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Managing Experimental 3D Structures in the Beyond-Rule-of-5 Chemical Space: The Case of Rifampicin.

ABSTRACT: The beyond-Rule-of-5 (bRo5) chemical space is a source of new oral drugs and includes large and flexible compounds. Because of their size and conformational variability, bRo5 molecules assume different privileged conformations in the compartments of human body, i. e., they can exhibit chameleonic properties. The elucidation of the ensemble of 3D structures explored by such molecules under different conditions is therefore critical to check the role played by chameleonicity to modulate cell permeability. Here we characterized the conformational ensembles of rifampicin, a bRo5 drug, in polar and nonpolar solvents and in the solid state. We performed NMR experiments, analyzed their results with a novel algorithm and set-up a pool of ad hoc in silico strategies to investigate crystallographic structures retrieved from the CSD. Moreover, a polarity descriptor often related to permeability (SA-3D-PSA) was calculated for all the conformers and its variation with the environment analyzed. Results showed that the conformational behavior of rifampicin in solution and in the solid state is not superposable. The identification of dynamic intramolecular hydrogen bonds can be assessed by NMR spectroscopy but not by X-ray structures. Moreover, SA-3D-PSA revealed that dynamic IMHBs do not provide rifampicin with chameleonic properties. Overall, this study highlights that the peculiarity of rifampicin, which is cell permeable probably because of the presence of static IMHBs but is devoid of any chameleonic behavior, can be assessed by a proper analysis of experimental 3D structures.

SUBMITTER: Ermondi G

PROVIDER: S-EPMC8361677 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The structural identification of unknown biochemical compounds in complex biofluids continues to be a major challenge in metabolomics research. Using LC/MS, there are currently two major options for solving this problem: searching small biochemical databases, which often do not contain the unknown of interest or searching large chemical databases which include large numbers of nonbiochemical compounds. Searching larger chemical databases (larger chemical space) increases the odds of identifying an unknown biochemical compound, but only if nonbiochemical structures can be eliminated from consideration. In this paper we present BioSM; a cheminformatics tool that uses known endogenous mammalian biochemical compounds (as scaffolds) and graph matching methods to identify endogenous mammalian biochemical structures in chemical structure space. The results of a comprehensive set of empirical experiments suggest that BioSM identifies endogenous mammalian biochemical structures with high accuracy. In a leave-one-out cross validation experiment, BioSM correctly predicted 95% of 1388 Kyoto Encyclopedia of Genes and Genomes (KEGG) compounds as endogenous mammalian biochemicals using 1565 scaffolds. Analysis of two additional biological data sets containing 2330 human metabolites (HMDB) and 2416 plant secondary metabolites (KEGG) resulted in biochemical annotations of 89% and 72% of the compounds, respectively. When a data set of 3895 drugs (DrugBank and USAN) was tested, 48% of these structures were predicted to be biochemical. However, when a set of synthetic chemical compounds (Chembridge and Chemsynthesis databases) were examined, only 29% of the 458,207 structures were predicted to be biochemical. Moreover, BioSM predicted that 34% of 883,199 randomly selected compounds from PubChem were biochemical. We then expanded the scaffold list to 3927 biochemical compounds and reevaluated the above data sets to determine whether scaffold number influenced model performance. Although there were significant improvements in model sensitivity and specificity using the larger scaffold list, the data set comparison results were very similar. These results suggest that additional biochemical scaffolds will not further improve our representation of biochemical structure space and that the model is reasonably robust. BioSM provides a qualitative (yes/no) and quantitative (ranking) method for endogenous mammalian biochemical annotation of chemical space and, thus, will be useful in the identification of unknown biochemical structures in metabolomics. BioSM is freely available at http://metabolomics.pharm.uconn.edu.

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Managing Experimental 3D Structures in the Beyond-Rule-of-5 Chemical Space: The Case of Rifampicin.

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