Development and validation of a genetic algorithm for flexible docking

The Potential of Molecular Docking for Predictive Toxicology

Molecular modeling techniques are widely used in medicinal chemistry for the study of biological targets, the rational design of new drugs, or the investigation of their mechanism of action.They are also applied in toxicology to identify chemical potential harmful effects.Molecular docking is a computational technique to predict the ligand binding mode and evaluate the interaction energy with a biological target.This chapter describes a computational workflow to predict possible endocrine disruptors on peroxisome proliferator-activated receptor alpha (PPARα), a nuclear receptor involved in glucose and lipid metabolism. The analyzed compounds are food contact chemicals, natural or synthetic substances intentionally added to food or released from the package or during production or technological processes.

Simulation- and AI-directed optimization of 4,6-substituted 1,3,5-triazin-2(1H)-ones as inhibitors of human DNA topoisomerase IIα

The 4,6-substituted-1,3,5-triazin-2(1H)-ones are promising inhibitors of human DNA topoisomerase IIα. To further develop this chemical class targeting the enzyme´s ATP binding site, the triazin-2(1H)-one substitution position 6 was optimized. Inspired by binding of preclinical substituted 9H-purine derivative, bicyclic substituents were incorporated at position 6 and the utility of this modification was validated by a combination of molecular simulations, dynamic pharmacophores, and free energy calculations. Considering also predictions of Deepfrag, a software developed for structure-based lead optimization based on deep learning, compounds with both bicyclic and monocyclic substitutions were synthesized and investigated for their inhibitory activity. The SAR data showed that the bicyclic substituted compounds exhibited good inhibition of topo IIα, comparable to their mono-substituted counterparts. Further evaluation on a panel of human protein kinases showed selectivity for the inhibition of topo IIα. Mechanistic studies indicated that the compounds acted predominantly as catalytic inhibitors, with some exhibiting topo IIα poison effects at higher concentrations. Integration of STD NMR experiments and molecular simulations, provided insights into the binding model and highlighted the importance of the Asn120 interaction and hydrophobic interactions with substituents at positions 4 and 6. In addition, NCI-60 screening demonstrated cytotoxicity of the compounds with bicyclic substituents and identified sensitive human cancer cell lines, underlining the translational relevance of our findings for further preclinical development of this class of compounds. The study highlights the synergy between simulation and AI-based approaches in efficiently guiding molecular design for drug optimization, which has implications for further preclinical development of this class of compounds.

Investigation of in silico studies for cytochrome P450 isoforms specificity

Cytochrome P450 (CYP450) enzymes comprise a highly diverse superfamily of heme-thiolate proteins that responsible for catalyzing over 90 % of enzymatic reactions associated with xenobiotic metabolism in humans. Accurately predicting whether chemicals are substrates or inhibitors of different CYP450 isoforms can aid in pre-selecting hit compounds for the drug discovery process, chemical toxicology studies, and patients treatment planning. In this work, we investigated in silico studies on CYP450s specificity over past twenty years, categorizing these studies into structure-based and ligand-based approaches. Subsequently, we utilized 100 of the most frequently prescribed drugs to test eleven machine learning-based prediction models which were published between 2015 and 2024. We analyzed various aspects of the evaluated models, such as their datasets, algorithms, and performance. This will give readers with a comprehensive overview of these prediction models and help them choose the most suitable one to do prediction. We also provide our insights for future research trend in both structure-based and ligand-based approaches in this field.

LC-ESI-UHR-QqTOF-MS/MS profiling and anti-inflammatory potential of the cultivated Opuntia ficus-indica (L.) Mill. and the wild Opuntia stricta (Haw.) Haw. fruits from the Algerian region

Opuntia plants are abundant but still underexplored edible resources of the Algerian region. This work chemically characterizes extracts of different parts of the fruit of the commercial Opuntia ficus-indica (L.) Mill. and the wild Opuntia stricta (Haw.) Haw. growing in Bejaia, and evaluates their anti-inflammatory potential through different cell and cell-free bioassays. The LC-ESI-UHR-QqTOF-MS/MS analysis enabled the identification of 18 compounds, with azelaic acid and 1-O-vanilloyl-β-d-glucose reported here for the first time. Aqueous extracts of seeds were the most effective in scavenging superoxide anion radical (IC50 = 111.08 μg/mL) and presented the best anti-inflammatory potential in LPS-stimulated macrophages (IC50 = 206.30 μg/mL). The pulp of O. stricta suggested potential for addressing post-inflammatory hyperpigmentation, with piscidic and eucomic acids predicted with the strongest binding affinity towards tyrosinase, exhibiting higher scoring values than the reference inhibitor kojic acid. This pioneer study brings valuable perspectives for the pharmacological, nutritional and economic valorization of the wild O. stricta for functional foods.

Crystal structure of dihydroneopterin aldolase from Mycobacterium tuberculosis associated with 8-mercaptoguanine, and development of novel S8-functionalized analogues as inhibitors: Synthesis, enzyme inhibition, in vitro toxicity and antitubercular activity

The crystallographic structure of the FolB enzyme from Mycobacterium tuberculosis (MtFolB), complexed with its inhibitor 8-mercaptoguanine (8-MG), was elucidated at a resolution of 1.95 Å. A novel series of S8-functionalized 8-MG derivatives were synthesised and evaluated as in vitro inhibitors of dihydroneopterin aldolase (DHNA, EC 4.1.2.25) activity of MtFolB. These compounds exhibited IC50 values in the submicromolar range. Evaluation of the activity for five compounds indicated their inhibition mode and inhibition constants. Molecular docking analyses were performed to determine the enzyme-inhibitor intermolecular interactions and ligand conformations upon complex formation. The inhibitory activities of all compounds against the M. tuberculosis H37Rv strain were evaluated. Compound 3e exhibited a minimum inhibitory concentration in the micromolar range. Finally, Compound 3e showed no apparent toxicity in both HepG2 and Vero cells. The findings presented herein will advance the quest for novel, specific inhibitors targeting MtFolB, an attractive molecular target for TB drug development.

Effect of functional groups in strawberry flavoring on pea protein-flavor interactions: Potential applicable in flavor formulation for plant-based protein aqueous foods

This research aimed to explore binding interactions between pea protein isolate (PPI) and selected strawberry flavorings including vanillin, γ-decalactone, furaneol, and (Z)-3-hexen-1-ol within an aqueous system. The results showed that binding affinities of PPI with all various functional group of flavor compounds decreased as temperature increased from 5 °C to 25 °C. Notably, at 25 °C, γ-decalactone displayed the highest binding affinity, followed by vanillin, (Z)-3-hexen-1-ol, and furaneol. Lowest binding was observed for furaneol, explained by its greater lipophilicity (lower partition coefficient values or LogP value) and molecular structure in each functional group in the flavor compounds. Thermodynamically, the interaction between PPI and each selected flavor compound was spontaneous, with evidence suggesting primary forces being hydrophobic interactions or hydrogen bonding/van der Waals forces. Computational molecular docking further confirmed these interaction types. This research provides insights into the interactions between PPI and strawberry flavorings, aiding in the selection of optimal flavor compound proportion for protein-rich products.

Exploitation of the nitro- and/or 4-Trifluoromethyl-decorated phenyl fragment to develop small inhibitors of Alpha-Syn fibril aggregation

Here, we report new 2-nitro and/or 4-trifluoromethylphenyl-based small molecules developed as inhibitors of alpha-Syn fibril formation. The set of eighteen compounds was inspired by well-known alpha-Syn aggregation modulators retrieved from literature. The preliminary biochemical data suggested that the two molecules out of eighteen compounds exerted activity comparable to that of reference compound SynuClean-D (SC-D, 5-nitro-6-(3-nitrophenyl)-2-oxo-4-(trifluoromethyl)-1H-pyridine-3-carbonitrile), according to Thioflavin T kinetics. Pharmacophore modelling deciphered the main structural requirements for alpha-Syn aggregation modulators. Moreover, docking and molecular dynamics simulations depicted the binding mode with the targeted alpha-Syn fibrils. The structural data of these new potential α-Syn binders might furnish additional information for understanding the mechanism of action of the ligands that specifically target the NAC domain as theranostic agents for α-synucleopathies.

Protein-Protein Stabilization in VIVO/8-Hydroxyquinoline-Lysozyme Adducts

The binding of the potential drug [VIVO(8-HQ)2], where 8-HQ is 8-hydroxyquinolinato, with hen egg white lysozyme (HEWL) was evaluated through spectroscopic (electron paramagnetic resonance, EPR, and UV-visible), spectrometric (electrospray ionization-mass spectrometry, ESI-MS), crystallographic (X-ray diffraction, XRD), and computational (DFT and docking) studies. ESI-MS indicates the interaction of [VIVO(8-HQ)(H2O)]+ and [VIVO(8-HQ)2(H2O)] species with HEWL. Room temperature EPR spectra suggest both covalent and non-covalent binding of the two different V-containing fragments. XRD analyses confirm these findings, showing that [VIVO(8-HQ)(H2O)]+ interacts covalently with the solvent exposed Asp119, while cis-[VIVO(8-HQ)2(H2O)] non-covalently with Arg128 and Lys96 from a symmetry mate. The covalent binding of [VIVO(8-HQ)(H2O)]+ to Asp119 is favored by a π-π contact with Trp62 and a H-bond with Asn103 of a symmetry-related molecule. Additionally, the covalent binding of VVO2 + to Asp48 and non-covalent binding of other V-containing fragments to Arg5, Cys6, and Glu7 are revealed. Molecular docking indicates that, in the absence of the interactions occurring at the protein-protein interface close to Asp119, the covalent binding to Glu35 or Asp52 should be preferred. Such a protein-protein stabilization could be more common than what believed up today, at least in the solid state, and should be considered in the characterization of metal-protein adducts.

Peptide-Based Strategies: Combating Alzheimer's Amyloid β Aggregation through Ergonomic Design and Fibril Disruption

Amyloidosis of amyloid-β (Aβ) triggers a cascade of events, leading to oxidative damage and neuronal death. Therefore, inhibiting Aβ amyloidosis or disrupting the matured fibrils is the primary target to combat progressive Alzheimer's disease (AD) pathogenesis. Here, we undertake optimization strategies to improve the antiamyloid efficiency of our previously reported NF11 (NAVRWSLMRPF) peptide. Among the series of peptides tested, nontoxic and serum-stable peptide 1 or P1 containing an anthranilic acid residue shows immense potential in not only inhibiting the Aβ42 amyloid formation but also disrupting the mature Aβ42 fibrils into nontoxic small molecular weight soluble species. Our studies provide high-resolution characterization of the peptide's mechanism of action. With a binding affinity within the micromolar range for both the monomer and aggregated Aβ42, this α/β hybrid peptide can efficiently modulate Aβ amyloidosis while facilitating the clearance of toxic aggregates and enforcing protection from apoptosis. Thus, our studies highlight that incorporating a β-amino acid not only imparts protection from proteolytic degradation and improved stability but also functions effectively as a β breaker, redirecting the aggregation kinetics toward off-pathway fibrillation.

Exploring the antiviral activities of the FDA-approved drug sulfadoxine and its derivatives against Chikungunya virus

BACKGROUND

Currently, there is no antiviral licensed to treat chikungunya fever, a disease caused by the infection with Alphavirus chikungunya (CHIKV). Treatment is based on analgesic and anti-inflammatory drugs to relieve symptoms. Our study aimed to evaluate the antiviral activity of sulfadoxine (SFX), an FDA-approved drug, and its derivatives complexed with silver(I) (AgSFX), salicylaldehyde Schiff base (SFX-SL), and with both Ag and SL (AgSFX-SL) against CHIKV.

METHODS

The anti-CHIKV activity of SFX and its derivatives was investigated using BHK-21 cells infected with CHIKV-nanoluc, a marker virus-carrying nanoluciferase reporter. Dose-response and time of drug-addition assays were performed in order to assess the antiviral effects of the compounds, as well as in silico data and ATR-FTIR analysis for insights on their mechanisms of action.

RESULTS

The SFX inhibited 34% of CHIKV replication, while AgSFX, SFX-SL, and AgSFX-SL enhanced anti-CHIKV activity to 84%, 89%, and 95%, respectively. AgSFX, SFX-SL, and AgSFX-SL significantly decreased viral entry and post-entry to host cells, and the latter also protected cells against infection. Additionally, molecular docking calculations and ATR-FTIR analysis demonstrated interactions of SFX-SL, AgSFX, and AgSFX-SL with CHIKV.

CONCLUSIONS

Collectively, our findings suggest that the addition of metal ions and/or Schiff base to SFX improved its antiviral activity against CHIKV.

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

SAR Analysis of Novel Coxsackie virus A9 Capsid Binders

Enterovirus infections are common in humans, yet there are no approved antiviral treatments. In this study we concentrated on inhibition of one of the Enterovirus B (EV-B), namely Coxsackievirus A9 (CVA9), using a combination of medicinal chemistry, virus inhibition assays, structure determination from cryogenic electron microscopy and molecular modeling, to determine the structure activity relationships for a promising class of novel N-phenylbenzylamines. Of the new 29 compounds synthesized, 10 had half maximal effective concentration (EC50) values between 0.64-10.46 μM, and of these, 7 had 50% cytotoxicity concentration (CC50) values higher than 200 μM. In addition, this new series of compounds showed promising physicochemical properties and act through capsid stabilization, preventing capsid expansion and subsequent release of the genome.

Novel DNA Repair Inhibitors Targeting XPG to Enhance Cisplatin Therapy in Non-Small Cell Lung Cancer: Insights from In Silico and Cell-Based Studies

NSCLC is marked by low survival and resistance to platinum-based chemotherapy. The XPG endonuclease has emerged as a promising biomarker for predicting the prognosis of cisplatin-treated patients and its downregulation having been reported to increase cisplatin efficacy. This study presents an integrated strategy for identifying small molecule inhibitors of XPG to improve cisplatin therapy in NSCLC. A structure-based virtual screening approach was adopted, including a structural and physicochemical analysis of the protein, and a library of small molecules with reported inhibitory activities was retrieved. This analysis identified Lys84 as a crucial residue for XPG activity by targeting its interaction with DNA. After molecular docking and virtual screening calculations, 61 small molecules were selected as potential XPG inhibitors, acquired from the ChemBridge database and then validated in H1299 cells, a NSCLC cell line exhibiting the highest ERCC5 expression. The MTS assay was performed as a first screening approach to determine whether these potential inhibitors could enhance cisplatin-induced cytotoxicity. Overall, among the eight compounds identified as the most promising, three of them revealed to significantly increase the impact of cisplatin. The inherent cytotoxicity of these compounds was further investigated in a non-tumoral lung cell line (BEAS-2B cells), which resulted in the identification of two non-cytotoxic candidates to be used in combination with cisplatin in order to improve its efficacy in NSCLC therapy.

Novel PROTAC probes targeting KDM3 degradation to eliminate colorectal cancer stem cells through inhibition of Wnt/β-catenin signaling

It has been demonstrated that the KDM3 family of histone demethylases (KDM3A and KDM3B) epigenetically control the functional properties of colorectal cancer stem cells (CSCs) through Wnt/β-catenin signaling. Meanwhile, a broad-spectrum histone demethylase inhibitor, IOX1, suppresses Wnt-induced colorectal tumorigenesis predominantly through inhibiting the enzymatic activity of KDM3. In this work, several cereblon (CRBN)-recruiting PROTACs with various linker lengths were designed and synthesized using IOX1 as a warhead to target KDM3 proteins for degradation. Two of the synthesized PROTACs demonstrated favorable degradation profile and selectivity towards KDM3A and KDM3B. Compound 4 demonstrated favorable in vitro metabolic profile in liver enzymes as well as no hERG-associated cardiotoxicity. Compound 4 also showed dramatic ability in suppressing oncogenic Wnt signaling to eliminate colorectal CSCs and inhibit tumor growth, with around 10- to 35-fold increased potency over IOX1. In summary, this study suggests that PROTACs provide a unique molecular tool for the development of novel small molecules from the IOX1 skeleton for selective degradation of KDM3 to eliminate colorectal CSCs via suppressing oncogenic Wnt signaling.

Predicting of novel homoserine dehydrogenase inhibitors against Paracoccidioides brasiliensis: integrating in silico and in vitro approaches

Aim: To search for potential inhibitors to homoserine dehydrogenase (HSD) in Paracoccidioides brasiliensis the causative agent of paracoccidioidomycosis, an infection with a high mortality rate in Brazil.Materials & methods: The enzyme was modeled and used in the virtual screening of the compounds. The library was first screened by the Autodock, in which 66 molecules were better ranked than substrate, and then, also evaluated by the Molegro and Gold programs.Results: The HS23 and HS87 molecules were selected in common by the three programs, and ADME/Tox evaluation indicates they are not toxic. The molecular dynamics of PbHSD bonded to ligands showed stable complexes until 50 ns. To validate the results, compounds were purchased for assays of minimum inhibitory concentration (MIC), minimum fungicidal concentration (MFC), synergic profile with Amphotericin B (AmB) and cytotoxicity. The two molecules presented MIC of 32 μg/ml and MFC of 64 μg/ml against the P. brasiliensis (strain Pb18). They also showed synergistic activity with AmB and a lack of toxicity against Hela and Vero cell lines.Conclusion: These results suggest that the HS23 and HS87 are promising candidates as PbHSD inhibitors and may be used as hits for the development of new drugs against paracoccidioidomycosis.

Searching for Protein Off-Targets of Prostate-Specific Membrane Antigen-Targeting Radioligands in the Salivary Glands

Background: Prostate specific membrane antigen (PSMA)-targeted radioligand therapies represent a highly effective treatment for metastatic prostate cancer. However, high and sustain uptake of PSMA-ligands in the salivary glands led to dose limiting dry mouth (xerostomia), especially with α-emitters. The expression of PSMA and histologic analysis couldn't directly explain the toxicity, suggesting a potential off-target mediator for uptake. In this study, we set out to search for possible off-target non-PSMA protein(s) in the salivary glands. Methods: A machine-learning based quantitative structure activity relationship (QSAR) model was built for seeking the possible off-target(s). The resulting target candidates from the model prediction were subjected to further analysis for salivary protein expression and structural homology at key regions required for PSMA-ligand binding. Furthermore, cellular binding assays were performed utilizing multiple cell lines with high expression of the candidate proteins and low expression of PSMA. Finally, PSMA knockout (PSMA-/-) mice were scanned by small animal PET/MR using [68Ga]Ga-PSMA-11 for in-vivo validation. Results: The screening of the trained QSAR model did not yield a solid off-target protein, which was corroborated in part by cellular binding assays. Imaging using PSMA-/- mice further demonstrated markedly reduced PSMA-radioligand uptake in the salivary glands. Conclusion: Uptake of the PSMA-targeted radioligands in the salivary glands remains primarily PSMA-mediated. Further investigations are needed to illustrate a seemingly different process of uptake and retention in the salivary glands than that in prostate cancer.

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.

Exploring the Esterase Catalytic Activity of Minimalist Heptapeptide Amyloid Fibers

This paper investigates the esterase activity of minimalist amyloid fibers composed of short seven-residue peptides, IHIHIHI (IH7) and IHIHIQI (IH7Q), with a particular focus on the role of the sixth residue position within the peptide sequence. Through computational simulations and analyses, we explore the molecular mechanisms underlying catalysis in these amyloid-based enzymes. Contrary to initial hypotheses, our study reveals that the twist angle of the fiber, and thus the catalytic site's environment, is not notably affected by the sixth residue. Instead, the sixth residue interacts with the p-nitrophenylacetate (pNPA) substrate, particularly through its -NO2 group, potentially enhancing catalysis. Quantum mechanics/molecular mechanics (QM/MM) simulations of the reaction mechanism suggest that the polarizing effect of glutamine enhances catalytic activity by forming a stabilizing network of hydrogen bonds with pNPA, leading to lower energy barriers and a more exergonic reaction. Our findings provide valuable insights into the intricate interplay between peptide sequence, structural arrangement, and catalytic function in amyloid-based enzymes, offering potentially valuable information for the design and optimization of biomimetic catalysts.

An artificial intelligence accelerated virtual screening platform for drug discovery

Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

Going beyond the Ordered Bulk: A Perspective on the Use of the Cambridge Structural Database for Predictive Materials Design

When Olga Kennard founded the Cambridge Crystallographic Data Centre in 1965, the Cambridge Structural Database was a pioneering attempt to collect scientific data in a standard format. Since then, it has evolved into an indispensable resource in contemporary molecular materials science, with over 1.25 million structures and comprehensive software tools for searching, visualizing and analyzing the data. In this perspective, we discuss the use of the CSD and CCDC tools to address the multiscale challenge of predictive materials design. We provide an overview of the core capabilities of the CSD and CCDC software and demonstrate their application to a range of materials design problems with recent case studies drawn from topical research areas, focusing in particular on the use of data mining and machine learning techniques. We also identify several challenges that can be addressed with existing capabilities or through new capabilities with varying levels of development effort.

In silico evidence of bitopertin's broad interactions within the SLC6 transporter family

The Glycine Transporter Type 1 (GlyT1) significantly impacts central nervous system functions, influencing glycinergic and glutamatergic neurotransmission. Bitopertin, the first GlyT1 inhibitor in clinical trials, was developed for schizophrenia treatment but showed limited efficacy. Despite this, bitopertin's repositioning could advance treating various pathologies. This study aims to understand bitopertin's mechanism of action using computational methods, exploring off-target effects, and providing a comprehensive pharmacological profile. Similarity Ensemble Approach (SEA) and SwissTargetPrediction initially predicted targets, followed by molecular modeling on SWISS-MODEL and GalaxyWeb servers. Binding sites were identified using PrankWeb, and molecular docking was performed with DockThor and GOLD software. Molecular dynamics analyses were conducted on the Visual Dynamics platform. Reverse screening on SEA and SwissTargetPrediction identified GlyT1 (SLC6A9), GlyT2 (SLC6A5), PROT (SLC6A7), and DAT (SLC6A3) as potential bitopertin targets. Homology modeling on SwissModel generated high-resolution models, optimized further on GalaxyWeb. PrankWeb identified similar binding sites in GlyT1, GlyT2, PROT, and DAT, indicating potential interaction. Docking studies suggested bitopertin's interaction with GlyT1 and proximity to GlyT2 and PROT. Molecular dynamics confirmed docking results, highlighting bitopertin's target stability beyond GlyT1. The study concludes that bitopertin potentially interacts with multiple SLC6 family targets, indicating a broader pharmacological property.

New Sources of Resistance to Terbinafine Revealed and Squalene Epoxidase Modelled in the Dermatophyte Fungus Trichophyton interdigitale From Australia

BACKGROUND

Terbinafine is widely used to treat onychomycosis caused by dermatophyte fungi. Terbinafine resistance in recent years is causing concern. Resistance has so far been associated with single-nucleotide substitutions in the DNA sequence of the enzyme squalene epoxidase (SQLE) but how this affects SQLE functionality is not understood.

OBJECTIVES

The aim of this study was to understand newly discovered resistance in two Australian strains of Trichophyton interdigitale.

PATIENTS/METHODS

Resistance to terbinafine was tested in four newly isolated strains. Three-dimensional SQLE models were prepared to investigate how the structure of their SQLE affected the binding of terbinafine.

RESULTS

This study found the first Australian occurrences of terbinafine resistance in two T. interdigitale strains. Both strains had novel deletion mutations in erg1 and frameshifts during translation. Three-dimensional models had smaller SQLE proteins and open reading frames as well as fewer C-terminal α-helices than susceptible strains. In susceptible strains, the lipophilic tail of terbinafine was predicted to dock stably into a hydrophobic pocket in SQLE lined by over 20 hydrophobic amino acids. In resistant strains, molecular dynamics simulations showed that terbinafine docking was unstable and so terbinafine did not block squalene metabolism and ultimately ergosterol production. The resistant reference strain ATCC MYA-4438 T. rubrum showed a single erg1 mutation that resulted in frameshift during translation, leading to C-terminal helix deletion.

CONCLUSIONS

Modelling their effects on their SQLE proteins will aid in the design of potential new treatments for these novel resistant strains, which pose clinical problems in treating dermatophyte infections with terbinafine.

3D structure of acetolactate synthase explains why the Asp-376-Glu point mutation does not give the same resistance level to different imidazolinone herbicides

Resistance to ALS-inhibiting herbicides has dramatically increased worldwide due to the persisting evolution of target site mutations that reduce the affinity between the herbicide and the target. We evaluated the effect of the well-known ALS Asp-376-Glu target site mutation on different imidazolinone herbicides, including imazamox and imazethapyr. Greenhouse dose response experiments indicate that the Amaranthus retroflexus biotype carrying Asp-376-Glu was fully controlled by applying the field recommended dose of imazamox, whereas it displayed high level of resistance to imazethapyr. Likewise, Sorghum halepense, carrying Asp-376-Glu showed resistance to field recommended doses of imazethapyr but not of imazamox. Biochemical inhibition and kinetic characterization of the Asp-376-Glu mutant enzyme heterologously expressed using different plant sequence backbones, indicate that the Asp-376-Glu shows high level of insensitivity to imazethapyr but not to imazamox, corroborating the greenhouse results. Docking simulations revealed that imazamox can still inhibit the Asp-376-Glu mutant enzyme through a chalcogen interaction between the oxygen of the ligand and the sulfur atom of the ALS Met200, while imazethapyr does not create such interaction. These results explain the different sensitivity of the Asp-376-Glu mutation towards imidazolinone herbicides, thus providing novel information that can be exploited for defining stewardship guidelines to manage fields infested by weeds harboring the Asp-376-Glu mutation.

TnP and AHR-CYP1A1 Signaling Crosstalk in an Injury-Induced Zebrafish Inflammation Model

Aryl Hydrocarbon Receptor (AHR) signaling is crucial for regulating the biotransformation of xenobiotics and physiological processes like inflammation and immunity. Meanwhile, Thalassophryne nattereri Peptide (TnP), a promising anti-inflammatory candidate from toadfish venom, demonstrates therapeutic effects through immunomodulation. However, its influence on AHR signaling remains unexplored. This study aimed to elucidate TnP's molecular mechanisms on the AHR-cytochrome P450, family 1 (CYP1) pathway upon injury-induced inflammation in wild-type (WT) and Ahr2-knockdown (KD) zebrafish larvae through transcriptomic analysis and Cyp1a reporters. TnP, while unable to directly activate AHR, potentiated AHR activation by the high-affinity ligand 6-Formylindolo [3,2-b]carbazole (FICZ), implying a role as a CYP1A inhibitor, confirmed by in vitro studies. This interplay suggests TnP's ability to modulate the AHR-CYP1 complex, prompting investigations into its influence on biotransformation pathways and injury-induced inflammation. Here, the inflammation model alone resulted in a significant response on the transcriptome, with most differentially expressed genes (DEGs) being upregulated across the groups. Ahr2-KD resulted in an overall greater number of DEGs, as did treatment with the higher dose of TnP in both WT and KD embryos. Genes related to oxidative stress and inflammatory response were the most apparent under inflamed conditions for both WT and KD groups, e.g., Tnfrsf1a, Irf1b, and Mmp9. TnP, specifically, induces the expression of Hspa5, Hsp90aa1.2, Cxcr3.3, and Mpeg1.2. Overall, this study suggests an interplay between TnP and the AHR-CYP1 pathway, stressing the inflammatory modulation through AHR-dependent mechanisms. Altogether, these results may offer new avenues in novel therapeutic strategies, such as based on natural bioactive molecules, harnessing AHR modulation for targeted and sustained drug effects in inflammatory conditions.

Cyclization of acyl thiosemicarbazides led to new Helicobacter pylori α-carbonic anhydrase inhibitors

The eradication of Helicobacter pylori, the etiologic agent of gastric ulcer and adenocarcinoma, is a big concern in clinics due to the increasing drug resistance phenomena and the limited number of efficacious treatment options. The exploitation of the H. pylori carbonic anhydrases (HpCAs) as promising pharmacological targets has been validated by the antibacterial activity of previously reported CA inhibitors due to the role of these enzymes in the bacterium survival in the gastric mucosa. The development of new HpCA inhibitors seems to be on the way to filling the existing antibiotics gap. Due to the recent evidence on the ability of the coumarin scaffold to inhibit microbial α-CAs, a large library of derivatives has been developed by means of a pH-regulated cyclization reaction of coumarin-bearing acyl thiosemicarbazide intermediates. The obtained 1,3,4-thiadiazoles (10-18a,b) and 1,2,4-triazole-3-thiones (19-26a,b) were found to strongly and selectively inhibit HpαCA and computational studies were fundamental to gaining an understanding of the interaction networks governing the enzyme-inhibitor complex. Antibacterial evaluations on H. pylori ATCC 43504 highlighted some compounds that maintained potency on a resistant clinical isolate. Also, their combinations with metronidazole decreased both the minimal inhibitory concentration and minimal bactericidal concentration values of the antibiotic, with no synergistic effect.

QM/MM Study of the Reaction Mechanism of L-Tyrosine Hydroxylation Catalyzed by the Enzyme CYP76AD1

We have studied the hydroxylation mechanism of l-Tyr by the heme-dependent enzyme CYP76AD1 from the sugar beet (Beta vulgaris). This enzyme has a promising biotechnological application in modified yeast strains to produce medicinal alkaloids, an alternative to the traditional opium poppy harvest. A generative machine learning software based on AlphaFold was used to build the structure of CYP76AD1 since there are no structural data for this specific enzyme. After model validation, l-Tyr was docked in the active site of CYP76AD1 to assemble the reactive complex, whose catalytic distances remained stable throughout the 100 ns of MD simulation. Subsequent QM/MM calculations elucidated that l-Tyr hydroxylation occurs in two steps: hydrogen abstraction from l-Tyr by CpdI, forming an l-Tyr radical, and subsequent radical rebound, corresponding to a rate-limiting step of 16.0 kcal·mol-1. Our calculations suggest that the hydrogen abstraction step should occur in the doublet state, while the radical rebound should happen in the quartet state. The clarification of the reaction mechanism of CYP76AD1 provides insights into the rational optimization of the biosynthesis of alkaloids to eliminate the use of opium poppy.

Computational toolbox for the analysis of protein-glycan interactions

Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

Discovery of Novel, Selective, and Nonbasic Agonists for the Kappa-Opioid Receptor Determined by Salvinorin A-Based Virtual Screening

Modulating the kappa-opioid receptor (KOR) is a promising strategy for treating various human diseases. KOR agonists show potential for treating pain, pruritus, and epilepsy, while KOR antagonists show potential for treating depression, anxiety, and addiction. The diterpenoid Salvinorin A (SalA), a secondary metabolite of Salvia divinorum, is a potent and selective KOR agonist. Unlike typical opioids, SalA lacks a basic nitrogen, which encouraged us to search for nonbasic KOR ligands. Through structure-based virtual screening using 3D pharmacophore models based on the binding mode of SalA, we identified novel, nonbasic, potent, and selective KOR agonists. In vitro studies confirmed two virtual hits, SalA-VS-07 and SalA-VS-08, as highly selective for the KOR and showing G protein-biased KOR agonist activity. Both KOR ligands share a novel spiro-moiety and a nonbasic scaffold. Our findings provide novel starting points for developing therapeutics aimed at treating pain and other conditions in which KOR is a central player.

Alteration of Neuropilin-1 and Heparan Sulfate Interaction Impairs Murine B16 Tumor Growth

Neuropilin-1 acts as a coreceptor with vascular endothelial growth factor receptors to facilitate binding of its ligand, vascular endothelial growth factor. Neuropilin-1 also binds to heparan sulfate, but the functional significance of this interaction has not been established. A combinatorial library screening using heparin oligosaccharides followed by molecular dynamics simulations of a heparin tetradecasaccharide suggested a highly conserved binding site composed of amino acid residues extending across the b1 and b2 domains of murine neuropilin-1. Mutagenesis studies established the importance of arginine513 and lysine514 for binding of heparin to a recombinant form of Nrp1 composed of the a1, a2, b1, and b2 domains. Recombinant Nrp1 protein bearing R513A,K514A mutations showed a significant loss of heparin-binding, heparin-induced dimerization, and heparin-dependent thermal stabilization. Isothermal calorimetry experiments suggested a 1:2 complex of heparin tetradecasaccharide:Nrp1. To study the impact of altered heparin binding in vivo, a mutant allele of Nrp1 bearing the R513A,K514A mutations was created in mice (Nrp1D) and crossbred to Nrp1+/- mice to examine the impact of altered heparan sulfate binding. Analysis of tumor formation showed variable effects on tumor growth in Nrp1D/D mice, resulting in a frank reduction in tumor growth in Nrp1D/- mice. Expression of mutant Nrp1D protein was normal in tissues, suggesting that the reduction in tumor growth was due to the altered binding of heparin/heparan sulfate to neuropilin-1. These findings suggest that the interaction of neuropilin-1 with heparan sulfate modulates its stability and its role in tumor formation and growth.

FitScore: a fast machine learning-based score for 3D virtual screening enrichment

Enhancing virtual screening enrichment has become an urgent problem in computational chemistry, driven by increasingly large databases of commercially available compounds, without a commensurate drop in in vitro screening costs. Docking these large databases is possible with cloud-scale computing. However, rapid docking necessitates compromises in scoring, often leading to poor enrichment and an abundance of false positives in docking results. This work describes a new scoring function composed of two parts - a knowledge-based component that predicts the probability of a particular atom type being in a particular receptor environment, and a tunable weight matrix that converts the probability predictions into a dimensionless score suitable for virtual screening enrichment. This score, the FitScore, represents the compatibility between the ligand and the binding site and is capable of a high degree of enrichment across standardized docking test sets.

HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery

We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactions in protein-ligand binding. We designed an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assessed our approach using established public benchmarks based on the CASF-2016 core set, achieving top-tier results in affinity and pose prediction (Pearson's r = 0.86, RMSE = 1.15, Top-1 = 0.95). We introduced a novel approach for interaction profiling, aimed at detecting potential biases within both the model and data sets. This approach not only enhanced interpretability but also reinforced the impartiality of our methodology. Finally, we demonstrated HydraScreen's ability to generalize effectively across novel proteins and ligands through a temporal split. We also provide insights into potential avenues for future development aimed at enhancing the robustness of machine learning scoring functions. HydraScreen (accessible at http://hydrascreen.ro5.ai/paper) provides a user-friendly GUI and a public API, facilitating the easy-access assessment of protein-ligand complexes.

Targeting N-Acetylglucosaminidase in Staphylococcus aureus with Iminosugar Inhibitors

Bacteria are capable of remarkable adaptations to their environment, including undesirable bacterial resistance to antibacterial agents. One of the most serious cases is an infection caused by multidrug-resistant Staphylococcus aureus, which has unfortunately also spread outside hospitals. Therefore, the development of new effective antibacterial agents is extremely important to solve the increasing problem of bacterial resistance. The bacteriolytic enzyme autolysin E (AtlE) is a promising new drug target as it plays a key role in the degradation of peptidoglycan in the bacterial cell wall. Consequently, disruption of function can have an immense impact on bacterial growth and survival. An in silico and in vitro evaluation of iminosugar derivatives as potent inhibitors of S. aureus (AtlE) was performed. Three promising hit compounds (1, 3 and 8) were identified as AtlE binders in the micromolar range as measured by surface plasmon resonance. The most potent compound among the SPR response curve hits was 1, with a KD of 19 μM. The KD value for compound 8 was 88 μM, while compound 3 had a KD value of 410 μM.

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.

Repurposing Hsp90 inhibitors as antimicrobials targeting two-component systems identifies compounds leading to loss of bacterial membrane integrity

The discovery of antimicrobials with novel mechanisms of action is crucial to tackle the foreseen global health crisis due to antimicrobial resistance. Bacterial two-component signaling systems (TCSs) are attractive targets for the discovery of novel antibacterial agents. TCS-encoding genes are found in all bacterial genomes and typically consist of a sensor histidine kinase (HK) and a response regulator. Due to the conserved Bergerat fold in the ATP-binding domain of the TCS HK and the human chaperone Hsp90, there has been much interest in repurposing inhibitors of Hsp90 as antibacterial compounds. In this study, we explore the chemical space of the known Hsp90 inhibitor scaffold 3,4-diphenylpyrazole (DPP), building on previous literature to further understand their potential for HK inhibition. Six DPP analogs inhibited HK autophosphorylation in vitro and had good antimicrobial activity against Gram-positive bacteria. However, mechanistic studies showed that their antimicrobial activity was related to damage of bacterial membranes. In addition, DPP analogs were cytotoxic to human embryonic kidney cell lines and induced the cell arrest phenotype shown for other Hsp90 inhibitors. We conclude that these DPP structures can be further optimized as specific disruptors of bacterial membranes providing binding to Hsp90 and cytotoxicity are lowered. Moreover, the X-ray crystal structure of resorcinol, a substructure of the DPP derivatives, bound to the HK CheA represents a promising starting point for the fragment-based design of novel HK inhibitors.

IMPORTANCE

The discovery of novel antimicrobials is of paramount importance in tackling the imminent global health crisis of antimicrobial resistance. The discovery of novel antimicrobials with novel mechanisms of actions, e.g., targeting bacterial two-component signaling systems, is crucial to bypass existing resistance mechanisms and stimulate pharmaceutical innovations. Here, we explore the possible repurposing of compounds developed in cancer research as inhibitors of two-component systems and investigate their off-target effects such as bacterial membrane disruption and toxicity. These results highlight compounds that are promising for further development of novel bacterial membrane disruptors and two-component system inhibitors.

Synthesis and Biological Properties of Ferrocenyl and Organic Methotrexate Derivatives

Synthesis and biological activity of two series of modified side chain methotrexate (MTX) derivatives are presented, one with a ferrocenyl moiety inserted between the pteroyl and glutamate portions of the molecule and the other with glutamate substituted for short chain amino acids. Ferrocenyl derivatives of MTX turned out to be rather moderate inhibitors of dihydrofolate reductase (DHFR) although molecular modeling suggested more effective interactions between these compounds and the target enzyme. More interestingly, ferrocene-decorated MTX derivatives were able to impede the proliferation of four murine and human cell lines as well as their methotrexate-resistant counterparts, overcoming the multidrug resistance (MDR) barrier. They were also able to directly interact with Abcc1, an MDR protein. Of the amino acid pteroyl conjugates, the γ-aminobutyric acid derivative was an efficient inhibitor of DHFR but had no effect on cell proliferation in the concentration range studied while a taurine conjugate was a poor DHFR inhibitor but able to affect cell viability. We postulate that modification of the methotrexate side chain may be an efficient strategy to overcome efflux-dependent methotrexate resistance.

Structural insights of an LCP protein-LytR-from Streptococcus dysgalactiae subs. dysgalactiae through biophysical and in silico methods

The rise of antibiotic-resistant bacterial strains has become a critical health concern. According to the World Health Organization, the market introduction of new antibiotics is alarmingly sparse, underscoring the need for novel therapeutic targets. The LytR-CpsA-Psr (LCP) family of proteins, which facilitate the insertion of cell wall glycopolymers (CWGPs) like teichoic acids into peptidoglycan, has emerged as a promising target for antibiotic development. LCP proteins are crucial in bacterial adhesion and biofilm formation, making them attractive for disrupting these processes. This study investigated the structural and functional characteristics of the LCP domain of LytR from Streptococcus dysgalactiae subsp. dysgalactiae. The protein structure was solved by X-ray Crystallography at 2.80 Å resolution. Small-angle X-ray scattering (SAXS) data were collected to examine potential conformational differences between the free and ligand-bound forms of the LytR LCP domain. Additionally, docking and molecular dynamics (MD) simulations were used to predict the interactions and conversion of ATP to ADP and AMP. Experimental validation of these predictions was performed using malachite green activity assays. The determined structure of the LCP domain revealed a fold highly similar to those of homologous proteins while SAXS data indicated potential conformational differences between the ligand-free and ligand-bound forms, suggesting a more compact conformation during catalysis, upon ligand binding. Docking and MD simulations predicted that the LytR LCP domain could interact with ADP and ATP and catalyze their conversion to AMP. These predictions were experimentally validated by malachite green activity assays, confirming the protein's functional versatility. The study provides significant insights into the structural features and functional capabilities of the LCP domain of LytR from S. dysgalactiae subsp. dysgalactiae. These findings pave the way for designing targeted therapies against antibiotic-resistant bacteria and offer strategies to disrupt bacterial biofilm formation.

Azobenzenesulfonamide Carbonic Anhydrase Inhibitors as New Weapons to Fight Helicobacter pylori: Synthesis, Bioactivity Evaluation, In Vivo Toxicity, and Computational Studies

Research into novel anti-Helicobacter pylori agents represents an important approach for the identification of new treatments for chronic gastritis and peptic ulcers, which are associated with a high risk of developing gastric carcinoma. In this respect, two series of azobenzenesulfonamides were designed, synthesized, and tested against a large panel of human and bacterial CAs to evaluate their inhibitory activity. In addition, computational studies of the novel primary benzenesulfonamides (4a-j) were performed to predict the putative binding mode to both HpCAs. Then, the antimicrobial activity versus H. pylori of the two series was also studied. The best-in-class compounds were found to be 4c and 4e among the primary azobenzenesulfonamides and 5c and 5f belonging to the secondary azobenzenesulfonamides series, showing themselves to exert a promising anti-H. pylori activity, with MIC values of 4-8 μg/mL and MBCs between 4 and 16 μg/mL. Moreover, the evaluation of their toxicity on a G. mellonella larva in vivo model indicated a safe profile for 4c,e and 5c,f. The collected results warrant considering these azobenzenesulfonamides as an interesting starting point for the development of a new class of anti-H. pylori agents.

Suppressed basal mitophagy drives cellular aging phenotypes that can be reversed by a p62-targeting small molecule

Selective degradation of damaged mitochondria by autophagy (mitophagy) is proposed to play an important role in cellular homeostasis. However, the molecular mechanisms and the requirement of mitochondrial quality control by mitophagy for cellular physiology are poorly understood. Here, we demonstrated that primary human cells maintain highly active basal mitophagy initiated by mitochondrial superoxide signaling. Mitophagy was found to be mediated by PINK1/Parkin-dependent pathway involving p62 as a selective autophagy receptor (SAR). Importantly, this pathway was suppressed upon the induction of cellular senescence and in naturally aged cells, leading to a robust shutdown of mitophagy. Inhibition of mitophagy in proliferating cells was sufficient to trigger the senescence program, while reactivation of mitophagy was necessary for the anti-senescence effects of NAD precursors or rapamycin. Furthermore, reactivation of mitophagy by a p62-targeting small molecule rescued markers of cellular aging, which establishes mitochondrial quality control as a promising target for anti-aging interventions.

In silico identification of D449-0032 compound as a putative SARS-CoV-2 Mpro inhibitor

The SARS-CoV-2 pandemic originated the urgency in developing therapeutic resources for the treatment of COVID-19. Despite the current availability of vaccines and some antivirals, the occurence of severe cases of the disease and the risk of the emergence of new virus variants still motivate research in this field. In this context, this study aimed at the computational prospection of likely inhibitors of the main protease (Mpro) of SARS-CoV-2 since inhibiting this enzyme leads to disruption of the viral replication process. The virtual screening of the antiviral libraries Asinex, ChemDiv, and Enamine targeting SARS-CoV-2 Mpro was performed, indicating the D449-0032 compound as a promising inhibitor. Molecular dynamics simulations showed the stability of the protein-ligand complex and in silico predictions of toxicity and pharmacokinetic parameters indicated the probable drug-like behavior of the compound. In vitro and in vivo studies are essential to confirm the Mpro inhibition by the D449-0032.Communicated by Ramaswamy H. Sarma.

Cyclopenta[b]indoles as novel antimicrotubule agents with antileukemia activity

Acute leukemias present therapeutic challenges despite advances in treatments. Microtubule inhibitors have played a pivotal role in cancer therapy, inspiring exploration into novel compounds like C2E1 from the cyclopenta[b]indole class. In the present study, we investigated C2E1's potential as a therapeutic agent for acute leukemia at molecular, cellular, and genetic levels. C2E1 demonstrated tubulin depolarization activity, significantly reducing leukemia cell viability. Its impact involved multifaceted mechanisms: inducing apoptosis, arrest of cell cycle progression, and inhibition of clonogenicity and migration in leukemia cells. At a molecular level, C2E1 triggered DNA damage, antiproliferative, and apoptosis markers and altered gene expression related to cytoskeletal regulation, disrupting essential cellular processes crucial for leukemia cell survival and proliferation. These findings highlight C2E1's promise as a potential candidate for novel anti-cancer therapies. Notably, its distinct mode of action from conventional microtubule-targeting drugs suggests the potential to bypass common resistance mechanisms encountered with existing treatments. In summary, C2E1 emerges as a compelling compound with diverse effects on leukemia cells, showcasing promising antineoplastic properties. Its ability to disrupt critical cellular functions selective to leukemia cells positions it as a candidate for future therapeutic development.

Synthesis, identification and evaluation of molecular docking and experimental anti-cancer and antioxidant activity of new spiro four membered ring derivatives bearing 5-nitro isatin

Spiro-5-nitro isatino aza-β-lactams were produced by a [2 + 2] cycloaddition of 5-nitro isatin Schiff bases [1-5] with different aromatic isocyanate and thioisocyanate. 1HNMR and 13CNMR as well as FTIR spectroscopies, were used to identify the structures of the obtained compounds. These spiro-5-nitro isatin aza- β-lactams interest to us due to their potential antioxidant and anticancer properties. The MTT assay was used to examine in vitro bioactivity testing against breast cancer (MCF-7) cell lines. From result data, compound 14 displayed IC50 values that were lower than those of the clinically used anticancer drug tamoxifen toward MCF-7 cells after 24 h while compound 9 after 48 h synthesized compounds [6-20] were evaluated for against antioxidant activity by using DPPH assay. In molecular docking, Promising compounds were used to reveal potential cytotoxic activity mechanisms.

Combining the Fragment Molecular Orbital and GRID Approaches for the Prediction of Ligand-Metalloenzyme Binding Affinity: The Case Study of hCA II Inhibitors

Polarization and charge-transfer interactions play an important role in ligand-receptor complexes containing metals, and only quantum mechanics methods can adequately describe their contribution to the binding energy. In this work, we selected a set of benzenesulfonamide ligands of human Carbonic Anhydrase II (hCA II)-an important druggable target containing a Zn2+ ion in the active site-as a case study to predict the binding free energy in metalloprotein-ligand complexes and designed specialized computational methods that combine the ab initio fragment molecular orbital (FMO) method and GRID approach. To reproduce the experimental binding free energy in these systems, we adopted a machine-learning approach, here named formula generator (FG), considering different FMO energy terms, the hydrophobic interaction energy (computed by GRID) and logP. The main advantage of the FG approach is that it can find nonlinear relations between the energy terms used to predict the binding free energy, explicitly showing their mathematical relation. This work showed the effectiveness of the FG approach, and therefore, it might represent an important tool for the development of new scoring functions. Indeed, our scoring function showed a high correlation with the experimental binding free energy (R2 = 0.76-0.95, RMSE = 0.34-0.18), revealing a nonlinear relation between energy terms and highlighting the relevant role played by hydrophobic contacts. These results, along with the FMO characterization of ligand-receptor interactions, represent important information to support the design of new and potent hCA II inhibitors.

Accurate prediction of dynamic protein-ligand binding using P-score ranking

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

Advances in Parkinson's disease research - A computational network pharmacological approach

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is projected to see a significant rise in incidence over the next three decades. The precise treatment of PD remains a formidable challenge, prompting ongoing research into early diagnostic methodologies. Network pharmacology, a burgeoning field grounded in systems biology, examines the intricate networks of biological systems to identify critical signal nodes, facilitating the development of multi-target therapeutic molecules. This approach systematically maps the components of Parkinson's disease, thereby reducing its complexity. In this review, we explore the application of network pharmacology workflows in PD, discuss the techniques employed in this field, and evaluate the current advancements and status of network pharmacology in the context of Parkinson's disease. The comprehensive insights will pave newer paths to explore early disease biomarkers and to develop diagnosis with a holistic in silico, in vitro, in vivo and clinical studies.

Novel Inhibitory Site Revealed by Mode of Action on the Metabotropic Glutamate 7 Receptor Venus Flytrap Domain

Metabotropic glutamate (mGlu) receptors play a key role in modulating most synapses in the brain. The mGlu7 receptors inhibit presynaptic neurotransmitter release and offer therapeutic possibilities for post-traumatic stress disorders or epilepsy. Screening campaigns provided mGlu7-specific allosteric modulators as the inhibitor XAP044 (Gee et al. J. Biol. Chem. 2014). In contrast to other mGlu receptor allosteric modulators, XAP044 does not bind in the transmembrane domain but to the extracellular domain of the mGlu7 receptor and not at the orthosteric site. Here, we identified the mode of action of XAP044, combining synthesis of derivatives, modeling and docking experiments, and mutagenesis. We propose a unique mode of action of these inhibitors, preventing the closure of the Venus flytrap agonist binding domain. While acting as a noncompetitive antagonist of L-AP4, XAP044 and derivatives act as apparent competitive antagonists of LSP4-2022. These data revealed more potent XAP044 analogues and new possibilities to target mGluRs.

Are the metal identity and stoichiometry of metal complexes important for colchicine site binding and inhibition of tubulin polymerization?

Quite recently we discovered that copper(II) complexes with isomeric morpholine-thiosemicarbazone hybrid ligands show good cytotoxicity in cancer cells and that the molecular target responsible for this activity might be tubulin. In order to obtain better lead drug candidates, we opted to exploit the power of coordination chemistry to (i) assemble structures with globular shape to better fit the colchicine pocket and (ii) vary the metal ion. We report the synthesis and full characterization of bis-ligand cobalt(III) and iron(III) complexes with 6-morpholinomethyl-2-formylpyridine 4N-(4-hydroxy-3,5-dimethylphenyl)-3-thiosemicarbazone (HL1), 6-morpholinomethyl-2-acetylpyridine 4N-(4-hydroxy-3,5-dimethylphenyl)-3-thiosemicarbazone (HL2), and 6-morpholinomethyl-2-formylpyridine 4N-phenyl-3-thiosemicarbazone (HL3), and mono-ligand nickel(II), zinc(II) and palladium(II) complexes with HL1, namely [CoIII(HL1)(L1)](NO3)2 (1), [CoIII(HL2)(L2)](NO3)2 (2), [CoIII(HL3)(L3)](NO3)2 (3), [FeIII(L2)2]NO3 (4), [FeIII(HL3)(L3)](NO3)2 (5), [NiII(L1)]Cl (6), [Zn(L1)Cl] (7) and [PdII(HL1)Cl]Cl (8). We discuss the effect of the metal identity and metal complex stoichiometry on in vitro cytotoxicity and antitubulin activity. The high antiproliferative activity of complex 4 correlated well with inhibition of tubulin polymerization. Insights into the mechanism of antiproliferative activity were supported by experimental results and molecular docking calculations.

A Robust Induced Fit Docking Approach with the Combination of the Hybrid All-Atom/United-Atom/Coarse-Grained Model and Simulated Annealing

Molecular docking remains an indispensable tool in computational biology and structure-based drug discovery. However, the correct prediction of binding poses remains a major challenge for molecular docking, especially for target proteins where a substrate binding induces significant reorganization of the active site. Here, we introduce an Induced Fit Docking (IFD) approach named AA/UA/CG-SA-IFD, which combines a hybrid All-Atom/United-Atom/Coarse-Grained model with Simulated Annealing. In this approach, the core region is represented by the All-Atom(AA) model, while the protein environment beyond the core region and the solvent are treated with either the United-Atom (UA) or the Coarse-Grained (CG) model. By combining the Elastic Network Model (ENM) for the CG region, the hybrid model ensures a reasonable description of ligand binding and the environmental effects of the protein, facilitating highly efficient and reliable sampling of ligand binding through Simulated Annealing (SA) at a high temperature. Upon validation with two testing sets, the AA/UA/CG-SA-IFD approach demonstrates remarkable accuracy and efficiency in induced fit docking, even for challenging cases where the docked poses significantly deviate from crystal structures.

Exploring a New Generation of Pyrimidine and Pyridine Derivatives as Anti-Influenza Agents Targeting the Polymerase PA-PB1 Subunits Interaction

The limited range of available flu treatments due to virus mutations and drug resistance have prompted the search for new therapies. RNA-dependent RNA polymerase (RdRp) is a heterotrimeric complex of three subunits, i.e., polymerase acidic protein (PA) and polymerase basic proteins 1 and 2 (PB1 and PB2). It is widely recognized as one of the most promising anti-flu targets because of its critical role in influenza infection and high amino acid conservation. In particular, the disruption of RdRp complex assembly through protein-protein interaction (PPI) inhibition has emerged as a valuable strategy for discovering a new therapy. Our group previously identified the 3-cyano-4,6-diphenyl-pyridine core as a privileged scaffold for developing PA-PB1 PPI inhibitors. Encouraged by these findings, we synthesized a small library of pyridine and pyrimidine derivatives decorated with a thio-N-(m-tolyl)acetamide side chain (compounds 2a-n) or several amino acid groups (compounds 3a-n) at the C2 position. Interestingly, derivative 2d, characterized by a pyrimidine core and a phenyl and 4-chloro phenyl ring at the C4 and C6 positions, respectively, showed an IC50 value of 90.1 μM in PA-PB1 ELISA, an EC50 value of 2.8 μM in PRA, and a favorable cytotoxic profile, emerging as a significant breakthrough in the pursuit of new PPI inhibitors. A molecular modeling study was also completed as part of this project, allowing us to clarify the biological profile of these compounds.

Systematic computational strategies for identifying protein targets and lead discovery

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein-ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.

Uncovering potential CDK9 inhibitors from natural compound databases through docking-based virtual screening and MD simulations

CONTEXT

Cyclin-dependent kinase 9 (CDK9) plays a significant role in gene regulation and RNA polymerase II transcription under basal and stimulated conditions. The upregulation of transcriptional homeostasis by CDK9 leads to various malignant tumors and therefore acts as a valuable drug target in addressing cancer incidences. Ongoing drug development endeavors targeting CDK9 have yielded numerous clinical candidate molecules currently undergoing investigation as potential CDK9 modulators, though none have yet received Food and Drug Administration (FDA) approval.

METHODS

In this study, we employ in silico approaches including the molecular docking and molecular dynamics simulations for the virtual screening over the natural compounds library to identify novel promising selective CDK9 inhibitors. The compounds derived from the initial virtual screening were subsequently employed for molecular dynamics simulations and binding free energy calculations to study the compound's stability under virtual physiological conditions. The first-generation CDK inhibitor Flavopiridol was used as a reference to compare with our novel hit compound as a CDK9 antagonist. The 500-ns molecular dynamics simulation and binding free energy calculation showed that two natural compounds showed better binding affinity and interaction mode with CDK9 receptors over the reference Flavopiridol. They also showed reasonable figures in the predicted absorption, distribution, metabolism, excretion, and toxicity (ADMET) calculations as well as in computational cytotoxicity predictions. Therefore, we anticipate that the proposed scaffolds could contribute to developing potential and selective CDK9 inhibitors subjected to further validations.