Understanding the Polypharmacological Profiles of Triple Reuptake Inhibitors by Molecular Simulation

Dimerization and antidepressant recognition at noradrenaline transporter

The noradrenaline transporter has a pivotal role in regulating neurotransmitter balance and is crucial for normal physiology and neurobiology1. Dysfunction of noradrenaline transporter has been implicated in numerous neuropsychiatric diseases, including depression and attention deficit hyperactivity disorder2. Here we report cryo-electron microscopy structures of noradrenaline transporter in apo and substrate-bound forms, and as complexes with six antidepressants. The structures reveal a noradrenaline transporter dimer interface that is mediated predominantly by cholesterol and lipid molecules. The substrate noradrenaline binds deep in the central binding pocket, and its amine group interacts with a conserved aspartate residue. Our structures also provide insight into antidepressant recognition and monoamine transporter selectivity. Together, these findings advance our understanding of noradrenaline transporter regulation and inhibition, and provide templates for designing improved antidepressants to treat neuropsychiatric disorders.

Exploring novel lead scaffolds for SGLT2 inhibitors: Insights from machine learning and molecular dynamics simulations

Sodium-glucose cotransporter 2 (SGLT2) plays a pivotal role in mediating glucose reabsorption within the renal filtrate, representing a well-known target in type 2 diabetes and heart failure. Recent emphasis has been directed toward designing SGLT2 inhibitors, with C-glycoside inhibitors emerging as front-runners. The architecture of SGLT2 has been successfully resolved using cryo-electron microscopy. However, comprehension of the pharmacophores within the binding site of SGLT2 remains unclear. Here, we use machine learning and molecular dynamics simulations on SGLT2 bound with its inhibitors in preclinical or clinical development to shed light on this issue. Our dataset comprises 1240 SGLT2 inhibitors amalgamated from diverse sources, forming the basis for constructing machine learning models. SHapley Additive exPlanation (SHAP) elucidates the crucial fragments that contribute to inhibitor activity, specifically Morgan_3, 162, 310, 325, 366, 470, 597, 714, 926, and 975. Furthermore, the computed binding free energies and per-residue contributions for SGLT2-inhibitor complexes unveil crucial fragments of inhibitors that interact with residues Asn-75, His-80, Val-95, Phe-98, Val-157, Leu-274, and Phe-453 in the binding site of SGLT2. This comprehensive investigation enhances understanding of the binding mechanism for SGLT2 inhibitors, providing a robust framework for evaluating and discovering novel lead scaffolds within this domain.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Machine learning methods applied for the prediction of biological activities of triple reuptake inhibitors

Major depressive disorder (MDD) is characterized by a series of disabling symptoms like anhedonia, depressed mood, lack of motivation for daily tasks and self-extermination thoughts. The monoamine deficiency hypothesis states that depression is mainly caused by a deficiency of monoamine at the synaptic cleft. Thus, major efforts have been made to develop drugs that inhibit serotonin (SERT), norepinephrine (NET) and dopamine (DAT) transporters and increase the availability of these monoamines. Current gold standard treatment of MDD uses drugs that target one or more monoamine transporters. Triple reuptake inhibitors (TRIs) can target SERT, NET, and DAT simultaneously, and are believed to have the potential to be early onset antidepressants. Quantitative structure-activity relationship models were developed using machine learning algorithms in order to predict biological activities of a series of triple reuptake inhibitor compounds that showed in vitro inhibitory activity against multiple targets. The results, using mostly interpretable descriptors, showed that the internal and external predictive ability of the models are adequate, particularly of the DAT and NET by Random Forest and Support Vector Machine models. The current work shows that models developed from relatively simple, chemically interpretable descriptors can predict the activity of TRIs with similar structure in the applicability domain using ML methods.Communicated by Ramaswamy H. Sarma.

The Important Role of Transporter Structures in Drug Disposition, Efficacy, and Toxicity

The ATP-binding cassette (ABC) and solute carrier (SLC) transporters are critical determinants of drug disposition, clinical efficacy, and toxicity as they specifically mediate the influx and efflux of various substrates and drugs. ABC transporters can modulate the pharmacokinetics of many drugs via mediating the translocation of drugs across biologic membranes. SLC transporters are important drug targets involved in the uptake of a broad range of compounds across the membrane. However, high-resolution experimental structures have been reported for a very limited number of transporters, which limits the study of their physiologic functions. In this review, we collected structural information on ABC and SLC transporters and described the application of computational methods in structure prediction. Taking P-glycoprotein (ABCB1) and serotonin transporter (SLC6A4) as examples, we assessed the pivotal role of structure in transport mechanisms, details of ligand-receptor interactions, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms. The data collected contributes toward safer and more effective pharmacological treatments. SIGNIFICANCE STATEMENT: The experimental structure of ATP-binding cassette and solute carrier transporters was collected, and the application of computational methods in structure prediction was described. P-glycoprotein and serotonin transporter were used as examples to reveal the pivotal role of structure in transport mechanisms, drug selectivity, the molecular mechanisms of drug-drug interactions, and differences caused by genetic polymorphisms.

ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators

The development of small molecules that selectively target the cannabinoid receptor subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat neurodegeneration, as well as to contrast the onset and progression of cancer. In this context, in-silico tools able to predict CB2R affinity and selectivity with respect to the subtype 1 (CB1R), whose modulation is responsible for undesired psychotropic effects, are highly desirable. In this work, we developed a series of machine learning classifiers trained on high-quality bioactivity data of small molecules acting on CB2R and/or CB1R extracted from ChEMBL v30. Our classifiers showed strong predictive power in accurately determining CB2R affinity, CB1R affinity, and CB2R/CB1R selectivity. Among the built models, those obtained using random forest as algorithm proved to be the top-performing ones (AUC in validation ≥0.96) and were made freely accessible through a user-friendly web platform developed ad hoc and called ALPACA (https://www.ba.ic.cnr.it/softwareic/alpaca/). Due to its user-friendly interface and robust predictive power, ALPACA can be a valuable tool in saving both time and resources involved in the design of selective CB2R modulators.

Structure-Based Discovery of a Novel Allosteric Inhibitor against Human Dopamine Transporter

Human dopamine transporter (hDAT) regulates the reuptake of extracellular dopamine (DA) and is an essential therapeutic target for central nervous system (CNS) diseases. The allosteric modulation of hDAT has been identified for decades. However, the molecular mechanism underlying the transportation is still elusive, which hinders the rational design of allosteric modulators against hDAT. Here, a systematic structure-based method was performed to explore allosteric sites on hDAT in inward-open (IO) conformation and to screen compounds with allosteric affinity. First, the model of the hDAT structure was constructed based on the recently reported Cryo-EM structure of the human serotonin transporter (hSERT) and Gaussian-accelerated molecular dynamics (GaMD) simulation was further utilized for the identification of intermediate energetic stable states of the transporter. Then, with the potential druggable allosteric site on hDAT in IO conformation, virtual screening of seven enamine chemical libraries (∼440,000 compounds) was processed, resulting in 10 compounds being purchased for in vitro assay and with Z1078601926 discovered to allosterically inhibit hDAT (IC₅₀ = 0.527 [0.284; 0.988] μM) when nomifensine was introduced as an orthosteric ligand. Finally, the synergistic effect underlying the allosteric inhibition of hDAT by Z1078601926 and nomifensine was explored using additional GaMD simulation and postbinding free energy analysis. The hit compound discovered in this work not only provides a good starting point for lead optimization but also demonstrates the usability of the method for the structure-based discovery of novel allosteric modulators of other therapeutic targets.

Structure based virtual screening, molecular dynamic simulation to identify the oxadiazole derivatives as inhibitors of Enterococcus D-Ala-D-Ser ligase for combating vancomycin resistance

Vancomycin resistance in enterococci mainly arises due to alteration in terminal peptidoglycan dipeptide. A comprehensive structural analysis for substrate specificity of dipeptide modifying d-Alanine: d-Serine ligase (Ddls) is essential to screen its inhibitors for combating vancomycin resistance. In this study modeled 3D structure of EgDdls from E. gallinarum was used for structure based virtual screening (SBVS) of oxadiazole derivatives. Initially, fifteen oxadiazole derivatives were identified as inhibitors at the active site of EgDdls from PubChem database. Further, four EgDdls inhibitors were evaluated using pharmacokinetic profile and molecular docking. The results of molecular docking showed that oxadiazole inhibitors could bind preferentially at ATP binding pocket with the lowest binding energy. Further, molecular dynamics simulation results showed stable behavior of EgDdls in complex with screened inhibitors. The residues Phe172, Lys174, Glu217, Phe292, and Asn302 of EgDdls were mainly involved in interactions with screened inhibitors. Furthermore, MM-PBSA calculation showed electrostatic and van der Waals interactions mainly contribute to overall binding energy. The PCA analysis showed motion of central domain and omega loop of EgDdls. This is involved in the formation of native dipeptide and stabilized after binding of 2-(1-(Ethylsulfonyl) piperidin-4-yl)-5-(furan-2-yl)-1,3,4-oxadiazole, which could be reason for the inhibition of EgDdls. Hence, in this study we have screened inhibitors of EgDdls which could be useful to alleviate the vancomycin resistance problem in enterococci, involved in hospital-acquired infections, especially urinary tract infections (UTI).

"Identification of alkaloid compounds as potent inhibitors of Mycobacterium tuberculosis NadD using computational strategies"

Mycobacterium tuberculosis is leading cause of death worldwide. NAD participates in a host of redox reactions in energy landscape of organisms. Several studies implicate surrogate energy pathways involving NAD pools as important in survival of active as well as dormant mycobacteria. One of the NAD metabolic pathway enzyme, nicotinate mononucleotide adenylyltransferase (NadD) is indispensable in mycobacterial NAD metabolism and is perceived as an attractive drug target in pathogen. In this study, we have employed in silico screening, simulation and MM-PBSA strategies to identify potentially important alkaloid compounds against mycobacterial NadD for structure-based inhibitor development. We have performed an exhaustive structure-based virtual screening of an alkaloid library, ADMET, DFT profiling followed by Molecular Dynamics (MD) simulation, and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculation to identify 10 compounds which exhibit favourable drug like properties and interactions. Interaction energies of these 10 alkaloid molecules range between -190 kJ/mol and -250 kJ/mol. These compounds could be promising starting point in the development of selective inhibitors against Mycobacterium tuberculosis.

Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (Mpro) using molecular docking and deep learning methods

Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the academia. Effective drugs to treat people with COVID-19 have yet to be developed to reduce mortality and transmission. Studies on the SARS-CoV-2 virus identified that its main protease (M^pro) might be a potential therapeutic target for drug development, as this enzyme plays a key role in viral replication. In search of potential inhibitors of M^pro, we developed a phytochemical library consisting of 2431 phytochemicals from 104 Korean medicinal plants that exhibited medicinal and antioxidant properties. The library was screened by molecular docking, followed by revalidation by re-screening with a deep learning method. Recurrent Neural Networks (RNN) computing system was used to develop an inhibitory predictive model using SARS coronavirus M^pro dataset. It was deployed to screen the top 12 compounds based on their docked binding affinity that ranged from -8.0 to -8.9 kcal/mol. The top two lead compounds, Catechin gallate and Quercetin 3-O-malonylglucoside, were selected depending on inhibitory potency against M^pro. Interactions with the target protein active sites, including His41, Met49, Cys145, Met165, and Thr190 were also examined. Molecular dynamics simulation was performed to analyze root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RG), solvent accessible surface area (SASA), and number of hydrogen bonds. Results confirmed the inflexible nature of the docked complexes. Absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as bioactivity prediction confirmed the pharmaceutical activities of the lead compound. Findings of this research might help scientists to optimize compatible drugs for the treatment of COVID-19 patients.

Unbiased molecular dynamics simulation of a first-in-class small molecule inhibitor binds to oncostatin M

Small molecule inhibitors (SMIs) targeting oncostatin M (OSM) signaling pathway represent new therapeutics to combat cancer, inflammatory bowel disease (IBD) and CNS disease. Recently, the first-in-class SMI named SMI-10B that target OSM and block its interaction with receptor (OSMR) were reported. However, the binding pocket and interaction mode of the compound on OSM remain poorly understood, which hampering the rational design of SMIs that target OSM. Here, using SMI-10B as a probe, the multiple pockets on OSM for small molecules binding were extensively explored by unbiased molecular dynamics (MD) simulations. Then, the near-native structure of the complex was identified by molecular mechanics generalized Born surface area (MM/GBSA) binding energy funnel. Moreover, the binding stabilities of the protein-ligand complexes in near- and non-native conformations were verified by additional independent MD runs and absolute free energy perturbation (FEP) calculation. In summary, the unique feature of SMI-10B spontaneously binds to OSM characterized here not only provide detailed information for understanding the molecular mechanism of SMI-10B binding to OSM, but also will facilitate the rational design of novel and more potent SMIs to block OSM signaling.

Discovery and Development of Monoamine Transporter Ligands

Monoamine transporters (MATs) are targets of a wide range of compounds that have been developed as therapeutic treatments for various neuropsychiatric and neurodegenerative disorders such as depression, ADHD, neuropathic pain, anxiety disorders, stimulant use disorders, epilepsy, and Parkinson's disease. The MAT family is comprised of three main members - the dopamine transporter (DAT), the norepinephrine transporter (NET), and the serotonin transporter (SERT). These transporters are through reuptake responsible for the clearance of their respective monoamine substrates from the extracellular space. The determination of X-ray crystal structures of MATs and their homologues bound with various substrates and ligands has resulted in a surge of structure-function-based studies of MATs to understand the molecular basis of transport function and the mechanism of various ligands that ultimately result in their behavioral effects. This review focusses on recent examples of ligand-based structure-activity relationship studies trying to overcome some of the challenges associated with previously developed MAT inhibitors. These studies have led to the discovery of unique and novel structurally diverse MAT ligands including allosteric modulators. These novel molecular scaffolds serve as leads for designing more effective therapeutic interventions by modulating the activities of MATs and ultimately their associated neurotransmission and behavioral effects.

Structure-based drug design of novel M. tuberculosis InhA inhibitors based on fragment molecular orbital calculations

2-trans enoyl-acyl carrier protein reductase (InhA) is a promising target for developing novel chemotherapy agents for tuberculosis, and their inhibitory effects on InhA activity were widely investigated by the physicochemical experiments. However, the reason for the wide range of their inhibitory effects induced by similar agents was not explained by only the difference in their chemical structures. In our previous molecular simulations, a series of heteroaryl benzamide derivatives were selected as candidate inhibitors against InhA, and their binding properties with InhA were investigated to propose novel derivatives with higher binding affinity to InhA. In the present study, we extended the simulations for a series of 4-hydroxy-2-pyridone derivatives to search widely for more potent inhibitors against InhA. Using ab initio fragment molecular orbital (FMO) calculations, we elucidated the specific interactions between InhA residues and the derivatives at an electronic level and highlighted key interactions between InhA and the derivatives. The FMO results clearly indicated that the most potent inhibitor has strong hydrogen bonds with the backbones of Tyr158, Thr196, and NADH of InhA. This finding may provide informative structural concepts for designing novel 4-hydroxy-2-pyridone derivatives with higher binding affinity to InhA. Our previous and present molecular simulations could provide important guidelines for the rational design of more potent InhA inhibitors.

Recognition of quinolone antibiotics by the multidrug efflux transporter MexB of Pseudomonas aeruginosa

The drug/proton antiporter MexB is the engine of the major efflux pump MexAB-OprM in Pseudomonas aeruginosa. This protein is known to transport a large variety of compounds, including antibiotics, thus conferring a multi-drug resistance phenotype. Due to the difficulty of producing co-crystals, only two X-ray structures of MexB in a complex with ligands are available to date, and mechanistic aspects are largely hypothesized based on the body of data collected for the homologous protein AcrB of Escherichia coli. In particular, a recent study (Ornik-Cha, Wilhelm, Kobylka et al., Nat. Commun., 2021, 12, 6919) reported a co-crystal structure of AcrB in a complex with levofloxacin, an antibiotic belonging to the important class of (fluoro)-quinolones. In this work, we performed a systematic ensemble docking campaign coupled to the cluster analysis and molecular-mechanics optimization of docking poses to study the interaction between 36 quinolone antibiotics and MexB. We additionally investigated surface complementarity between each molecule and the transporter and thoroughly assessed the computational protocol adopted against the known experimental data. Our study reveals different binding preferences of the investigated compounds towards the sub-sites of the large deep binding pocket of MexB, supporting the hypothesis that MexB substrates oscillate between different binding modes with similar affinity. Interestingly, small changes in the molecular structure translate into significant differences in MexB–quinolone interactions. All the predicted binding modes are available for download and visualization at the following link: https://www.dsf.unica.it/dock/mexb/quinolones.

Putative binding modes (BMs) of quinolones to the bacterial efflux transporter MexB were identified. Multiple interaction patterns are possible, supporting the hypothesis that substrates oscillate between different BMs with similar affinity.

Molecular Modeling Study on the Interaction Mechanism between the LRRK2 G2019S Mutant and Type I Inhibitors by Integrating Molecular Dynamics Simulation, Binding Free Energy Calculations, and Pharmacophore Modeling

Leucine-rich repeat kinase 2 (LRRK2) has been reported in the pathogenesis of Parkinson's disease (PD). G2019S mutant is the most common pathogenic mutation in LRRK2-related PD patients. Inhibition of LRRK2 kinase activity is proposed to be a new therapeutic approach for PD treatment. Therefore, understanding the molecular basis of the interaction between LRRK2 and its inhibitors will be valuable for the discovery and design of LRRK2 inhibitors. However, the structure of human LRRK2 in complex with the inhibitor has not been determined, and the inhibitory mechanism underlying LRRK2 still needs to be further investigated. In this study, molecular dynamics (MD) simulation combined with the molecular mechanics generalized born surface area (MM-GBSA) binding free energy calculation and pharmacophore modeling methods was employed to explore the critical residues in LRRK2 for binding of inhibitors and to investigate the general structural features of the inhibitors with diverse scaffolds. The results from MD simulations suggest that the hinge region residues Glu1948 and Ala1950 play a significant role in maintaining the intermolecular hydrogen bond interaction with the G2019S LRRK2 protein and inhibitor. The strong hinge hydrogen bond with an occupancy rate of more than 95% represents the high activity of LRRK2 inhibitors, and the hydrogen bond interaction with the kinase catalytic loop region could compromise selectivity. Further pharmacophore modeling reveals that the high activity LRRK2 inhibitor should have one aromatic ring, one hydrogen bond acceptor, and one hydrogen bond donor. Hence, the obtained results can provide valuable information to understand the interactions of LRRK2 inhibitors at the atomic level that will be helpful in designing potent inhibitors of LRRK2.

Molecular Mechanism for the Allosteric Inhibition of the Human Serotonin Transporter by Antidepressant Escitalopram

Human serotine transporter (hSERT) is one of the most influential drug targets, and its allosteric modulators (e.g., escitalopram) have emerged to be the next-generation medication for psychiatric disorders. However, the molecular mechanism underlying the allosteric modulation of hSERT is still elusive. Here, the simulation strategies of conventional (cMD) and steered (SMD) molecular dynamics were applied to investigate this molecular mechanism from distinct perspectives. First, cMD simulations revealed that escitalopram's binding to hSERT's allosteric site simultaneously enhanced its binding to the orthosteric site. Then, SMD simulation identified that the occupation of hSERT's allosteric site by escitalopram could also block its dissociation from the orthosteric site. Finally, by comparing the simulated structures of two hSERT-escitalopram complexes with and without allosteric modulation, a new conformational coupling between an extracellular (Arg104-Glu494) and an intracellular (Lys490-Glu494) salt bridge was identified. In summary, this study explored the mechanism underlying the allosteric modulation of hSERT by collectively applying two MD simulation strategies, which could facilitate our understanding of the allosteric modulations of not only hSERT but also other clinically important therapeutic targets.

Diastereoselective desymmetrization reactions of prochiral para-quinamines with cyclopropenes generated in situ: access to fused hydroindol-5-one scaffolds

Interesting desymmetric [3 + 2] annulation reactions between p-quinamines as prochiral N-donors and 2-aroyl-1-chlorocyclopropanecarboxylates facilitated by a base are reported. This successive double Michael reaction delivered a unique class of cyclopropane-fused hydoindol-5-one frameworks, each having four contiguous stereogenic centers, with three of them being fully substituted. Moreover, this method was found to provide acceptable chemical yields with promising diastereoselectivities (dr of up to ≤95 : 5) and to work with a variety of substrates. Importantly, a polycyclic tacrine analogue used to treat Alzheimer's disease was synthesized using our developed method.