Docking and scoring with ICM: the benchmarking results and strategies for improvement

A high-affinity, cis-on photoswitchable beta blocker to optically control β2-adrenergic receptors in vitro and in vivo

This study introduces (S)-Opto-prop-2, a second-generation photoswitchable ligand designed for precise modulation of β2-adrenoceptor (β2AR). Synthesised by incorporating an azobenzene moiety with propranolol, (S)-Opto-prop-2 exhibited a high PSScis (photostationary state for cis isomer) percentage (∼90 %) and a favourable half-life (>10 days), facilitating diverse bioassay measurements. In vitro, the cis-isomer displayed substantially higher β2AR binding affinity than the trans-isomer (1000-fold), making (S)-Opto-prop-2 one of the best photoswitchable GPCR (G protein-coupled receptor) ligands reported so far. Molecular docking of (S)-Opto-prop-2 in the X-ray structure of propranolol-bound β2AR followed by site-directed mutagenesis studies, identified D1133.32, N3127.39 and F2896.51 as crucial residues that contribute to ligand-receptor interactions at the molecular level. In vivo efficacy was assessed using a rabbit ocular hypertension model, revealing that the cis isomer mimicked propranolol's effects in reducing intraocular pressure, while the trans isomer was inactive. Dynamic optical modulation of β2AR by (S)-Opto-prop-2 was demonstrated in two different cAMP bioassays and using live-cell confocal imaging, indicating reversible and dynamic control of β2AR activity using the new photopharmacology tool. In conclusion, (S)-Opto-prop-2 emerges as a promising photoswitchable ligand for precise and reversible β2AR modulation with light. The new tool shows superior cis-on binding affinity, one of the largest reported differences in affinity (1000-fold) between its two configurations, in vivo efficacy, and dynamic modulation. This study contributes valuable insights into the evolving field of photopharmacology, offering a potential avenue for targeted therapy in β2AR-associated pathologies.

Synthesis, Biological, and Computational Evaluations of Conformationally Restricted NAD-Mimics as Discriminant Inhibitors of Human NMN-Adenylyltransferase Isozymes

Nicotinamide adenine dinucleotide (NAD) cofactor metabolism plays a significant role in cancer development. Tumor cells have an increased demand for NAD and ATP to support rapid growth and proliferation. Limiting the amount of available NAD by targeting critical NAD biosynthesis enzymes has emerged as a promising anticancer therapeutic approach. In mammals, the enzyme nicotinamide/nicotinic acid adenylyltransferase (NMNAT) catalyzes a crucial downstream reaction for all known NAD synthesis routes. Novel nicotinamide/nicotinic acid adenine dinucleotide (NAD/NaAD) analogues 1-4, containing a methyl group at the ribose 2'-C and 3'-C-position of the adenosine moiety, were synthesized as inhibitors of the three isoforms of human NMN-adenylyltransferase, named hNMNAT-1, hNMNAT-2, and hNMNAT-3. An NMR-based conformational analysis suggests that individual NAD-analogues (1-4) have distinct conformational preferences. Biological evaluation of dinucleotides 1-4 as inhibitors of hNMNAT isoforms revealed structural relationships between different conformations (North-anti and South-syn) and enzyme-inhibitory activity. Among the new series of NAD analogues synthesized and tested, the 2'-C-methyl-NAD analogue 1 (Ki = 15 and 21 µM towards NMN and ATP, respectively) emerged as the most potent and selective inhibitor of hNMNAT-2 reported so far. Finally, we rationalized the in vitro bioactivity and selectivity of methylated NAD analogues with in silico studies, helping to lay the groundwork for rational scaffold optimization.

ABC transporter inhibition by beauvericin partially overcomes drug resistance in Leishmania tropica

Leishmaniasis is a neglected tropical disease infecting the world's poorest populations. Miltefosine (ML) remains the primary oral drug against the cutaneous form of leishmaniasis. The ATP-binding cassette (ABC) transporters are key players in the xenobiotic efflux, and their inhibition could enhance the therapeutic index. In this study, the ability of beauvericin (BEA) to overcome ABC transporter-mediated resistance of Leishmania tropica to ML was assessed. In addition, the transcription profile of genes involved in resistance acquisition to ML was inspected. Finally, we explored the efflux mechanism of the drug and inhibitor. The efficacy of ML against all developmental stages of L. tropica in the presence or absence of BEA was evaluated using an absolute quantification assay. The expression of resistance genes was evaluated, comparing susceptible and resistant strains. Finally, the mechanisms governing the interaction between the ABC transporter and its ligands were elucidated using molecular docking and dynamic simulation. Relative quantification showed that the expression of the ABCG sub-family is mostly modulated by ML. In this study, we used BEA to impede resistance of Leishmania tropica. The IC50 values, following BEA treatment, were significantly reduced from 30.83, 48.17, and 16.83 µM using ML to 8.14, 11.1, and 7.18 µM when using a combinatorial treatment (ML + BEA) against promastigotes, axenic amastigotes, and intracellular amastigotes, respectively. We also demonstrated a favorable BEA-binding enthalpy to L. tropica ABC transporter compared to ML. Our study revealed that BEA partially reverses the resistance development of L. tropica to ML by blocking the alternate ATP hydrolysis cycle.

PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs

Proteolysis-targeting chimeras (PROTACs) that engage two biological targets at once are a promising technology in degrading clinically relevant protein targets. Since factors that influence the biological activities of PROTACs are more complex than those of a small molecule drug, we explored a combination of computational chemistry and deep learning strategies to forecast PROTAC activity and enable automated design. A new method named PROTACable was developed for the de novo design of PROTACs, which includes a robust 3-D modeling workflow to model PROTAC ternary complexes using a library of E3 ligase and linker and an SE(3)-equivariant graph transformer network to predict the activity of newly designed PROTACs. PROTACable is available at https://github.com/giaguaro/PROTACable/.

Recognition and remodeling of endosomal zones by sorting nexins

The proteolipid code determines how cytosolic proteins find and remodel membrane surfaces. Here, we investigate how this process works with sorting nexins Snx1 and Snx3. Both proteins form sorting machines by recognizing membrane zones enriched in phosphatidylinositol 3-phosphate (PI3P), phosphatidylserine (PS) and cholesterol. This co-localized combination forms a unique "lipid codon" or lipidon that we propose is responsible for endosomal targeting, as revealed by structures and interactions of their PX domain-based readers. We outline a membrane recognition and remodeling mechanism for Snx1 and Snx3 involving this code element alongside transmembrane pH gradients, dipole moment-guided docking and specific protein-protein interactions. This generates an initial membrane-protein assembly (memtein) that then recruits retromer and additional PX proteins to recruit cell surface receptors for sorting to the trans-Golgi network (TGN), lysosome and plasma membranes. Post-translational modification (PTM) networks appear to regulate how the sorting machines form and operate at each level. The commonalities and differences between these sorting nexins show how the proteolipid code orchestrates parallel flows of molecular information from ribosome emergence to organelle genesis, and illuminates a universally applicable model of the membrane.

In silico characterization of the novel SDR42E1 as a potential vitamin D modulator

The short-chain dehydrogenase/reductase (SDR) superfamily encompasses enzymes that play essential roles in the metabolism of steroid hormones and lipids. Despite an enigmatic function, recent genetic studies have linked the novel SDR 42 extended-1 (SDR42E1) gene to 25-hydroxyvitamin D levels. This study investigated the potential SDR42E1 functions and interactions with vitamin D using bioinformatics and molecular docking studies. Phylogenetic analysis unveiled that the nucleotide sequences of human SDR42E1 exhibit high evolutionary conservation across nematodes and fruit flies. Molecular docking analysis identified strong binding affinities between SDR42E1 and its orthologs with vitamin D3 and essential precursors, 8-dehydrocholesterol, followed by 7-dehydrocholesterol and 25-hydroxyvitamin D. The hydrophobic interactions observed between the protein residues and vitamin D compounds supported the predicted transmembrane localization of SDR42E1. Our investigation provides valuable insights into the potential role of SDR42E1 in skin vitamin D biosynthesis throughout species. This provides the foundation for future research and development of targeted therapies for vitamin D deficiency and related health conditions.

Design and Evaluation of Nanoscale Materials with Programmed Responsivity towards Epigenetic Enzymes

Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing 'intelligent' encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly (acetyl L-lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles' hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We further demonstrated the biocompatibility and cellular effects of these materials and conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs.

Pharmacological Characterization and Radiolabeling of VUF15485, a High-Affinity Small-Molecule Agonist for the Atypical Chemokine Receptor ACKR3

Atypical chemokine receptor 3 (ACKR3), formerly referred to as CXCR7, is considered to be an interesting drug target. In this study, we report on the synthesis, pharmacological characterization and radiolabeling of VUF15485, a new ACKR3 small-molecule agonist, that will serve as an important new tool to study this β-arrestin-biased chemokine receptor. VUF15485 binds with nanomolar affinity (pIC50 = 8.3) to human ACKR3, as measured in [125I]CXCL12 competition binding experiments. Moreover, in a bioluminescence resonance energy transfer-based β-arrestin2 recruitment assay VUF15485 acts as a potent ACKR3 agonist (pEC50 = 7.6) and shows a similar extent of receptor activation compared with CXCL12 when using a newly developed, fluorescence resonance energy transfer-based ACKR3 conformational sensor. Moreover, the ACKR3 agonist VUF15485, tested against a (atypical) chemokine receptor panel (agonist and antagonist mode), proves to be selective for ACKR3. VUF15485 labeled with tritium at one of its methoxy groups ([3H]VUF15485), binds ACKR3 saturably and with high affinity (K d = 8.2 nM). Additionally, [3H]VUF15485 shows rapid binding kinetics and consequently a short residence time (<2 minutes) for binding to ACKR3. The selectivity of [3H]VUF15485 for ACKR3, was confirmed by binding studies, whereupon CXCR3, CXCR4, and ACKR3 small-molecule ligands were competed for binding against the radiolabeled agonist. Interestingly, the chemokine ligands CXCL11 and CXCL12 are not able to displace the binding of [3H]VUF15485 to ACKR3. The radiolabeled VUF15485 was subsequently used to evaluate its binding pocket. Site-directed mutagenesis and docking studies using a recently solved cryo-EM structure propose that VUF15485 binds in the major and the minor binding pocket of ACKR3. SIGNIFICANCE STATEMENT: The atypical chemokine receptor atypical chemokine receptor 3 (ACKR3) is considered an interesting drug target in relation to cancer and multiple sclerosis. The study reports on new chemical biology tools for ACKR3, i.e., a new agonist that can also be radiolabeled and a new ACKR3 conformational sensor, that both can be used to directly study the interaction of ACKR3 ligands with the G protein-coupled receptor.

iBio-GATS-A Semi-Automated Workflow for Structural Modelling of Insect Odorant Receptors

Insects utilize seven transmembrane (7TM) odorant receptor (iOR) proteins, with an inverted topology compared to G-protein coupled receptors (GPCRs), to detect chemical cues in the environment. For pest biocontrol, chemical attractants are used to trap insect pests. However, with the influx of invasive insect pests, novel odorants are urgently needed, specifically designed to match 3D iOR structures. Experimental structural determination of these membrane receptors remains challenging and only four experimental iOR structures from two evolutionarily distant organisms have been solved. Template-based modelling (TBM) is a complementary approach, to generate model structures, selecting templates based on sequence identity. As the iOR family is highly divergent, a different template selection approach than sequence identity is needed. Bio-GATS template selection for GPCRs, based on hydrophobicity correspondence, has been morphed into iBio-GATS, for template selection from available experimental iOR structures. This easy-to-use semi-automated workflow has been extended to generate high-quality models from any iOR sequence from the selected template, using Python and shell scripting. This workflow was successfully validated on Apocrypta bakeri Orco and Machilis hrabei OR5 structures. iBio-GATS models generated for the fruit fly iOR, OR59b and Orco, yielded functional ligand binding results concordant with experimental mutagenesis findings, compared to AlphaFold2 models.

Dissecting the Interaction Fingerprints and Binding Affinity of BYL719 Analogs Targeting PI3Kα

Phosphatidylinositol-3-kinase Alpha (PI3Kα) is a lipid kinase which regulates signaling pathways involved in cell proliferation. Dysregulation of these pathways promotes several human cancers, pushing for the development of anticancer drugs to target PI3Kα. One such medicinal chemistry campaign at Novartis led to the discovery of BYL719 (Piqray, Alpelicib), a PI3Kα inhibitor approved by the FDA in 2019 for treatment of HR+/HER2-advanced breast cancer with a PIK3CA mutation. Structure-based drug design played a key role in compound design and optimization throughout the discovery process. However, further characterization of potency drivers via structural dynamics and energetic analyses can be advantageous for ensuing PI3Kα programs. Here, our goal is to employ various in-silico techniques, including molecular simulations and machine learning, to characterize 14 ligands from the BYL719 analogs and predict their binding affinities. The structural insights from molecular simulations suggest that although the ligand-hinge interaction is the primary driver of ligand stability at the pocket, the R group positioning at C2 or C6 of pyridine/pyrimidine also plays a major role. Binding affinities predicted via thermodynamic integration (TI) are in good agreement with previously reported IC50s. Yet, computationally demanding techniques such as TI might not always be the most efficient approach for affinity prediction, as in our case study, fast high-throughput techniques were capable of classifying compounds as active or inactive, and one docking approach showed accuracy comparable to TI.

Molecular determinants of antagonist interactions with chemokine receptors CCR2 and CCR5

By driving monocyte chemotaxis, the chemokine receptor CCR2 shapes inflammatory responses and the formation of tumor microenvironments. This makes it a promising target in inflammation and immuno-oncology; however, despite extensive efforts, there are no FDA-approved CCR2-targeting therapeutics. Cited challenges include the redundancy of the chemokine system, suboptimal properties of compound candidates, and species differences that confound the translation of results from animals to humans. Structure-based drug design can rationalize and accelerate the discovery and optimization of CCR2 antagonists to address these challenges. The prerequisites for such efforts include an atomic-level understanding of the molecular determinants of action of existing antagonists. In this study, using molecular docking and artificial-intelligence-powered compound library screening, we uncover the structural principles of small molecule antagonism and selectivity towards CCR2 and its sister receptor CCR5. CCR2 orthosteric inhibitors are shown to universally occupy an inactive-state-specific tunnel between receptor helices 1 and 7; we also discover an unexpected role for an extra-helical groove accessible through this tunnel, suggesting its potential as a new targetable interface for CCR2 and CCR5 modulation. By contrast, only shape complementarity and limited helix 8 hydrogen bonding govern the binding of various chemotypes of allosteric antagonists. CCR2 residues S1012.63 and V2446.36 are implicated as determinants of CCR2/CCR5 and human/mouse orthosteric and allosteric antagonist selectivity, respectively, and the role of S1012.63 is corroborated through experimental gain-of-function mutagenesis. We establish a critical role of induced fit in antagonist recognition, reveal strong chemotype selectivity of existing structures, and demonstrate the high predictive potential of a new deep-learning-based compound scoring function. Finally, this study expands the available CCR2 structural landscape with computationally generated chemotype-specific models well-suited for structure-based antagonist design.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Integrated workflow for the identification of new GABAA R positive allosteric modulators based on the in silico screening with further in vitro validation. Case study using Enamine's stock chemical space

Numerous studies reported an association between GABAA R subunit genes and epilepsy, eating disorders, autism spectrum disorders, neurodevelopmental disorders, and bipolar disorders. This study was aimed to find some potential positive allosteric modulators and was performed by combining the in silico approach with further in vitro evaluation of its real activity. We started from the GABAA R-diazepam complexes and assembled a lipid embedded protein ensemble to refine it via molecular dynamics (MD) simulation. Then we focused on the interaction of α1β2γ2 with some Z-drugs (non-benzodiazepine compounds) using an Induced Fit Docking (IFD) into the relaxed binding site to generate a pharmacophore model. The pharmacophore model was validated with a reference set and applied to decrease the pre-filtered Enamine database before the main docking procedure. Finally, we succeeded in identifying a set of compounds, which met all features of the docking model. The aqueous solubility and stability of these compounds in mouse plasma were assessed. Then they were tested for the biological activity using the rat Purkinje neurons and CHO cells with heterologously expressed human α1β2γ2 GABAA receptors. Whole-cell patch clamp recordings were used to reveal the GABA induced currents. Our study represents a convenient and tunable model for the discovery of novel positive allosteric modulators of GABAA receptors. A High-throughput virtual screening of the largest available database of chemical compounds resulted in the selection of 23 compounds. Further electrophysiological tests allowed us to determine a set of 3 the most outstanding active compounds. Considering the structural features of leader compounds, the study can develop into the MedChem project soon.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

Synthesis, molecular docking analysis and in vitro evaluation of new heterocyclic hybrids of 4-aza-podophyllotoxin as potent cytotoxic agents

Two different synthetic approaches to novel heterocyclic hybrid compounds of 4-azapodophyllotoxin were investigated. The obtained products were characterized by infrared spectroscopy, nuclear magnetic resonance spectroscopy, and high-resolution mass spectrometry. MTT protocol was then performed to examine the cytotoxic activity of these products against KB, HepG2, A549, MCF7, and Hek-293 cell lines. The cytotoxic assessment indicated that all products displayed moderate to high cytotoxicity against all tested cancer cell lines. The most active compound 13k containing the 2-methoxypyridin-4-yl group exhibited selective cytotoxicity against KB, A549, and HepG2 cell lines with the IC50 values ranging from 0.23 to 0.27 μM, which were between 5- to 10-fold more potent than the positive control ellipticine. Compounds 13a (HetAr = thiophen-3-yl) and 13d (HetAr = 5-bromofuran-2-yl) displayed high cytotoxic selectivity for A549 and HepG2 cancer cell lines when compared to the other cancer cell lines and low toxicity to the normal Hek-293 cell line. Molecular docking study was conducted to evaluate the interaction of new synthesized compounds with the colchicine-binding-site of tubulin. Besides that, physicochemical and pharmacokinetic properties of the most active compounds 13h,k were predicted.

The oxidation of steroid derivatives by the CYP125A6 and CYP125A7 enzymes from Mycobacterium marinum

The members of the bacterial cytochrome P450 (CYP) monooxygenase family CYP125, catalyze the oxidation of steroid derivatives including cholesterol and phytosterols, as the initial activating step in their catabolism. However, several bacterial species contain multiple genes encoding CYP125 enzymes and other CYP enzymes which catalyze cholesterol/cholest-4-en-3-one hydroxylation. An important question is why these bacterium have more than one enzyme with overlapping substrate ranges capable of catalyzing the terminal oxidation of the alkyl chain of these sterols. To further understand the role of these enzymes we investigated CYP125A6 and CYP125A7 from Mycobacterium marinum with various cholesterol analogues. These have modifications on the A and B rings of the steroid and we assessed the substrate binding and catalytic activity of these with each enzyme. CYP125A7 gave similar results to those reported for the CYP125A1 enzyme from M. tuberculosis. Differences in the substrate binding and catalytic activity with the cholesterol analogues were observed with CYP125A6. For example, while cholesteryl sulfate could bind to both enzymes it was only oxidized by CYP125A6 and not by CYP125A7. CYP125A6 generated higher levels of metabolites with the majority of C-3 and C-7 substituted cholesterol analogues such 7-ketocholesterol. However, 5α-cholestan-3β-ol was only oxidized by CYP125A7 enzyme. The cholest-4-en-3-one and 7-ketocholesterol-bound forms of the CYP125A6 and CYP125A7 enzymes were modelled using AlphaFold. The structural models highlighted differences in the binding modes of the steroid derivatives within the same enzyme. Significant changes in the binding mode of the steroids between these CYP125 enzymes and other bacterial cholesterol oxidizing enzymes, CYP142A3 and CYP124A1, were also seen. Despite this, all these models predicted the selectivity for terminal methyl hydroxylation, in agreement with the experimental data.

Exploring Photoswitchable Binding Interactions with Small-Molecule- and Peptide-Based Inhibitors of Trypsin

The ability to photochemically activate a drug, both when and where needed, requires optimisation of the difference in biological activity between each isomeric state. As a step to this goal, we report small-molecule- and peptide-based inhibitors of the same protease-trypsin-to better understand how photoswitchable drugs interact with their biological target. The best peptidic inhibitor displayed a more than fivefold difference in inhibitory activity between isomeric states, whereas the best small-molecule inhibitor only showed a 3.4-fold difference. Docking and molecular modelling suggest this result is due to a large change in 3D structure in the key binding residues of the peptidic inhibitor upon isomerisation; this is not observed for the small-molecule inhibitor. Hence, we demonstrate that significant structural changes in critical binding motifs upon irradiation are essential for maximising the difference in biological activity between isomeric states. This is an important consideration in the design of future photoswitchable drugs for clinical applications.

A generic binding pocket for small molecule IKs activators at the extracellular inter-subunit interface of KCNQ1 and KCNE1 channel complexes

The cardiac IKs ion channel comprises KCNQ1, calmodulin, and KCNE1 in a dodecameric complex which provides a repolarizing current reserve at higher heart rates and protects from arrhythmia syndromes that cause fainting and sudden death. Pharmacological activators of IKs are therefore of interest both scientifically and therapeutically for treatment of IKs loss-of-function disorders. One group of chemical activators are only active in the presence of the accessory KCNE1 subunit and here we investigate this phenomenon using molecular modeling techniques and mutagenesis scanning in mammalian cells. A generalized activator binding pocket is formed extracellularly by KCNE1, the domain-swapped S1 helices of one KCNQ1 subunit and the pore/turret region made up of two other KCNQ1 subunits. A few residues, including K41, A44 and Y46 in KCNE1, W323 in the KCNQ1 pore, and Y148 in the KCNQ1 S1 domain, appear critical for the binding of structurally diverse molecules, but in addition, molecular modeling studies suggest that induced fit by structurally different molecules underlies the generalized nature of the binding pocket. Activation of IKs is enhanced by stabilization of the KCNQ1-S1/KCNE1/pore complex, which ultimately slows deactivation of the current, and promotes outward current summation at higher pulse rates. Our results provide a mechanistic explanation of enhanced IKs currents by these activator compounds and provide a map for future design of more potent therapeutically useful molecules.

Development and Assessment of 1,5-Diarylpyrazole/Oxime Hybrids Targeting EGFR and JNK-2 as Antiproliferative Agents: A Comprehensive Study through Synthesis, Molecular Docking, and Evaluation

New 1,5-diarylpyrazole oxime hybrid derivatives (scaffolds A and B) were designed, synthesized, and then their purity was verified using a variety of spectroscopic methods. A panel of five cancer cell lines known to express EGFR and JNK-2, including human colorectal adenocarcinoma cell line DLD-1, human cervical cancer cell line Hela, human leukemia cell line K562, human pancreatic cell line SUIT-2, and human hepatocellular carcinoma cell line HepG2, were used to biologically evaluate for their in vitro cytotoxicity for all the synthesized compounds 7a-j, 8a-j, 9a-c, and 10a-c. The oxime containing compounds 8a-j and 10a-c were more active as antiproliferative agents than their non-oxime congeners 7a-j and 9a-c. Compounds 8d, 8g, 8i, and 10c inhibited EGFR with IC50 values ranging from 8 to 21 µM when compared with sorafenib. Compound 8i inhibited JNK-2 as effectively as sorafenib, with an IC50 of 1.0 µM. Furthermore, compound 8g showed cell cycle arrest at the G2/M phase in the cell cycle analysis of the Hela cell line, whereas compound 8i showed combined S phase and G2 phase arrest. According to docking studies, oxime hybrid compounds 8d, 8g, 8i, and 10c exhibited binding free energies ranging from -12.98 to 32.30 kcal/mol at the EGFR binding site whereas compounds 8d and 8i had binding free energies ranging from -9.16 to -12.00 kcal/mol at the JNK-2 binding site.

Therapeutic Switching of Rafoxanide: a New Approach To Fighting Drug-Resistant Bacteria and Fungi

Control and management of life-threatening bacterial and fungal infections are a global health challenge. Despite advances in antimicrobial therapies, treatment failures for resistant bacterial and fungal infections continue to increase. We aimed to repurpose the anthelmintic drug rafoxanide for use with existing therapeutic drugs to increase the possibility of better managing infection and decrease treatment failures. For this purpose, we evaluated the antibacterial and antifungal potential of rafoxanide. Notably, 70% (70/100) of bacterial isolates showed multidrug resistance (MDR) patterns, with higher prevalence among human isolates (73.5% [50/68]) than animal ones (62.5% [20/32]). Moreover, 22 fungal isolates (88%) were MDR and were more prevalent among animal (88.9%) than human (87.5%) sources. We observed alarming MDR patterns among bacterial isolates, i.e., Klebsiella pneumoniae (75% [30/40; 8 animal and 22 human]) and Escherichia coli (66% [40/60; 12 animal and 28 human]), and fungal isolates, i.e., Candida albicans (86.7% [13/15; 4 animal and 9 human]) and Aspergillus fumigatus (90% [9/10; 4 animal and 5 human]), that were resistant to at least one agent in three or more different antimicrobial classes. Rafoxanide had antibacterial and antifungal activities, with minimal inhibitory concentration (MICs) ranging from 2 to 128 μg/mL. Rafoxanide at sub-MICs downregulated the mRNA expression of resistance genes, including E. coli and K. pneumoniae blaCTX-M-1, blaTEM-1, blaSHV, MOX, and DHA, C. albicans ERG11, and A. fumigatus cyp51A. We noted the improvement in the activity of β-lactam and antifungal drugs upon combination with rafoxanide. This was apparent in the reduction in the MICs of cefotaxime and fluconazole when these drugs were combined with sub-MIC levels of rafoxanide. There was obvious synergism between rafoxanide and cefotaxime against all E. coli and K. pneumoniae isolates (fractional inhibitory concentration index [FICI] values ≤ 0.5). Accordingly, there was a shift in the patterns of resistance of 16.7% of E. coli and 22.5% of K. pneumoniae isolates to cefotaxime and those of 63.2% of C. albicans and A. fumigatus isolates to fluconazole when the isolates were treated with sub-MICs of rafoxanide. These results were confirmed by in silico and mouse protection assays. Based on the in silico study, one possible explanation for how rafoxanide reduced bacterial resistance is through its inhibitory effects on bacterial and fungal histidine kinase enzymes. In short, rafoxanide exhibited promising results in overcoming bacterial and fungal drug resistance. IMPORTANCE The drug repurposing strategy is an alternative approach to reducing drug development timelines with low cost, especially during outbreaks of disease caused by drug-resistant pathogens. Rafoxanide can disrupt the abilities of bacterial and fungal cells to adapt to stress conditions. The coadministration of antibiotics with rafoxanide can prevent the failure of treatment of both resistant bacteria and fungi, as the resistant pathogens could be made sensitive upon treatment with rafoxanide. From our findings, we anticipate that pharmaceutical companies will be able to utilize new combinations against resistant pathogens.

Improvement of binding pose prediction of the MR1 covalent ligands by inclusion of simple pharmacophore constraints and structural waters in the docking process

The major histocompatibility complex (MHC) class I-related molecule, MR1, is a key component of the immune system, presenting antigens to T-cell receptors (TCRs) and modulating the immune response against various antigens. MR1 possesses a compact ligand-binding pocket despite its ability to interact with ligands that can have either agonistic or antagonistic effects on the immune system. Agonistic ligands can stimulate the immune response, while antagonistic ligands do not elicit an immune response. In most cases, ligand binding to MR1 is mediated through a covalent bond with Lys43. However, recent studies have suggested that a variety of small molecules can interact with the MR1-binding site. In this study, we have used several approaches to improve the binding pose prediction of covalent ligands to MR1, including docking in mutated receptors, and imposing simple pharmacophore constraints and structural water molecules. The careful assignment of pharmacophore constraints and inclusion of structural water molecules in the challenging docking process of covalent docking improved the binding pose prediction and virtual screening performance. In a retrospective virtual screening, the proposed approach exhibited EF1% and EF2% values of 7.4 and 5.5, respectively. Conversely, when using the mutated receptor, both EF1% and EF2% were recorded as 0 for the conventional docking method. The performance of the pharmacophore constraints was also evaluated on other covalent docking cases, and compared to previously reported results for common covalent docking methods. The proposed approach achieved an average RMSD of 2.55, while AutoDock4, CovDock, FITTED, GOLD, ICM-Pro, and MOE exhibited average RMSD values of 3.0, 2.93, 3.04, 4.93, 2.44, and 3.36, respectively. Our results demonstrate that the inclusion of simple pharmacophore constraints and structural waters can improve the prediction of binding poses of covalent ligands to MR1, which can aid in the discovery of novel immunotherapeutic agents.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03694-w.

Identification and characterisation of MdUGT78T2 as a galactosyltransferase with dual activity on flavonol and anthocyanidin substrates in red-skinned apple fruit (Malus domestica L.)

Anthocyanidin and flavonol glycosides have been linked to the health-promoting effects associated with apple consumption. However, very few enzymes involved in flavonoid glycosylation have been characterised to date. Here, we present the identification and phylogenetic analysis of 234 putative glycosyltransferases involved in flavonoid biosynthesis, and detail the biochemical and structural characterisation of MdUGT78T2 as a strict galactosyltransferase involved in the formation of quercetin-3-O-galactoside and cyanidin-3-O-galactoside, the major glycoconjugates of flavonoids in apple. The enzyme is also active on other flavonoids but with a lower catalytic efficiency. Our data, complemented with gene expression analysis suggest that MdUGT78T2 synthesises the glycoconjugates at both the early and late stages of fruit development. This newly discovered type of catalytic activity can potentially be exploited for in vitro modification of flavonoids to increase their stability in food products and to modify apple fruits and other commercial crops through breeding approaches to enhance their health benefits.

Allosteric Inhibitors of Zika Virus NS2B-NS3 Protease Targeting Protease in "Super-Open" Conformation

The Zika virus (ZIKV), a member of the Flaviviridae family, is considered a major health threat causing multiple cases of microcephaly in newborns and Guillain-Barré syndrome in adults. In this study, we targeted a transient, deep, and hydrophobic pocket of the "super-open" conformation of ZIKV NS2B-NS3 protease to overcome the limitations of the active site pocket. After virtual docking screening of approximately seven million compounds against the novel allosteric site, we selected the top six candidates and assessed them in enzymatic assays. Six candidates inhibited ZIKV NS2B-NS3 protease proteolytic activity at low micromolar concentrations. These six compounds, targeting the selected protease pocket conserved in ZIKV, serve as unique drug candidates and open new opportunities for possible treatment against several flavivirus infections.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA

RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

Computer-aided drug design in seeking viral capsid modulators

Approved or licensed antiviral drugs have limited applications because of their drug resistance and severe adverse effects. By contrast, by stabilizing or destroying the viral capsid, compounds known as capsid modulators prevent viral replication by acting on new targets and, therefore, overcoming the problem of clinical drug resistance. For example. computer-aided drug design (CADD) methods, using strategies based on structures of biological targets (structure-based drug design; SBDD), such as docking, molecular dynamics (MD) simulations, and virtual screening (VS), have provided opportunities for fast and effective development of viral capsid modulators. In this review, we summarize the application of CADD in the discovery, optimization, and mechanism prediction of capsid-targeting small molecules, providing new insights into antiviral drug discovery modalities. Teaser: Computer-aided drug design will accelerate the development of viral capsid regulators, which brings new hope for the treatment of refractory viral diseases.

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

MetaDOCK: A Combinatorial Molecular Docking Approach

Molecular docking plays a major role in academic and industrial drug screening and discovery processes. Despite the availability of numerous docking software packages, there is a lot of scope for improvement for the docking algorithms in terms of becoming more reliable to replicate the experimental binding results. Here, we propose a combinatorial or consensus docking approach where complementary powers of the existing methods are captured. We created a meta-docking protocol by combining the results of AutoDock4.2, LeDock, and rDOCK programs as these are freely available, easy to use, and suitable for large-scale analysis and produced better performance on benchmarking studies. Rigorous benchmarking analyses were undertaken to evaluate the scoring, posing, and screening capability of our approach. Further, the performance measures were compared against one standard state-of-the-art commercial docking software, GOLD, and one freely available software, PLANTS. Performances of MetaDOCK for scoring, posing, and screening the protein-ligand complexes were found to be quite superior compared to the reference programs. Exhaustive molecular dynamics simulation and molecular mechanics Poisson-Boltzmann and surface area-based free energy estimation also suggest better energetic stability of the docking solutions produced by our meta-approach. We believe that the MetaDOCK approach is a useful packaging of the freely available software and provides a better alternative to the scientific community who are unable to afford costly commercial packages.

Impact of cancer-associated mutations in CC chemokine receptor 2 on receptor function and antagonism

CC chemokine receptor 2 (CCR2), a G protein-coupled receptor, plays a role in many cancer-related processes such as metastasis formation and immunosuppression. Since ∼ 20 % of human cancers contain mutations in G protein-coupled receptors, ten cancer-associated CCR2 mutants obtained from the Genome Data Commons were investigated for their effect on receptor functionality and antagonist binding. Mutations were selected based on either their vicinity to CCR2's orthosteric or allosteric binding sites or their presence in conserved amino acid motifs. One of the mutant receptors, namely S101P^2.63 with a mutation near the orthosteric binding site, did not express on the cell surface. All other studied mutants showed a decrease in or a lack of G protein activation in response to the main endogenous CCR2 ligand CCL2, but no change in potency was observed. Furthermore, INCB3344 and LUF7482 were chosen as representative orthosteric and allosteric antagonists, respectively. No change in potency was observed in a functional assay, but mutations located at F116^3.28 impacted orthosteric antagonist binding significantly, while allosteric antagonist binding was abolished for L134Q^3.46 and D137N^3.49 mutants. As CC chemokine receptor 2 is an attractive drug target in cancer, the negative effect of these mutations on receptor functionality and drugability should be considered in the drug discovery process.

Allosteric regulation of CAD modulates de novo pyrimidine synthesis during the cell cycle

Metabolism is a fundamental cellular process that is coordinated with cell cycle progression. Despite this association, a mechanistic understanding of cell cycle phase-dependent metabolic pathway regulation remains elusive. Here we report the mechanism by which human de novo pyrimidine biosynthesis is allosterically regulated during the cell cycle. Combining traditional synchronization methods and metabolomics, we characterize metabolites by their accumulation pattern during cell cycle phases and identify cell cycle phase-dependent regulation of carbamoyl-phosphate synthetase 2, aspartate transcarbamylase and dihydroorotase (CAD), the first, rate-limiting enzyme in de novo pyrimidine biosynthesis. Through systematic mutational scanning and structural modelling, we find allostery as a major regulatory mechanism that controls the activity change of CAD during the cell cycle. Specifically, we report evidence of two Animalia-specific loops in the CAD allosteric domain that involve sensing and binding of uridine 5'-triphosphate, a CAD allosteric inhibitor. Based on homology with a mitochondrial carbamoyl-phosphate synthetase homologue, we identify a critical role for a signal transmission loop in regulating the formation of a substrate channel, thereby controlling CAD activity.

High-throughput phenotypic screen identifies a new family of potent anti-amoebic compounds

Entamoeba histolytica is a disease-causing parasitic amoeba which affects an estimated 50 million people worldwide, particularly in socioeconomically vulnerable populations experiencing water sanitation issues. Infection with E. histolytica is referred to as amoebiasis, and can cause symptoms such as colitis, dysentery, and even death in extreme cases. Drugs exist that are capable of killing this parasite, but they are hampered by downsides such as significant adverse effects at therapeutic concentrations, issues with patient compliance, the need for additional drugs to kill the transmissible cyst stage, and potential development of resistance. Past screens of small and medium sized chemical libraries have yielded anti-amoebic candidates, thus rendering high-throughput screening a promising direction for new drug discovery in this area. In this study, we screened a curated 81,664 compound library from Janssen pharmaceuticals against E. histolytica trophozoites in vitro, and from it identified a highly potent new inhibitor compound. The best compound in this series, JNJ001, showed excellent inhibition activity against E. histolytica trophozoites with EC50 values at 0.29 μM, which is better than the current approved treatment, metronidazole. Further experimentation confirmed the activity of this compound, as well as that of several structurally related compounds, originating from both the Janssen Jump-stARter library, and from chemical vendors, thus highlighting a new structure-activity relationship (SAR). In addition, we confirmed that the compound inhibited E. histolytica survival as rapidly as the current standard of care and inhibited transmissible cysts of the related model organism Entamoeba invadens. Together these results constitute the discovery of a novel class of chemicals with favorable in vitro pharmacological properties. The discovery may lead to an improved therapy against this parasite and in all of its life stages.

Allosteric Modulators of Dopamine D2 Receptors for Fine-Tuning of Dopaminergic Neurotransmission in CNS Diseases: Overview, Pharmacology, Structural Aspects and Synthesis

Allosteric modulation of G protein-coupled receptors (GPCRs) is nowadays a hot topic in medicinal chemistry. Allosteric modulators, i.e., compounds which bind in a receptor site topologically distinct from orthosteric sites, exhibit a number of advantages. They are more selective, safer and display a ceiling effect which prevents overdosing. Allosteric modulators of dopamine D₂ receptor are potential drugs against a number of psychiatric and neurological diseases, such as schizophrenia and Parkinson's disease. In this review, an insightful summary of current research on D₂ receptor modulators is presented, ranging from their pharmacology and structural aspects of ligand-receptor interactions to their synthesis.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

A New 1,2,3-Triazole Scaffold with Improved Potency against Staphylococcus aureus Biotin Protein Ligase

Staphylococcus aureus, a key ESKAPE bacteria, is responsible for most blood-based infections and, as a result, is a major economic healthcare burden requiring urgent attention. Here, we report in silico docking, synthesis, and assay of N1-diphenylmethyl triazole-based analogues (7-13) designed to interact with the entire binding site of S. aureus biotin protein ligase (SaBPL), an enzyme critical for the regulation of gluconeogenesis and fatty acid biosynthesis. The second aryl ring of these compounds enhances both SaBPL potency and whole cell activity against S. aureus relative to previously reported mono-benzyl triazoles. Analogues 12 and 13, with added substituents to better interact with the adenine binding site, are particularly potent, with K_i values of 6.01 ± 1.01 and 8.43 ± 0.73 nM, respectively. These analogues are the most active triazole-based inhibitors reported to date and, importantly, inhibit the growth of a clinical isolate strain of S. aureus ATCC 49775, with minimum inhibitory concentrations of 1 and 8 μg/mL, respectively.

Novel Efficient Multistage Lead Optimization Pipeline Experimentally Validated for DYRK1B Selective Inhibitors

In addition to general challenges in drug discovery such as the identification of lead compounds in time- and cost-effective ways, specific challenges also exist. Particularly, it is necessary to develop pharmacological inhibitors that effectively discriminate between closely related molecular targets. DYRK1B kinase is considered a valuable target for cancer-specific mono- or combination chemotherapy; however, the inhibition of its closely related DYRK1A kinase is not beneficial. Existing inhibitors target both kinases with essentially the same efficiency, and the unavailability of the DYRK1B crystal structure makes the discovery of DYRK1B-specific inhibitors even more challenging. Here, we propose a novel multi-stage compound discovery pipeline aimed at in silico identification of both potent and selective small molecules from a large set of initial candidates. The method uses structure-based docking and ligand-based quantitative structure–activity relationship modeling. This approach allowed us to identify lead and runner-up small-molecule compounds targeting DYRK1B with high efficiency and specificity.

Combination of ligand and structure based virtual screening approaches for the discovery of potential PARP1 inhibitors

Poly (ADP-ribose) polymerase 1 (PARP1) has high therapeutic value as biomolecular target for research and development of small molecules with antineoplastic activity, since it is upregulated in many cancers, especially in ovarian and BRCA 1/2 mutated breast cancers. Decades of investigation of PARP inhibitors (PARPi) have led to the approval of several drug compounds, however clinical application of PARPi in cancer therapy is limited due to a number of factors, including low selectivity, weak affinity and undesired side effects. Thus, identification of novel drug-like chemical compounds with alternatives to the known PARPi chemical scaffolds, binding modes and interaction patterns with amino acid residues in the active site is of high therapeutic importance. In this study we applied a combination of ligand- and structure-based virtual screening approaches with the goal of identification of novel potential PARPi.

In Vitro Evaluation of In Silico Screening Approaches in Search for Selective ACE2 Binding Chemical Probes

New models for ACE2 receptor binding, based on QSAR and docking algorithms were developed, using XRD structural data and ChEMBL 26 database hits as training sets. The selectivity of the potential ACE2-binding ligands towards Neprilysin (NEP) and ACE was evaluated. The Enamine screening collection (3.2 million compounds) was virtually screened according to the above models, in order to find possible ACE2-chemical probes, useful for the study of SARS-CoV2-induced neurological disorders. An enzymology inhibition assay for ACE2 was optimized, and the combined diversified set of predicted selective ACE2-binding molecules from QSAR modeling, docking, and ultrafast docking was screened in vitro. The in vitro hits included two novel chemotypes suitable for further optimization.

Synergistic interactions of repurposed drugs that inhibit Nsp1, a major virulence factor for COVID-19

Nsp1 is one of the first proteins expressed from the SARS-CoV-2 genome and is a major virulence factor for COVID-19. A rapid multiplexed assay for detecting the action of Nsp1 was developed in cultured lung cells. The assay is based on the acute cytopathic effects induced by Nsp1. Virtual screening was used to stratify compounds that interact with two functional Nsp1 sites: the RNA-binding groove and C-terminal helix-loop-helix region. Experimental screening focused on compounds that could be readily repurposed to treat COVID-19. Multiple synergistic combinations of compounds that significantly inhibited Nsp1 action were identified. Among the most promising combinations are Ponatinib, Rilpivirine, and Montelukast, which together, reversed the toxic effects of Nsp1 to the same extent as null mutations in the Nsp1 gene.

Discovery of Triple Inhibitors of Both SARS-CoV-2 Proteases and Human Cathepsin L

One inhibitor of the main SARS-CoV-2 protease has been approved recently by the FDA, yet it targets only SARS-CoV-2 main protease (Mpro). Here, we discovered inhibitors containing thiuram disulfide or dithiobis-(thioformate) tested against three key proteases involved in SARS-CoV-2 replication, including Mpro, SARS-CoV-2 papain-like protease (PLpro), and human cathepsin L. The use of thiuram disulfide and dithiobis-(thioformate) covalent inhibitor warheads was inspired by an idea to find a better alternative than disulfiram, an approved treatment for chronic alcoholism that is currently in phase 2 clinical trials against SARS-CoV-2. Our goal was to find more potent inhibitors that target both viral proteases and one essential human protease to reduce the dosage, improve the efficacy, and minimize the adverse effects associated with these agents. We found that compounds coded as RI175, RI173, and RI172 were the most potent inhibitors in an enzymatic assay against SARS-CoV-2 Mpro, SARS-CoV-2 PLpro, and human cathepsin L, with IC₅₀s of 300, 200, and 200 nM, which is about 5-, 19-, and 11-fold more potent than disulfiram, respectively. In addition, RI173 was tested against SARS-CoV-2 in a cell-based and toxicity assay and was shown to have a greater antiviral effect than disulfiram. The identified compounds demonstrated the promising potential of thiuram disulfide or dithiobis-(thioformate) as a reactive functional group in small molecules that could be further developed for treatment of the COVID-19 virus or related variants.

C6 Hydroxymethyl-Substituted Carbapenem MA-1-206 Inhibits the Major Acinetobacter baumannii Carbapenemase OXA-23 by Impeding Deacylation

Acinetobacter baumannii has become a major nosocomial pathogen, as it is often multidrug-resistant, which results in infections characterized by high mortality rates. The bacterium achieves high levels of resistance to β-lactam antibiotics by producing β-lactamases, enzymes which destroy these valuable agents. Historically, the carbapenem family of β-lactam antibiotics have been the drugs of choice for treating A. baumannii infections. However, their effectiveness has been significantly diminished due to the pathogen’s production of carbapenem-hydrolyzing class D β-lactamases (CHDLs); thus, new antibiotics and inhibitors of these enzymes are urgently needed. Here, we describe a new carbapenem antibiotic, MA-1-206, in which the canonical C6 hydroxyethyl group has been replaced with hydroxymethyl. The antimicrobial susceptibility studies presented here demonstrated that this compound is more potent than meropenem and imipenem against A. baumannii producing OXA-23, the most prevalent CHDL of this pathogen, and also against strains producing the CHDL OXA-24/40 and the class B metallo-β-lactamase VIM-2. Our kinetic and mass spectrometry studies revealed that this drug is a reversible inhibitor of OXA-23, where inhibition takes place through a branched pathway. X-ray crystallographic studies, molecular docking, and molecular dynamics simulations of the OXA-23-MA-1-206 complex show that the C6 hydroxymethyl group forms a hydrogen bond with the carboxylated catalytic lysine of OXA-23, effectively preventing deacylation. These results provide a promising strategy for designing a new generation of CHDL-resistant carbapenems to restore their efficacy against deadly A. baumannii infections.

Discovery of MurA Inhibitors as Novel Antimicrobials through an Integrated Computational and Experimental Approach

The bacterial cell wall is essential for protecting bacteria from the surrounding environment and maintaining the integrity of bacteria cells. The MurA enzyme, which is an essential enzyme involved in bacterial cell wall synthesis, could be a good drug target for antibiotics. Although fosfomycin is used clinically as a MurA inhibitor, resistance to this antibiotic is a concern. Here we used molecular docking-based virtual screening approaches to identify potential MurA inhibitors from 1.412 million compounds from three databases. Thirty-three top compounds from virtual screening were experimentally tested in Listeria innocua (Gram-positive bacterium) and Escherichia coli (Gram-negative bacterium). Compound 2-Amino-5-bromobenzimidazole (S17) showed growth inhibition effect in both L. innocua and E. coli, with the same Minimum Inhibitory Concentration (MIC) value of 0.5 mg/mL. Compound 2-[4-(dimethylamino)benzylidene]-n-nitrohydrazinecarboximidamide (C1) had growth inhibition effect only in L. innocua, with a MIC value of 0.5 mg/mL. Two FDA-approved drugs, albendazole (S4) and diflunisal (S8), had a growth inhibition effect only in E. coli, with a MIC value of 0.0625 mg/mL. The identified MurA inhibitors could be potential novel antibiotics. Furthermore, they could be potential fosfomycin substitutes for the fosfomycin-resistant strains.

Neuroepigenetic Mechanisms of Action of Ultrashort Peptides in Alzheimer's Disease

Epigenetic regulation of gene expression is necessary for maintaining higher-order cognitive functions (learning and memory). The current understanding of the role of epigenetics in the mechanism of Alzheimer’s disease (AD) is focused on DNA methylation, chromatin remodeling, histone modifications, and regulation of non-coding RNAs. The pathogenetic links of this disease are the misfolding and aggregation of tau protein and amyloid peptides, mitochondrial dysfunction, oxidative stress, impaired energy metabolism, destruction of the blood–brain barrier, and neuroinflammation, all of which lead to impaired synaptic plasticity and memory loss. Ultrashort peptides are promising neuroprotective compounds with a broad spectrum of activity and without reported side effects. The main aim of this review is to analyze the possible epigenetic mechanisms of the neuroprotective action of ultrashort peptides in AD. The review highlights the role of short peptides in the AD pathophysiology. We formulate the hypothesis that peptide regulation of gene expression can be mediated by the interaction of short peptides with histone proteins, cis- and transregulatory DNA elements and effector molecules (DNA/RNA-binding proteins and non-coding RNA). The development of therapeutic agents based on ultrashort peptides may offer a promising addition to the multifunctional treatment of AD.

Molecular modelling and in vitro studies of Daruharidra as a potent alpha-amylase inhibitor

The present study aims at exploring the potential of the Daruharidra plant (stem and bark) for inhibition of alpha-amylase. Aqueous and ethanolic extraction yielded the highest total phenolic content (TPC) of 101.4 and 111.8 mcg of gallic acid equivalent. Methanol and ethanol extract had Total flavonoid content (TFC) of 319.6 and 288.3 mcg of quercetin equivalent, respectively. In contrast, petroleum ether extraction resulted in the lowest TPC of 23.6 and TFC of 8.33 mcg, respectively. Methanol (5.554 mg/ml), acetone (6.576 mg/ml), and ethanol (7.321 mg/ml) extract had the lowest IC50 values in alpha-amylase inhibition with the mode of inhibition being non-competitive inhibition. HR-LCMS was used for comprehension of phytoconstituents present in the extract. Amongst hundreds of hits observed 10 ligands of alkaloid nature were used for docking studies. Berbamine, alloxanthine, protopine and benazepril along with reference molecule (Acarbose) were subjected to Molecular dynamics (MD) simulation to analyze the stability of the docked protein-ligand complex. The values of RMSD, RMSF, RG, H-Bond and SASA, the interaction energy of all protein-ligand complexes were calculated after 150 ns of MD simulation. The results of screened complexes revealed good stability as compared to reference Acarbose. These screened ligands used for simulation have the most negative binding energies that interacted with alpha amylase enzyme having -9.28 kcal/mol, -7.51 kcal/mol, -7.73 kcal/mol and -8.00 kcal/mol, energies respectively. The results show significant alpha-amylase inhibitory activity and interaction of ligands targeting this enzyme, which can be used for cross-validation, in vitroCommunicated by Ramaswamy H. Sarma.

Control of Unsaturation in De Novo Fatty Acid Biosynthesis by FabA

Carrier protein-dependent biosynthesis provides a thiotemplated format for the production of natural products. Within these pathways, many reactions display exquisite substrate selectivity, a regulatory framework proposed to be controlled by protein-protein interactions (PPIs). In Escherichia coli, unsaturated fatty acids are generated within the de novo fatty acid synthase by a chain length-specific interaction between the acyl carrier protein AcpP and the isomerizing dehydratase FabA. To evaluate PPI-based control of reactivity, interactions of FabA with AcpP bearing multiple sequestered substrates were analyzed through NMR titration and guided high-resolution docking. Through a combination of quantitative binding constants, residue-specific perturbation analysis, and high-resolution docking, a model for substrate control via PPIs has been developed. The in silico results illuminate the mechanism of FabA substrate selectivity and provide a structural rationale with atomic detail. Helix III positioning in AcpP communicates sequestered chain length identity recognized by FabA, demonstrating a powerful strategy to regulate activity by allosteric control. These studies broadly illuminate carrier protein-dependent pathways and offer an important consideration for future inhibitor design and pathway engineering.