Discovery of novel-scaffold monoamine transporter ligands via in silico screening with the S1 pocket of the serotonin transporter

Unlocking the Potential of High-Quality Dopamine Transporter Pharmacological Data: Advancing Robust Machine Learning-Based QSAR Modeling

The dopamine transporter (DAT) plays a critical role in the central nervous system and has been implicated in numerous psychiatric disorders. The ligand-based approaches are instrumental to decipher the structure-activity relationship (SAR) of DAT ligands, especially the quantitative SAR (QSAR) modeling. By gathering and analyzing data from literature and databases, we systematically assemble a diverse range of ligands binding to DAT, aiming to discern the general features of DAT ligands and uncover the chemical space for potential novel DAT ligand scaffolds. The aggregation of DAT pharmacological activity data, particularly from databases like ChEMBL, provides a foundation for constructing robust QSAR models. The compilation and meticulous filtering of these data, establishing high-quality training datasets with specific divisions of pharmacological assays and data types, along with the application of QSAR modeling, prove to be a promising strategy for navigating the pertinent chemical space. Through a systematic comparison of DAT QSAR models using training datasets from various ChEMBL releases, we underscore the positive impact of enhanced data set quality and increased data set size on the predictive power of DAT QSAR models.

Mannich Base PIP-199 Is a Chemically Unstable Pan-Assay Interference Compound

Mannich base PIP-199 is the only reported small-molecule inhibitor of the Fanconi anemia complementation group M-RecQ-mediated genome instability protein (FANCM-RMI), a protein-protein interaction that governs genome instability in the genetic disorders Fanconi anemia and Bloom's syndrome. PIP-199 and analogues with the same indole-derived Mannich base scaffold have been used as tool compounds in diverse biological studies. We report the first published synthesis of PIP-199 and its analogues, demonstrating that PIP-199 immediately decomposes in common aqueous buffers and some organic solvents. Neither PIP-199 nor its more hydrolytically stable analogues show any observable activity in binding and competitive biophysical assays for FANCM-RMI. We conclude that PIP-199 is not an effective tool compound for biological studies and that apparent cellular activity likely arises from the nonspecific toxicity of breakdown products. More generally, apparent inhibitors that share this Mannich scaffold potentially represent a new family of pan-assay interference compounds (PAINS) that should be thoroughly assessed for aqueous stability prior to use in biological studies.

Taurine and Creatine Transporters as Potential Drug Targets in Cancer Therapy

Cancer cells are characterized by uncontrolled growth, proliferation, and impaired apoptosis. Tumour progression could be related to poor prognosis and due to this fact, researchers have been working on novel therapeutic strategies and antineoplastic agents. It is known that altered expression and function of solute carrier proteins from the SLC6 family could be associated with severe diseases, including cancers. These proteins were noticed to play important physiological roles through transferring nutrient amino acids, osmolytes, neurotransmitters, and ions, and many of them are necessary for survival of the cells. Herein, we present the potential role of taurine (SLC6A6) and creatine (SLC6A8) transporters in cancer development as well as therapeutic potential of their inhibitors. Experimental data indicate that overexpression of analyzed proteins could be connected with colon or breast cancers, which are the most common types of cancers. The pool of known inhibitors of these transporters is limited; however, one ligand of SLC6A8 protein is currently tested in the first phase of clinical trials. Therefore, we also highlight structural aspects useful for ligand development. In this review, we discuss SLC6A6 and SLC6A8 transporters as potential biological targets for anticancer agents.

Increasing the Sampling Efficiency of Protein Conformational Change by Combining a Modified Replica Exchange Molecular Dynamics and Normal Mode Analysis

Understanding conformational change at an atomic level is significant when determining a protein functional mechanism. Replica exchange molecular dynamics (REMD) is a widely used enhanced sampling method to explore protein conformational space. However, REMD with an explicit solvent model requires huge computational resources, immensely limiting its application. In this study, a variation of parallel tempering metadynamics (PTMetaD) with the omission of solvent-solvent interactions in exchange attempts and the use of low-frequency modes calculated by normal-mode analysis (NMA) as collective variables (CVs), namely ossPTMetaD, is proposed with the aim to accelerate MD simulations simultaneously in temperature and geometrical spaces. For testing the performance of ossPTMetaD, five protein systems with diverse biological functions and motion patterns were selected, including large-scale domain motion (AdK), flap movement (HIV-1 protease and BACE1), and DFG-motif flip in kinases (p38α and c-Abl). The simulation results showed that ossPTMetaD requires much fewer numbers of replicas than temperature REMD (T-REMD) with a reduction of ∼70% to achieve a similar exchange ratio. Although it does not obey the detailed balance condition, ossPTMetaD provides consistent results with T-REMD and experimental data. The high accessibility of the large conformational change of protein systems by ossPTMetaD, especially in simulating the very challenging DFG-motif flip of protein kinases, demonstrated its high efficiency and robustness in the characterization of the large-scale protein conformational change pathway and associated free energy profile.

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.

What Contributes to Serotonin-Norepinephrine Reuptake Inhibitors' Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation

Dual inhibition of serotonin and norepinephrine transporters (hSERT and hNET) gives greatly improved efficacy and tolerability for treating major depressive disorder (MDD) compared with selective reuptake inhibitors. Pioneer studies provided valuable information on structure, function, and pharmacology of drugs targeting both hSERT and hNET (serotonin-norepinephrine reuptake inhibitors, SNRIs), and the differential binding mechanism between SNRIs and selective inhibitors of 5-HT (SSRIs) or NE (sNRIs) to their corresponding targets was expected to be able to facilitate the discovery of a privileged drug-like scaffold with improved efficacy. However, the dual-target mechanism of SNRIs was still elusive, and the binding mode distinguishing SNRIs from SSRIs and sNRIs was also unclear. Herein, an integrated computational strategy was adopted to discover the binding mode shared by all FDA approved SNRIs. The comparative analysis of binding free energy at the per-residue level discovered that residues Phe335, Leu337, Gly338, and Val343 located at the transmembrane domain 6 (TM6) of hSERT (the corresponding residues Phe317, Leu319, Gly320, and Val325 in hNET) were the determinants accounting for SNRIs' dual-acting inhibition, while residues lining TM3 and 8 (Ile172, Ser438, Thr439, and Leu443 in hSERT; Val148, Ser419, Ser420, and Met424 in hNET) contributed less to the binding of SNRIs than that of SSRIs and sNRIs. Based on these results, the distances between an SNRI's centroid and the centroids of its two aromatic rings (measuring the depth of rings stretching into hydrophobic pockets) were discovered as the key to the SNRIs' dual-targeting mechanism. This finding revealed SNRIs' binding mechanism at an atomistic level, which could be further utilized as structural blueprints for the rational design of privileged drug-like scaffolds treating MDD.

Getting the most out of PubChem for virtual screening

INTRODUCTION

With the emergence of the 'big data' era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge.

AREAS COVERED

This article provides an overview of how PubChem's data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery.

EXPERT OPINION

PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.

Properties of an inward-facing state of LeuT: conformational stability and substrate release

The leucine transporter (LeuT) is a bacterial homolog of the human monoamine transporters, which are important pharmaceutical targets. There are no high-resolution structures of the human transporters available; however, LeuT has been crystallized in several different conformational states. Recently, an inward-facing conformation of LeuT was solved revealing an unexpectedly large movement of transmembrane helix 1a (TM1a). We have performed molecular dynamics simulations of the mutated and wild-type transporter, with and without the cocrystallized Fab antibody fragment, to investigate the properties of this inward-facing conformation in relation to transport by LeuT within the membrane environment. In all of the simulations, local conformational changes with respect to the crystal structure are consistently observed, especially in TM1a. Umbrella sampling revealed a soft potential for TM1a tilting. Furthermore, simulations of inward-facing LeuT with Na(+) ions and substrate bound suggest that one of the Na(+) ion binding sites is fully disrupted. Release of alanine and the second Na(+) ion is also observed, giving insight into the final stage of the translocation process in atomistic detail.

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery

Recent advances in molecular simulations have allowed scientists to investigate slower biological processes than ever before. Together with these advances came an explosion of data that has transformed a traditionally computing-bound into a data-bound problem. Here, we present HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery. So far, HTMD includes system building for CHARMM and AMBER force fields, projection methods, clustering, molecular simulation production, adaptive sampling, an Amazon cloud interface, Markov state models, and visualization. As a result, a single, short HTMD script can lead from a PDB structure to useful quantities such as relaxation time scales, equilibrium populations, metastable conformations, and kinetic rates. In this paper, we focus on the adaptive sampling and Markov state modeling features.

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.