In Silico Prediction of Human Sulfotransferase 1E1 Activity Guided by Pharmacophores from Molecular Dynamics Simulations

FMO-guided design of darunavir analogs as HIV-1 protease inhibitors

The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the need for antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR), responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is a potent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create DRV analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloud mode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Our findings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of our approach and its potential to be used in further studies for developing new antiretroviral drugs.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.

Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs

Cytosolic sulfotransferases (SULTs) are a family of enzymes responsible for the sulfation of small endogenous and exogenous compounds. SULTs contribute to the conjugation phase of metabolism and share substrates with the uridine 5'-diphospho-glucuronosyltransferase (UGT) family of enzymes. UGTs are considered to be the most important enzymes in the conjugation phase, and SULTs are an auxiliary enzyme system to them. Understanding how the regioselectivity of SULTs differs from that of UGTs is essential from the perspective of developing novel drug candidates. We present a general ligand-based SULT model trained and tested using high-quality experimental regioselectivity data. The current study suggests that, unlike other metabolic enzymes in the modification and conjugation phases, the SULT regioselectivity is not strongly influenced by the activation energy of the rate-limiting step of the catalysis. Instead, the prominent role is played by the substrate binding site of SULT. Thus, the model is trained only on steric and orientation descriptors, which mimic the binding pocket of SULT. The resulting classification model, which predicts whether a site is metabolized, achieved a Cohen's kappa of 0.71.

Identification of repurposing therapeutics toward SARS-CoV-2 main protease by virtual screening

SARS-CoV-2 causes the current global pandemic coronavirus disease 2019. Widely-available effective drugs could be a critical factor in halting the pandemic. The main protease (3CL^pro) plays a vital role in viral replication; therefore, it is of great interest to find inhibitors for this enzyme. We applied the combination of virtual screening based on molecular docking derived from the crystal structure of the peptidomimetic inhibitors (N3, 13b, and 11a), and experimental verification revealed FDA-approved drugs that could inhibit the 3CL^pro of SARS-CoV-2. Three drugs were selected using the binding energy criteria and subsequently performed the 3CL^pro inhibition by enzyme-based assay. In addition, six common drugs were also chosen to study the 3CL^pro inhibition. Among these compounds, lapatinib showed high efficiency of 3CL^pro inhibition (IC₅₀ value of 35 ± 1 μM and K_i of 23 ± 1 μM). The binding behavior of lapatinib against 3CL^pro was elucidated by molecular dynamics simulations. This drug could well bind with 3CL^pro residues in the five subsites S1’, S1, S2, S3, and S4. Moreover, lapatinib’s key chemical pharmacophore features toward SAR-CoV-2 3CL^pro shared important HBD and HBA with potent peptidomimetic inhibitors. The rational design of lapatinib was subsequently carried out using the obtained results. Our discovery provides an effective repurposed drug and its newly designed analogs to inhibit SARS-CoV-2 3CL^pro.

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 μM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 μM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 μM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.

Computational Analysis of Chemical Space of Natural Compounds Interacting with Sulfotransferases

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

Insights into the substrate binding mechanism of SULT1A1 through molecular dynamics with excited normal modes simulations

Sulfotransferases (SULTs) are phase II drug-metabolizing enzymes catalyzing the sulfoconjugation from the co-factor 3′-phosphoadenosine 5′-phosphosulfate (PAPS) to a substrate. It has been previously suggested that a considerable shift of SULT structure caused by PAPS binding could control the capability of SULT to bind large substrates. We employed molecular dynamics (MD) simulations and the recently developed approach of MD with excited normal modes (MDeNM) to elucidate molecular mechanisms guiding the recognition of diverse substrates and inhibitors by SULT1A1. MDeNM allowed exploring an extended conformational space of PAPS-bound SULT1A1, which has not been achieved up to now by using classical MD. The generated ensembles combined with docking of 132 SULT1A1 ligands shed new light on substrate and inhibitor binding mechanisms. Unexpectedly, our simulations and analyses on binding of the substrates estradiol and fulvestrant demonstrated that large conformational changes of the PAPS-bound SULT1A1 could occur independently of the co-factor movements that could be sufficient to accommodate large substrates as fulvestrant. Such structural displacements detected by the MDeNM simulations in the presence of the co-factor suggest that a wider range of drugs could be recognized by PAPS-bound SULT1A1 and highlight the utility of including MDeNM in protein–ligand interactions studies where major rearrangements are expected.

An activity-based fluorescent sensor for the detection of the phenol sulfotransferase SULT1A1 in living cells

Human phenol sulfotransferases mediate the transfer of a sulfuryl moiety from the activated sulfate donor PAPS to hydroxy-containing substrates, altering substrate solubility and charge to affect phase II metabolism and cell signaling. Here, we present the development, computational modeling, in vitro enzymology, and biological application of STS-3, an activity-based fluorescent sensor for the SULT1A1 isoform.

Functional Expression of All Human Sulfotransferases in Fission Yeast, Assay Development, and Structural Models for Isoforms SULT4A1 and SULT6B1

Cytosolic sulfotransferases (SULTs) catalyze phase II (conjugation) reactions of drugs and endogenous compounds. A complete set of recombinant fission yeast strains each expressing one of the 14 human SULTs was generated, including SULT4A1 and SULT6B1. Sulfation of test substrates by whole-cell biotransformation was successfully demonstrated for all enzymes for which substrates were previously known. The results proved that the intracellular production of the cofactor 3'-phosphoadenosine 5'-phosphosulfate (PAPS) necessary for SULT activity in fission yeast is sufficiently high to support metabolite production. A modified variant of sulfotransferase assay was also developed that employs permeabilized fission yeast cells (enzyme bags). Using this approach, SULT4A1-dependent sulfation of 1-naphthol was observed. Additionally, a new and convenient SULT activity assay is presented. It is based on the sulfation of a proluciferin compound, which was catalyzed by SULT1E1, SULT2A1, SULT4A1, and SULT6B1. For the latter two enzymes this study represents the first demonstration of their enzymatic functionality. Furthermore, the first catalytically competent homology models for SULT4A1 and SULT6B1 in complex with PAPS are reported. Through mechanistic molecular modeling driven by substrate docking, we pinned down the increased activity levels of these two isoforms to optimized substrate binding.

Binding Mode Prediction and Identification of New Lead Compounds from Natural Products as 3-OST Enzyme Inhibitors

Background and Introduction:

The availability of antiviral medicines for the treatment of viral diseases is limited, hence the discovery of novel bioactive molecules is required. The present investigation has been carried out to develop novel 3-O-sulfotransferase enzyme inhibitors to treat viral diseases.

Method:

Virtual screening study (QSAR, docking and pharmacophore analysis) and binding mode analysis have been performed on a dataset collected from the literature (synthetic and natural compounds).

Results:

The docking studies showed that Glu184, His186, Lys215 and Lys368 residues established the most important hydrogen bonding with several hit compounds. The QSAR results explained that the presence of electronegative atoms/groups in the aromatic or heteroaromatic rings confer increased activity. Furthermore, the flexibility and the aromatic rings with less polar groups have better activity than the compounds connected to purine rings. Finally, the structurebased pharmacophore studies illustrated that the ligand has many polar interaction sites, and the projected acceptor and donor groups in the molecules make a significant contribution to the pharmacophore model building.

Conclusion:

These studies identified two compounds, Phomoidride B and Barceloneic acid A, as potential 3-OST inhibitors.

Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens

In this review, applications of various molecular modelling methods in the study of estrogens and xenoestrogens are summarized. Selected biomolecules that are the most commonly chosen as molecular modelling objects in this field are presented. In most of the reviewed works, ligand docking using solely force field methods was performed, employing various molecular targets involved in metabolism and action of estrogens. Other molecular modelling methods such as molecular dynamics and combined quantum mechanics with molecular mechanics have also been successfully used to predict the properties of estrogens and xenoestrogens. Among published works, a great number also focused on the application of different types of quantitative structure-activity relationship (QSAR) analyses to examine estrogen's structures and activities. Although the interactions between estrogens and xenoestrogens with various proteins are the most commonly studied, other aspects such as penetration of estrogens through lipid bilayers or their ability to adsorb on different materials are also explored using theoretical calculations. Apart from molecular mechanics and statistical methods, quantum mechanics calculations are also employed in the studies of estrogens and xenoestrogens. Their applications include computation of spectroscopic properties, both vibrational and Nuclear Magnetic Resonance (NMR), and also in quantum molecular dynamics simulations and crystal structure prediction. The main aim of this review is to present the great potential and versatility of various molecular modelling methods in the studies on estrogens and xenoestrogens.

Mechanistic Insights into the Effect of Ligands on Structural Stability and Selectivity of Sulfotransferase 2A1 (SULT2A1)

Cytosolic sulfotransferases (SULTs) acting as phase II metabolic enzymes can be used in the sulfonation of small molecules by transferring a sulfonate group from the unique co-factor 3'-phosphoadenosine 5'-phosphosulfate (PAPS) to the substrates. In the present study, molecular dynamics (MD) simulations and ensemble docking study were employed to theoretically characterize the mechanism for the effect of co-factor (PAP) and ligands (LCA, raloxifene, α-hydroxytamoxifen, ouabain, and 3'-phosphoadenylyl sulfate) on structural stability and selectivity of SULT2A1 from the perspective of the dynamic behavior of SULT2A1 structures. Structural stability and network analyses indicated that the cooperation between PAP and LCA may enhance the thermal stability and compact communication in enzymes. During the MD simulations, the obviously rigid region and inward displacement were detected in the active-site cap (loop16) of the conformation containing PAP, which may be responsible for the significant changes in substrate accessibility and catalytic activity. The smaller substrates such as LCA could bind stably to the active pocket in the presence of PAP. However, the substrates or inhibitors with a large spatial structure needed to bind to the open conformation (without PAP) prior to PAPS binding.

Multiple Virtual Screening Strategies for the Discovery of Novel Compounds Active Against Dengue Virus: A Hit Identification Study

Dengue infection is caused by a mosquito-borne virus, particularly in children, which may even cause death. No effective prevention or therapeutic agents to cure this disease are available up to now. The dengue viral envelope (E) protein was discovered to be a promising target for inhibition in several steps of viral infection. Structure-based virtual screening has become an important technique to identify first hits in a drug screening process, as it is possible to reduce the number of compounds to be assayed, allowing to save resources. In the present study, pharmacophore models were generated using the common hits approach (CHA), starting from trajectories obtained from molecular dynamics (MD) simulations of the E protein complexed with the active inhibitor, flavanone (FN5Y). Subsequently, compounds presented in various drug databases were screened using the LigandScout 4.2 program. The obtained hits were analyzed in more detail by molecular docking, followed by extensive MD simulations of the complexes. The highest-ranked compound from this procedure was then synthesized and tested on its inhibitory efficiency by experimental assays.

Estrogen sulfotransferase in the metabolism of estrogenic drugs and in the pathogenesis of diseases

INTRODUCTION

Biotransformation is important in the metabolism of endobiotics and xenobiotics. This process comprises the activity of phase I and phase II enzymes. Estrogen sulfotransferase (SULT1E1 or EST) is a phase II conjugating enzyme that belongs to the family of cytosolic sulfotransferases. The expression of SULT1E1 can be detected in many tissues, including the liver. SULT1E1 catalyzes the transfer of a sulfate group from 3'-phosphoadenosine-5'-phosphosulfate (PAPS) to any available hydroxyl group in estrogenic molecules. The substrates of SULT1E1 include the endogenous and synthetic estrogens. Upon SULT1E1-mediated sulfation, the hydrosolubility of estrogens increases, preventing the binding between the sulfated estrogens and the estrogen receptor (ER). This sulfated state of the estrogens is not irreversible, as the steroid sulfatase (STS) can convert sulfoconjugated estrogens to free estrogens. The expression of SULT1E1 is inducible by several diseases that involve tissue inflammation, such as type 2 diabetes, sepsis, and ischemia-reperfusion injury. Areas covered: This systematic literature review aims to summarize the role of SULT1E1 in the metabolism of estrogenic drugs and xenobiotics, and the role of SULT1E1 in the pathogenesis of several diseases, including cancer, metabolic disease, sepsis, liver injury, and cystic fibrosis. Meanwhile, ablation or pharmacological inhibition of SULT1E1 can affect the outcomes of the aforementioned diseases. Expert opinion: In addition to its role in metabolizing estrogenic drugs, SULT1E1 is unexpectedly being unveiled as a mediator for the disease effect on estrogen metabolism and homeostasis. Meanwhile, because the expression and activity of SULT1E1 can affect the outcome of diseases, the same sulfotransferase and the reversing enzymes STS can be potential therapeutic targets to prevent or manage diseases. Accumulating evidence suggest that the physiological and pathophysiological effects of SULT1E1 can be estrogen-independent and it is necessary to elucidate what other possible substrates may be recognized by the enzyme. Moreover, human studies are paramount to confirm the human relevance of the animal studies.

Optimization of Pyrazoles as Phenol Surrogates to Yield Potent Inhibitors of Macrophage Migration Inhibitory Factor

Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that is implicated in the regulation of inflammation, cell proliferation, and neurological disorders. MIF is also an enzyme that functions as a keto-enol tautomerase. Most potent MIF tautomerase inhibitors incorporate a phenol, which hydrogen bonds to Asn97 in the active site. Starting from a 113-μm docking hit, we report results of structure-based and computer-aided design that have provided substituted pyrazoles as phenol alternatives with potencies of 60-70 nm. Crystal structures of complexes of MIF with the pyrazoles highlight the contributions of hydrogen bonding with Lys32 and Asn97, and aryl-aryl interactions with Tyr36, Tyr95, and Phe113 to the binding.

Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review

Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.

Comparing pharmacophore models derived from crystallography and NMR ensembles

NMR and X-ray crystallography are the two most widely used methods for determining protein structures. Our previous study examining NMR versus X-Ray sources of protein conformations showed improved performance with NMR structures when used in our Multiple Protein Structures (MPS) method for receptor-based pharmacophores (Damm, Carlson, J Am Chem Soc 129:8225-8235, 2007). However, that work was based on a single test case, HIV-1 protease, because of the rich data available for that system. New data for more systems are available now, which calls for further examination of the effect of different sources of protein conformations. The MPS technique was applied to Growth factor receptor bound protein 2 (Grb2), Src SH2 homology domain (Src-SH2), FK506-binding protein 1A (FKBP12), and Peroxisome proliferator-activated receptor-γ (PPAR-γ). Pharmacophore models from both crystal and NMR ensembles were able to discriminate between high-affinity, low-affinity, and decoy molecules. As we found in our original study, NMR models showed optimal performance when all elements were used. The crystal models had more pharmacophore elements compared to their NMR counterparts. The crystal-based models exhibited optimum performance only when pharmacophore elements were dropped. This supports our assertion that the higher flexibility in NMR ensembles helps focus the models on the most essential interactions with the protein. Our studies suggest that the "extra" pharmacophore elements seen at the periphery in X-ray models arise as a result of decreased protein flexibility and make very little contribution to model performance.

Structural and biochemical studies of sulphotransferase 18 from Arabidopsis thaliana explain its substrate specificity and reaction mechanism

Sulphotransferases are a diverse group of enzymes catalysing the transfer of a sulfuryl group from 3'-phosphoadenosine 5'-phosphosulphate (PAPS) to a broad range of secondary metabolites. They exist in all kingdoms of life. In Arabidopsis thaliana (L.) Heynh. twenty-two sulphotransferase (SOT) isoforms were identified. Three of those are involved in glucosinolate (Gl) biosynthesis, glycosylated sulphur-containing aldoximes containing chemically different side chains, whose break-down products are involved in stress response against herbivores, pathogens, and abiotic stress. To explain the differences in substrate specificity of desulpho (ds)-Gl SOTs and to understand the reaction mechanism of plant SOTs, we determined the first high-resolution crystal structure of the plant ds-Gl SOT AtSOT18 in complex with 3'-phosphoadenosine 5'-phosphate (PAP) alone and together with the Gl sinigrin. These new structural insights into the determination of substrate specificity were complemented by mutagenesis studies. The structure of AtSOT18 invigorates the similarity between plant and mammalian sulphotransferases, which illustrates the evolutionary conservation of this multifunctional enzyme family. We identified the essential residues for substrate binding and catalysis and demonstrated that the catalytic mechanism is conserved between human and plant enzymes. Our study indicates that the loop-gating mechanism is likely to be a source of the substrate specificity in plants.

Drug Metabolism in Preclinical Drug Development: A Survey of the Discovery Process, Toxicology, and Computational Tools

BACKGROUND

While establishing efficacy in translational models and humans through clinically-relevant endpoints for disease is of great interest, assessing the potential toxicity of a putative therapeutic drug is critical. Toxicological assessments in the pre-clinical discovery phase help to avoid future failure in the clinical phases of drug development. Many in vitro assays exist to aid in modular toxicological assessment, such as hepatotoxicity and genotoxicity. While these methods have provided tremendous insight into human toxicity by investigational new drugs, they are expensive, require substantial resources, and do not account for pharmacogenomics as well as critical ADME properties. Computational tools can fill this niche in toxicology if in silico models are accurate in relating drug molecular properties to toxicological endpoints as well as reliable in predicting important drug-target interactions that mediate known adverse events or adverse outcome pathways (AOPs).

METHODS

We undertook an unstructured search of multiple bibliographic databases for peer-reviewed literature regarding computational methods in predictive toxicology for in silico drug discovery. As this review paper is meant to serve as a survey of available methods for the interested reader, no focused criteria were applied. Literature chosen was based on the writers' expertise and intent in communicating important aspects of in silico toxicology to the interested reader.

CONCLUSION

This review provides a purview of computational methods of pre-clinical toxicologic assessments for novel small molecule drugs that may be of use for novice and experienced investigators as well as academic and commercial drug discovery entities.

Ligand Binding Ensembles Determine Graded Agonist Efficacies at a G Protein-coupled Receptor

G protein-coupled receptors constitute the largest family of membrane receptors and modulate almost every physiological process in humans. Binding of agonists to G protein-coupled receptors induces a shift from inactive to active receptor conformations. Biophysical studies of the dynamic equilibrium of receptors suggest that a portion of receptors can remain in inactive states even in the presence of saturating concentrations of agonist and G protein mimetic. However, the molecular details of agonist-bound inactive receptors are poorly understood. Here we use the model of bitopic orthosteric/allosteric (i.e. dualsteric) agonists for muscarinic M2 receptors to demonstrate the existence and function of such inactive agonist·receptor complexes on a molecular level. Using all-atom molecular dynamics simulations, dynophores (i.e. a combination of static three-dimensional pharmacophores and molecular dynamics-based conformational sampling), ligand design, and receptor mutagenesis, we show that inactive agonist·receptor complexes can result from agonist binding to the allosteric vestibule alone, whereas the dualsteric binding mode produces active receptors. Each agonist forms a distinct ligand binding ensemble, and different agonist efficacies depend on the fraction of purely allosteric (i.e. inactive) versus dualsteric (i.e. active) binding modes. We propose that this concept may explain why agonist·receptor complexes can be inactive and that adopting multiple binding modes may be generalized also to small agonists where binding modes will be only subtly different and confined to only one binding site.

More than a look into a crystal ball: protein structure elucidation guided by molecular dynamics simulations

The 'form follows function' principle implies that a structural determination of protein structures is indispensable to understand proteins in their biological roles. However, experimental methods still show shortcomings in the description of the dynamic properties of proteins. Therefore, molecular dynamics (MD) simulations represent an essential tool for structural biology to investigate proteins as flexible and dynamic entities. Here, we will give an overview on the impact of MD simulations on structural investigations, including studies that aim at a prediction of protein-folding pathways, protein-assembly processes and the sampling of conformational space by computational means.