Investigation of substrate recognition for cytochrome P450 1A2 mediated by water molecules using docking and molecular dynamics simulations

ATLAS: protein flexibility description from atomistic molecular dynamics simulations

Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.

Structural investigation of pathogenic variants in dihydropyrimidinase using molecular dynamics simulations

Dihydropyrimidinase (DHP) is an enzyme that catabolizes the degradation of pyrimidine and fluoropyrimidine drugs such as 5-fluorouracil. DHP deficiency triggers various clinical symptoms and increases the risk of fluoropyrimidine drug toxicity. Various pathogenic variants of DHP cause DHP deficiency, and their catalytic activities have been well studied. However, the three-dimensional structures of DHP variants have not been clarified. In this study, we investigated the effects of mutations on DHP structures using the molecular dynamics simulations. Simulations of the wild type and 10 variants were performed and compared. In the T68R, D81G, G278D, and L337P variants, the flexibilities of structures related to the interaction for oligomer formation increased in comparison with those of the wild type. W117R, T343A, and R412 M mutations affected the structures of stereochemistry gate loops or the substrate-binding pocket. The three-dimensional structures of W360R and G435R variants were suggested to collapse. On the other hand, only slight structural changes were observed in the R181W variant, whose experimentally observed activity was similar to that of the wild type. The computational results are expected to clarify the relationship between clinical mutations and structural effects of drug-metabolizing enzymes.

Heat Shock Protein 90 (HSP90) Inhibitors as Anticancer Medicines: A Review on the Computer-Aided Drug Discovery Approaches over the Past Five Years

Cancer is a disease caused by the uncontrolled, abnormal growth of cells in different anatomic sites. In 2018, it was predicted that the worldwide cancer burden would rise to 18.1 million new cases and 9.6 million deaths. Anticancer compounds, often known as chemotherapeutic medicines, have gained much interest in recent cancer research. These medicines work through various biological processes in targeting cells at various stages of the cell's life cycle. One of the most significant roadblocks to developing anticancer drugs is that traditional chemotherapy affects normal cells and cancer cells, resulting in substantial side effects. Recently, advancements in new drug development methodologies and the prediction of the targeted interatomic and intermolecular ligand interaction sites have been beneficial. This has prompted further research into developing and discovering novel chemical species as preferred therapeutic compounds against specific cancer types. Identifying new drug molecules with high selectivity and specificity for cancer is a prerequisite in the treatment and management of the disease. The overexpression of HSP90 occurs in patients with cancer, and the HSP90 triggers unstable harmful kinase functions, which enhance carcinogenesis. Therefore, the development of potent HSP90 inhibitors with high selectivity and specificity becomes very imperative. The activities of HSP90 as chaperones and cochaperones are complex due to the conformational dynamism, and this could be one of the reasons why no HSP90 drugs have made it beyond the clinical trials. Nevertheless, HSP90 modulations appear to be preferred due to the competitive inhibition of the targeted N-terminal adenosine triphosphate pocket. This study, therefore, presents an overview of the various computational models implored in the development of HSP90 inhibitors as anticancer medicines. We hereby suggest an extensive investigation of advanced computational modelling of the three different domains of HSP90 for potent, effective inhibitor design with minimal off-target effects.

Virtual Alanine Scan of the Main Protease Active Site in Severe Acute Respiratory Syndrome Coronavirus 2

Recently, inhibitors of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) have been proposed as potential therapeutic agents for COVID-19. Studying effects of amino acid mutations in the conformation of drug targets is necessary for anticipating drug resistance. In this study, with the structure of the SARS-CoV-2 Mpro complexed with a non-covalent inhibitor, we performed molecular dynamics (MD) simulations to determine the conformation of the complex when single amino acid residue in the active site is mutated. As a model of amino acid mutation, we constructed mutant proteins with one residue in the active site mutated to alanine. This method is called virtual alanine scan. The results of the MD simulations showed that the conformation and configuration of the ligand was changed for mutants H163A and E166A, although the structure of the whole protein and of the catalytic dyad did not change significantly, suggesting that mutations in His163 and Glu166 may be linked to drug resistance.

Substrate Selectivity of Coumarin Derivatives by Human CYP1 Enzymes: In Vitro Enzyme Kinetics and In Silico Modeling

Of the three enzymes in the human cytochrome P450 family 1, CYP1A2 is an important enzyme mediating metabolism of xenobiotics including drugs in the liver, while CYP1A1 and CYP1B1 are expressed in extrahepatic tissues. Currently used CYP substrates, such as 7-ethoxycoumarin and 7-ethoxyresorufin, are oxidized by all individual CYP1 forms. The main aim of this study was to find profluorescent coumarin substrates that are more selective for the individual CYP1 forms. Eleven 3-phenylcoumarin derivatives were synthetized, their enzyme kinetic parameters were determined, and their interactions in the active sites of CYP1 enzymes were analyzed by docking and molecular dynamic simulations. All coumarin derivatives and 7-ethoxyresorufin and 7-pentoxyresorufin were oxidized by at least one CYP1 enzyme. 3-(3-Methoxyphenyl)-6-methoxycoumarin (19) was 7-O-demethylated by similar high efficiency [21-30 ML/(min·mol CYP)] by all CYP1 forms and displayed similar binding in the enzyme active sites. 3-(3-Fluoro-4-acetoxyphenyl)coumarin (14) was selectively 7-O-demethylated by CYP1A1, but with low efficiency [0.16 ML/(min mol)]. This was explained by better orientation and stronger H-bond interactions in the active site of CYP1A1 than that of CYP1A2 and CYP1B1. 3-(4-Acetoxyphenyl)-6-chlorocoumarin (20) was 7-O-demethylated most efficiently by CYP1B1 [53 ML/(min·mol CYP)], followed by CYP1A1 [16 ML/(min·mol CYP)] and CYP1A2 [0.6 ML/(min·mol CYP)]. Variations in stabilities of complexes between 20 and the individual CYP enzymes explained these differences. Compounds 14, 19, and 20 are candidates to replace traditional substrates in measuring activity of human CYP1 enzymes.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Molecular Dynamics Simulations for Three-Dimensional Structures of Orotate Phosphoribosyltransferases Constructed from a Simplified Amino Acid Set

Proteins of modern terrestrial organisms are composed of nearly 20 amino acids; however, the amino acid sets of primitive organisms may have contained fewer than 20 amino acids. Furthermore, the full set of 20 amino acids is not required by some proteins to encode their function. Indeed, simplified variants of Escherichia coli (E. coli) orotate phosphoribosyltransferase (OPRTase) constructed by Akanuma et al. and composed of a limited amino acid set exhibit significant catalytic activity for the growth of E. coli. However, its structural details are currently unclear. Here, we predict the structures of simplified variants of OPRTase using molecular dynamics (MD) simulations and evaluate the accuracy of the MD simulations for simplified proteins. The three-dimensional structure of the wild-type was largely maintained in the simplified variants, but differences in the catalyst loop and C-terminal helix were observed. These results are considered sufficient to elucidate the differences in catalytic activity between the wild-type and simplified OPRTase variants. Thus, using MD simulations to make structural predictions appears to be a useful strategy when investigating non-wild-type proteins composed of reduced amino acid sets.

Integrating Molecular Docking and Molecular Dynamics Simulations

Computational methods, applied at the early stages of the drug design process, use current technology to provide valuable insights into the understanding of chemical systems in a virtual manner, complementing experimental analysis. Molecular docking is an in silico method employed to foresee binding modes of small compounds or macromolecules in contact with a receptor and to predict their molecular interactions. Moreover, the methodology opens up the possibility of ranking these compounds according to a hierarchy determined using particular scoring functions. Docking protocols assign many approximations, and most of them lack receptor flexibility. Therefore, the reliability of the resulting protein-ligand complexes is uncertain. The association with the costly but more accurate MD techniques provides significant complementary with docking. MD simulations can be used before docking since a series of "new" and broader protein conformations can be extracted from the processing of the resulting trajectory and employed as targets for docking. They also can be utilized a posteriori to optimize the structures of the final complexes from docking, calculate more detailed interaction energies, and provide information about the ligand binding mechanism. Here, we focus on protocols that offer the docking-MD combination as a logical approach to improving the drug discovery process.

Structure-Based Drug Design for Cytochrome P450 Family 1 Inhibitors

Cytochromes P450 are a class of metalloproteins which are responsible for electron transfer in a wide spectrum of reactions including metabolic biotransformation of endogenous and exogenous substrates. The superfamily of cytochromes P450 consists of families and subfamilies which are characterized by a specific structure and substrate specificity. Cytochromes P450 family 1 (CYP1s) play a distinctive role in the metabolism of drugs and chemical procarcinogens. In recent decades, these hemoproteins have been intensively studied with the use of computational methods which have been recently developed remarkably to be used in the process of drug design by the virtual screening of compounds in order to find agents with desired properties. Moreover, the molecular modeling of proteins and ligand docking to their active sites provide an insight into the mechanism of enzyme action and enable us to predict the sites of drug metabolism. The review presents the current status of knowledge about the use of the computational approach in studies of ligand-enzyme interactions for CYP1s. Research on the metabolism of substrates and inhibitors of CYP1s and on the selectivity of their action is particularly valuable from the viewpoint of cancer chemoprevention, chemotherapy, and drug-drug interactions.