A practical guide to large-scale docking

Computational insights into allosteric inhibition of focal adhesion kinase: A combined pharmacophore modeling and molecular dynamics approach

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that modulates integrin and growth factor signaling pathways and is implicated in cancer cell migration, proliferation, and survival. Over the past decade various, FAK kinase, FERM, and FAT domain inhibitors have been reported and a few kinase domain inhibitors are under clinical consideration. However, few of them were identified as multikinase inhibitors. In kinase drug design selectivity is always a point of concern, to improve selectivity allosteric inhibitor development is the best choice. The current research utilized a pharmacophore modeling (PM) approach to identify novel allosteric inhibitors of FAK. The all-available allosteric inhibitor bound 3D structures with PDB ids 4EBV, 4EBW, and 4I4F were utilized for the pharmacophore modeling. The validated PM models were utilized to map a database of 770,550 compounds prepared from ZINC, EXIMED, SPECS, ASINEX, and InterBioScreen, aiming to identify potential allosteric inhibitors. The obtained compounds from screening step were forwarded to molecular docking (MD) for the prediction of binding orientation inside the allosteric site and the results were evaluated with the known FAK allosteric inhibitor (REF). Finally, 14 FAK-inhibitor complexes were selected from the docking study and were studied under molecular dynamics simulations (MDS) for 500 ns. The complexes were ranked according to binding free energy (BFE) and those demonstrated higher affinity for allosteric site of FAK than REF inhibitors were selected. The selected complexes were further analyzed for intermolecular interactions and finally, three potential allosteric inhibitor candidates for the inhibition of FAK protein were identified. We believe that identified scaffolds may help in drug development against FAK as an anticancer agent.

G Protein-Coupled Receptors: A Century of Research and Discovery

GPCRs (G protein-coupled receptors), also known as 7 transmembrane domain receptors, are the largest receptor family in the human genome, with ≈800 members. GPCRs regulate nearly every aspect of human physiology and disease, thus serving as important drug targets in cardiovascular disease. Sharing a conserved structure comprised of 7 transmembrane α-helices, GPCRs couple to heterotrimeric G-proteins, GPCR kinases, and β-arrestins, promoting downstream signaling through second messengers and other intracellular signaling pathways. GPCR drug development has led to important cardiovascular therapies, such as antagonists of β-adrenergic and angiotensin II receptors for heart failure and hypertension, and agonists of the glucagon-like peptide-1 receptor for reducing adverse cardiovascular events and other emerging indications. There continues to be a major interest in GPCR drug development in cardiovascular and cardiometabolic disease, driven by advances in GPCR mechanistic studies and structure-based drug design. This review recounts the rich history of GPCR research, including the current state of clinically used GPCR drugs, and highlights newly discovered aspects of GPCR biology and promising directions for future investigation. As additional mechanisms for regulating GPCR signaling are uncovered, new strategies for targeting these ubiquitous receptors hold tremendous promise for the field of cardiovascular medicine.

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

Optical Control of Cell-Surface and Endomembrane-Exclusive β-Adrenergic Receptor Signaling

Beta-adrenergic receptors (βARs) are G protein-coupled receptors (GPCRs) that mediate catecholamine hormone-induced stress responses, such as elevation of heart rate. Besides those that are plasma membrane-bound, endomembrane βARs are also signaling competent. Dysregulation of βAR pathways underlies severe pathological conditions. Emerging evidence indicates pathological molecular signatures in deeper endomembrane βARs signaling, likely contributing to conditions such as cardiomyocyte hypertrophy and apoptosis. However, the lack of approaches to control endomembrane β1ARs has impeded linking signaling with pathology. Informed by the β1AR-catecholamine interactions, we engineered an efficient photo-labile pro-ligand (OptoIso) to trigger βAR signaling exclusively in endomembrane regions using blue light stimulation. Not only does OptoIso undergo blue light deprotection in seconds, but also efficiently enters cells and allows examination of G protein heterotrimer activation exclusively at endomembranes. OptoIso also allows optical activation of plasma membrane βAR signaling in selected single cells with native fidelity, which can be reversed by terminating blue light. Thus, OptoIso will be a valuable experimental tool to elicit spatial and temporal control of βAR signaling in user-defined endomembrane or plasma membrane regions in unmodified cells with native fidelity.

In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations

The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods. Each challenge contains two phases, hit-finding and follow-up optimization, each of which is followed by experimental validation of the computational predictions. For the CACHE Challenge #1, the Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 Repeat (WDR) domain was selected as the target for in silico hit-finding and optimization. Mutations in LRRK2 are the most common genetic cause of the familial form of Parkinson's disease. The LRRK2 WDR domain is an understudied drug target with no known molecular inhibitors. Herein we detail the first phase of our winning submission to the CACHE Challenge #1. We developed a framework for the high-throughput structure-based virtual screening of a chemically diverse small molecule space. Hit identification was performed using the large-scale Deep Docking (DD) protocol followed by absolute binding free energy (ABFE) simulations. ABFEs were computed using an automated molecular dynamics (MD)-based thermodynamic integration (TI) approach. 4.1 billion ligands from Enamine REAL were screened with DD followed by ABFEs computed by MD TI for 793 ligands. 76 ligands were prioritized for experimental validation, with 59 compounds successfully synthesized and 5 compounds identified as hits, yielding a 8.5% hit rate. Our results demonstrate the efficacy of the combined DD and ABFE approaches for hit identification for a target with no previously known hits. This approach is widely applicable for the efficient screening of ultra-large chemical libraries as well as rigorous protein-ligand binding affinity estimation leveraging modern computational resources.

Assessment of Nucleobase Protomeric and Tautomeric States in Nucleic Acid Structures for Interaction Analysis and Structure-Based Ligand Design

With increasing interest in RNA as a therapeutic and a potential target, the role of RNA structures has become more important. Even slight changes in nucleobases, such as modifications or protomeric and tautomeric states, can have a large impact on RNA structure and function, while local environments in turn affect protonation and tautomerization. In this work, the application of empirical tools for pKa and tautomer prediction for RNA modifications was elucidated and compared with ab initio quantum mechanics (QM) methods and expanded toward macromolecular RNA structures, where QM is no longer feasible. In this regard, the Protonate3D functionality within the molecular operating environment (MOE) was expanded for nucleobase protomer and tautomer predictions and applied to reported examples of altered protonation states depending on the local environment. Overall, observations of nonstandard protomers and tautomers were well reproduced, including structural C+G:C(A) and A+GG motifs, several mismatches, and protonation of adenosine or cytidine as the general acid in nucleolytic ribozymes. Special cases, such as cobalt hexamine-soaked complexes or the deprotonation of guanosine as the general base in nucleolytic ribozymes, proved to be challenging. The collected set of examples shall serve as a starting point for the development of further RNA protonation prediction tools, while the presented Protonate3D implementation already delivers reasonable protonation predictions for RNA and DNA macromolecules. For cases where higher accuracy is needed, like following catalytic pathways of ribozymes, incorporation of QM-based methods can build upon the Protonate3D-generated starting structures. Likewise, this protonation prediction can be used for structure-based RNA-ligand design approaches.

Structure-based virtual screening of vast chemical space as a starting point for drug discovery

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.

Technologies for the discovery of G protein-coupled receptor-targeting biologics

G protein-coupled receptors (GPCRs) are important pharmaceutical targets, working as entry points for signaling pathways involved in metabolic, neurological, and cardiovascular diseases. Although small molecules remain the major GPCR drug type, biologic therapeutics, such as peptides and antibodies, are increasingly found among clinical trials and Food and Drug Administration (FDA)-approved drugs. Here, we review state-of-the-art technologies for the engineering of biologics that target GPCRs, as well as proof-of-principle technologies that are ripe for this application. Looking ahead, inexpensive DNA synthesis will enable the routine generation of computationally predesigned libraries for use in display assays for the rapid discovery of GPCR binders. Advances in synthetic biology are enabling the increased throughput of functional GPCR assays to the point that they can be used to directly identify biologics that modulate GPCR activity. Finally, we give an overview of adjacent technologies that are ripe for application to discover biologics that target human GPCRs.

DiffBindFR: an SE(3) equivariant network for flexible protein-ligand docking

Molecular docking, a key technique in structure-based drug design, plays pivotal roles in protein-ligand interaction modeling, hit identification and optimization, in which accurate prediction of protein-ligand binding mode is essential. Conventional docking approaches perform well in redocking tasks with known protein binding pocket conformation in the complex state. However, in real-world docking scenario without knowing the protein binding conformation for a new ligand, accurately modeling the binding complex structure remains challenging as flexible docking is computationally expensive and inaccurate. Typical deep learning-based docking methods do not explicitly consider protein side chain conformations and fail to ensure the physical plausibility and detailed atomic interactions. In this study, we present DiffBindFR, a full-atom diffusion-based flexible docking model that operates over the product space of ligand overall movements and flexibility and pocket side chain torsion changes. We show that DiffBindFR has higher accuracy in producing native-like binding structures with physically plausible and detailed interactions than available docking methods. Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design. DiffBindFR provides a powerful flexible docking tool for modeling accurate protein-ligand binding structures.

Non-clinical investigations about cytotoxic and anti-platelet activities of gamma-terpinene

Gamma-terpinene (γ-TPN) is a cyclohexane monoterpene isolated from plant essential oils, such as tea tree (Melaleuca alternifolia), oregano (Origanum vulgare), rosemary (Rosmarinus officinalis L.), thyme (Thymus vulgaris Marchand), and eucalyptus (Eucalyptus sp.). Terpenes are widely studied molecules pharmacologically active on the cardiovascular system, hemostasis, and antioxidant actions. Herein, it was investigated the cytotoxic and antiplatelet activity of γ-TPN using different non-clinical laboratory models. For in silico evaluation, the PreADMET, SwissADME, and SwissTargetPrediction softwares were used. Molecular docking was performed using the AutoDockVina and BIOVIA Discovery Studio databases. The cytotoxicity of γ-TPN was analyzed by the MTT assay upon normal murine endothelial SVEC4-10 and fibroblast L-929 cells. Platelet aggregation was evaluated with platelet-rich (PRP) and platelet-poor (PPP) plasma from spontaneously hypertensive rats (SHR), in addition to SVEC4-10 cells pre-incubated with γ-TPN (50, 100, and 200 µM) for 24 h. SHR animals were pre-treated by gavage with γ-TPN for 7 days and divided into four groups (negative control, 25, 50, and 100 mg/kg). Blood samples were collected to measure nitrite using the Griess reagent. Gamma-TPN proved to be quite lipid-soluble (Log P = +4.50), with a qualified profile of similarity to the drug, good bioavailability, and adequate pharmacokinetics. It exhibited affinity mainly for the P2Y12 receptor (6.450 ± 0.232 Kcal/mol), moderate cytotoxicity for L-929 (CC50 = 333.3 µM) and SVEC 4-10 (CC50 = 366.7 µM) cells. The presence of γ-TPN in SVEC 4-10 cells was also able to reduce platelet aggregation by 51.57 and 44.20% at lower concentrations (50 and 100 µM, respectively). Then, γ-TPN has good affinity with purinergic receptors and an effect on the reversal of platelet aggregation and oxidative stress, being promising and safe for therapeutic targets and subsequent studies on the control of thromboembolic diseases.

Structure-based discovery of CFTR potentiators and inhibitors

The cystic fibrosis transmembrane conductance regulator (CFTR) is a crucial ion channel whose loss of function leads to cystic fibrosis, whereas its hyperactivation leads to secretory diarrhea. Small molecules that improve CFTR folding (correctors) or function (potentiators) are clinically available. However, the only potentiator, ivacaftor, has suboptimal pharmacokinetics and inhibitors have yet to be clinically developed. Here, we combine molecular docking, electrophysiology, cryo-EM, and medicinal chemistry to identify CFTR modulators. We docked ∼155 million molecules into the potentiator site on CFTR, synthesized 53 test ligands, and used structure-based optimization to identify candidate modulators. This approach uncovered mid-nanomolar potentiators, as well as inhibitors, that bind to the same allosteric site. These molecules represent potential leads for the development of more effective drugs for cystic fibrosis and secretory diarrhea, demonstrating the feasibility of large-scale docking for ion channel drug discovery.

MolModa: accessible and secure molecular docking in a web browser

Molecular docking advances early-stage drug discovery by predicting the geometries and affinities of small-molecule compounds bound to drug-target receptors, predictions that researchers can leverage in prioritizing drug candidates for experimental testing. Unfortunately, existing docking tools often suffer from poor usability, data security, and maintainability, limiting broader adoption. Additionally, the complexity of the docking process, which requires users to execute a series of specialized steps, often poses a substantial barrier for non-expert users. Here, we introduce MolModa, a secure, accessible environment where users can perform molecular docking entirely in their web browsers. We provide two case studies that illustrate how MolModa provides valuable biological insights. We further compare MolModa to other docking tools to highlight its strengths and limitations. MolModa is available free of charge for academic and commercial use, without login or registration, at https://durrantlab.com/molmoda.

Structure-based virtual screening of unbiased and RNA-focused libraries to identify new ligands for the HCV IRES model system

Targeting RNA including viral RNAs with small molecules is an emerging field. The hepatitis C virus internal ribosome entry site (HCV IRES) is a potential target for translation inhibitor development to raise drug resistance mutation preparedness. Using RNA-focused and unbiased molecule libraries, a structure-based virtual screening (VS) by molecular docking and pharmacophore analysis was performed against the HCV IRES subdomain IIa. VS hits were validated by a microscale thermophoresis (MST) binding assay and a Förster resonance energy transfer (FRET) assay elucidating ligand-induced conformational changes. Ten hit molecules were identified with potencies in the high to medium micromolar range proving the suitability of structure-based virtual screenings against RNA-targets. Hit compounds from a 2-guanidino-quinazoline series, like the strongest binder, compound 8b with an EC50 of 61 μM, show low molecular weight, moderate lipophilicity and reduced basicity compared to previously reported IRES ligands. Therefore, it can be considered as a potential starting point for further optimization by chemical derivatization.

Unveiling the Multifaceted Capabilities of Endophytic Aspergillus flavus Isolated from Annona squamosa Fruit Peels against Staphylococcus Isolates and HCoV 229E-In Vitro and In Silico Investigations

Recently, there has been a surge towards searching for primitive treatment strategies to discover novel therapeutic approaches against multi-drug-resistant pathogens. Endophytes are considered unexplored yet perpetual sources of several secondary metabolites with therapeutic significance. This study aims to isolate and identify the endophytic fungi from Annona squamosa L. fruit peels using morphological, microscopical, and transcribed spacer (ITS-rDNA) sequence analysis; extract the fungus's secondary metabolites by ethyl acetate; investigate the chemical profile using UPLC/MS; and evaluate the potential antibacterial, antibiofilm, and antiviral activities. An endophytic fungus was isolated and identified as Aspergillus flavus L. from the fruit peels. The UPLC/MS revealed seven compounds with various chemical classes. The antimicrobial activity of the fungal ethyl acetate extract (FEA) was investigated against different Gram-positive and Gram-negative standard strains, in addition to resistant clinical isolates using the agar diffusion method. The CPE-inhibition assay was used to identify the potential antiviral activity of the crude fungal extract against low pathogenic human coronavirus (HCoV 229E). Selective Gram-positive antibacterial and antibiofilm activities were evident, demonstrating pronounced efficacy against both methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive Staphylococcus aureus (MSSA). However, the extract exhibited very weak activity against Gram-negative bacterial strains. The ethyl acetate extract of Aspergillus flavus L exhibited an interesting antiviral activity with a half maximal inhibitory concentration (IC50) value of 27.2 µg/mL against HCoV 229E. Furthermore, in silico virtual molecular docking-coupled dynamics simulation highlighted the promising affinity of the identified metabolite, orienting towards three MRSA biotargets and HCoV 229E main protease as compared to reported reference inhibitors/substrates. Finally, ADME analysis was conducted to evaluate the potential oral bioavailability of the identified metabolites.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Using In Silico Molecular Docking to Explain Differences in Receptor Binding Behavior of HHC and THCV Isomers: Revealing New Binding Modes

Even slight structural differences between phytocannabinoid isomers are usually enough to cause a change in their biological properties. In this study, we used in vitro CB1 agonism/antagonism assays to compare the receptor binding functionality of THCV (tetrahydrocannabivarin) and HHC (hexahydrocannabinol) isomers and applied molecular docking to provide an explanation for the difference in the activities. No CB1 agonism was observed for ∆9- and ∆8-THCV. Instead, both isomers antagonized CP 55940, with ∆9-THCV being approximately two times more potent than the ∆8 counterpart (IC50 = 52.4 nM and 119.6 nM for ∆9- and ∆8-THCV, respectively). Docking simulations found two binding poses for THCV isomers, one very similar to ∆9-THC and one newly discovered pose involving the occupation of side pocket 1 of the CB1 receptor by the alkyl chain of the ligand. We suggested the latter as a potential antagonist pose. In addition, our results established 9R-HHC and 9S-HHC among partial agonists of the CB1 receptor. The 9R-HHC (EC50 = 53.4 nM) isomer was a significantly more potent agonist than 9S (EC50 = 624.3 nM). ∆9-THC and 9R-HHC showed comparable binding poses inside the receptor pocket, whereas 9S-HHC adopted a new and different binding posture that can explain its weak agonist activity.

A Hybrid Approach Combining Shape-Based and Docking Methods to Identify Novel Potential P2X7 Antagonists from Natural Product Databases

P2X7 is an ATP-activated purinergic receptor implicated in pro-inflammatory responses. It is associated with the development of several diseases, including inflammatory and neurodegenerative conditions. Although several P2X7 receptor antagonists have recently been reported in the literature, none of them is approved for clinical use. However, the structure of the known antagonists can serve as a scaffold for discovering effective compounds in clinical therapy. This study aimed to propose an improved virtual screening methodology for the identification of novel potential P2X7 receptor antagonists from natural products through the combination of shape-based and docking approaches. First, a shape-based screening was performed based on the structure of JNJ-47965567, a P2X7 antagonist, using two natural product compound databases, MEGx (~5.8 × 103 compounds) and NATx (~32 × 103 compounds). Then, the compounds selected by the proposed shape-based model, with Shape-Tanimoto score values ranging between 0.624 and 0.799, were filtered for drug-like properties. Finally, the compounds that met the drug-like filter criteria were docked into the P2X7 allosteric binding site, using the docking programs GOLD and DockThor. The docking poses with the best score values were submitted to careful visual inspection of the P2X7 allosteric binding site. Based on our established visual inspection criteria, four compounds from the MEGx database and four from the NATx database were finally selected as potential P2X7 receptor antagonists. The selected compounds are structurally different from known P2X7 antagonists, have drug-like properties, and are predicted to interact with key P2X7 allosteric binding pocket residues, including F88, F92, F95, F103, M105, F108, Y295, Y298, and I310. Therefore, the combination of shape-based screening and docking approaches proposed in our study has proven useful in selecting potential novel P2X7 antagonist candidates from natural-product-derived compounds databases. This approach could also be useful for selecting potential inhibitors/antagonists of other receptors and/or biological targets.

Leveraging computational tools to combat malaria: assessment and development of new therapeutics

As the world grapples with the relentless challenges posed by diseases like malaria, the advent of sophisticated computational tools has emerged as a beacon of hope in the quest for effective treatments. In this study we delve into the strategies behind computational tools encompassing virtual screening, molecular docking, artificial intelligence (AI), and machine learning (ML). We assess their effectiveness and contribution to the progress of malaria treatment. The convergence of these computational strategies, coupled with the ever-increasing power of computing systems, has ushered in a new era of drug discovery, holding immense promise for the eradication of malaria. SCIENTIFIC CONTRIBUTION: Computational tools remain pivotal in drug design and development. They provide a platform for researchers to explore various treatment options and save both time and money in the drug development pipeline. It is imperative to assess computational techniques and monitor their effectiveness in disease control. In this study we examine renown computational tools that have been employed in the battle against malaria, the benefits and challenges these tools have presented, and the potential they hold in the future eradication of the disease.

Rational Design of Drugs Targeting G-Protein-Coupled Receptors: Ligand Search and Screening

G protein-coupled receptors (GPCRs) are transmembrane proteins that participate in many physiological processes and represent major pharmacological targets. Recent advances in structural biology of GPCRs have enabled the development of drugs based on the receptor structure (structure-based drug design, SBDD). SBDD utilizes information about the receptor-ligand complex to search for suitable compounds, thus expanding the chemical space of possible receptor ligands without the need for experimental screening. The review describes the use of structure-based virtual screening (SBVS) for GPCR ligands and approaches for the functional testing of potential drug compounds, as well as discusses recent advances and successful examples in the application of SBDD for the identification of GPCR ligands.

Molecular Docking, Synthesis, and Characterization of Furanyl-Pyrazolyl Acetamide and 2,4-Thiazolidinyl-Furan-3-Carboxamide Derivatives as Neuroinflammatory Protective Agents

Microglia are key immune cells in the brain that maintain homeostasis and defend against immune threats. Targeting the dysfunctional microglia is one of the most promising approaches to inhibit neuroinflammation. In the current study, a diverse series of molecular hybrids were designed and screened through molecular docking against two neuroinflammatory targets, namely HMGB1 (2LY4) and HMGB1 Box A (4QR9) proteins. Based on the outcomes of docking scores fifteen compounds; ten furanyl-pyrazolyl acetamides 11(a-j), and five 2,4-thiazolidinyl-furan-3-carboxamide 15(a-e) derivatives were selected for further synthesis, followed by biological evaluation. The selected compounds, 11(a-j) and 15(a-e) were successfully synthesized with moderate to good yields, and structures were confirmed by IR, NMR, and mass spectra. The in-vitro cytotoxicity was evaluated on microglial cells namely BV-2, N-9, HMO6, leukemic HAP1, and human fibroblast cells. Further western-blot analysis revealed that 11h, 11f, 11c, 11j, 15d, 15c, 15e, and 15b compounds significantly suppressed anti-inflammatory markers such as TNF-α, IL-1, IL-6, and Bcl-2. All derivatives were moderate in potency compared to reference doxorubicin and could potentially act as novel anti-neuroinflammatory agents. This study can act as a beacon for further research in the application of furan-pyrazole and furan-2,4-thiazolidinediones as lead moieties for anti-neuroinflammatory and related diseases.

Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists

Introduction: 3D pharmacophore models describe the ligand's chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design. Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening. Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031. Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.

One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse

The increasing availability of experimental and computational protein structures entices their use for function prediction. Here we develop an automated procedure to identify enzymes involved in metabolic reactions by assessing substrate conformations docked to a library of protein structures. By screening AlphaFold-modeled vitamin B6-dependent enzymes, we find that a metric based on catalytically favorable conformations at the enzyme active site performs best (AUROC Score=0.84) in identifying genes associated with known reactions. Applying this procedure, we identify the mammalian gene encoding hydroxytrimethyllysine aldolase (HTMLA), the second enzyme of carnitine biosynthesis. Upon experimental validation, we find that the top-ranked candidates, serine hydroxymethyl transferase (SHMT) 1 and 2, catalyze the HTMLA reaction. However, a mouse protein absent in humans (threonine aldolase; Tha1) catalyzes the reaction more efficiently. Tha1 did not rank highest based on the AlphaFold model, but its rank improved to second place using the experimental crystal structure we determined at 2.26 Å resolution. Our findings suggest that humans have lost a gene involved in carnitine biosynthesis, with HTMLA activity of SHMT partially compensating for its function.

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening

Deep learning methods that predict protein-ligand binding have recently been used for structure-based virtual screening. Many such models have been trained using protein-ligand complexes with known crystal structures and activities from the PDBBind data set. However, because PDBbind only includes 20K complexes, models typically fail to generalize to new targets, and model performance is on par with models trained with only ligand information. Conversely, the ChEMBL database contains a wealth of chemical activity information but includes no information about binding poses. We introduce BigBind, a data set that maps ChEMBL activity data to proteins from the CrossDocked data set. BigBind comprises 583 K ligand activities and includes 3D structures of the protein binding pockets. Additionally, we augmented the data by adding an equal number of putative inactives for each target. Using this data, we developed Banana (basic neural network for binding affinity), a neural network-based model to classify active from inactive compounds, defined by a 10 μM cutoff. Our model achieved an AUC of 0.72 on BigBind's test set, while a ligand-only model achieved an AUC of 0.59. Furthermore, Banana achieved competitive performance on the LIT-PCBA benchmark (median EF1% 1.81) while running 16,000 times faster than molecular docking with Gnina. We suggest that Banana, as well as other models trained on this data set, will significantly improve the outcomes of prospective virtual screening tasks.

Regression-Based Active Learning for Accessible Acceleration of Ultra-Large Library Docking

Structure-based drug discovery is a process for both hit finding and optimization that relies on a validated three-dimensional model of a target biomolecule, used to rationalize the structure-function relationship for this particular target. An ultralarge virtual screening approach has emerged recently for rapid discovery of high-affinity hit compounds, but it requires substantial computational resources. This study shows that active learning with simple linear regression models can accelerate virtual screening, retrieving up to 90% of the top-1% of the docking hit list after docking just 10% of the ligands. The results demonstrate that it is unnecessary to use complex models, such as deep learning approaches, to predict the imprecise results of ligand docking with a low sampling depth. Furthermore, we explore active learning meta-parameters and find that constant batch size models with a simple ensembling method provide the best ligand retrieval rate. Finally, our approach is validated on the ultralarge size virtual screening data set, retrieving 70% of the top-0.05% of ligands after screening only 2% of the library. Altogether, this work provides a computationally accessible approach for accelerated virtual screening that can serve as a blueprint for the future design of low-compute agents for exploration of the chemical space via large-scale accelerated docking. With recent breakthroughs in protein structure prediction, this method can significantly increase accessibility for the academic community and aid in the rapid discovery of high-affinity hit compounds for various targets.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach

The present contribution introduces a novel computational protocol called PyRMD2Dock, which combines the Ligand-Based Virtual Screening (LBVS) tool PyRMD with the popular docking software AutoDock-GPU (AD4-GPU) to enhance the throughput of virtual screening campaigns for drug discovery. By implementing PyRMD2Dock, we demonstrate that it is possible to rapidly screen massive chemical databases and identify those with the highest predicted binding affinity to a target protein. Our benchmarking and screening experiments illustrate the predictive power and speed of PyRMD2Dock and highlight its potential to accelerate the discovery of novel drug candidates. Overall, this study showcases the value of combining AI-powered LBVS tools with docking software to enable effective and high-throughput virtual screening of ultralarge molecular databases in drug discovery. PyRMD and the PyRMD2Dock protocol are freely available on GitHub (https://github.com/cosconatilab/PyRMD) as an open-source tool.

Multitargeted molecular docking and dynamics simulation studies of flavonoids and volatile components from the peel of Citrus sinensis L. (Osbeck) against specific tumor protein markers

Citrus sinensis (L.) Osbeck (Rutaceae), commonly known as the sweet orange, is a popular and widely consumed fruit with several medicinal properties. The present study aimed to perform the in silico screening of 18 flavonoids and eight volatile components from the peel of C. sinensis against apoptotic and inflammatory proteins, metalloprotease, and tumor suppressor markers. Flavonoids obtained higher probabilities than volatile components against selected anti-cancer drug targets. Hence, the data from the binding energies against the essential apoptotic and cell proliferation proteins substantiate that they may be promising compounds in developing effective candidates to block cell growth, proliferation, and induced cell death by activating the apoptotic pathway. Further, the binding stability of the selected targets and the corresponding molecules were analyzed by 100 ns molecular dynamics (MD) simulations. Chlorogenic acid has the most binding affinity against the important anti-cancer targets iNOS, MMP-9, and p53. The congruent binding mode to different drug targets focused on cancer shown by chlorogenic acid suggests that it may be a compound with significant therapeutic potential. Moreover, the binding energy predictions indicated that the compound had stable electrostatic and van der Waal energies. Thus, our data reinforce the medicinal importance of flavonoids from C. sinensis and expand the need for more studies, seeking to optimize results and amplify the impacts of further in vitro and in vivo studies. Communicated by Ramaswamy H. Sarma.

Large library docking for cannabinoid-1 receptor agonists with reduced side effects

Large library docking can reveal unexpected chemotypes that complement the structures of biological targets. Seeking new agonists for the cannabinoid-1 receptor (CB1R), we docked 74 million tangible molecules, prioritizing 46 high ranking ones for de novo synthesis and testing. Nine were active by radioligand competition, a 20% hit-rate. Structure-based optimization of one of the most potent of these (Ki = 0.7 uM) led to '4042, a 1.9 nM ligand and a full CB1R agonist. A cryo-EM structure of the purified enantiomer of '4042 ('1350) in complex with CB1R-Gi1 confirmed its docked pose. The new agonist was strongly analgesic, with generally a 5-10-fold therapeutic window over sedation and catalepsy and no observable conditioned place preference. These findings suggest that new cannabinoid chemotypes may disentangle characteristic cannabinoid side-effects from their analgesia, supporting the further development of cannabinoids as pain therapeutics.

Thompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases

Over the last five years, virtual screening of ultralarge synthesis on-demand libraries has emerged as a powerful tool for hit identification in drug discovery programs. As these libraries have grown to tens of billions of molecules, we have reached a point where it is no longer cost-effective to screen every molecule virtually. To address these challenges, several groups have developed heuristic search methods to rapidly identify the best molecules on a virtual screen. This article describes the application of Thompson sampling (TS), an active learning approach that streamlines the virtual screening of large combinatorial libraries by performing a probabilistic search in the reagent space, thereby never requiring the full enumeration of the library. TS is a general technique that can be applied to various virtual screening modalities, including 2D and 3D similarity search, docking, and application of machine-learning models. In an illustrative example, we show that TS can identify more than half of the top 100 molecules from a docking-based virtual screen of 335 million molecules by evaluating 1% of the data set.

Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures

Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Optical Control of Cell-Surface and Endomembrane-Exclusive β-Adrenergic Receptor Signaling

Beta-adrenergic receptors (βARs) are G protein-coupled receptors (GPCRs) that mediate catecholamine-induced stress responses, such as heart rate increase and bronchodilation. In addition to signals from the cell surface, βARs also broadcast non-canonical signaling activities from the cell interior membranes (endomembranes). Dysregulation of these receptor pathways underlies severe pathological conditions. Excessive βAR stimulation is linked to cardiac hypertrophy, leading to heart failure, while impaired stimulation causes compromised fight or flight stress responses and homeostasis. In addition to plasma membrane βAR, emerging evidence indicates potential pathological implications of deeper endomembrane βARs, such as inducing cardiomyocyte hypertrophy and apoptosis, underlying heart failure. However, the lack of approaches to control their signaling in subcellular compartments exclusively has impeded linking endomembrane βAR signaling with pathology. Informed by the β1AR-catecholamine interactions, we engineered an efficiently photo-labile, protected hydroxy β1AR pro-ligand (OptoIso) to trigger βAR signaling at the cell surface, as well as exclusive endomembrane regions upon blue light stimulation. Not only does OptoIso undergo blue light deprotection in seconds, but it also efficiently enters cells and allows examination of G protein heterotrimer activation exclusively at endomembranes. In addition to its application in the optical interrogation of βARs in unmodified cells, given its ability to control deep organelle βAR signaling, OptoIso will be a valuable experimental tool.

PopShift: A Thermodynamically Sound Approach to Estimate Binding Free Energies by Accounting for Ligand-Induced Population Shifts from a Ligand-Free Markov State Model

Obtaining accurate binding free energies from in silico screens has been a long-standing goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein's thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking─producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation─and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.

DockOpt: A Tool for Automatic Optimization of Docking Models

Molecular docking is a widely used technique for leveraging protein structure for ligand discovery, but it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress toward automation, docking still requires expert guidance, hindering its adoption by a broader range of investigators. To make docking more accessible, we developed a new utility called DockOpt, which automates the creation, evaluation, and optimization of docking models prior to their deployment in large-scale prospective screens. DockOpt outperforms our previous automated pipeline across all 43 targets in the DUDE-Z benchmark data set, and the generated models for 84% of targets demonstrate sufficient enrichment to warrant their use in prospective screens, with normalized LogAUC values of at least 15%. DockOpt is available as part of the Python package Pydock3 included in the UCSF DOCK 3.8 distribution, which is available for free to academic researchers at https://dock.compbio.ucsf.edu and free for everyone upon registration at https://tldr.docking.org.

DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

While significant advances have been made in predicting static protein structures, the inherent dynamics of proteins, modulated by ligands, are crucial for understanding protein function and facilitating drug discovery. Traditional docking methods, frequently used in studying protein-ligand interactions, typically treat proteins as rigid. While molecular dynamics simulations can propose appropriate protein conformations, they're computationally demanding due to rare transitions between biologically relevant equilibrium states. In this study, we present DynamicBind, a deep learning method that employs equivariant geometric diffusion networks to construct a smooth energy landscape, promoting efficient transitions between different equilibrium states. DynamicBind accurately recovers ligand-specific conformations from unbound protein structures without the need for holo-structures or extensive sampling. Remarkably, it demonstrates state-of-the-art performance in docking and virtual screening benchmarks. Our experiments reveal that DynamicBind can accommodate a wide range of large protein conformational changes and identify cryptic pockets in unseen protein targets. As a result, DynamicBind shows potential in accelerating the development of small molecules for previously undruggable targets and expanding the horizons of computational drug discovery.

Exploring the combinatorial explosion of amine-acid reaction space via graph editing

Amines and carboxylic acids are abundant chemical feedstocks that are nearly exclusively united via the amide coupling reaction. The disproportionate use of the amide coupling leaves a large section of unexplored reaction space between amines and acids: two of the most common chemical building blocks. Herein we conduct a thorough exploration of amine-acid reaction space via systematic enumeration of reactions involving a simple amine-carboxylic acid pair. This approach to chemical space exploration investigates the coarse and fine modulation of physicochemical properties and molecular shapes. With the invention of reaction methods becoming increasingly automated and bringing conceptual reactions into reality, our map provides an entirely new axis of chemical space exploration for rational property design.

Computational Insights into the Interaction of Pinostrobin with Bcl-2 Family Proteins: A Molecular Docking Analysis

BACKGROUND

Cancer research has emphasized the Bcl-2 family of proteins because of their interaction in apoptosis process, a critical mechanism that regulates cellular survival and death. Recently small molecules from diverse sources have gained much attention in anticancer research due to their promising inhibitory action against Bcl-2 and Bcl-XL that are pointedly known as the members of anti-apoptotic Bcl-2 family of proteins. Pinostrobin (PN) is a natural flavonoid with diverse pharmacological potential emerged as a molecule of interest as anticancer agent. The present study aims to screen the interaction of PN with anti-apoptotic protagonists Bcl-2 and Bcl- XL at the molecular level through docking studies.

METHOD

The molecular docking was performed using the Schrodinger software. The docking score of PN with the Bcl-2 (4IEH) and Bcl-XL (3ZK6) and their molecular interactions was examined and analysed.

RESULTS

The result of the molecular docking analysis showed that PN and the anti-apoptotic proteins 4IEH and 3ZK6 had significant interactions and docking energy scores (ΔG) were found to be -5.112 kcal/mol and -7.822 kcal/mol respectively. The small molecule PN illustrated effective interaction with the active site amino acids of the Bcl-2 and Bcl-XL proteins and has been associated through traditional hydrogen bond with 4IEH. Further, it was observed that PN and anti-apoptotic Bcl-2 proteins interaction was stabilized by other non-covalent interactions, such as π-alkyl or π-π interactions and van der Waals forces.

CONCLUSIONS

This was the first study to reveal the inhibitory action of PN against anti-apoptotic Bcl-2 and Bcl-XL proteins at the molecular level. The findings of this study concludes that PN ability to inhibit anti-apoptotic proteins, Bcl-2 and Bcl-XL could be useful to induce intracellular apoptosis in tumorous cells.

Drug discovery: In silico dry data can bypass biological wet data?

The recent and extraordinary increase in computer power, along with the availability of efficient algorithms based on artificial intelligence, has prompted a large number of inexperienced scientists to challenge the complex and yet competitive world of drug discovery, by pretending to identify new hits through the sole use of computer aided drug design (CADD). Does the golden era of dry data run the risk of overshadowing the importance of wet data and, in doing so, forget that in silico and biological data need each other in successful preclinical drug discovery programmes?

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Novel Polystyrene-Binding Nanobody for Enhancing Immunoassays: Insights into Affinity, Immobilization, and Application Potential

Nanobodies, which represent the next generation of antibodies due to their unique properties, face a significant limitation in their poor physical adsorption on solid supports. In this study, we successfully discovered polystyrene binding nanobodies from a synthetic nanobody library. Notably, bivalent nanobody B2 exhibited high affinity for polystyrene (0.7 nM for ELISA saturation binding analysis and 15.6 nM for isothermal titration calorimetry), displaying a pH-dependent behavior. Remarkably, hydrophobic and electrostatic interactions contribute minimally to the binding process. Molecular modeling provided insights into the interaction between B2 and polystyrene, revealing that the Trp51 residue within the CDR2 loop formed an aromatic H-bond with polystyrene at a distance of 2.74 Å, thus explaining the observed reduction in B2 affinity caused by Trp51 mutations. To explore B2's potential in protein immobilization, we constructed a bispecific nanobody by fusing B2 to an anticarcinoembryonic antigen nanobody 11C12, which cannot be immobilized on polystyrene through passive adsorption. Remarkably, the fusion construct achieved effective immobilization on polystyrene within 5 min by passing the need for periplasmic protein purification despite its low expression level. Moreover, the fusion construct demonstrated excellent linearity in the chemiluminescent enzyme immunoassay. For the first time, this study reports a simplified and seamless platform for the oriented immobilization of nanobody. Importantly, the entire process eliminated the need for protein purification, enabling efficient and rapid immobilization of fusion proteins directly from crude cell extracts, even when the expression level was low. Our developed process dramatically reduced the processing time from 2.5 days to just 5 min.

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Repurposing dye ligands as antivirals via a docking approach on viral membrane and globular proteins - SARS-CoV-2 and HPV-16

A series of dye ligands are docked to three different proteins, E and 3a of severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) and E6 of human papilloma virus type 16 (HPV-16) using three different software. A four-level selection algorithm is used based on nonparametric statistics of numerical key values such as the "rank" derived from (i) averaged estimated binding energies (EBEs) and (ii) absolute EBE value of each of the software, (iii) frequency of ranking and (iv) rank of the area-under-curve values (AUCs) from decoy docking. A series of repurposing drugs and known antivirals used in experimental studies are docked for comparison. One dye ligand is ranked best for all proteins using the selection algorithm levels i - iii. Another three dye ligands are ranked top for the proteins individually when using all four levels.

Considerations Around Structure-Based Drug Discovery for KRAS Using DOCK

Molecular docking is a popular computational tool in drug discovery. Leveraging structural information, docking software predicts binding poses of small molecules to cavities on the surfaces of proteins. Virtual screening for ligand discovery is a useful application of docking software. In this chapter, using the enigmatic KRAS protein as an example system, we endeavor to teach the reader about best practices for performing molecular docking with UCSF DOCK. We discuss methods for virtual screening and docking molecules on KRAS. We present the following six points to optimize our docking setup for prosecuting a virtual screen: protein structure choice, pocket selection, optimization of the scoring function, modification of sampling spheres and sampling procedures, choosing an appropriate portion of chemical space to dock, and the choice of which top scoring molecules to pick for purchase.

HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries

Recent rapid expansion of make-on-demand, purchasable, chemical libraries comprising dozens of billions or even trillions of molecules has challenged the efficient application of traditional structure-based virtual screening methods that rely on molecular docking. We present a novel computational methodology termed HIDDEN GEM (HIt Discovery using Docking ENriched by GEnerative Modeling) that greatly accelerates virtual screening. This workflow uniquely integrates machine learning, generative chemistry, massive chemical similarity searching and molecular docking of small, selected libraries in the beginning and the end of the workflow. For each target, HIDDEN GEM nominates a small number of top-scoring virtual hits prioritized from ultra-large chemical libraries. We have benchmarked HIDDEN GEM by conducting virtual screening campaigns for 16 diverse protein targets using Enamine REAL Space library comprising 37 billion molecules. We show that HIDDEN GEM yields the highest enrichment factors as compared to state of the art accelerated virtual screening methods, while requiring the least computational resources. HIDDEN GEM can be executed with any docking software and employed by users with limited computational resources.

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries

Summary

The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking and generative AI, have greatly expanded the chemical diversity of small molecules for drug discovery. Selecting compounds for experimental validation requires filtering these molecules based on favourable druglike properties, such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET). We developed ADMET-AI, a machine learning platform that provides fast and accurate ADMET predictions both as a website and as a Python package. ADMET-AI has the highest average rank on the TDC ADMET Benchmark Group leaderboard, and it is currently the fastest web-based ADMET predictor, with a 45% reduction in time compared to the next fastest ADMET web server. ADMET-AI can also be run locally with predictions for one million molecules taking just 3.1 hours.

Availability and Implementation

The ADMET-AI platform is freely available both as a web server at admet.ai.greenstonebio.com and as an open-source Python package for local batch prediction at github.com/swansonk14/admet_ai (also archived on Zenodo at doi.org/10.5281/zenodo.10372930 ). All data and models are archived on Zenodo at doi.org/10.5281/zenodo.10372418 .

Calculation of Protein Folding Thermodynamics Using Molecular Dynamics Simulations

Despite advances in artificial intelligence methods, protein folding remains in many ways an enigma to be solved. Accurate computation of protein folding energetics could help drive fields such as protein and drug design and genetic interpretation. However, the challenge of calculating the state functions governing protein folding from first-principles remains unaddressed. We present here a simple approach that allows us to accurately calculate the energetics of protein folding. It is based on computing the energy of the folded and unfolded states at different temperatures using molecular dynamics simulations. From this, two essential quantities (ΔH and ΔCp) are obtained and used to calculate the conformational stability of the protein (ΔG). With this approach, we have successfully calculated the energetics of two- and three-state proteins, representatives of the major structural classes, as well as small stability differences (ΔΔG) due to changes in solution conditions or variations in an amino acid residue.

Structure-based molecular docking and molecular dynamics simulations study for the identification of dipeptidyl peptidase 4 inhibitors in type 2 diabetes

Inhibition of dipeptidyl peptidase-4 (DPP4) activity has emerged as a promising therapeutic approach for the treatment of type 2 diabetes mellitus (T2DM). Bioinformatics-driven approaches have emerged as crucial tools in drug discovery. Molecular docking and molecular dynamics (MD) simulations are effective tools in drug discovery, as they reduce the time and cost associated with experimental screening. In this study, we employed structure-assisted in-silico methods, including molecular docking and MD simulations, to identify SRT2183, a small molecule that may potentially inhibit the activity of DPP4 enzyme. The interaction between the small molecule "SRT2183" and DPP4 exhibited a binding affinity of -9.9 Kcal/Mol, leading to the formation of hydrogen bonds with the amino acid residues MET348, SER376, and THR351 of DPP4. The MD simulations over a period of 100 ns indicated stable protein-ligand interactions, with no significant conformational rearrangements observed within the simulated timeframe. In conclusion, our results suggest that the small molecule SRT2183 may have the potential to inhibit the DPP4 enzyme and pave the way for the therapeutics of T2DM.Communicated by Ramaswamy H. Sarma.

Exploring the Fatty Acid Binding Pocket in the SARS-CoV-2 Spike Protein - Confirmed and Potential Ligands

Severe Acute Respiratory syndrome 2 (SARS-CoV-2) is a respiratory virus responsible for coronavirus disease 19 (COVID-19) and the still ongoing and unprecedented global pandemic. The key viral protein for cell infection is the spike glycoprotein, a surface-exposed fusion protein that both recognizes and mediates entry into host cells. Within the spike glycoprotein, a fatty acid binding pocket (FABP) was confirmed, with the crystallization of linoleic acid (LA) occupying a well-defined site. Importantly, when the pocket is occupied by a fatty acid, an inactive conformation is stabilized, and cell recognition is hindered. In this review, we discuss ligands reported so far for this site, correlating their activity predicted through in silico studies with antispike experimental activity, assessed by either binding assays or cell-infection assays. LA was the first confirmed ligand, cocrystallized in a cryo-EM structure of the spike protein, resulting in increased stability of the inactive conformation of the spike protein. The next identified ligand, lifitegrast, was also experimentally confirmed as a ligand with antiviral activity, suggesting the potential for diverse chemical scaffolds to bind this site. Finally, SPC-14 was also confirmed as a ligand, although no inhibition assays were performed. In this review, we identified 20 studies describing small-molecule compounds predicted to bind the pocket in in silico studies and with confirmed binding or in vitro activity, either inhibitory activity against the spike-ACE2 interaction or antiviral activity in cell-based assays. When considering all ligands confirmed with in vitro assays, a good overall occupation of the pocket should be complemented with the ability to make direct interactions, both hydrophilic and hydrophobic, with key amino acid residues defining the pocket surface. Among the active compounds, long flexible carbon chains are recurrent, with retinoids capable of binding the FABP, although bulkier systems are also capable of affecting viral fitness. Compounds able to bind this site with high affinity have the potential to stabilize the inactive conformation of the SARS-CoV-2 spike protein and therefore reduce the virus's ability to infect new cells. Since this pocket is conserved in highly pathogenic human coronaviruses, including MERS-CoV and SARS-CoV, this effect could be exploited for the development of new antiviral agents, with broad-spectrum anticoronavirus activity.

Docking for EP4R antagonists active against inflammatory pain

The lipid prostaglandin E2 (PGE2) mediates inflammatory pain by activating G protein-coupled receptors, including the prostaglandin E2 receptor 4 (EP4R). Nonsteroidal anti-inflammatory drugs (NSAIDs) reduce nociception by inhibiting prostaglandin synthesis, however, the disruption of upstream prostanoid biosynthesis can lead to pleiotropic effects including gastrointestinal bleeding and cardiac complications. In contrast, by acting downstream, EP4R antagonists may act specifically as anti-inflammatory agents and, to date, no selective EP4R antagonists have been approved for human use. In this work, seeking to diversify EP4R antagonist scaffolds, we computationally dock over 400 million compounds against an EP4R crystal structure and experimentally validate 71 highly ranked, de novo synthesized molecules. Further, we show how structure-based optimization of initial docking hits identifies a potent and selective antagonist with 16 nanomolar potency. Finally, we demonstrate favorable pharmacokinetics for the discovered compound as well as anti-allodynic and anti-inflammatory activity in several preclinical pain models in mice.