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

In silico validation of allosteric inhibitors targeting Zika virus NS2B-NS3 protease

The Zika virus (ZIKV), a member of the Flaviviridae family, poses a major threat to human health because of the lack of effective antiviral drugs. Although the NS2B-NS3 protease of ZIKV (NS2B-NS3pro) is regarded as a major target for antiviral inhibitors, viral mutations can lead to ineffective competitive inhibitors. Allosteric inhibitors bind to highly conserved nonprotease active sites, induce conformational changes in the protease active site, and prevent substrate binding. Currently, no molecular simulation techniques are available for accurately predicting and analysing conformational changes in the protease catalytic domain. In this study, we developed a combined approach that involves blind docking, Gaussian accelerated molecular dynamics, two-dimensional potential of mean force profiling, density functional theory (DFT) calculations, and interaction region indicator (IRI) analysis and employed it to examine the allosteric inhibitor-01 molecule and its interaction with ZIKV NS2B-NS3pro. Our results indicated that the binding of inhibitor-01 to NS2B-NS3pro resulted in two major conformational states, state I and state II, which in turn changed the volume of the protease active site from 1014 Å3 to 710 and 820 Å3, respectively. These two states had an inactive catalytic domain (residues His116, Asp140, and Ser200). DFT and IRI analyses revealed that, in state I, Lys138 and Gln139 formed hydrogen bonds with inhibitor-01, whereas Lys138, Leu214, Asn217, Val220, and Ile221 engaged in van der Waals interactions with inhibitor-01. Advancements in computational techniques and power are expected to facilitate further progress in overcoming challenges associated with designing allosteric inhibitors for viral proteases.

Molecular Mechanism-Driven Discovery of Novel Small Molecule Inhibitors against Drug-Resistant SARS-CoV-2 Mpro Variants

Under the selective pressure of nirmatrelvir, a peptidomimetic covalent drug targeting SARS-CoV-2 Mpro, various drug-resistant mutations on Mpro have been acquired in vitro. Among the mutations, L50F and E166V, along with the combination of L50F and E166V, are particularly representative and pose considerable obstacles to the effective treatment of COVID-19. Our previous study identified NMI-001 and NMI-002 as novel nonpeptide inhibitors that target SARS-CoV-2 Mpro, possessing unique scaffolds and binding modes different from those of nirmatrelvir. In view of these findings, we proposed a drug design strategy aimed at rapidly identifying inhibitors that can combat mutation-induced drug resistance. Initially, molecular dynamics (MD) simulation was employed to investigate the binding mechanisms of NMI-001 and NMI-002 against the three drug-resistant mutants (Mpro_L50F, Mpro_E166V, and Mpro_L50F+E166V). Then, we conducted two phases of high-throughput virtual screening. In the first phase, NMI-001 served as a template to perform scaffold hopping-based similarity search in a library of 15,742,661 compounds. In the second phase, 968 compounds exhibiting similarity to NMI-001 were evaluated via molecular docking and MD simulations. Six compounds that may be effective against at least one mutant were identified, and five compounds were procured for conducting in vitro assays. Finally, the compound Z1557501297 (NMI-003) exhibiting inhibitory effects against the E166V (IC50 = 27.81 ± 2.65 μM) and L50F+E166V (IC50 = 8.78 ± 0.74 μM) mutants was discovered. The binding modes referring to NMI-003-Mpro_E166V and NMI-003-Mpro_L50F+E166V were further elucidated at the atomic level. In summary, NMI-003 reported herein is the first compound with activity against E166V and L50F+E166V, which provides a good starting point to design novel antiviral drugs for the treatment of drug-resistant SARS-CoV-2.

Structural Models of Human Norepinephrine Transporter Ensemble Reveal the Allosteric Sites and Ligand-Binding Mechanism

The norepinephrine transporter (NET) plays a pivotal role in recycling norepinephrine (NE) from the synaptic cleft. However, the structures referring to the conformational heterogeneity of NET during the transport cycle remain poorly understood. Here, three structural models of NE bound to the orthosteric site of NET in outward-open (OOholo), outward-occluded (OCholo), and inward-open (IOholo) conformations were first obtained using the multistate structures of serotonin transporter as templates and further characterized through Gaussian-accelerated molecular dynamics and free energy reweighting. Analysis of the structures revealed eight potential allosteric sites on the functional-specific states of NET. One of the pharmacologically relevant pockets located at the extracellular vestibule was further verified by simulating the binding behaviors of a clinical trial drug χ-MrIA that is allosterically regulating NET. These structural and energetic insights into NET advanced our understanding of NE reuptake and paved the way for discovering novel molecules targeting the allosteric sites.

The binding pattern of ferric iron and iron-binding protein in Botrytis cinerea

Iron-binding protein (Ibp) has protective effect on pathogen exposed to H2O2 in defense response of plants. Ibp in Botrytis cinerea (BcIbp) is related to its virulence. Bcibp mutation lead to virulence deficiencies in B. cinerea. BcIbp is involved in the Fe3+ homeostasis regulation. Recognition the binding site and binding pattern of ferric iron and iron-binding protein in B. cinerea are vital to understand its function. In this study, molecular dynamics (MD) simulations, gaussian accelerated molecular dynamics (GaMD) simulations, dynamic cross correlation analysis and quantum chemical energy calculation were used to explore binding pattern of ferric iron. MD results showed that the C-terminal region had little effect on the stability of residues in the Fe3+-binding pocket. Energy calculations suggested the most likely coordination pattern for ferric iron in iron-binding protein. These results will help to understand the binding of ferric iron to iron-binding protein and provide new ideas for regulating the virulence of B. cinerea.

DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders

In response to the shortcomings in data quality and coverage for neurological and psychiatric disorders (NPDs) in existing comprehensive databases, this paper introduces the DTNPD database, specifically designed for NPDs. DTNPD contains detailed information on 30 NPDs types, 1847 drugs, 514 drug targets, 64 drug combinations, and 61 potential target combinations, forming a network with 2389 drug-target associations. The database is user-friendly, offering open access and downloadable data, which is crucial for network pharmacology studies. The key strength of DTNPD lies in its robust networks of drug and target combinations, as well as drug-target networks, facilitating research and development in the field of NPDs. The development of the DTNPD database marks a significant milestone in understanding and treating NPDs. For accessing the DTNPD database, the primary URL is http://dtnpd.cnsdrug.com, complemented by a mirror site available at http://dtnpd.lyhbio.com.

CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design

Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.

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.

Novel Scaffold Agonists of the α2A Adrenergic Receptor Identified via Ensemble-Based Strategy

The α2A adrenergic receptor (α2A-AR) serves as a critical molecular target for sedatives and analgesics. However, α2A-AR ligands with an imidazole ring also interact with an imidazoline receptor as well as other proteins and lead to undesirable effects, motivating us to develop more novel scaffold α2A-AR ligands. For this purpose, we employed an ensemble-based ligand discovery strategy, integrating long-term molecular dynamics (MD) simulations and virtual screening, to identify new potential α2A-AR agonists with novel scaffold. Our results showed that compounds SY-15 and SY-17 exhibited significant biological effects in the preliminary evaluation of protein kinase A (PKA) redistribution assays. They also reduced levels of intracellular cyclic adenosine monophosphate (cAMP) in a dose-dependent manner. Upon treatment of the cells with 100 μM concentrations of SY-15 and SY-17, there was a respective decrease in the intracellular cAMP levels by 63.43% and 53.83%. Subsequent computational analysis was conducted to elucidate the binding interactions of SY-15 and SY-17 with the α2A-AR. The binding free energies of SY-15 and SY-17 calculated by MD simulations were -45.93 and -71.97 kcal/mol. MD simulations also revealed that both compounds act as bitopic agonists, occupying the orthosteric site and a novel exosite of the receptor simultaneously. Our findings of integrative computational and experimental approaches could offer the potential to enhance ligand affinity and selectivity through dual-site occupancy and provide a novel direction for the rational design of sedatives and analgesics.

Characterizing the Molecular Mechanism of the Lethal C423D Mutation in FgMyoI: A Molecular Perspective

The lethal mutation C423D in Fusarium graminearum myosin I (FgMyoI) occurs close to the binding pocket of the allosteric inhibitor phenamacril and causes severe inhibition on mycelial growth of F. graminearum strain PH-1. Here, based on extensive Gaussian accelerated molecular dynamics simulations and wet experiments, we elucidate the underlying molecular mechanism of the abnormal functioning of the FgMyoIC423D mutant at the atomistic level. Our results suggest that the damaging mutation C423D exhibits a synergistic allosteric inhibition mechanism similar to but more robust than that of phenamacril, including effects on the active site and actin binding. Unlike phenamacril-induced closure of Switch2, the mutation results in unfolding of the N-terminal relay helix with a partially opened Switch2 and blocks the structural rearrangement of the relay/SH1 helices, impairing the proper initiation of the recovery stroke. Due to the significant influence of C423D mutation on the function of FgMyoI, designing covalent inhibitors targeting this site holds tremendous potential.

Mechanistic Insights into Peptide Binding and Deactivation of an Adhesion G Protein-Coupled Receptor

Adhesion G protein-coupled receptors (ADGRGs) play critical roles in the reproductive, neurological, cardiovascular, and endocrine systems. In particular, ADGRG2 plays a significant role in Ewing sarcoma cell proliferation, parathyroid cell function, and male fertility. In 2022, a cryo-EM structure was reported for the active ADGRG2 bound by an optimized peptide agonist IP15 and the Gs protein. The IP15 peptide agonist was also modified to antagonists 4PH-E and 4PH-D with mutations of the 4PH residue to Glu and Asp, respectively. However, experimental structures of inactive antagonist-bound ADGRs remain to be resolved, and the activation mechanism of ADGRs such as ADGRG2 is poorly understood. Here, we applied Gaussian accelerated molecular dynamics (GaMD) simulations to probe conformational dynamics of the agonist- and antagonist-bound ADGRG2. By performing GaMD simulations, we were able to identify important low-energy conformations of ADGRG2 in the active, intermediate, and inactive states, as well as explore the binding conformations of each peptide. Moreover, our simulations revealed critical peptide-receptor residue interactions during the deactivation of ADGRG2. In conclusion, through GaMD simulations, we uncovered mechanistic insights into peptide (agonist and antagonist) binding and deactivation of the ADGRG2. These findings will potentially facilitate rational design of new peptide modulators of ADGRG2 and other ADGRs.

Allosteric inhibition of myosin by phenamacril: a synergistic mechanism revealed by computational and experimental approaches

BACKGROUND

Myosin plays a crucial role in cellular processes, while its dysfunction can lead to organismal malfunction. Phenamacril (PHA), a highly species-specific and non-competitive inhibitor of myosin I (FgMyoI) from Fusarium graminearum, has been identified as an effective fungicide for controlling plant diseases caused by partial Fusarium pathogens, such as wheat scab and rice bakanae. However, the molecular basis of its action is still unclear.

RESULTS

This study used multiple computational approaches first to elucidate the allosteric inhibition mechanism of FgMyoI by PHA at the atomistic level. The results indicated the increase of adenosine triphosphate (ATP) binding affinity upon PHA binding, which might impede the release of hydrolysis products. Furthermore, simulations revealed a broadened outer cleft and a significantly more flexible interface for actin binding, accompanied by a decrease in signaling transduction from the catalytic center to the actin-binding interface. These various effects might work together to disrupt the actomyosin cycle and hinder the ability of motor to generate force. Our experimental results further confirmed that PHA reduces the enzymatic activity of myosin and its binding with actin.

CONCLUSION

Therefore, our findings demonstrated that PHA might suppress the function of myosin through a synergistic mechanism, providing new insights into myosin allostery and offering new avenues for drug/fungicide discovery targeting myosin. © 2023 Society of Chemical Industry.