Structure-based Virtual Screening Approach Research Articles

Structure-Guided Discovery of a Potent Inhibitor of the Ferric Citrate Binding Protein FecB in Vibrio Bacteria.

Infections caused by Gram-negative bacteria present a significant risk to human health worldwide. Novel strategies are needed to deal with the challenge caused by drug-resistant bacteria. Here, we report a new approach to combat infections by targeting iron-binding proteins to suppress bacterial growth. We investigated the function of the conserved periplasmic binding protein FecB from Vibrio alginolyticus. FecB was known to play a crucial role in the bacterial growth and to relate with biofilm formation. We then solved the crystal structures and elucidated the binding mechanism of FecB with ferric ion chelated by citrate. The results indicated that FecB binds weakly to one citrate molecule and strongly to the Fe3+-(citrate)2 complex. Based on these results, a structure-based virtual screening approach was conducted against FecB to identify small molecules that block ferric citrate uptake. Further evaluations in vivo and in vitro demonstrated that salvianolic acid C significantly suppressed bacterial growth, indicating that targeting bacterial nutrient absorption is a promising strategy for identifying potential antibacterial drugs.

Structure-based virtual screening and drug repurposing studies indicate potential inhibitors of bovine papillomavirus E6 oncoprotein.

Bovine papillomavirus type 1 (BPV1) is an oncogenic virus that causes lesions and cancer in infected cattle. Despite being one of the most studied genotypes in the family and occurring in herds worldwide, there are currently no vaccines or drugs for its control. The viral E6 oncoprotein plays a crucial role in infection by this virus, making it a promising target for the development of new therapies. In this regard, we integrated structure-based virtual screening approaches, drug repositioning, and molecular dynamics to identify approved drugs with the potential to inhibit BPV1 E6. Our results reveal that Lumacaftor and MK-3207 are promising candidates for controlling BPV1 infection. The findings of this study may contribute to the development of E6 oncoprotein blockers in an accelerated and cost-effective manner.

Computational assessment of targeting angiotensin-converting enzyme for hypertension management: A structure-based virtual screening approach

Background: Hypertension is a growing public health concern globally. The angiotensin-converting enzyme (ACE) is an enzyme that cleaves the carboxy-terminal His-Leu dipeptide from angiotensin I, yielding the potent vasopressor octapeptide, angiotensin II. ACE inhibitors are a primary treatment option for hypertension, heart failure, and myocardial infarction. However, the use of synthetic ACE inhibitors has been linked to a number of side effects. Therefore, the development of novel and safe ACE inhibitors is a need of time.Methods: This study used a computational screening of a library of known compounds with anti-inflammatory activities against the active site of ACE using the PyRx-Python 0.8 tool to find more potent ACE inhibitors with less or no side effects. The physicochemical properties of the anti-inflammatory compounds were obtained from the Life Chemicals website.Result: The five hits, specifically F3398-2114, F0193-0245, F0163-0089, F0193-0237, and F0302-0060, exhibited notable interactions within the ACE binding pocket and demonstrated greater binding energy compared to the control compound, Lisinopril. All of these compounds displayed favorable physicochemical characteristics and aligned to Lipinski's rule.Conclusion: The compounds F3398-2114, F0193-0245, F0163-0089, F0193-0237, and F0302-0060 have the potential to be used as ACE inhibitors; however, further experimental validation is required to optimize them as ACE inhibitors.Keywords: Hypertension; Angiotensin-converting enzyme; Heart failure; Lipinski's rule

A structure-based virtual screening identifies a novel MDM2 antagonist in the activation of the p53 signaling and inhibition of tumor growth.

p53, a tumor suppressor protein, has a vital role in the regulation of the cell cycle, apoptosis, and DNA damage repair. The degradation of p53 is predominantly controlled by the murine double minute 2 (MDM2) protein, a ubiquitin E3 ligase. The overexpression or amplification of MDM2 is commonly observed in various human cancers bearing wild-type p53 alleles, leading to the rapid degradation of the p53 protein and the attenuation of p53 tumor suppression functions. Thus, a major effort in p53-based cancer therapy has been to research MDM2 antagonists that specifically stabilize and activate p53, leading to the suppression of tumor growth. However, despite numerous efforts to develop MDM2 antagonists, to date they have failed to reach clinical use, largely because of the cytotoxicity associated with these small molecules. This study used our newly designed structure-based virtual screening approach on a commercial compound library to identify a novel compound, CGMA-Q18, which directly binds to MDM2, leading to the activation of p53, the induction of apoptosis, and cell cycle arrest in cancer cells. Notably, CGMA-Q18 significantly inhibited tumor xenograft growth in nude mice without observable toxicity. These findings highlight our useful virtual screening protocol and CGMA-Q18 as a putative MDM2 antagonist.

Harnessing marine natural products to inhibit PAD4 triple mutant: A structure-based virtual screening approach for rheumatoid arthritis therapy

Peptidylarginine deiminase type4 (PAD4) is a pivotal pro-inflammatory protein within the human immune system, intricately involved in both inflammatory processes and immune responses. Its role extends to the generation of diverse immune cell types, including T cells, B cells, natural killer cells, and dendritic cells. PAD4 has recently garnered attention due to its association with a spectrum of inflammatory and autoimmune disorders, notably rheumatoid arthritis (RA). Mutations in the PAD4 gene, leading to the conversion of arginine to citrulline, have emerged as significant factors in the pathogenesis of RA and related conditions. As a calcium-dependent enzyme, PAD4 is central to the citrullination process, a crucial post-translational modification implicated in disease pathophysiology. Its critical role in autoimmune disorders and inflammation makes PAD4 a prime candidate for therapeutic intervention in RA. Inhibiting PAD4 presents a promising avenue for mitigating inflammatory responses and curtailing joint degradation and impairment. To explore its therapeutic potential, a structure-based virtual screening (SBVS) approach was employed, harnessing an array of marine natural products (MNPs) sourced from databases such as CMNPD, MNPD, and Seaweed. Notably, MNPD10752, CMNPD12680, and CMNPD2751 emerged as potential hit molecules, exhibiting adherence to essential pharmacokinetic properties and favorable toxicity profiles. Quantum mechanics studies using density functional theory (DFT) calculations revealed the inhibitory potential of these identified natural products. Further structural elucidation through molecular dynamics simulations (MDS) and principal component-based free energy landscape (FEL) analysis shed light on the stability of MNP-bound PAD4 complexes. In conclusion, this computational study serves as a stepping stone for further experimental evaluation, aiming to explore the potential of MNPs in addressing PAD4-related human pathologies.

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

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

Discovering Dually Active Anti-cancer Compounds with a Hybrid AI-structure-based Approach.

Cancer's persistent growth often relies on its ability to maintain telomere length and tolerate the accumulation of DNA damage. This study explores a computational approach to identify compounds that can simultaneously target both G-quadruplex (G4) structures and poly(ADP-ribose) polymerase (PARP)1 enzyme, offering a potential multipronged attack on cancer cells. We employed a hybrid virtual screening (VS) protocol, combining the power of machine learning with traditional structure-based methods. PyRMD, our AI-powered tool, was first used to analyze vast chemical libraries and to identify potential PARP1 inhibitors based on known bioactivity data. Subsequently, a structure-based VS approach selected compounds from these identified inhibitors for their G4 stabilization potential. This two-step process yielded 50 promising candidates, which were then experimentally validated for their ability to inhibit PARP1 and stabilize G4 structures. Ultimately, four lead compounds emerged as promising candidates with the desired dual activity and demonstrated antiproliferative effects against specific cancer cell lines. This study highlights the potential of combining Artificial Intelligence and structure-based methods for the discovery of multitarget anticancer compounds, offering a valuable approach for future drug development efforts.

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.

Structure-based drug-development study against fibroblast growth factor receptor 2: molecular docking and Molecular dynamics simulation approaches

Developing new therapeutic strategies to target specific molecular pathways has become a primary focus in modern drug discovery science. Fibroblast growth factor receptor 2 (FGFR2) is a critical signaling protein involved in various cellular processes and implicated in numerous diseases, including cancer. Existing FGFR2 inhibitors face limitations like drug resistance and specificity issues. In this study, we present an integrated structure-based bioinformatics analysis to explore the potential of FGFR2 inhibitors-like compounds from the PubChem database with the Tanimoto threshold of 80%. We conducted a structure-based virtual screening approach on a dataset comprising 2336 compounds sourced from the PubChem database. Primarily, the selection of promising compounds was based on several criteria, such as drug-likeness, binding affinities, docking scores, and selectivity. Further, we conducted all-atom molecular dynamics (MD) simulations for 200 ns, followed by an essential dynamics analysis. Finally, a promising FGFR2 inhibitor with PubChem CID:507883 (1-[7-(1H-benzimidazol-2-yl)-4-fluoro-1H-indol-3-yl]-2-(4-benzoylpiperazin-1-yl)ethane-1,2-dione) was screened out from the study. This compound indicates a higher potential for inhibiting FGFR2 than the control inhibitor, Zoligratinib. The identified compound, CID:507883 shows >80% structural similarity with Zoligratinib. ADMET analysis showed promising pharmacokinetic potential of the screened compound. Overall, the findings indicate that the compound CID:507883 may have promising potential to serve as a lead candidate against FGFR2 and could be further exploited in therapeutic development.

Structure-based Virtual Screening and Molecular Dynamic Simulation Approach for the Identification of Terpenoids as Potential DPP-4 Inhibitors.

Diabetes mellitus is a metabolic disorder where insulin secretion is compromised, leading to hyperglycemia. DPP-4 is a viable and safer target for type 2 diabetes mellitus. Computational tools have proven to be an asset in the process of drug discovery. In the present study, tools like structure-based virtual screening, MM/GBSA, and pharmacokinetic parameters were used to identify natural terpenoids as potential DPP-4 inhibitors for treating diabetes mellitus. Structure-based virtual screening, a cumulative mode of elimination technique, was adopted, identifying the top five potent hit compounds depending on the docking score and nonbonding interactions. According to the docking data, the most important contributors to complex stability are hydrogen bonding, hydrophobic interactions, and Pi-Pi stacking interactions. The dock scores ranged from -6.492 to -5.484 kcal/mol, indicating robust ligand-protein interactions. The pharmacokinetic characteristics of top-scoring hits (CNP0309455, CNP0196061, CNP0122006, CNP0 221869, CNP0297378) were also computed in this study, confirming their safe administration in the human body. Also, based on the synthetic accessibility score, all top-scored hits are easily synthesizable. Compound CNP0309455 was quite stable during molecular dynamic simulation studies. Virtual database screening yielded new leads for developing DPP-4 inhibitors. As a result, the findings of this study can be used to design and develop natural terpenoids as DPP-4 inhibitors for the medication of diabetes mellitus.

Combined structure-based virtual screening and machine learning approach for the identification of potential dual inhibitors of ACC and DGAT2

Acetyl-coenzyme A carboxylase (ACC) and diacylglycerol acyltransferase 2 (DGAT2) are recognized as potential therapeutic targets for nonalcoholic fatty liver disease (NAFLD). Inhibitors targeting ACC and DGAT2 have exhibited the capacity to reduce hepatic fat in individuals afflicted with NAFLD. However, there are no reports of dual inhibitors targeting ACC and DGAT2 for the treatment of NAFLD. Here, we aimed to identify potential dual inhibitors of ACC and DGAT2 using an integrated in silico approach. Machine learning-based virtual screening of commercial molecule databases yielded 395,729 hits, which were subsequently subjected to molecular docking aimed at both the ACC and DGAT2 binding sites. Based on the docking scores, nine compounds exhibited robust interactions with critical residues of both ACC and DGAT2, displaying favorable drug-like features. Molecular dynamics simulations (MDs) unveiled the substantial impact of these compounds on the conformational dynamics of the proteins. Furthermore, binding free energy assessments highlighted the notable binding affinities of specific compounds (V003–8107, G340–0503, Y200–1700, E999–1199, V003–6429, V025–4981, V006–1474, V025–0499, and V021–8916) to ACC and DGAT2. The compounds proposed in this study, identified using a multifaceted computational strategy, warrant experimental validation as potential dual inhibitors of ACC and DGAT2, with implications for the future development of novel drugs targeting NAFLD.

Comprehensive approach to in silico identification and in vitro validation of anti-filarial hit molecules targeting the dimer interface of thioredoxin peroxidase 1 in Wuchereria bancrofti: a progress in anti-filariasis drug development.

Lymphatic filariasis (LF) remains a significant health challenge for populations in developing countries. LF is a parasitic disease transmitted by mosquitoes, mainly caused by the filarial nematode, Wuchereria bancrofti, prevalent in tropical and subtropical regions. Since the present drugs develop complications, including adverse side effects, lack of specificity, and development of drug resistance, the present study focused on developing the potential anti-filariasis drugs targeting crucial proteins for the nematode life cycle. We have identified the therapeutic compounds by targeting the enzyme thioredoxin peroxidase 1 (WbTPx1), which facilitates the conversion of hydrogen peroxide into water, an essential mechanism by which the nematode survives against oxidative stress in the host. This approach might resolve treatment efficacy and activity difficulties at various stages of filarial parasitic worms. We modeled the structure of WbTPx1 and employed the structure-based virtual screening approach, focusing on the dimer interface region of the protein. ADMET prediction profiles of the non-toxic, top-ranked hits with higher docking scores demonstrate higher affinity to the nematode protein than its human homolog. The molecular dynamic simulation studies show WbTPx1-hit complexes' stability and the intactness of hits in the binding site. Further, in vitro validation of identified hits using Setaria digitata, a cattle nematode, showed better IC50 and higher inhibition than the standard drug ivermectin, indicating the potential to inhibit enzyme activity and the development of drug candidates for controlling LF.

Discovery of an EP300 Inhibitor using Structure-based Virtual Screening and Bioactivity Evaluation.

EP300 (E1A binding protein p300) played a significant role in serial diseases such as cancer, neurodegenerative disease. Therefore, it became a significant target. Targeting EP300 discovery of a novel drug to alleviate these diseases. In this paper, 17 candidate compounds were obtained using a structure-based virtual screening approach, 4449-0460, with an IC50 of 5.89 ± 2.08 uM, which was identified by the EP300 bioactivity test. 4449-0460 consisted of three rings. The middle benzene ring connected the 5-ethylideneimidazolidine-2,4-dione group and the 3-F-Phenylmethoxy group. Furthermore, the interaction mechanism between 4449-0460 and EP300 was explored by combining molecular dynamics (MD) simulations and binding free energy calculation methods. The binding free energy of EP300 with 4449-0460 was -10.93 kcal/mol, and mainly came from the nonpolar energy term (ΔGnonpolar). Pro1074, Phe1075, Val1079, Leu1084, and Val1138 were the key residues in EP300/4449-0460 binding with more -1 kcal/mol energy contribution. 4449-0460 was a promising inhibitor targeting EP300, which had implications for the development of drugs for EP300-related diseases.

Identification of vilazodone as a novel plasminogen activator inhibitor to overcome Alzheimer's disease through virtual screening, molecular dynamics simulation, and biological evaluation.

Urokinase-type plasminogen activator (PLAU), a member of the S1 serine peptidase family in Clan PA, plays a crucial role in the conversion of plasminogen into active plasmin. However, the precise role of PLAU in the central nervous system remains incompletely elucidated, particularly, in relation to Alzheimer's disease (AD). In this study, we successfully identified that PLAU could promote cell senescence in neurons, indicating it as a potential target for ADtreatment through a systematic approach, which included both bioinformatics analysis and experimental verification. Subsequently, a structure-based virtual screening approach was employed to identify a potential PLAU inhibitor from theFood and Drug Administration-approved drug database. After analyzing docking scores and thoroughly examining the receptor-ligand complex interaction modes, vilazodone emerges as a highly promising PLAU inhibitor. Additionally, molecular docking and molecular dynamics simulations were performed to generate a complex structure between the relatively stable inhibitor vilazodone and PLAU. Of note, vilazodone exhibited superior cytotoxicity against senescent cells, showing a senolytic activity through targeting PLAU and ultimately producing an anti-AD effect. These findings suggest that targeting PLAU could represent a promising therapeutic strategy for AD. Furthermore, investigating the inhibitory potential and structural modifications based on vilazodone may provide valuable insights for future drug development targeting PLAU in AD disorders.

Pharmacophore Virtual Screening Identifies Riboflavin as an Inhibitor of the Schistosome Cathepsin B1 Protease with Antiparasitic Activity.

Schistosomiasis is a neglected disease of poverty that affects over 200 million people worldwide and relies on a single drug for therapy. The cathepsin B1 cysteine protease (SmCB1) of Schistosoma mansoni has been investigated as a potential target. Here, a structure-based pharmacophore virtual screening (VS) approach was used on a data set of approved drugs to identify potential antischistosomal agents targeting SmCB1. Pharmacophore (PHP) models underwent validation through receiver operating characteristics curves achieving values >0.8. The data highlighted riboflavin (RBF) as a compound of particular interest. A 1 μs molecular dynamics simulation demonstrated that RBF altered the conformation of SmCB1, causing the protease's binding site to close around RBF while maintaining the protease's overall integrity. RBF inhibited the activity of SmCB1 at low micromolar values and killed the parasite in vitro. Finally, in a murine model of S. mansoni infection, oral administration of 100 mg/kg RBF for 7 days significantly decreased worm burdens by ∼20% and had a major impact on intestinal and fecal egg burdens, which were decreased by ∼80%.

Utilizing a structure-based virtual screening approach to discover potential LSD1 inhibitors

BackgroundLysine-specific demethylase 1 (LSD1) is highly expressed in a variety of malignant tumors, rendering it a crucial epigenetic target for anti-tumor therapy. Therefore, the inhibition of LSD1 activity has emerged as a promising innovative therapeutic approach for targeted cancer treatment.MethodsIn our study, we employed innovative structure-based drug design methods to meticulously select compounds from the ZINC15 database. Utilizing virtual docking, we evaluated docking scores and binding modes to identify potential inhibitors. To further validate our findings, we harnessed molecular dynamic simulations and conducted meticulous biochemical experiments to deeply analyze the binding interactions between the protein and compounds.ResultsOur results showcased that ZINC10039815 exhibits an exquisite binding mode with LSD1, fitting perfectly into the active pocket and forming robust interactions with multiple critical residues of the protein.ConclusionsWith its significant inhibitory effect on LSD1 activity, ZINC10039815 emerges as a highly promising candidate for the development of novel LSD1 inhibitors.

Identification of potential JNK3 inhibitors through virtual screening, molecular docking and molecular dynamics simulation as therapeutics for Alzheimer's disease.

Alzheimer's disease (AD) is a complex neurological disorder and no effective drug is available for its treatment. Numerous pathological conditions are believed to be responsible for the initiation and development of AD including c-Jun N-terminal kinases (JNKs). The JNKs are one of the enzymes from the mitogen-activated protein kinase (MAPK) family that controls the phosphorylation of various transcription factors on serine and threonine residues, and hold significant responsibilities in tasks like gene expression, cell proliferation, differentiation, and apoptosis. Since, JNK3 is primarily expressed in the brain hence its increased levels in the brain are associated with the AD pathology promoting neurofibrillary tangles, senile plaques, neuroinflammation, and nerve cell apoptosis. The current research work is focused on the development of novel JNK inhibitors as therapeutics for AD employing a structure-based virtual screening (SBVS) approach. The ZINC database (14634052 compounds) was investigated after employing pan assay interference (PAINs), drug-likeness, and diversity picking filter to distinguish molecules interacting with JNK3 by following three docking precision criteria: High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP) & MMGBSA. Five lead molecules showed a better docking score in the range of -13.091 to -14.051kcal/mol better than the reference compound (-11.828kcal/mol). The lead compounds displayed acceptable pharmacokinetic properties and were subjected to molecular dynamic simulations of 100ns and binding free energy calculations. All the lead molecules showed stable RMSD and hydrogen bond interactions throughout the trajectory. The ∆GMM/PBSA_total score for the lead compounds ZINC220382956, ZINC147071339, ZINC207081127, ZINC205151456, ZINC1228819126, and CC-930 was calculated and found to be -31.39, -42.8, -37.04, -39.01, -36.5, -34.16kcal/mol, respectively. Thus, it was concluded that the lead molecules identified in these studies have the potential to be explored as potent JNK3 inhibitors.

Abstract 2088: Targeting transcriptional factor IWS1 as a potential treatment of advanced dedifferentiated liposarcoma

Abstract Introduction. Surgery remains the only curative treatment for patients with liposarcoma (LPS). The translation of novel therapies has been hampered, and there has been no significant improvement in prognosis for 30 years. Our recent findings demonstrate that IWS1, a transcription elongation and alternative splicing factor, is elevated in dedifferentiated LPS (ddLPS) patient tumor samples and is associated with worse survival and a shorter time to relapse in ddLPS. In this study, we present preliminary results on a novel treatment for advanced ddLPS: an IWS1 inhibitor, serving as a way to discover a spliceosome as a novel therapeutic target. Methods. Utilizing computer-aided, structure-based virtual screening approaches, we identified a class of compounds that exhibited inhibitory potential. A subset of FDA-approved drugs was evaluated by molecular docking calculations, and compounds with the highest calculated theoretical potency were selected for further analysis. To assess the ability of specific compounds to disrupt IWS1/Spt6 binding, Co-immunoprecipitation (Co-IP) experiments on ddLPS cell line Lipo863 were conducted. Transwell invasion and migration assays were performed to evaluate the effect of the drug candidate on the metastatic potential of cells. Additionally, to evaluate the impact of IWS1 inhibition on spheroid formation, a matrigel extracellular matrix scaffold was employed. Results. Through virtual screening, we identified a class of compounds characterized by a specific chemical pattern featuring a tricyclic fused-ring structure. This class of compounds includes the well-known FDA-approved drug Ketotifen, an antihistamine and mast cell stabilizer. Co-immunoprecipitation of IWS1/Spt6 complex after 48-hour treatment with Ketotifen showed a decrease of over 50% of the isolated complex. Migration and invasion of the ddLPS cell line, Lipo863, were reduced in a dose-dependent manner by 18-44% and 17-52%, respectively, when treated with Ketotifen, without affecting cell viability. In a 3D model, a 14-day treatment of Ketotifen significantly reduced spheroid formation, confirming its impact on tumor growth and cell proliferation. Conclusion. Ketotifen effectively disrupted IWS1/Spt6 complex in vitro. Importantly, it did not affect cell viability while reducing the migratory and invasive potential of ddLPS. The dose-dependent effect of Ketotifen suggests that there may be an optimal in vivo concentration to prevent cells from invading nearby structures and/or metastasizing. We are currently testing the ability of these compounds in preclinical mouse models of ddLPS. Citation Format: Marina Goryunova, Yiran He, Nipin Sp, Elizaveta K. Titerina, Sayumi Tahara, Carson Karakis, Alessandro La Ferlita, Michael Sorkin, Alex Kim, Hua Zhu, Christopher M. Hadad, Raphael E. Pollock, Joal D. Beane. Targeting transcriptional factor IWS1 as a potential treatment of advanced dedifferentiated liposarcoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2088.

In silico exploration of the potential inhibitory activities of in-house and ZINC database lead compounds against alpha-glucosidase using structure-based virtual screening and molecular dynamics simulation approach

Inhibitors of α-glucosidase have been used to treat type-2 diabetes (T2DM) by preventing the breakdown of carbohydrates into glucose and prevent enhancing glucose conversion. Structure-based virtual screening (SBVS) was used to generate novel chemical scaffold-ligand α-glucosidase inhibitors. The databases were screened against the receptor α-glucosidase using SBVS and molecular dynamics simulation (MDS) techniques in this study. Based on molecular docking studies, three and two compounds of α-glucosidase inhibitors were chosen from a commercial database (ZINC) and an In-house database for this study respectively. The mode of binding interactions of the selected compounds later predicted their α-glucosidase inhibitory potential. Finally, one out of three lead compound from ZINC and one out of two lead compound from In-house database were shortlisted based on interactions. Furthermore, MDS and post-MDS strategies were used to refine and validate the shortlisted leads along with the reference acarbose/α-glucosidase. The Hits’ ability to inhibit α-glucosidase was predicted by SBVS, indicating that these compounds have good inhibitory activities. The lead inhibitor’s structure may serve as templates for the design of novel inhibitors, and in vitro testing to confirm their anti-diabetic potential is necessary. These insights can help rationally design new effective anti-diabetic drugs. Communicated by Ramaswamy H. Sarma

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor.

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of HDAC11 shares low sequence identity with other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold is a machine learning approach that can predict the 3D structure of proteins with high accuracy even in absence of similar structures. However, the fact that AlphaFold models are predicted in the absence of small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an HDAC11 AlphaFold model by adding the catalytic zinc ion and minimization in the presence of reported HDAC11 inhibitors. In the current study, we implement a comparative structure-based virtual screening approach utilizing the previously optimized HDAC11 AlphaFold model to identify novel and selective HDAC11 inhibitors. The stepwise virtual screening approach was successful in identifying a hit that was subsequently tested using an in vitro enzymatic assay. The hit compound showed an IC50 value of 3.5 µM for HDAC11 and could selectively inhibit HDAC11 over other HDAC subtypes at 10 µM concentration. In addition, we carried out molecular dynamics simulations to further confirm the binding hypothesis obtained by the docking study. These results reinforce the previously presented AlphaFold optimization approach and confirm the applicability of AlphaFold models in the search for novel inhibitors for drug discovery.