Pharmacophore design and database searching for selective monoamine neurotransmitter transporter ligands

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.

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.

Hierarchical virtual screening of the dual MMP-2/HDAC-6 inhibitors from natural products based on pharmacophore models and molecular docking

The dual-target inhibitors tend to improve the response rate in treating tumors, comparing with the single-target inhibitors. Matrix metalloproteinase-2 (MMP-2) and histone deacetylase-6 (HDAC-6) are attractive targets for cancer therapy. In this study, the hierarchical virtual screening of dual MMP-2/HDAC-6 inhibitors from natural products is investigated. The pharmacophore model of MMP-2 inhibitors is built based on ligands, but the pharmacophore model of HDAC-6 inhibitors is built based on the experimental crystal structures of multiple receptor-ligand complexes. The reliability of these two pharmacophore models is validated subsequently. The hierarchical virtual screening, combining these two different pharmacophore models of MMP-2 and HDAC-6 inhibitors with molecular docking, is carried out to identify the dual MMP-2/HDAC-6 inhibitors from a database of natural products. The four potential dual MMP-2/HDAC-6 inhibitors of natural products, STOCK1 N-46177, STOCK1 N-52245, STOCK1 N-55477, and STOCK1 N-69706, are found. The studies of binding modes show that the screened four natural products can simultaneously well bind with the MMP-2 and HDAC-6 active sites by different kinds of interactions, to inhibit the MMP-2 and HDAC-6 activities. In addition, the ADMET properties of screened four natural products are assessed. These found dual MMP-2/HDAC-6 inhibitors of natural products could serve as the lead compounds for designing the new dual MMP-2/HDAC-6 inhibitors having higher biological activities by carrying out structural modifications and optimizations in the future studies.

Designing modulators of monoamine transporters using virtual screening techniques

The plasma-membrane monoamine transporters (MATs), including the serotonin (SERT), norepinephrine (NET) and dopamine (DAT) transporters, serve a pivotal role in limiting monoamine-mediated neurotransmission through the reuptake of their respective monoamine neurotransmitters. The transporters are the main target of clinically used psychostimulants and antidepressants. Despite the availability of several potent and selective MAT substrates and inhibitors the continuing need for therapeutic drugs to treat brain disorders involving aberrant monoamine signaling provides a compelling reason to identify novel ways of targeting and modulating the MATs. Designing novel modulators of MAT function have been limited by the lack of three dimensional structure information of the individual MATs. However, crystal structures of LeuT, a bacterial homolog of MATs, in a substrate-bound occluded, substrate-free outward-open, and an apo inward-open state and also with competitive and non-competitive inhibitors have been determined. In addition, several structures of the Drosophila DAT have also been resolved. Together with computational modeling and experimental data gathered over the past decade, these structures have dramatically advanced our understanding of several aspects of SERT, NET, and DAT transporter function, including some of the molecular determinants of ligand interaction at orthosteric substrate and inhibitor binding pockets. In addition progress has been made in the understanding of how allosteric modulation of MAT function can be achieved. Here we will review all the efforts up to date that has been made through computational approaches employing structural models of MATs to design small molecule modulators to the orthosteric and allosteric sites using virtual screening techniques.

SLC transporters as therapeutic targets: emerging opportunities

Solute carrier (SLC) transporters - a family of more than 300 membrane-bound proteins that facilitate the transport of a wide array of substrates across biological membranes - have important roles in physiological processes ranging from the cellular uptake of nutrients to the absorption of drugs and other xenobiotics. Several classes of marketed drugs target well-known SLC transporters, such as neurotransmitter transporters, and human genetic studies have provided powerful insight into the roles of more-recently characterized SLC transporters in both rare and common diseases, indicating a wealth of new therapeutic opportunities. This Review summarizes knowledge on the roles of SLC transporters in human disease, describes strategies to target such transporters, and highlights current and investigational drugs that modulate SLC transporters, as well as promising drug targets.

Dual binding site and selective acetylcholinesterase inhibitors derived from integrated pharmacophore models and sequential virtual screening

In this study, we have employed in silico methodology combining double pharmacophore based screening, molecular docking, and ADME/T filtering to identify dual binding site acetylcholinesterase inhibitors that can preferentially inhibit acetylcholinesterase and simultaneously inhibit the butyrylcholinesterase also but in the lesser extent than acetylcholinesterase. 3D-pharmacophore models of AChE and BuChE enzyme inhibitors have been developed from xanthostigmine derivatives through HypoGen and validated using test set, Fischer's randomization technique. The best acetylcholinesterase and butyrylcholinesterase inhibitors pharmacophore hypotheses Hypo1_A and Hypo1_B, with high correlation coefficient of 0.96 and 0.94, respectively, were used as 3D query for screening the Zinc database. The screened hits were then subjected to the ADME/T and molecular docking study to prioritise the compounds. Finally, 18 compounds were identified as potential leads against AChE enzyme, showing good predicted activities and promising ADME/T properties.

Identification of novel serotonin transporter compounds by virtual screening

The serotonin (5-hydroxytryptamine, 5-HT) transporter (SERT) plays an essential role in the termination of serotonergic neurotransmission by removing 5-HT from the synaptic cleft into the presynaptic neuron. It is also of pharmacological importance being targeted by antidepressants and psychostimulant drugs. Here, five commercial databases containing approximately 3.24 million drug-like compounds have been screened using a combination of two-dimensional (2D) fingerprint-based and three-dimensional (3D) pharmacophore-based screening and flexible docking into multiple conformations of the binding pocket detected in an outward-open SERT homology model. Following virtual screening (VS), selected compounds were evaluated using in vitro screening and full binding assays and an in silico hit-to-lead (H2L) screening was performed to obtain analogues of the identified compounds. Using this multistep VS/H2L approach, 74 active compounds, 46 of which had K(i) values of ≤1000 nM, belonging to 16 structural classes, have been identified, and multiple compounds share no structural resemblance with known SERT binders.

New strategy for receptor-based pharmacophore query construction: a case study for 5-HT₇ receptor ligands

In this paper, a new approach for generating receptor-based 3D pharmacophore models for rapid in silico virtual screening is presented. The method combines information from docking poses of known ligands of different structures and further ligand-receptor complexes analyses using structural interaction fingerprints (SIFts). Next, the best linear combination of three-, four-, and five-feature pharmacophores in terms of selected performance parameter (i.e., recall, F-score, and MCC) is constructed. The resultant queries showed significantly better VS performance and new scaffold recognition when compared with the known ligand- and receptor-based pharmacophore models. The approach was developed and validated on 5-HT₇ receptor homology models created on available crystal structure templates. The efficiency of the obtained linear combinations exhibited only a minor dependence on the template selection.

Monoamine transporter structure, function, dynamics, and drug discovery: a computational perspective

With the breakthrough crystallization of the bacterial leucine transporter protein LeuT, the first available X-ray structure for the neurotransmitter/sodium symporter family, development of 3-D computational models is suddenly essential for structure-function studies on the plasmalemmal monoamine transporters (MATs). LeuT-based MAT models have been used to guide elucidation of substrate and inhibitor binding pockets, and molecular dynamics simulations using these models are providing insight into conformations involved in the substrate translocation cycle. With credible MAT models finally in hand, structure-based virtual screening for novel ligands is yielding lead compounds toward the development of new medications for psychostimulant dependence, attention deficit hyperactivity, depression, anxiety, schizophrenia, and other disorders associated with dopamine, norepinephrine, or serotonin dysregulation.

Insights from molecular dynamics: the binding site of cocaine in the dopamine transporter and permeation pathways of substrates in the leucine and dopamine transporters

The dopamine transporter (DAT) facilitates the regulation of synaptic neurotransmitter levels. As a target for therapeutic and illicit psycho-stimulant drugs like antidepressants and cocaine, DAT has been studied intensively. Despite a wealth of mutational and physiological data regarding DAT, the structure remains unsolved and details of the transport mechanism, binding sites and conformational changes remain debated. A bacterial homolog of DAT, the leucine transporter (LeuT(Aa)) has been used as a template and framework for modeling and understanding DAT. Free energy profiles obtained from Multi-Configuration Thermodynamic Integration simulations allowed us to correctly identify the primary and secondary binding pockets of LeuT(Aa). A comparison of free energy profiles for dopamine and cocaine in DAT suggests that the binding site of cocaine is located in a secondary pocket, not the primary substrate site. Two recurring primary pathways for intracellular substrate release from the primary pocket are identified in both transporters using the Random Acceleration Molecular Dynamics method. One pathway appears to follow transmembranes (TMs) 1a and 6b while the other pathway follows along TMs 6b and 8. Interestingly, we observe that a single sodium ion is co-transported with leucine during both simulation types.

QSAR study and synthesis of new phenyltropanes as ligands of the dopamine transporter (DAT)

The dopamine transporter (DAT) plays a pivotal role in the regulation of dopamine neurotransmission, and is involved in a number of physiological functions and brain disorders. Furthermore the DAT analysis by molecular imaging techniques is a useful tool for the diagnosis and follow up treatment of diseases involving the DAT. In order to predict the affinity of new derivatives for the DAT, different QSAR molecular modeling models based on cocaine were compared. We have evaluated in these models tropane derivatives synthesized with original synthons which coupled properties of both fluorine and iodine atoms. One compound showed a high in vitro affinity and selectivity for the DAT (K(i)=0.87±0.04 nM). This compound should be radiolabeled with radioiodine for further investigations by SPECT.

Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors

Mesenchymal epithelial transition factor (c-Met) is an attractive target for cancer therapy. Three-dimensional pharmacophore hypotheses were built based on a set of known structurally diverse c-Met inhibitors. The best pharmacophore model, which identified inhibitors with an associated correlation coefficient of 0.983 between their experimental and estimated IC(50) values, consisted of two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic feature. The highly predictive power of the model was rigorously validated by test set prediction and Fischer's randomization method. The high values of enrichment factor and receiver operating characteristic (ROC) score indicated the model performed fairly well at distinguishing active from inactive compounds. The model was then applied to screen compound database for potential c-Met inhibitors. A filtering protocol, including druggability and molecular docking, were also applied in hits selection. The final 38 molecules, which exhibited good estimated activities, desired binding mode and favorable drug likeness were identified as potential c-Met inhibitors. Their novel backbone structures could be served as scaffolds for further study, which may facilitate the discovery and rational design of potent c-Met kinase inhibitors.

Computational methods for the discovery of mood disorder therapies

INTRODUCTION

Despite the significant progress, research is still needed to reveal details of the complex and dynamic chemical processes operating in the central nervous system (CNS) and their relationship to psychological effects such as mood disorders. The incidence of behavioral depression is widely spread worldwide, with an estimated 14.8 million adults diagnosed yearly in the United States alone. The efficacy of current antidepressants on 50 - 60% of patients, their slow onset of action and the prevalence of adverse side effects highlight the need for developing a new generation of improved antidepressants. Computational methods have the potential to aid in the discovery of mood modulators.

AREAS COVERED

This review contains three main sections: historical evolution of marketed antidepressants, physicochemical and structural properties of antidepressant compounds reported in the ChEMBL database and recent efforts in the design and discovery of antidepressants using computational methods. The authors provide details of the computational methods employed, from chemoinformatic analyses to molecular modeling.

EXPERT OPINION

While there have been numerous and important findings in depression research, the high cost and time spent on research into new therapies for brain disorders is a risky undertaking. Computational methodologies can be employed to speed up the discovery of new antidepressants and to detect new sources of chemical compounds with potential antidepressant activity. Compound collections containing compounds already approved in the pharmaceutical and food industries that cover the property space and complement the structural space of CNS drugs represent a promising starting point for the discovery of new antidepressant agents.

Molecular mechanism of serotonin transporter inhibition elucidated by a new flexible docking protocol

The two main groups of antidepressant drugs, the tricyclic antidepressants (TCAs) and the selective serotonin reuptake inhibitors (SSRIs), as well as several other compounds, act by inhibiting the serotonin transporter (SERT). However, the binding mode and molecular mechanism of inhibition in SERT are not fully understood. In this study, five classes of SERT inhibitors were docked into an outward-facing SERT homology model using a new 4D ensemble docking protocol. Unlike other docking protocols, where protein flexibility is not considered or is highly dependent on the ligand structure, flexibility was here obtained by side chain sampling of the amino acids of the binding pocket using biased probability Monte Carlo (BPMC) prior to docking. This resulted in the generation of multiple binding pocket conformations that the ligands were docked into. The docking results showed that the inhibitors were stacked between the aromatic amino acids of the extracellular gate (Y176, F335) presumably preventing its closure. The inhibitors interacted with amino acids in both the putative substrate binding site and more extracellular regions of the protein. A general structure-docking-based pharmacophore model was generated to explain binding of all studied classes of SERT inhibitors. Docking of a test set of actives and decoys furthermore showed that the outward-facing ensemble SERT homology model consistently and selectively scored the majority of active compounds above decoys, which indicates its usefulness in virtual screening.

Discovery of novel selective serotonin reuptake inhibitors through development of a protein-based pharmacophore

The serotonin transporter (SERT), a member of the neurotransmitter sodium symporter (NSS) family, is responsible for the reuptake of serotonin from the synaptic cleft to maintain neurotransmitter homeostasis. SERT is established as an important target in the treatment of anxiety and depression. Because a high-resolution crystal structure is not available, a computational model of SERT was built based upon the X-ray coordinates of the leucine transporter LeuT, a bacterial NSS homologue. The model was used to develop the first SERT structure-based pharmacophore. Virtual screening (VS) of a small molecule structural library using the generated SERT computational model yielded candidate ligands of diverse scaffolds. Pharmacological analysis of the VS hits identified two SERT-selective compounds, potential lead compounds for further SERT-related medication development.

A three-dimensional pharmacophore modelling of ITK inhibitors and virtual screening for novel inhibitors

Interleukin-2-inducible T-cell kinase (ITK) is a key member of the Tec family of non-receptor tyrosine kinases, and has been found to be a novel target for a number of inflammatory and autoimmune diseases. A three-dimensional pharmacophore model has been generated for protein ITK from its known inhibitors. The best HypoGen model consisted of four pharmacophore features: one hydrogen bond acceptor, one hydrogen bond donor and two hydrophobic rings. This model showed a correlation coefficient of 0.947, a root mean square deviation of 0.914 and a configuration cost of 16.866. The model was validated using test set prediction and Fischer's test. A test set containing 204 compounds showed an r(2) of 0.745 between estimated activity and activity measured experimentally. Fisher's test gave a confidence level of 95%. The best pharmacophore model (Hypo1) was then employed for virtual screening (3D database searching), including Lipinsiki's filter, to obtain a pool of more drug-like molecules. The molecular pool thus retrieved was subjected to docking analysis with a study protein to remove any molecules showing false positive activity for ITK.

Investigation on the isoform selectivity of histone deacetylase inhibitors using chemical feature based pharmacophore and docking approaches

A three dimensional (3D) chemical feature based pharmacophore model was developed for selective histone deacetylase 1 (HDAC1) inhibitors, which provides an efficient way to discuss the isoform selectivity of HDAC inhibitors. In contrast to the classical pan-HDAC pharmacophore, two hydrophobic features (HY and HYAr2) were found in the chemical feature based pharmacophore model, which might be responsible for the selectivity of HDAC1 inhibitions. Molecular docking also highlighted the two hydrophobic features, which are located in the internal cavity adjacent to the active site. The results contribute to our understanding of the molecular mechanism underlying the selectivity of HDAC1 inhibitors and suggest a possible target region to design novel selective HDAC1 inhibitors.