Virtual Screening Research Articles

Computational identification of promising therapeutics via BACE1 Targeting: Implications for Alzheimer's disease.

Alzheimer's disease (AD) is a significant global healthcare challenge, particularly in the elderly population. This neurodegenerative disorder is characterized by impaired memory and progressive decline in cognitive function. BACE1, a transmembrane protein found in neurons, oligodendrocytes, and astrocytes, exhibits varying levels across different neural subtypes. Abnormal BACE1 activity in the brains of individuals with AD leads to the formation of beta-amyloid proteins. The complex interplay between myelin sheath formation, BACE1 activity, and beta-amyloid accumulation suggests a critical role in understanding the pathological mechanisms of AD. The primary objective of this study was to identify molecular inhibitors that target Aβ. Structure-based virtual screening (SBVS) was employed using the MCULE database, which houses over 2 million chemical compounds. A total of 59 molecules were selected after the toxicity profiling. Subsequently, five compounds conforming to the Egan-Egg permeation predictive model of the ADME rules were selected and subjected to molecular docking using AutoDock Vina on the Mcule drug discovery platform. The top two ligands and the positive control, 5HA, were subjected to molecular dynamics simulation for five nanoseconds. Toxicity profiling, physiochemical properties, lipophilicity, solubility, pharmacokinetics, druglikeness, medicinal chemistry attributes, average potential energy, RMSD, RMSF, and Rg analyses were conducted to identify the ligand MCULE-9199128437-0-2 as a promising inhibitor of BACE1.

Computational insights into inhibiting EphA2: Integrating structure-based virtual screening, docking, and molecular dynamics simulations for small molecule discovery.

Elevated expression and dysfunction of ephrin type A receptor-2 (EphA2) have been implicated in the initiation and progression of cancer, metastasis, and unfavorable clinical outcomes. A promising strategy to counteract this dysregulation involves the development of small-molecule inhibitors that target EphA2. Our study focuses on this objective. To initiate Structure-Based Virtual Screening (SBVS), we leveraged an advanced online platform, the Mcule database, which houses an extensive collection of millions of chemical compounds. Using drug similarity filters, we efficiently identified ten thousand potential hits. By further refining the selection through toxicity profiling, we prudently narrowed down the candidates to a more manageable set of 100 molecules. Using the Mcule Single Click, DockThor, and SwissDock tools, we conducted multi-scoring docking assessments of thirty-seven compounds that satisfied the ADME standards. A comprehensive evaluation of Gibbs binding free energy terms, as derived from these docking tools, facilitated the identification of top-ranking docking hits. Remarkably, among the known inhibitors, dasatinib displayed the most robust binding to EphA2 with an average ΔG of -9.0 kcal/mol. Intriguingly, alternatives have emerged in recent years. Notably, small molecules such as Mcule-1579910267 (ΔG: -9.3 kcal/mol), Mcule-1893218381 (ΔG: -9.2 kcal/mol), Mcule-3981378344 (ΔG: -9.3 kcal/mol), and Mcule-8617639093 (ΔG: -9.1 kcal/mol) exhibited a notably strong binding affinity to EphA2, rivaling dasatinib. Subsequently, the four leading ligands along with dasatinib were selected for the MD simulations. Our rigorous analyses during the MD simulation phase encompassing RMSD, RMSF, SASA, ΔGsolv, and Rg underscored the favorable stability of Mcule-8617639093. This compelling evidence ultimately signifies the potential for selective EphA2 inhibition.

Multi-property prediction and high-throughput screening of polyimides: An application case for interpretable machine learning

Polyimide (PI), as a high-performance polymer widely used in aerospace, optoelectronics, microelectronics, etc., the properties it focused on in different areas of application were diverse. However, most of the past machine learning studies in polyimide property prediction were focused only on the prediction of a single property. This study focused on four major categories of properties of polyimide, including thermal (Tg, Td5, Td10, and CTE), mechanical (Ts and TM), dielectric (ε), and optical (λcutoff, T400, nav, and Δn), totaling eleven properties. PI data synthesized from previous studies were collected. MorGan fingerprints, improved MorGan fingerprints, RDkit and Mordred descriptors were selected as feature representations. Four ML models such as DNN, RF, XGBoost and BT were also built. Collectively, 176 machine learning models were trained for 11 predictions of properties. The performance and generalization ability of the models were confirmed by experimental validation, external validation and leave-one-out cross-validation. SHAP analysis was used to explain the optimal model for each property prediction from a physicochemical point of view and structural aspects, and three PIs with different structures were designed accordingly. Finally, a high-throughput virtual screening of nearly 7.6 million polyimides was performed based on the trained model. SA_scores was used to evaluate the ease of synthesis of each PI, and finally high-performance PIs with potential and easy to synthesize were selected for each field. This study could be expected to provide a guideline and a design framework for the application of PIs in various fields in the future.

Targeting LIMK1 in Alzheimer's Disease: A Multifaceted Computational Investigation Involving ADMET, Virtual Screening, Molecular Docking, and Molecular Dynamics

LIM domain kinases (LIMKs), which include LIMK1 and LIMK2, are key proteins in actin dynamics. On this basis, the inhibition of LIMK1 enhances dendritic spine density and size in dementia, reducing Alzheimer's disease (AD) effects. Therefore, several small molecules were discovered as potential therapeutic targets for AD. Herein, a pharmacophore-based virtual screening was employed to identify novel potential LIMK1 inhibitors. The pharmacophore model derived from the co-crystallized receptor structure of PubChem-329823760: LIMK1 (PDB ID: 5NXC) was then used for virtual screening. After applying Lipinski's rules and pharmacophore filters, 29 potential hits were identified. Molecular docking simulations were performed to determine the binding affinities of these candidates against LIMK1, with results ranging from -5.20 to -10.60 kcal/mol. Notably, PubChem-136621040 showed the highest binding affinity against the target protein, with a docking score of -10.60 kcal/mol, slightly surpassing the native ligand, PubChem-329823760, possessing a lower docking score of -9.80 kcal/mol. The drug-likeness and toxicity properties of target compounds were assessed through ADMET evaluations. A series of 75 nanosecond molecular dynamics (MD) simulations were conducted on the complexes generated by the best-docked molecule and the native ligand. RMSD, RMSF, SASA, and Rg calculations of their trajectories were also calculated. PubChem-136621040 possessed an average RMSD value of 0.23 nm, lower than the native ligand's 0.31 nm, indicating a greater binding stability. The RMSF results also revealed that the best-docked compound had a lower value (0.10 nm), while the native ligand possessed a value of 0.12 nm. The SASA values for both the native ligand and the best-docked compound were nearly identical, at 150.20 nm2 and 150.80 nm2, respectively. The Rg results demonstrated that both complexes maintained their rigidity throughout the simulation, with similar average values of 2.04 nm for the native ligand and 2.06 nm for the best-docked compound.

Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE.

Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

Teaching old docks new tricks with machine learning enhanced ensemble docking

We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound’s spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

An artificial intelligence accelerated virtual screening platform for drug discovery

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

Antimicrobial activity of compounds identified by artificial intelligence discovery engine targeting enzymes involved in Neisseria gonorrhoeae peptidoglycan metabolism

BackgroundNeisseria gonorrhoeae (Ng) causes the sexually transmitted disease gonorrhoea. There are no vaccines and infections are treated principally with antibiotics. However, gonococci rapidly develop resistance to every antibiotic class used and there is a need for developing new antimicrobial treatments. In this study we focused on two gonococcal enzymes as potential antimicrobial targets, namely the serine protease L,D-carboxypeptidase LdcA (NgO1274/NEIS1546) and the lytic transglycosylase LtgD (NgO0626/NEIS1212). To identify compounds that could interact with these enzymes as potential antimicrobials, we used the AtomNet virtual high-throughput screening technology. We then did a computational modelling study to examine the interactions of the most bioactive compounds with their target enzymes. The identified compounds were tested against gonococci to determine minimum inhibitory and bactericidal concentrations (MIC/MBC), specificity, and compound toxicity in vitro.ResultsAtomNet identified 74 compounds that could potentially interact with Ng-LdcA and 84 compounds that could potentially interact with Ng-LtgD. Through MIC and MBC assays, we selected the three best performing compounds for both enzymes. Compound 16 was the most active against Ng-LdcA, with a MIC50 value < 1.56 µM and MBC50/90 values between 0.195 and 0.39 µM. In general, the Ng-LdcA compounds showed higher activity than the compounds directed against Ng-LtgD, of which compound 45 had MIC50 values of 1.56–3.125 µM and MBC50/90 values between 3.125 and 6.25 µM. The compounds were specific for gonococci and did not kill other bacteria. They were also non-toxic for human conjunctival epithelial cells as judged by a resazurin assay. To support our biological data, in-depth computational modelling study detailed the interactions of the compounds with their target enzymes. Protein models were generated in silico and validated, the active binding sites and amino acids involved elucidated, and the interactions of the compounds interacting with the enzymes visualised through molecular docking and Molecular Dynamics Simulations for 50 ns and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA).ConclusionsWe have identified bioactive compounds that appear to target the N. gonorrhoeae LdcA and LtgD enzymes. By using a reductionist approach involving biological and computational data, we propose that compound Ng-LdcA-16 and Ng-LtgD-45 are promising anti-gonococcal compounds for further development.

In Silico Structure-Activity Study of Selected Triterpenoids as Potential Inhibitors of Mycolic Acid Transporter of Mycobacterial MmpL3 Receptor Protein

Structural features of the triterpenoid skeleton that are necessary for antimycobacterial activity are not well understood. Following the isolation of the triterpenoids ergosterol-5,8-endoperoxide, 6β-hydroxykulactone, 12β-hydroxykulactone, (24R)-24,25-epoxycycloartan-3-one and (3β,24R)-24,25-epoxycycloartan-3-ol, and (3β,24R)-24,25-epoxycycloartan-3-acetate with varying antimycobacterial activity ranging between MIC of 1 µg/ml to128 µg/ml prompted us to study this class of compounds further to shade light on the structural features necessary for their antimycobacterial activity. This in silico study involved docking the triterpenoids on the mycobacterial multi-pharmacophore receptor protein MmpL3. The docking results were compared with the MmpL3 receptor protein co-crystallized TB drug candidate, AU1235, (1-(2-adamantyl)-3-[2,3,4-tris(fluoranyl)phenyl] urea). The virtual screening revealed key structural features in the triterpenoid skeleton, including the C-3 keto and β-hydroxy group on C-3 or C-6, as important for antimycobacterial activity. Also, the decreased binding affinity for compounds 2 and 7 with an acetate group on C-3 were in tandem with those observed in vitro. Toxicity predictions revealed that this class of compounds had no mutagenic effects and displayed favorable pharmacokinetic parameters. The study reveals the potential of the triterpenoid skeleton exemplified by the readily available ergosterol-5,8-endoperoxide as a useful scaffold in searching and developing effective therapeutic lead entities to facilitate anti-tuberculosis drug discovery.

In silico approach for identification of potential tetracyclic triterpenoids from mushroom as HMG-CoA reductase inhibitor

Cardiovascular disease is estimated to be responsible for one-third of all global deaths annually. It occurs mostly due to hyperlipidemia, a condition where excessive cholesterol deposits in blood vessels. A favorable target for treating hyperlipidemia involves the crucial role of inhibition of a specific enzyme known as 3-Hydroxy-3-methylglutaryl coenzyme A reductase (HMG-CoA reductase). The primary goal of this present study is to identify potential HMG-CoA reductase inhibitors containing tetracyclic triterpene nucleus derived from mushrooms. A library of 86 myco-constituents bearing a tetracyclic triterpene scaffold was prepared and screened to identify potential HMG-CoA reductase inhibitors targeting proteins 1HW8 and 1HW9. For this purpose, molecular docking, ADME prediction, and molecular dynamics (MD) simulation studies were performed on this in-house prepared database. The virtual screening results exhibited M_02(c) as the best hit with promising SP Glide scores compared to standard statin drugs. In order to assess the stability and interactions, a 100 ns MD simulation was performed. Further, M_02(c) was also analysed for MMGBSA binding energy to access and validate the thermodynamic stability of the protein-ligand complex. The results of this study revealed that M_02(c) is a promising hit molecule and may emerge as a potent HMG-CoA reductase inhibitor in preventing and treating hyperlipidemia.

AI-Based Discovery and CryoEM Structural Elucidation of a KATP Channel Pharmacochaperone.

Pancreatic KATP channel trafficking defects underlie congenital hyperinsulinism (CHI) cases unresponsive to the KATP channel opener diazoxide, the mainstay medical therapy for CHI. Current clinically used KATP channel inhibitors have been shown to act as pharmacochaperones and restore surface expression of trafficking mutants; however, their therapeutic utility for KATP trafficking impaired CHI is hindered by high-affinity binding, which limits functional recovery of rescued channels. Recent structural studies of KATP channels employing cryo-electron microscopy (cryoEM) have revealed a promiscuous pocket where several known KATP pharmacochaperones bind. The structural knowledge provides a framework for discovering KATP channel pharmacochaperones with desired reversible inhibitory effects to permit functional recovery of rescued channels. Using an AI-based virtual screening technology AtomNet® followed by functional validation, we identified a novel compound, termed Aekatperone, which exhibits chaperoning effects on KATP channel trafficking mutations. Aekatperone reversibly inhibits KATP channel activity with a half-maximal inhibitory concentration (IC50) ~ 9 μM. Mutant channels rescued to the cell surface by Aekatperone showed functional recovery upon washout of the compound. CryoEM structure of KATP bound to Aekatperone revealed distinct binding features compared to known high affinity inhibitor pharmacochaperones. Our findings unveil a KATP pharmacochaperone enabling functional recovery of rescued channels as a promising therapeutic for CHI caused by KATP trafficking defects.

Innovative virtual screening of PD-L1 inhibitors: the synergy of molecular similarity, neural networks and GNINA docking.

Aims: Immune checkpoint inhibitors targeting PD-L1 are crucial in cancer research for preventing cancer cells from evading the immune system.Materials & methods: This study developed a screening model combining ANN, molecular similarity, and GNINA 1.0 docking to target PD-L1. A database of 2044 substances was compiled from patents.Results: For molecular similarity, the AVALON emerged as the most effective fingerprint, demonstrating an AUC-ROC of 0.963. The ANN model outperformed the Random Forest andSupport Vector Classifier in cross-validation and external validation, achieving an average precision of 0.851 and an F1 score of 0.790. GNINA 1.0 was validated through redocking and retrospective control, achieving an AUC of 0.975.Conclusions: From 15235 DrugBank compounds, 22 candidates were shortlisted. Among which (3S)-1-(4-acetylphenyl)-5-oxopyrrolidine-3-carboxylic acid emerged as the most promising.

Discovery of Aldehyde Dehydrogenase as a Potential Fungicide Target and Screening of its Natural Inhibitors against Fusarium verticillioides.

Fusarium verticillioides is the primary pathogen causing ear rot and stalk rot in corn (Zea mays). It not only affects yields but also produces mycotoxins endangering both human and animal health. Aldehyde dehydrogenase (ALDH) is essential for the oxidation of aldehydes in living organisms, making it a potential target for human drug design. However, there are limited reports on its function in plant pathogenic fungus. In this study, we analyzed the expression levels and gene knockout mutants, revealing that ALDH genes FvALDH-43 and FvALDH-96 in F. verticillioides played significant roles in pathogenicity and resistance to low-temperature stress by affecting antioxidant capacity. Virtual screening for natural product inhibitors and molecular docking were performed targeting FvALDH-43 and FvALDH-96. Following the biological activity analysis, three natural flavonoid compounds featuring a 2-hydroxyphenol chromene were identified. Among these, Taxifolin exhibited the highest biological activity and low toxicity. Both in vitro and in vivo biological evaluations confirmed that Taxifolin targeted ALDH and inhibited its activity. These findings indicate that aldehyde dehydrogenase may serve as a promising target for the design of novel fungicides.

Integrating computational 3D-QSAR and pharmacophore modelling in diverse natural phytochemicals library for targeted therapy against breast cancer estrogen receptors

Breast cancer stands as a pervasive global health challenge, profoundly impacting the physical and emotional well-being of individuals. The relentless growth of malignant cells within breast tissues necessitates comprehensive research into the disease's origin, early detection, and treatment strategies. This study focused on constructing pharmacophores and 3D-QSAR models to dissect the crucial structure attributes essential for inhibiting Estrogen receptors. By employing ligand-based pharmacophore modeling and Atom-based-3D-QSAR techniques within the Schrödinger Phase module, we analyzed the inhibitory activity of 202 natural compounds and Tamoxifen as the control drug. To identify promising candidates, five molecules (Genistein, Coumestrol, Apigenin, Emodin, and Daidzein) were strategically chosen based on prediction from the 3D-QSAR pharmacophore model. These molecules are further assessed through model validation and virtual screening. The generated pharmacophore model (ADHR.10) reveals key features like hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), and hydrophobic interactions. The resulting 3D-QSAR model predicted and stability was validated through the Xternal validation plus tool. The ADHR.10 model guided the systematic screening of natural compounds, resulting in the identification of the top five hits with promising pharmacological activity. Molecular docking scores demonstrated the compound's binding affinity with ESR1 (PDB ID: 6PSJ) and ESR2 (PDB ID: 1QKM). One standout compound, Coumestrol, displayed an exceptional binding affinity with ESR2 outperforming Tamoxifen. The top compounds exhibited both strong binding and stability, making them a promising candidate. Furthermore, Molecular Dynamics Simulation (MDS) demonstrated Coumestrol's superior stability compared to the control drug against the ESR1 and ERS2 proteins. Coumestrol exhibits lower average fluctuation in RMSD and residue-specific dynamics (RMSF) indicating stability than control. Specifically, designed compounds, rooted in natural sources, demonstrated excellent potential as breast cancer treatment candidates. These findings highlight them as promising lead candidates for the development of targeted breast cancer therapies and emphasize the potential of computational approaches in discovering new therapeutic avenues.

In silico study exploiting chromosomal replication initiator protein as a molecular target for new antibiotics in Shigella dysenteriae

Introduction: DNA chromosomal replication initiator protein, DnaA is a component of bacterial division machinery. Objetives: virtual screening of phytochemicals for new antibiotics at novel molecular targets. Materials and Methods: SWISS-Model tool was used in homology modeling of this protein and validation tools were also used to check for its accuracy. The evaluation tools were ERRAT, PROCHECK and Molprobity servers. The refined model by GalaxyWEB server, then employed in molecular docking experiments. A total of 300 natural products were used in virtual screening for lead compounds targeting the model by AutoDock Vina software. Results and Discussion: Corylin among then best ligands that have binding affinities lower than ADP as control. Interactions in the form of hydrogen bonds and hydrophobic interactions were analyzed by LigPlot. The best compounds were submitted into iGEMDOCK docking tool for consensus scoring approach. admetSAR 2.0 was used to predict for pharmacokinetics, interactions and toxicities of these ligands inside the human body. Conclusions: Therefore, preliminary screening of lead compounds must be accompanied by studying pharmacologic properties.

Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint.

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), and their combinations, are frequently conducted in modern drug discovery campaigns. As a form of combination, an amalgamation of methods from ligand- and structure-based information, termed hybrid VS approaches, has been extensively investigated such as using interaction fingerprints (IFPs) in combination with machine learning (ML) models. This approach has the potential to prioritize active compounds in terms of protein-ligand binding and ligand structural characteristics, which is assumed to be difficult using either one of the approaches. Herein, we present an IFP, named the fragmented interaction fingerprint (FIFI), for hybrid VS approaches. FIFI is constructed from the extended connectivity fingerprint atom environments of a ligand proximal to the protein residues in the binding site. Each unique ligand substructure within each amino acid residue is encoded as a bit in FIFI while retaining sequence order. From the retrospective evaluation of activity prediction using a limited number and variety of active compounds for six biological targets, FIFI consistently showed higher prediction accuracy than that using previously proposed IFPs. For the same data sets, the screening performance of LBVS, SBVS sequential VS, parallel VS, and other hybrid VS approaches was investigated. Compared to these approaches, FIFI in combination with ML showed overall stable and high prediction accuracy, except for one target: the kappa opioid receptor, where the extended connectivity fingerprint combined with ML models showed better performance than other approaches by wide margins.

Identification of potential NUDT5 inhibitors from marine bacterial natural compounds via molecular dynamics and free energy landscape analysis.

NUDIX hydrolase 5 (NUDT5) is an enzyme involved in the hydrolysis of nucleoside diphosphates linked to other moieties, such as ADP-ribose. This cofactor is vital in redox reactions and is essential for the activity of sirtuins and poly(ADP-ribose) polymerases, which are involved in DNA repair and genomic stability. It has been shown that NUDT5 activity can also influence NAD+ homeostasis, thereby affecting cancer cell metabolism and survival. In this regard, the discovery of NUDT5 inhibitors has emerged as a potential therapeutic approach in cancer treatment. In this study, we conducted a high-throughput virtual screening of marine bacterial compounds against the NUDT5 enzyme and four molecules were selected based on their docking scores. These compounds established strong interactions within the NUDT5 active site, with molecular analysis highlighting the key role of Trp28A and Trp46B residues. Molecular dynamics simulations over 200 ns indicated a stable behavior, in association with root mean square deviation values always below 3 Å, suggesting conformational stability. Free energy landscape analysis further supported their potential as NUDT5 inhibitors, offering avenues for novel therapeutic strategies against NUDT5-associated breast cancer.

Structure-Based Virtual Screening and Protein–Protein Docking Analysis of ERBB2 and Associated Proteins for Pediatric Cancer Therapeutic Approaches

Abstract Introduction The Erythroblastosis Oncogene B homolog 2 (ERBB2) protein, also known as human epidermal growth factor receptor 2 (HER2), is a key player in cancer growth, especially in neuroblastoma and gastric cancers. Targeting ERBB2 has led to successful therapies, making it an important focus in cancer research with the potential to improve treatment for HER2-positive cancers. Objective The primary goal of this research is to employ a multifaceted computational approach to identify potential drug candidates targeting ERBB2. We aim to combine virtual screening, protein–protein docking, and functional partner prediction to provide insights into the molecular interactions and potential efficacy of the identified compounds. Additionally, we intend to assess the safety profiles of these compounds using advanced toxicity prediction tools. Methods Relevant protein sequence and structural data for ERBB2 and epidermal growth factor receptor (EGFR) were sourced from publicly available databases. Potential inhibitors from the Enamine and LifeChemicals databases were identified through virtual screening using AutoDock Vina. Functional partners of ERBB2 were explored using STRING, KEGG, and REACTOME servers. The identified compounds were subjected to toxicity prediction using the ProTox-II server. Results Virtual screening led to the selection of 10 compounds with favorable binding energies (–8.346 to –6.296 kcal/mol) and specific amino acid interactions (Thr5, Arg412, Leu414, and Ser441) with the receptor. On the other hand, EGFR was identified as the best functional partner for ERBB2. The EGFR residues Gln408, Lys463, Phe412, and Asp436 found key residues for the complex formation. The toxicity prediction analysis revealed that the majority of compounds exhibited acceptable safety profiles, although a subset of compounds showed lower prediction scores, suggesting the need for further consideration. Conclusion This comprehensive computational approach, integrating virtual screening, protein–protein docking, functional partner identification, and toxicity prediction, offers a systematic framework for efficient drug discovery. The identification of potential lead compounds targeting ERBB2, with emphasis on both binding affinity and safety, underscores the significance of such an approach in streamlining the drug development process. By prioritizing compounds with promising efficacy, functional relevance, and acceptable toxicity profiles, this study advances our understanding of potential therapeutic agents, enhancing the likelihood of successful translation from computational predictions to real-world drug candidates.

Ligand and structure-based virtual screening approaches in drug discovery: minireview.

The compilation of ligand and structure-based molecular modeling methods has become an important practice in virtual screening applied to drug discovery. This systematic review addresses and ranks various virtual screening strategies to drive the selection of the optimal method for studies that have as their starting point a multi-ligand investigation and investigation based on the protein structure of a therapeutic target. This study shows examples of applications and an evaluation based on the objective and problematic of a series of virtual screening studies present in the ScienceDirect® database. The results showed that the molecular docking technique is widely used in scientific production, indicating that approaches that use protein structure as a starting point are the most promising strategy for drug discovery that relies on virtual screening-based research.

Unraveling the molecular basis for effective regulation of integrin α5β1 for enhanced therapeutic interventions

Cell attachment to the extracellular matrix significantly impacts the integrity of tissues and human health. The integrin α5β1 is a heterodimer of α5 and β1 subunits and has been identified as a crucial modulator in several human carcinomas. Integrin α5β1 significantly regulates cell proliferation, angiogenesis, inflammation, tumor metastasis, and invasion. This regulatory role of integrin α5β1 in tumor metastasis makes it an appealing target for cancer therapy. The majority of the drugs targeting integrin α5β1 are limited only to clinical trials. In our study, we have performed 94287 compounds screening to determine potential drugs against α5β1 integrin. We have used ATN-161 as a reference and employed combined bioinformatic methodologies, including molecular modelling, virtual screening, MM-GBSA, cell-line cytotoxicity prediction, ADMET, Density Functional Theory (DFT), Non-covalent Interactions (NCI) and molecular simulation, to identify putative integrin α5β1 inhibitors. We found Taxifolin, PD133053, and Acebutolol that possess inhibitory activity against α5β1 integrin and could act as effective drug for the cancer treatment. Taxifolin, PD133053, and Acebutolol exhibited excellent binding to the druggable pocket of integrin α5β1, and also maintained a unique binding mechanism with extra hydrophobic contacts at molecular level. Overall, our study gives new pharmacological candidates that may act as a potential drug against integrin α5β1.