibuprofen. These results demonstrate the feasibility of predicting drug-target relationships for eukaryotic proteins using high-throughput metabolomics.
| S-SCDT-MSB-2021-10767 | biostudies-other
Project description:A growing consensus is emerging that optimizing the drug-target affinity alone under equilibrium conditions does not necessarily translate into higher potency in vivo and that instead binding kinetic parameters should be optimized to ensure better efficacy. Therefore, in silico methods are needed to predict the kinetic parameters and the mechanistic determinants of drug-protein binding. Here we demonstrate the application of COMparative BINding Energy (COMBINE) analysis to derive quantitative structure-kinetics relationships (QSKRs) for the dissociation rate constants (k off) of inhibitors of heat shock protein 90 (HSP90) and HIV-1 protease. We derived protein-specific scoring functions by correlating k off rate constants with a subset of weighted interaction energy components determined from the energy-minimized structures of drug-protein complexes. As the QSKRs derived for these sets of chemically diverse compounds have good predictive ability and provide insights into important drug-protein interactions for optimizing k off, COMBINE analysis offers a promising approach for binding kinetics-guided lead optimization.
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Project description:BackgroundComputational approaches in the identification of drug targets are expected to reduce time and effort in drug development. Advances in genomics and proteomics provide the opportunity to uncover properties of druggable genomes. Although several studies have been conducted for distinguishing drug targets from non-drug targets, they mainly focus on the sequences and functional roles of proteins. Many other properties of proteins have not been fully investigated.MethodsUsing the DrugBank (version 3.0) database containing nearly 6,816 drug entries including 760 FDA-approved drugs and 1822 of their targets and human UniProt/Swiss-Prot databases, we defined 1578 non-redundant drug target and 17,575 non-drug target proteins. To select these non-redundant protein datasets, we built four datasets (A, B, C, and D) by considering clustering of paralogous proteins.ResultsWe first reassessed the widely used properties of drug target proteins. We confirmed and extended that drug target proteins (1) are likely to have more hydrophobic, less polar, less PEST sequences, and more signal peptide sequences higher and (2) are more involved in enzyme catalysis, oxidation and reduction in cellular respiration, and operational genes. In this study, we proposed new properties (essentiality, expression pattern, PTMs, and solvent accessibility) for effectively identifying drug target proteins. We found that (1) drug targetability and protein essentiality are decoupled, (2) druggability of proteins has high expression level and tissue specificity, and (3) functional post-translational modification residues are enriched in drug target proteins. In addition, to predict the drug targetability of proteins, we exploited two machine learning methods (Support Vector Machine and Random Forest). When we predicted drug targets by combining previously known protein properties and proposed new properties, an F-score of 0.8307 was obtained.ConclusionsWhen the newly proposed properties are integrated, the prediction performance is improved and these properties are related to drug targets. We believe that our study will provide a new aspect in inferring drug-target interactions.
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Project description:Bortezomib is a reversible proteasome inhibitor with potent antineoplastic activity that exhibits dose- and time-dependent pharmacokinetics (PK). Proteasome-mediated bortezomib disposition is proposed as the primary source of its nonlinear and apparent nonstationary PK behavior. Single intravenous (IV) doses of bortezomib (0.25 and 1 mg/kg) were administrated to BALB/c mice, with blood and tissue samples obtained over 144 h, which were analyzed by LC/MS/MS. A physiologically based pharmacokinetic (PBPK) model incorporating tissue drug-target binding was developed to test the hypothesis of proteasome-mediated bortezomib disposition. The final model reasonably captured bortezomib plasma and tissue PK profiles, and parameters were estimated with good precision. The rank-order of model estimated tissue target density correlated well with experimentally measured proteasome concentrations reported in the literature, supporting the hypothesis that binding to proteasome influences bortezomib disposition. The PBPK model was further scaled-up to humans to assess the similarity of bortezomib disposition among species. Human plasma bortezomib PK profiles following multiple IV dosing (1.3 mg/m(2)) on days 1, 4, 8, and 11 were simulated by appropriately scaling estimated mouse parameters. Simulated and observed bortezomib concentrations after multiple dosing were in good agreement, suggesting target-mediated bortezomib disposition is likely for both mice and humans. Furthermore, the model predicts that renal impairment should exert minimal influence on bortezomib exposure in humans, confirming that bortezomib dose adjustment is not necessary for patients with renal impairment.
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