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.

Discovery of Novel Noncovalent KRAS G12D Inhibitors through Structure-Based Virtual Screening and Molecular Dynamics Simulations.

The development of effective inhibitors targeting the Kirsten rat sarcoma viral proto-oncogene (KRASG12D) mutation, a prevalent oncogenic driver in cancer, represents a significant unmet need in precision medicine. In this study, an integrated computational approach combining structure-based virtual screening and molecular dynamics simulation was employed to identify novel noncovalent inhibitors targeting the KRASG12D variant. Through virtual screening of over 1.7 million diverse compounds, potential lead compounds with high binding affinity and specificity were identified using molecular docking and scoring techniques. Subsequently, 200 ns molecular dynamics simulations provided critical insights into the dynamic behavior, stability, and conformational changes of the inhibitor-KRASG12D complexes, facilitating the selection of lead compounds with robust binding profiles. Additionally, in silico absorption, distribution, metabolism, excretion (ADME) profiling, and toxicity predictions were applied to prioritize the lead compounds for further experimental validation. The discovered noncovalent KRASG12D inhibitors exhibit promises as potential candidates for targeted therapy against KRASG12D-driven cancers. This comprehensive computational framework not only expedites the discovery of novel KRASG12D inhibitors but also provides valuable insights for the development of precision treatments tailored to this oncogenic mutation.

Discovery of the Cryptic Sites of SARS-CoV-2 Papain-like Protease and Analysis of Its Druggability.

In late 2019, a new coronavirus (CoV) caused the outbreak of a deadly respiratory disease, resulting in the COVID-19 pandemic. In view of the ongoing pandemic, there is an immediate need to find drugs to treat patients. SARS-CoV-2 papain-like cysteine protease (PLpro) not only plays an important role in the pathogenesis of the virus but is also a target protein for the development of inhibitor drugs. Therefore, to develop targeted inhibitors, it is necessary to analyse and verify PLpro sites and explore whether there are other cryptic binding pockets with better activity. In this study, first, we detected the site of the whole PLpro protein by sitemap of Schrödinger (version 2018), the cavity of LigBuilder V3, and DeepSite, and roughly judged the possible activated binding site area. Then, we used the mixed solvent dynamics simulation (MixMD) of probe molecules to induce conformational changes in the protein to find the possible cryptic active sites. Finally, the TRAPP method was used to predict the druggability of cryptic pockets and analyse the changes in the physicochemical properties of residues around these sites. This work will help promote the research of SARS-CoV-2 PLpro inhibitors.

In Silico Study of the Acquired Resistance Caused by the Secondary Mutations of KRAS G12C Protein Using Long Time Molecular Dynamics Simulation and Markov State Model Analysis.

Kirsten rat sarcoma viral oncogene homolog (KRAS) is a small GTPase protein which plays an important role in the treatment of KRAS mutant cancers. The FDA-approved AMG510 and MRTX849 (phase III clinical trials) are two potent KRASG12C-selective inhibitors that target KRAS G12C. However, the drug resistance caused by the second-site mutation in KRAS has emerged, and the mechanisms of drug resistance at atom level are still unclear. To clarify the mechanisms of drug resistance, we conducted long time molecular dynamics simulations (75 µs in total) to study the structural and energetic features of KRAS G12C and its four drug resistant variants to inhibitors. The combined binding free energy calculation and protein-ligand interaction fingerprint revealed that these second-site mutations indeed caused KRAS to produce different degrees of resistance to AMG510 and MRTX849. Furthermore, Markov State Models and 2D-free energy landscapes analysis revealed the difference in conformational changes of mutated KRAS bound with and without inhibitors. Furthermore, the comparative analysis of these systems showed that there were differences in their allosteric signal pathways. These findings provide the molecular mechanism of drug resistance, which helps to guide novel KRAS G12C inhibitor design to overcome drug resistance.

Copper (Cu2+) ion-induced misfolding of tau protein R3 peptide revealed by enhanced molecular dynamics simulation.

Tau misfolding plays a significant role in some neurodegenerative diseases such as Alzheimer's disease (AD). It is intrinsically disordered and highly soluble under normal physiological conditions. While the protein will aggregate to form paired helical filaments (PHFs) under copper homeostasis at pathological conditions, which is the main substance of neurofibrillary tangles (NFTs) in the brain of AD patients. However, the molecular mechanism underlying the copper (Cu2+) ion-induced tau misfolding is not fully understood. In this study, using the 1/2 third repeat fragment (R3 peptide) of tau protein (residues 318-335: VTSKCGSLGNIHHKPGGG) as a model, a Gaussian accelerated molecular dynamics (GaMD) method followed by efficient trajectory analysis was carried out to investigate the influences of Cu2+ on the tau about the protein fold and the free energy landscape along the simulation. The two-dimensional potential of mean force (PMF) profiles obtained from reweighting of the GaMD simulations as well as clustering analysis revealed the Cu2+ ion induced α-helix fold R3 peptide located at the low-energy wells of free energy map, which is in agreement with the reported experimental result. In contrast, there is no α-helix fold of R3 peptide that appeared during the GaMD simulation without Cu2+ ion existing. Furthermore, the definition of secondary structure of protein (DSSP) analysis indicated that the R3 peptide with Cu2+ ion forms a stable structure of the helix (Lys321-His330 interval of the peptide) at between 400 and 500 ns. Therefore, the structures and free energy profiles from GaMD simulations proposed that Cu2+ triggers the aggregation of R3 peptide into toxic PHFs through a stable α-helix fold form.

Subtype-selective mechanisms of negative allosteric modulators binding to group I metabotropic glutamate receptors.

Group I metabotropic glutamate receptors (mGlu1 and mGlu5) are promising targets for multiple psychiatric and neurodegenerative disorders. Understanding the subtype selectivity of mGlu1 and mGlu5 allosteric sites is essential for the rational design of novel modulators with single- or dual-target mechanism of action. In this study, starting from the deposited mGlu1 and mGlu5 crystal structures, we utilized computational modeling approaches integrating docking, molecular dynamics simulation, and efficient post-trajectory analysis to reveal the subtype-selective mechanism of mGlu1 and mGlu5 to 10 diverse drug scaffolds representing known negative allosteric modulators (NAMs) in the literature. The results of modeling identified six pairs of non-conserved residues and four pairs of conserved ones as critical features to distinguish the selective NAMs binding to the corresponding receptors. In addition, nine pairs of residues are beneficial to the development of novel dual-target NAMs of group I metabotropic glutamate receptors. Furthermore, the binding modes of a reported dual-target NAM (VU0467558) in mGlu1 and mGlu5 were predicted to verify the identified residues that play key roles in the receptor selectivity and the dual-target binding. The results of this study can guide rational structure-based design of novel NAMs, and the approach can be generally applicable to characterize the features of selectivity for other G-protein-coupled receptors.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains.

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics.

Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Here, we describe an update of the Therapeutic target database (TTD) previously featured in NAR. This update includes: (i) 2000 drug resistance mutations in 83 targets and 104 target/drug regulatory genes, which are resistant to 228 drugs targeting 63 diseases (49 targets of 61 drugs with patient prevalence data); (ii) differential expression profiles of 758 targets in the disease-relevant drug-targeted tissue of 12 615 patients of 70 diseases; (iii) expression profiles of 629 targets in the non-targeted tissues of 2565 healthy individuals; (iv) 1008 target combinations of 1764 drugs and the 1604 target combination of 664 multi-target drugs; (v) additional 48 successful, 398 clinical trial and 21 research targets, 473 approved, 812 clinical trial and 1120 experimental drugs, and (vi) ICD-10-CM and ICD-9-CM codes for additional 482 targets and 262 drugs against 98 disease conditions. This update makes TTD more useful for facilitating the patient focused research, discovery and clinical investigations of the targeted therapeutics. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.

Prediction of the binding mode and resistance profile for a dual-target pyrrolyl diketo acid scaffold against HIV-1 integrase and reverse-transcriptase-associated ribonuclease H.

The rapid emergence of drug-resistant variants is one of the most common causes of highly active antiretroviral therapeutic (HAART) failure in patients infected with HIV-1. Compared with the existing HAART, the recently developed pyrrolyl diketo acid scaffold targeting both HIV-1 integrase (IN) and reverse transcriptase-associated ribonuclease H (RNase H) is an efficient approach to counteract the failure of anti-HIV treatment due to drug resistance. However, the binding mode and potential resistance profile of these inhibitors with important mechanistic principles remain poorly understood. To address this issue, an integrated computational method was employed to investigate the binding mode of inhibitor JMC6F with HIV-1 IN and RNase H. By using per-residue binding free energy decomposition analysis, the following residues: Asp64, Thr66, Leu68, Asp116, Tyr143, Gln148 and Glu152 in IN, Asp443, Glu478, Trp536, Lys541 and Asp549 in RNase H were identified as key residues for JMC6F binding. And then computational alanine scanning was carried to further verify the key residues. Moreover, the resistance profile of the currently known major mutations in HIV-1 IN and 2 mutations in RNase H against JMC6F was predicted by in silico mutagenesis studies. The results demonstrated that only three mutations in HIV-1 IN (Y143C, Q148R and N155H) and two mutations in HIV-1 RNase H (Y501R and Y501W) resulted in a reduction of JMC6F potency, thus indicating their potential role in providing resistance to JMC6F. These data provided important insights into the binding mode and resistance profile of the inhibitors with a pyrrolyl diketo acid scaffold in HIV-1 IN and RNase H, which would be helpful for the development of more effective dual HIV-1 IN and RNase H inhibitors.

Computational characterization of the selective inhibition of human norepinephrine and serotonin transporters by an escitalopram scaffold.

Human norepinephrine and serotonin transporters (hNET and hSERT) are closely related monoamine transporters (MATs) that regulate neurotransmitter signaling in neurons and are primary targets for a wide range of therapeutic drugs used in the treatment of mood disorders. The subtle modifications of an escitalopram scaffold exhibit distinct selective inhibition profiles of hNET and hSERT. However, the structural details of escitalopram scaffold binding to hSERT and (or) hNET are poorly understood and still remain a great challenge. In this work, on the basis of more recently solved X-ray crystallographic structure of hSERT in complex with escitalopram, 3 µs long all-atom MD simulations and binding free energy calculations via MM/GB(PB)SA, thermodynamic integration (TI) and MM/3D-RISM methods were performed to reproduce experimental free energies. And both MM/GBSA and TI have a high correlation coefficient (R2 = 0.95 and 0.96, respectively) between the relative binding free energies of the calculated and experimental values. Furthermore, MM/GBSA per-residue energy decomposition, molecular interaction fingerprints and thermodynamics-structure relationship analysis were employed to investigate and characterize the selectivity of the escitalopram scaffold with three modifications (escitalopram, ligand10 and talopram) to hNET and hSERT. As a result, 4 warm spots (A73, Y151, A477 and I481) in hNET and 4 warm spots (A96, A173, T439 and L443) in hSERT were thus discovered to exert a pronounced effect on the selective inhibition of hNET and hSERT by the studied ligands. These simulation results would provide great insight into the design of inhibitors with the desired selectivity to hNET and hSERT, thus further promoting the research of more efficacious antidepressants.

Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder.

Amitifadine, the only drug ever clinically tested in Phase 3 for treating depression, is a triple reuptake inhibitor (TRI) that simultaneously interacts with human monoamine transporters (MATs) including hSERT, hNET and hDAT. This novel multi-target strategy improves drug efficacy and reduces the toxic side effects of drugs. However, the binding modes accounting for amitifadine's polypharmacological mode of action are still elusive, and extensive exploration of the amitifadine-target interactions between amitifadine and MATs is urgently needed. In this study, a total of 0.63 µs molecular dynamics (MD) simulations with an explicit solvent as well as endpoint binding free energy (BFE) calculation were carried out. MD simulation results identified a shared binding mode involving eleven key residues at the S1 site of MATs for the binding of amitifadine, and the results of the BFE calculations were in good agreement with experimental reports. Moreover, by analyzing the per-residue energy contribution variation at the S1 site of three MATs and additional cross-mutagenesis simulations, the variation in the inhibition ratio of amitifadine between hSERT and two other MATs was discovered to mainly come from non-conserved residues (Y95, I172 and T439 in hNET and Y95, I172, A169 and T439 in hDAT). As the rational inhibition ratio of multi-target drugs among various therapeutic targets was found to be the key to their safety and tolerance, the findings of this study may further facilitate the rational design of more potent but less toxic multi-target antidepressant drugs.

Prediction of GluN2B-CT1290-1310/DAPK1 Interaction by Protein⁻Peptide Docking and Molecular Dynamics Simulation.

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290⁻1310 of GluN2B (GluN2B-CT1290-1310) remain elusive and the interaction between GluN2B-CT1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT1290-1310 was predicted by computational simulation methods including protein⁻peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT1290⁻1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein⁻peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT1290-1310 and DAPK1 interface.

Pathways and mechanism of MRTX1133 binding to KRAS G12D elucidated by molecular dynamics simulations and Markov state models.

KRAS G12D is the most common oncogenic mutation identified in several types of cancer. Therefore, design of inhibitors targeting KRAS G12D represents a promising strategy for anticancer therapy. MRTX1133 is a highly potent inhibitor (approximate experiment Kd ≈ 0.0002 nM) of KRAS G12D and is currently in Phase 1/2 study, however, pathways of the compound binding to KRAS G12D has remained unknown, and the mechanism underlying the complicated dynamic process are challenging to capture experimentally, which hinder the structure-based anti-cancer drug design. Here, using MRTX1133 as a probe, unbiased molecular dynamics (MD) was used to simulate the process of MRTX1133 spontaneously binding to KRAS G12D. In six of 42 independent MD simulation (a total of 99 µs), MRTX1133 was observed to successfully associate with KRAS G12D. The kinetically metastable states refer to the potential pathways of MRTX1133 binding to KRAS G12D were revealed by Markov state models (MSM) analysis. Additionally, 8 key residues that are essential for MRTX1133 recognition and tight binding at the preferred low energy states were identified by MM/GBSA analysis. In sum, this study provides a new perspective on understanding the pathways and mechanism of MRTX1133 binding to KRAS G12D.

Computer-aided discovery of novel aryl hydrocarbon receptor ligands to regulate CYP1A1 expression in inflammatory macrophages.

The environmental factor aryl hydrocarbon receptor (AhR), a key protein connecting the external environmental signals (e.g., environmental endocrine disruptor TCDD) to internal cellular processes, is involved in the activation of peripheral macrophages and inflammatory response in human body. Thus, there is widespread interest in finding compounds to anti-inflammatory response in macrophages by targeting human AhR. Here, ensemble docking based-virtual screening was first used to screen a library (~200,000 compounds) against human AhR ligand binding domain (LBD) and 25 compounds were identified as potential inhibitors. Then, 9 out of the 25 ligands were found to down-regulate the mRNA expression of CYP1A1 (a downstream gene of AhR signaling) in AhR overexpressing macrophages. The most potent compound AE-411/41415610 was selected for further study and found to reduce both mRNA and protein expressions level of CYP1A1 in mouse peritoneal macrophage. Moreover, protein chip signal pathway analysis indicated that AE-411/41415610 play a role in regulating JAK-STAT and AKT-mTOR pathways. In sum, the discovered hits with novel scaffolds provided a starting point for future design of more effective AhR-targeted lead compounds to regulate CYP1A1 expression of inflammatory peritoneal macrophages.

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.

Design and synthesis of isatin derivatives as effective SARS-CoV-2 3CL protease inhibitors.

SARS-CoV-2 chymotrypsin-like cysteine protease (3CLpro ) is one of the most widely developed drug targets for COVID-19. This study aimed to design and synthesize isatin derivatives to target SARS-CoV-2 3CLpro in a covalent binding manner. Through the process, a potent 3CLpro inhibitor (5g) was discovered with an IC50 value of 0.43 ± 0.17 µM. To understand the binding affinity and specificity of 5g as a candidate inhibitor of SARS-CoV-2 3CLpro , several assays were conducted, including FRET enzyme activity assays, thermodynamic-based and kinetic-based validation of inhibitor-target interactions, and cell-based FlipGFP assays. The interaction mechanism between 3CLpro -5g was characterized by docking. Overall, these findings suggest that 5g is a new potent SARS-CoV-2 3CLpro inhibitor for the treatment of COVID-19.

Multi-state Model-Based Identification of Cryptic Allosteric Sites on Human Serotonin Transporter.

Serotonin transporter (SERT) plays a fundamental role in taking the synaptic cleft serotonin back to the presynaptic neuron. The discovery of allosteric SERT modulators represents the next-generation medication for psychiatric disorders such as depression. Here, based on the cryo-EM structures of ibogaine in complex with SERT in distinct conformations, the multiple functional structures of the transporter bound to serotonin, including outward-open (OOholo), outward-occluded (OCholo), and inward-open (IOholo and IOholo'), were carefully characterized by induced-fit docking Gaussian-accelerated molecular dynamics (IFD-GaMD) simulation and the free-energy landscape analysis. Further MM/GBSA binding free energy, per-residue contribution, and molecular interaction fingerprint calculations revealed the interaction variations of serotonin with SERT in functional structures, which confirmed the allostery of SERT during serotonin reuptake. Moreover, five unique cryptic allosteric sites, which are druggable and capable of targeting by small molecules, were identified on the characterized multistate structures. These results provide structural and energetic information for the molecular mechanism of serotonin reuptake and will provide opportunities for the development of novel therapeutics based on the identified new allosteric sites on SERT.

Recent Advances in Computer-aided Antiviral Drug Design Targeting HIV-1 Integrase and Reverse Transcriptase Associated Ribonuclease H.

Acquired immunodeficiency syndrome (AIDS) has been a chronic, life-threatening disease for a long time. Though, a broad range of antiretroviral drug regimens is applicable for the successful suppression of virus replication in human immunodeficiency virus type 1 (HIV-1) infected people. The mutation-induced drug resistance problems during the treatment of AIDS forced people to continuously look for new antiviral agents. HIV-1 integrase (IN) and reverse transcriptase associated ribonuclease (RT-RNase H), two pivotal enzymes in HIV-1 replication progress, have gained popularity as druggable targets for designing novel HIV-1 antiviral drugs. During the development of HIV-1 IN and/or RT-RNase H inhibitors, computer-aided drug design (CADD), including homology modeling, pharmacophore, docking, molecular dynamics (MD) simulation and binding free energy calculation, represent a significant tool to accelerate the discovery of new drug candidates and reduce costs in antiviral drug development. In this review, we summarized the recent advances in the design of single- and dual-target inhibitors against HIV-1 IN or/and RT-RNase H as well as the prediction of mutation-induced drug resistance based on computational methods. We highlighted the results of the reported literatures and proposed some perspectives on the design of novel and more effective antiviral drugs in the future.

Understanding the Polypharmacological Profiles of Triple Reuptake Inhibitors by Molecular Simulation.

The triple reuptake inhibitors (TRIs) class is a class of effective inhibitors of human monoamine transporters (hMATs), which includes dopamine, norepinephrine, and serotonin transporters (hDATs, hNETs, and hSERTs). Due to the high degree of structural homology of the binding sites of those transporters, it is a great challenge to design potent TRIs with fine-tuned binding profiles. The molecular determinants responsible for the binding selectivity of TRIs to hDATs, hNETs, and hSERTs remain elusive. In this study, the solved X-ray crystallographic structure of hSERT in complex with escitalopram was used as a basis for modeling nine complexes of three representative TRIs (SEP225289, NS2359, and EB1020) bound to their corresponding targets. Molecular dynamics (MD) and effective post-trajectory analysis were performed to estimate the drug binding free energies and characterize the selective profiles of each TRI to hMATs. The common binding mode of studied TRIs to hMATs was revealed by hierarchical clustering analysis of the per-residue energy. Furthermore, the combined protein-ligand interaction fingerprint and residue energy contribution analysis indicated that several conserved and nonconserved "Warm Spots" such as S149, V328, and M427 in hDAT, F317, F323, and V325 in hNET and F335, F341, and V343 in hSERT were responsible for the TRI-binding selectivity. These findings provided important information for rational design of a single drug with better polypharmacological profiles through modulating multiple targets.

Recent Advances and Challenges of the Drugs Acting on Monoamine Transporters.

BACKGROUND: The human Monoamine Transporters (hMATs), primarily including hSERT, hNET and hDAT, are important targets for the treatment of depression and other behavioral disorders with more than the availability of 30 approved drugs. OBJECTIVE: This paper is to review the recent progress in the binding mode and inhibitory mechanism of hMATs inhibitors with the central or allosteric binding sites, for the benefit of future hMATs inhibitor design and discovery. The Structure-Activity Relationship (SAR) and the selectivity for hit/lead compounds to hMATs that are evaluated by in vitro and in vivo experiments will be highlighted. METHODS: PubMed and Web of Science databases were searched for protein-ligand interaction, novel inhibitors design and synthesis studies related to hMATs. RESULTS: Literature data indicate that since the first crystal structure determinations of the homologous bacterial Leucine Transporter (LeuT) complexed with clomipramine, a sizable database of over 100 experimental structures or computational models has been accumulated that now defines a substantial degree of structural variability hMATs-ligands recognition. In the meanwhile, a number of novel hMATs inhibitors have been discovered by medicinal chemistry with significant help from computational models. CONCLUSION: The reported new compounds act on hMATs as well as the structures of the transporters complexed with diverse ligands by either experiment or computational modeling have shed light on the poly-pharmacology, multimodal and allosteric regulation of the drugs to transporters. All of the studies will greatly promote the Structure-Based Drug Design (SBDD) of structurally novel scaffolds with high activity and selectivity for hMATs.