Hit Identification Research Articles

Assessing the Performance of GOLD, Glide and MM-GBSA on a Dataset of Hydrazide-hydrazone-based Tuberculostatics

Background: Tuberculosis is considered a global health problem; hence, the screening and synthesis of novel tuberculostatic drugs are a necessity. Molecular docking could drastically reduce the time of hit identification; however, initial validation is required to reduce the false-positive results. Objective: Assessment of several searching and scoring algorithms for a custom dataset of hydrazidehydrazone- based tuberculostatics was conducted to obtain a reliable docking protocol for future virtual screening. Methods: Modification in the scoring functions, size of the grid space, and presence of active waters of a GOLD 5.3 docking protocol was conducted. Subsequently, side-chain flexibility and ensemble docking were carried out to assess the role of protein flexibility in the correlation coefficient. In addition, docking simulations with Glide and free binding energy calculations with MM-GBSA were implemented. The Pearson correlation coefficient between the experimental and the acquired in silico data was calculated after each work step. The major interactions between the top-scored ligands and the active site of 2X22 were visualized applying Discovery Studio. Results: An optimized GOLD 5.3 docking protocol led to a drastically enhanced Pearson correlation coefficient of the training set, from 0.461 to 0.823, as well as an excellent pairwise correlation coefficient in the test set - 0,8405. Interestingly, the Glide docking scores and the free binding energy calculations with MM-GBSA did not achieve reliable results. During the visualization of the top-ranked compounds, it was observed that Lys165 played a major role in the formation of stable complexes. Conclusion: It could be concluded that the performance of the optimized GOLD 5.3 docking protocol demonstrated significantly higher reliability against the hydrazide-hydrazone dataset when compared to Glide docking simulations and MM-GBSA free binding energy calculations. The results could be utilized for future virtual screenings.

Backbone 1H, 15N and 13C resonance assignments for an E2 ubiquitin conjugating enzyme-UBE2T.

Ubiquitin-conjugating enzyme E2 T (UBE2T) plays important roles in ubiquitination of proteins through participation in transferring ubiquitin to its substrate. Due to its importance in protein modifications, UBE2T associates with diverse diseases and serves as an important target for drug discovery and development. The crystal structure of UBE2T has been determined and the structure reveals the lack of a druggable pocket for binding to small molecules for clinical applications. Despite the challenge, effort has been made to develop UBE2T inhibitors. We obtained UBE2T constructs with and without the C-terminal region which is flexible in solution. Herein, we report the backbone resonance assignments for human UBE2T without the C-terminal region. The backbone dynamics of UBE2T was also explored. The available assignments will be helpful for hit identification, determining ligand binding site and understanding the mechanism of action of UBE2T inhibitors.

De Novo Generation of Chemical Structures of Inhibitor and Activator Candidates for Therapeutic Target Proteins by a Transformer-Based Variational Autoencoder and Bayesian Optimization.

Deep generative models for molecular generation have been gaining much attention as structure generators to accelerate drug discovery. However, most previously developed methods are chemistry-centric approaches, and comprehensive biological responses in the cell have not been taken into account. In this study, we propose a novel computational method, TRIOMPHE-BOA (transcriptome-based inference and generation of molecules with desired phenotypes using the Bayesian optimization algorithm), to generate new chemical structures of inhibitor or activator candidates for therapeutic target proteins by integrating chemically and genetically perturbed transcriptome profiles. In the algorithm, the substructures of multiple molecules that were selected based on the transcriptome analysis are fused in the design of new chemical structures by exploring the latent space of a Transformer-based variational autoencoder using Bayesian optimization. Our results demonstrate the usefulness of the proposed method in terms of having high reproducibility of existing ligands for 10 therapeutic target proteins when compared with previous methods. Moreover, this method can be applied to proteins without detailed 3D structures or known ligands and is expected to become a powerful tool for more efficient hit identification.

Target immobilization on glass microfiber membranes as a label-free strategy for hit identification.

The discovery of novel chemical entities targeting G protein-coupled receptors (GPCRs) is usually guided by their receptor affinity. However, traditional affinity assay methods and hit identification procedures are usually laborious and expensive. In this work, the type-2 vasopressin receptor (V2R) was chosen as a prototypical GPCR. Membrane fragments from cells highly expressing SNAP-V2R were immobilized on the surface of a glass microfiber (GMF) coated with O6-benzylguanine (BG). This was achieved by transferring the benzyl group of BG to the active site of the SNAP-tag through a nucleophilic substitution reaction. As a result, a biofilm called SNAP-V2R@GMF-BG was produced that showed good specificity and stability. The adsorption ratio for each V2R ligand treated with SNAP-V2R@GMF-BG was determined by HPLC and exhibited a good linear correlation with the Ki value determined by displacement assays. Furthermore, a Ki prediction assay was performed by comparing the data with that generated by a homogeneous time-resolved fluorescence (HTRF) assay. SNAP-V2R@GMF-BG was also used to screen hit compounds from natural products. After SNAP-V2R@GMF-BG was incubated with the total extract, the ligand that binds to V2R could be separated and subjected to LC‒MS analysis for identification. Baicalein was screened from Clerodendranthus spicatus and verified as a potential V2R antagonist. This V2R-immobilized GMF platform can help determine the affinity of V2R-binding hit compounds and screen the compounds efficiently and accurately.

Targeting MDM2-p53 Axis through Drug Repurposing for Cancer Therapy: A Multidisciplinary Approach.

Cancer remains a major cause of morbidity and mortality worldwide, and while current therapies, such as chemotherapy, immunotherapy, and cell therapy, have been effective in many patients, the development of novel therapeutic options remains an urgent priority. Mouse double minute 2 (MDM2) is a key regulator of the tumor suppressor protein p53, which plays a critical role in regulating cellular growth, apoptosis, and DNA repair. Consequently, MDM2 has been the subject of extensive research aimed at developing novel cancer therapies. In this study, we employed a machine learning-based approach to establish a quantitative structure-activity relationship model capable of predicting the potential in vitro efficacy of small molecules as MDM2 inhibitors. Our model was used to screen 5883 FDA-approved drugs, resulting in the identification of promising hits that were subsequently evaluated using molecular docking and molecular dynamics simulations. Two antihistamine drugs, cetirizine (CZ) and rupatadine (RP), exhibited particularly favorable results in the initial in silico analyses. To further assess their potential use as the activators of the p53 pathway, we investigated the antiproliferative capability of the abovementioned drugs on human glioblastoma and neuroblastoma cell lines. Both the compounds exhibited significant antiproliferative effects on the abovementioned cell lines in a dose-dependent manner. The half-maximal inhibitory concentration (IC50) of CZ was found to be 697.87 and 941.37 μM on U87 and SH-SY5Y cell lines, respectively, while the IC50 of RP was found to be 524.28 and 617.07 μM on the same cell lines, respectively. Further investigation by quantitative reverse transcriptase polymerase chain reaction analysis revealed that the CZ-treated cell lines upregulate the expression of the p53-regulated genes involved in cell cycle arrest, apoptosis, and DNA damage response compared to their respective vehicle controls. These findings suggest that CZ activates the p53 pathway by inhibiting MDM2. Our results provide compelling preclinical evidence supporting the potential use of CZ as a modulator of the MDM2-p53 axis and its plausible repurposing for cancer treatment.

Multiplexed experimental strategies for fragment library screening against challenging drug targets using SPR biosensors

Surface plasmon resonance (SPR) biosensor methods are ideally suited for fragment-based lead discovery. However, generally applicable experimental procedures and detailed protocols are lacking, especially for structurally or physico-chemically challenging targets or when tool compounds are not available. Success depends on accounting for the features of both the target and the chemical library, purposely designing screening experiments for identification and validation of hits with desired specificity and mode-of-action, and availability of orthogonal methods capable of confirming fragment hits. The range of targets and libraries amenable to an SPR biosensor-based approach for identifying hits is considerably expanded by adopting multiplexed strategies, using multiple complementary surfaces or experimental conditions. Here we illustrate principles and multiplexed approaches for using flow-based SPR biosensor systems for screening fragment libraries of different sizes (90 and 1056 compounds) against a selection of challenging targets. It shows strategies for the identification of fragments interacting with 1) large and structurally dynamic targets, represented by acetyl choline binding protein (AChBP), a Cys-loop receptor ligand gated ion channel homologue, 2) targets in multi protein complexes, represented by lysine demethylase 1 and a corepressor (LSD1/CoREST), 3) structurally variable or unstable targets, represented by farnesyl pyrophosphate synthase (FPPS), 4) targets containing intrinsically disordered regions, represented by protein tyrosine phosphatase 1B (PTP1B), and 5) aggregation-prone proteins, represented by an engineered form of human tau (tau K18M). Practical considerations and procedures accounting for the characteristics of the proteins and libraries, and that increase robustness, sensitivity, throughput and versatility are highlighted. The study shows that the challenges for addressing these types of targets is not identification of potentially useful fragments per se, but establishing methods for their validation and evolution into leads.

Identification of hits as anti-obesity agents against human pancreatic lipase via docking, drug-likeness, in-silico ADME(T), pharmacophore, DFT, molecular dynamics, and MM/PB(GB)SA analysis

Obesity, characterized by excessive fat accumulation, is a major health concern. Inhibition of human pancreatic lipase, an enzyme involved in fat digestion, offers a potential strategy for weight loss and obesity treatment. This study aimed to identify polyphenols capable of forming stable complexes with human pancreatic lipase to block its activity. Molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and MMPBGBSA calculations were employed to evaluate ligand binding, stability, and energy profiles. Pharmacophore modeling was also performed to identify key structural features for effective inhibition. Virtual screening identified ZINC000015120539, ZINC000000899200, ZINC000001531702, and ZINC000013340267 as potential candidates, exhibiting favorable binding and stable interactions over 100 ns MD simulations. These findings provide insights into the inhibitory potential of selected polyphenols on human pancreatic lipase and support further experimental investigations for obesity treatment. Communicated by Ramaswamy H. Sarma

Free Fatty Acid Receptors (FFARs): Emerging Therapeutic Targets for the Management of Diabetes Mellitus.

Free fatty acids (FFAs) present in our dietary fats not only act as vital nutrients but also function as signalling molecules and modulate key biological functions through their active involvement in a multitude of energy metabolism pathways. However, it has been reported that excessive intake of dietary fat contributes to the development of different types of Diabetes mellitus. Free fatty acid receptors are the key regulators of most metabolic disorders. Among them, diabetes mellitus is a severe growing disorder and found in every corner of the world. For various metabolic disorders, particularly type 2 diabetes mellitus, these different free fatty acid receptors are being explored as drug targets. In the present review, various FFAs sensing G-protein coupled receptors (GPR) like GPR40 (FFAR1), GPR43 (FFAR2), GPR41 (FFAR3), GPR120 (FFAR4), and GPR84 are being explored as emerging novel therapeutic targets for antidiabetic drugs. Additionally, this review has covered pre-clinical discovery and development of different selective ligands targeted to these receptors starting from hit identification to lead optimization via chemical modification and the challenges and tactics selected by different medicinal chemists to improve potency, physicochemical properties, safety profiles, and pharmacokinetics of different FFAR agonists for making a potential drug candidate. Several molecules have been withdrawn in the clinical trials without reporting any reasons. We believe that this review will help the researchers to find a new direction in the discovery of new antidiabetic drugs.

Exploring the medicinal potential of Dark Chemical Matters (DCM) to design promising inhibitors for PLpro of SARS-CoV-2 using molecular screening and simulation approaches

The growing concerns and cases of COVID-19 with the appearance of novel variants i.e., BA.2.75. BA.5 and XBB have prompted demand for more effective treatment options that could overcome the risk of immune evasion. For this purpose, discovering novel small molecules to inhibit druggable proteins such as PLpro required for viral pathogenesis, replication, survival, and spread is the best choice. Compounds from the Dark chemical matter (DCM) database is consistently active in various screening tests and offer intriguing possibilities for finding drugs that are extremely selective or active against uncommon targets. Considering the essential role of PLpro, the current study uses DCMdatabase for the identification of potential hits using in silico virtual molecular screening and simulation approaches to inhibit the current and emerging variants of SARS-CoV-2. Our results revealed the 10 best compounds with docking scores between −7.99 to −7.03 kcal/mol better than the control drug (GRL0617) among which DC 5977–0726, DC 6623–2024, DC C879-0379 and DC D135-0154 were observed as the best hits. Structural-dynamics properties such as dynamic stability, protein packing, and residue flexibility demonstrated the pharmacologically favorable properties of these top hits in contrast to GRL0617. The hydrogen bonding half-life revealed that Asp164, Arg166, Tyr264, and Tyr268 have major contributions to the hydrogen bonding during the simulation. However, some of the important hydrogen bonds were missing in the control drug (GRL0617). Finally, the total binding free energy was reported to be −34.41 kcal/mol for GRL0617 (control), −41.03 kcal/mol for the DC5977-0726-PLpro, for the DC6623-2024-Plpro complex the TBE was −48.87 kcal/mol, for the for DCC879-0379-Plpro complex the TBE was −45.66 kcal/mol while for the DCD135-0154-PLpro complex the TBE was calculated to be −40.09 kcal/mol respectively, which shows the stronger potency of these compounds against PLpro and further in in vivo and in vitro test are required for the possible usage as potential drug against SARS-CoV-2.

Identification of Potential Hits against Fungal Lysine Deacetylase Rpd3 via Molecular Docking, Molecular Dynamics Simulation, DFT, In-Silico ADMET and Drug-Likeness Assessment

Identification of Potential Hits against Fungal Lysine Deacetylase Rpd3 via Molecular Docking, Molecular Dynamics Simulation, DFT, In-Silico ADMET and Drug-Likeness Assessment

Novel starting points for fragment-based drug design against mycobacterial thioredoxin reductase identified using crystallographic fragment screening.

The increasing number of people dying from tuberculosis and the existence of extensively drug-resistant strains has led to an urgent need for new antituberculotic drugs with alternative modes of action. As part of the thioredoxin system, thioredoxin reductase (TrxR) is essential for the survival of Mycobacterium tuberculosis (Mtb) and shows substantial differences from human TrxR, making it a promising and most likely selective target. As a model organism for Mtb, crystals of Mycobacterium smegmatis TrxR that diffracted to high resolution were used in crystallographic fragment screening to discover binding fragments and new binding sites. The application of the 96 structurally diverse fragments from the F2X-Entry Screen revealed 56 new starting points for fragment-based drug design of new TrxR inhibitors. Over 200 crystal structures were analyzed using FragMAXapp, which includes processing and refinement by largely automated software pipelines and hit identification via the multi-data-set analysis approach PanDDA. The fragments are bound to 11 binding sites, of which four are positioned at binding pockets or important interaction sites and therefore show high potential for possible inhibition of TrxR.

Identification of 5-HT2 Serotonin Receptor Modulators through the Synthesis of a Diverse, Tropane- and Quinuclidine-alkaloid-Inspired Compound Library.

The recombination of natural product (NP) fragments in unprecedented ways has emerged as an important strategy for bioactive compound discovery. In this context, we propose that privileged primary fragments predicted to be enriched in activity against a specific target class can be coupled to diverse secondary fragments to engineer selectivity among closely related targets. Here, we report the synthesis of an alkaloid-inspired compound library enriched in spirocyclic ring fusions, comprising 58 compounds from 12 tropane- or quinuclidine-containing scaffolds, all of which can be considered pseudo-NPs. The library displays excellent predicted drug-like properties including high Fsp3 content and Lipinski's rule-of-five compliance. Targeted screening against selected members of the serotonin and dopamine G protein-coupled receptor family led to the identification of several hits that displayed significant agonist or antagonist activity against 5-HT2A and/or 5-HT2C, and subsequent optimization of one of these delivered a lead dual 5-HT2B/C antagonist with a highly promising selectivity profile.

Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study.

Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the ID4Inno drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC50 is ∼0.80 μM) against PI5P4K-β, a novel anti-cancer target. Furthermore, by applying the tools implemented in ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-β. These results demonstrate the effectiveness of ID4inno in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.

Deep Learning Approach for the Discovery of Tumor-Targeting Small Organic Ligands from DNA-Encoded Chemical Libraries.

DNA-Encoded Chemical Libraries (DELs) have emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against carbonic anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell renal cell carcinoma. We present a new ligand identification and hit-to-lead strategy driven by machine learning models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX-screening datasets obtained from three different DELs were used to train machine learning models for generating novel hits, dissimilar to elements present in the original DELs. Out of the 152 novel potential hits that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC50 < 1 μM). To generate lead compounds that are functionalized with anticancer payloads, analogues of top hits were prioritized for synthesis based on the predicted CAIX affinity and synthetic feasibility. Three lead candidates showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed an in vitro KD of 5.7 nM and selectively targeted tumors in mice bearing human renal cell carcinoma lesions. Our results demonstrate the synergy between DEL and machine learning for the identification of novel hits and for the successful translation of lead candidates for in vivo targeting applications.

Rational repurposing, synthesis, in vitro and in silico studies of chromone-peptidyl hybrids as potential agents against Leishmania donovani

A chromone-peptidyl hybrids series was synthesised and rationally repurposed towards identification of potential antileishmanial hits against visceral leishmaniasis. Three hybrids 7c, 7n, and 7h showed potential IC50 values (9.8, 10, and 12 µM, respectively) which were comparable to erufosine IC50 (9.8 µM) but lower potency than miltefosine IC50 (3.5 µM). Preliminary assessment of cytotoxicity using human THP-1 cells presented chromone-peptidyl hybrids 7c and 7n as non-cytotoxic up to 100 µM while erufosine and miltefosine had CC50 of 19.4 µM and >40 µM, respectively. In silico studies pinpointed the N-p-methoxyphenethyl substituent at the peptidyl moiety together with the oxygen-based substituted functions of the phenyl ring of the chromone moiety as crucial players in binding to LdCALP. Together, these findings present chromone-peptidyl hybrids 7c and 7n as potential and anticipated non-cytotoxic antileishmanial hit compounds for possible development of potential antileishmanial agents against visceral leishmaniasis.

De Novo Design of Molecules with Multiaction Potential from Differential Gene Expression using Variational Autoencoder.

The modulating effect of chemical compounds and therapeutics on gene transcription is well-reported and has been intensively studied for both clinical and research purposes. Emerging research points toward the utility of drug-induced transcriptional alterations in de novo molecular design and highlights the idea of phenotype-matching an expression signature of interest to the structures being designed. In this work, we build an autoencoder-based generative model, BiCEV, around this concept. Our generative autoencoder has demonstrably generated a set of new molecules from gene expression input with notable validity (96%), uniqueness (98%), and internal diversity (0.77). Further, we attempted to validate BiCEV by testing the model on gene-knockdown profiles and combined signatures of synergistic drug pairs. From these investigations, we found the designed structures to be consistently high in collective quality. However, when their similarities to the supposed functional equivalents as determined by shared targets were considered, the findings were somewhat mixed. In spite of this, we believe the generative model merits further development in conjunction with in vitro corroboration to lend itself to being an assistive tool for drug discovery experts, particularly to support the initial stages of hit identification and lead optimization.

Application of automated machine learning in the identification of multi-target-directed ligands blocking PDE4B, PDE8A, and TRPA1 with potential use in the treatment of asthma and COPD.

Asthma and COPD are characterized by complex pathophysiology associated with chronic inflammation, bronchoconstriction, and bronchial hyperresponsiveness resulting in airway remodeling. A possible comprehensive solution that could fully counteract the pathological processes of both diseases are rationally designed multi-target-directed ligands (MTDLs), combining PDE4B and PDE8A inhibition with TRPA1 blockade. The aim of the study was to develop AutoML models to search for novel MTDL chemotypes blocking PDE4B, PDE8A, and TRPA1. Regression models were developed for each of the biological targets using "mljar-supervised". On their basis, virtual screenings of commercially available compounds derived from the ZINC15 database were performed. A common group of compounds placed within the top results was selected as potential novel chemotypes of multifunctional ligands. This study represents the first attempt to discover the potential MTDLs inhibiting three biological targets. The obtained results prove the usefulness of AutoML methodology in the identification of hits from the big compound databases.

Micelle-Mediated Sonogashira Coupling for DNA-Encoded Library Synthesis.

DNA-Encoded Libraries (DELs) are becoming widely established as a hit identification strategy for drug discovery campaigns. Their successful application relies on the availability and efficiency of the reactions that can be carried out on DNA. These reactions should proceed with high conversion to the desired product and have a broad substrate scope to synthesise chemically diverse and drug-like DELs. The Sonogashira coupling provides a unique means of coupling an sp-hybridized carbon centre to an aryl halide and methods to achieve this reaction on DNA are highly desirable. We report the application of our micellar technology for on-DNA chemistry to the Sonogashira reaction. This method gives highly efficient conversions for the coupling of (hetero)aromatic and aliphatic alkynes to (hetero)aryl iodides and bromides allowing the preparation of highly diverse DELs.

Hit identification of novel small molecules interfering with MALAT1 triplex by a structure-based virtual screening.

Nowadays, RNA is an attractive target for the design of new small molecules with different pharmacological activities. Among several RNA molecules, long noncoding RNAs (lncRNAs) are extensively reported to be involved in cancer pathogenesis. In particular, the overexpression of lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays an important role in the development of multiple myeloma (MM). Starting from the crystallographic structure of the triple-helical stability element at the 3'-end of MALAT1, we performed a structure-based virtual screening of a large commercial database, previously filtered according to the drug-like properties. After a thermodynamic analysis, we selected five compounds for the in vitro assays. Compound M5, characterized by a diazaindene scaffold, emerged as the most promising molecule enabling the destabilization of the MALAT1 triplex structure and antiproliferative activity on in vitro models of MM. M5 is proposed as a lead compound to be further optimized for improving its affinity toward MALAT1.

Identification of new hit to lead magmas inhibitors as potential therapeutics for glioblastoma

In continuation of our previous efforts for the development of potent small molecules against brain cancer, herein we synthesized seventeen new compounds and tested their anti-gliomapotential against established glioblastoma cell lines, namely, D54MG, U251, and LN-229 as well as patient derived cell lines (DB70 and DB93). Among them, the carboxamide derivatives, BT-851 and BT-892 were found to be the most active leads in comparison to our established hit compound BT#9.The SAR studies of our hit BT#9 compound resulted in the development of two new lead compounds by hit to lead strategy. The detailed biological studies are currently underway. The active compounds could possibly act as template for the future development of newer anti-glioma agents.