A mechanistic approach to explore novel HDAC1 inhibitor using pharmacophore modeling, 3D- QSAR analysis, molecular docking, density functional and molecular dynamics simulation study

Synergizing GA-XGBoost and QSAR modeling: Breaking down activity aliffs in HDAC1 inhibitors

The work being presented now combines severe gradient boosting with Shapley values, a thriving merger within the field of explainable artificial intelligence. We also use a genetic algorithm to analyse the HDAC1 inhibitory activity of a broad pool of 1274 molecules experimentally reported for HDAC1 inhibition. We conduct this analysis to ascertain the HDAC1 inhibitory activity of these molecules. Based on a rigorous investigation of extreme gradient boosting, the proposed method suggests using a genetic algorithm to identify pharmacophoric features. The statistical acceptability of extreme gradient boosting analysis is robust, with parameters such as R2tr = 0.8797, R2L10 % = 0.8831, Q2F1 = 0.9459, Q2F2 = 0.9452, and Q2F3 = 0.9474. This is the driving force behind the invention of nine Py-descriptor-containing genetic algorithms. Shapley additive explanations formed the basis for the interpretation, assigning a significant value to each variable in the model. This is followed by the use of counterfactual cases to analyse the impact of the discovered molecular descriptors on HDAC1 inhibition. An examination of the molecular descriptors, which include acc_N_3B, fsp2NringC8B, fsp3NC7B, and sp2N_sp3C_3B, demonstrates that these descriptors provide insight into the function that the nitrogen atom plays in influencing HDAC1's inhibitory activity. Furthermore, the investigation shed light on the significant role that the hybridized carbon atoms located in sp2 and sp3 play in HDAC1 inhibition. Thus, the QSAR results are in conformity with the reported findings. In addition, activity cliff analysis supports the QSAR findings. Thus, the genetic algorithm-extreme gradient-boosting GA-XGBoost model is easy to understand and makes decent predictions. Based on this, it indicates that "explainable AI" may prove to be beneficial in the future for the purpose of identifying and using structural features in the process of medication development.

Screening of Phytocompounds for Identification of Prospective Histone Deacetylase 1 (HDAC1) Inhibitor: An In Silico Molecular Docking, Molecular Dynamics Simulation, and MM-GBSA Approach

The upregulation of HDAC1 facilitate the induction of epigenetic repression of genes responsible for suppressing tumourigenesis, thereby triggering the development of cancer. HDAC1 inhibitors have thus emerged as possible therapeutic approaches against a variety of human malignancies, as they can inhibit the activity of certain HDACs, repair the overexpression of tumour suppressor genes, and induce cell differentiation, cell cycle arrest, and apoptosis. In this study, among 810 virtually screened compounds, Pinocembrin (PHUB000396) had a significant binding affinity (-7.99 kcal/mol). In molecular dynamics simulation (MD) studies for 200 ns time scale, the compound Pinocembrin effectively undergoes conformational optimization, thereby enabling its accommodation within the active site of the receptor. This outcome serves as a rational for the observed binding affinity. The optimal binding free energy calculations using the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) (-35.86 ± 7.52 kcal/mol) showed the significant role of van der Waals forces and Coulomb interactions in the stability of the respective complex. The pharmacokinetic study showed its potential as a lead compound. The in-silico cytotoxicity prediction also confirmed its potential as an active anticancer phytocompound in lung and brain cancer. Therefore, it can be predicted that Pinocembrin could be a useful bioactive compound as an HDAC1 inhibitor and could be used in developing epigenetic therapy in cancer such as brain cancer and lung cancer to regulate gene expression.

Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics

Histone deacetylase 1 (HDAC1), a class I HDAC enzyme, is crucial for histone modification. Currently, it is emerged as one of the important biological targets for designing small molecule drugs through cancer epigenetics. Along with synthetic inhibitors different natural inhibitors are showing potential HDAC1 inhibitions. In order to gain insights into the relationship between the molecular structures of the natural inhibitors and HDAC1, different molecular modelling techniques (Bayesian classification, recursive partitioning, molecular docking and molecular dynamics simulations) have been applied on a dataset of 155 HDAC1 nature-inspired inhibitors with diverse scaffolds. The Bayesian study showed acceptable ROC values for both the training set and test sets. The Recursive partitioning study produced decision tree 1 with 6 leaves. Further, molecular docking study was processed for generating the protein ligand complex which identified some potential amino acid residues such as F205, H28, L271, P29, F150, Y204 for the binding interactions in case of natural inhibitors. Stability of these HDAC1-natutal inhibitors complexes has been also evaluated by molecular dynamics simulation study. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints among different natural compounds modulating HDAC1 inhibition.Communicated by Ramaswamy H. Sarma.

An in silico drug repurposing approach to identify HDAC1 inhibitors against glioblastoma

Despite considerable improvement in therapy and diagnosis, brain tumors remain a global public health concern. Among all brain tumors, 80% are due to Glioblastoma. The average survival rate of a patient once diagnosed with glioblastoma is 15 months. Lately, the role of peptidase enzymes, especially Neprilysin, a neutral endopeptidase, is gaining attention for its role in tumor growth regulation. Neprilysin expressions are positively correlated with several tumors including GBM and reduced expression of NEP protein is associated with the pathogenesis of multiple tumors. One of the main reasons for NEP protein downregulation is the action of Histone deacetylase (HDAC) enzymes, especially HDAC1. Additionally, studies have reported that increased levels of HDAC1 are responsible for downregulating NEP gene expression. Hence, HDAC1 inhibition can be a good target to elevate NEP levels, which can be a good therapeutic approach to GBM. This study utilizes the computational drug repurposing tool, Schrodinger Maestro to identify HDAC1 inhibitors from the ZINC15 database.1379 FDA-approved drugs from the ZINC15 database were screened through molecular docking. Based on docking score and ligand-protein interaction, the top ten molecules were selected which were then subjected to binding energy calculation and molecular dynamics (MD) simulations. The three most active drugs from the MD simulations- ZINC22010649 (Panobinostat), ZINC4392649 (Tasimelteon) and ZINC1673 (Melphalan), were tested on C6 and U87 MG glioblastoma cells for cytotoxicity and HDAC1 protein levels using western blot analysis. Among the three drugs, Panobinostat exhibited potent cytotoxic action and showed a significant reduction in the HDAC1 protein levels.Communicated by Ramaswamy H. Sarma.

Structural and functional characterization of 6-phosphogluconate dehydrogenase in Plasmodium falciparum (3D7) and identification of its potent inhibitors

The malarial parasite Plasmodium falciparum predominantly causes severe malaria and deaths worldwide. Moreover, resistance developed by P. falciparum to frontline drugs in recent years has markedly increased malaria-related deaths in South Asian Countries. Ribulose 5-phosphate and NADPH synthesized by Pentose Phosphate Pathway (PPP) act as a direct precursor for nucleotide synthesis and P. falciparum survival during oxidative challenges in the intra-erythrocytic growth phase . In the present study, we have elucidated the structure and functional characteristics of 6-phosphogluconate dehydrogenase (6PGD) in P. falciparum and have identified potent hits against 6PGD by pharmacophore-based virtual screening with ZINC and ChemBridge databases. Molecular docking and Molecular dynamics simulation, binding free energies (MMGBSA & MMPBSA), and Density Functional Theory (DFT) calculations were integratively employed to validate and prioritize the most potential hits. The 6PGD structure was found to have an open and closed conformation during MD simulation. The apo form of 6PGD was found to be in closed conformation, while a open conformation attributed to facilitating binding of cofactor. It was also inferred from the conformational analysis that the small domain of 6PGD has a high influence in altering the conformation that may aid in open/closed conformation of 6PGD. The top three hits identified using pharmacophore hypotheses were ChemBridge_11084819, ChemBridge_80178394, and ChemBridge_17912340. Though all three hits scored a high glide score, MMGBSA, and favorable ADMET properties, ChemBridge_11084819 and ChemBrdige_17912340 showed higher stability and binding free energy. Moreover, these hits also featured stable H-bond interactions with the active loop of 6PGD with binding free energy comparable to substrate-bound complex. Therefore, the ChemBridge_11084819 and ChemBridge_17912340 moieties demonstrate to have high therapeutic potential against 6PGD in P. falciparum.Communicated by Ramaswamy H. Sarma.

In silico studies of alkaloids and their derivatives against N-acetyltransferase EIS protein from Mycobacterium tuberculosis

Antibiotic resistance against Mycobacterium tuberculosis (M.tb.) has been a significant cause of death worldwide. The Enhanced intracellular survival (EIS) protein of the bacteria is an acetyltransferase that multiacetylates aminoglycoside antibiotics, preventing them from binding to the bacterial ribosome. To overcome the EIS-mediated antibiotics resistance of M.tb., we compiled 888 alkaloids and derivatives from five different databases and virtually screened them against the EIS receptor. The compound library was filtered down to 87 compounds, which underwent additional analysis and filtration. Moreover, the top 15 most prominent phytocompounds were obtained after the drug-likeness prediction and ADMET screening. Out of 15, nine compounds confirmed the maximum number of hydrogen bond interactions and reliable binding energies during molecular docking. Additionally, the Molecular dynamics (MD) simulation of nine compounds showed the three most stable complexes, further verified by re-docking with mutated protein. The density functional theory (DFT) calculation was performed to identify the HOMO-LUMO energy gaps of the selected three potential compounds. Finally, our selected top lead compounds i.e., Alkaloid AQC2 (PubChem85634496), Nobilisitine A (ChEbi68116), and N-methylcheilanthifoline (ChEbi140673) demonstrated more favourable outcomes when compared with reference compounds (i.e., 39b and 2i) in all parameters used in this study. Therefore, we anticipate that our findings will help to explore and develop natural compound therapy against multi and extensively drug-resistant strains of M.tb.Communicated by Ramaswamy H. Sarma.

HDAC1 PREDICTOR: a simple and transparent application for virtual screening of histone deacetylase 1 inhibitors

Histone deacetylases play an important role in regulating gene expression by modifying histones and changing chromatin conformation. HDAC dysregulation is involved in many diseases, such as cancer, autoimmune and neurodegenerative diseases. Histone deacetylase 1 (HDAC1) inhibitors represent an important class of drugs. Quantitative Structure-Activity Relationship (QSAR) classification models were developed using 2D RDKit molecular descriptors; ECPF4 (Extended Connectivity Fingerprint) circular fingerprints; and the Random Forest, Gradient Boosting, and Support Vector Machine methods. The developed models were integrated into the HDAC1 PREDICTOR application, which is freely available at the link https://ovttiras-hdac1-inhibitors-hdac1-predictor-app-z3mrbr.streamlitapp.com. The HDAC1 PREDICTOR web application allows one to reveal the compounds for which the predicted activity to inhibit HDAC1 is higher than that of the reference Vorinostat compound (IC₅₀ = 11.08 nM). The algorithm implemented in HDAC1 PREDICTOR for determining the contributions of molecular fragments to the inhibitory activity can be used to find the molecule segments that increase or decrease the activity, enabling the researcher to conduct a rational molecular design of new highly active HDAC1 inhibitors. The developed QSAR models and the code for their construction in the Python programming language are freely available on the GitHub platform at https://github.com/ovttiras/HDAC1-inhibitors.

Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.

In Vitro and In Vivo Studies for the Investigation of γ-Globin Gene Induction by Adhatoda vasica: A Pre-Clinical Study of HbF Inducers for β-Thalassemia

Fetal hemoglobin (HbF) is a potent genetic modifier, and the γ-globin gene induction has proven to be a sustainable therapeutic approach for the management of β-thalassemia. In this study, we have evaluated the HbF induction ability of A. vasica in vitro and in vivo, and the identification of potential therapeutic compounds through a bioassay-guided approach. In vitro benzidine-Hb assay demonstrated strong erythroid differentiation of K562 cells by A. vasica extracts. Subsequently, an in vivo study with an aqueous extract of A. vasica (100 mg/kg) showed significant induction of the γ-globin gene and HbF production. While in the acute study, the hematological and biochemical indices were found to be unaltered at the lower dose of A. vasica. Following the bioassay-guided approach, two isolated compounds, vasicinol (1) and vasicine (2) strongly enhanced HbF levels and showed prominent cellular growth kinetics with ample accumulation of total hemoglobin in K562 cultures. High HbF levels were examined by immunofluorescence and flow cytometry analysis, concomitant with the overexpression in the γ-globin gene level. Compound 1 (0.1 µM) and compound 2 (1 µM) resulted in a greater increase in F-cells (90 and 83%) with marked up (8-fold and 5.1-fold) expression of the γ-globin gene, respectively. Molecular docking studies indicated strong binding affinities of (1) and (2) with HDAC2 and KDM1 protein that predict the possible mechanism of compounds in inhibition of these epigenetic regulators in the γ-globin gene reactivation. Altogether, these observations demonstrated the therapeutic usefulness of A. vasica for fostering HbF production in clinical implications for blood disorders.

Computational screening of potential inhibitors targeting MurF of Brugia malayi Wolbachia through multi-scale molecular docking, molecular dynamics and MM-GBSA analysis

Lymphatic filariasis is a parasitic disease caused by the worms Wuchereria bancrofti, Brugia malayi and Brugia timori. Three anti-filarial drugs namely Diethylcarbamazine, Ivermectin and Albendazole and their combinations are used as the control strategies for filariasis. The disease has received much attention in drug discovery due to the unavailability of vaccines and the toxic pharmaceutical properties of the existing drugs. In Wolbachia endosymbiont Brugia malayi, the UDP-N-acetylmuramoyl-tripeptide-d-alanyl-d-alanine ligase (MurF) plays a key role in peptidoglycan biosynthesis pathway and therefore can be considered as effective drug target against filariasis disease. Therefore, in the present study, MurF was selected as the therapeutic target to identify specific inhibitors against filariasis. Homology modeling was performed to predict the three-dimensional structure of MurF due to the absence of the experimental structure. Further molecular dynamics simulation and structure-based high throughput virtual screening with three different chemical databases (Zinc, Maybridge and Specs) were carried out to identify potent inhibitors and also to check their conformations inside the binding site of MurF, respectively. Top three compounds with high docking score and high relative binding affinity against MurF were selected. Further, validation studies, including predicted ADME (Absorption, Distribution, Metabolism, Excretion) assessment, binding free energy using MM-GBSA (Molecular Mechanics Generalized Born Surface Area) and DFT (Density Functional Theory) calculations were performed for the top three compounds. From the results, it was observed that all the three compounds were predicted to show high reactivity, acceptable range of pharmacokinetic properties and high binding affinity with the drug target MurF. Overall, the results could provide more understanding on the inhibition of MurF enzyme and the screened compounds could lead to the development of new specific anti-filarial drugs.

WaterMap and Molecular Dynamic Simulation-Guided Discovery of Potential PAK1 Inhibitors Using Repurposing Approaches

p21-Activated kinase 1 (PAK1) is positioned at the nexus of several oncogenic signaling pathways. Currently, there are no approved inhibitors for disabling the transfer of phosphate in the active site directly, as they are limited by lower affinity, and poor kinase selectivity. In this work, a repurposing study utilizing FDA-approved drugs from the DrugBank database was pursued with an initial selection of 27 molecules out of ∼2162 drug molecules, based on their docking energies and molecular interaction patterns. From the molecules that were considered for WaterMap analysis, seven molecules, namely, Mitoxantrone, Labetalol, Acalabrutinib, Sacubitril, Flubendazole, Trazodone, and Niraparib, ascertained the ability to overlap with high-energy hydration sites. Considering many other displaced unfavorable water molecules, only Acalabrutinib, Flubendazole, and Trazodone molecules highlighted their prominence in terms of binding affinity gains through ΔΔG that ranges between 6.44 and 2.59 kcal/mol. Even if Mitoxantrone exhibited the highest docking score and greater interaction strength, it did not comply with the WaterMap and molecular dynamics simulation results. Moreover, detailed MD simulation trajectory analyses suggested that the drug molecules Flubendazole, Niraparib, and Acalabrutinib were highly stable, observed from their RMSD values and consistent interaction pattern with Glu315, Glu345, Leu347, and Asp407 including the hydrophobic interactions maintained in the three replicates. However, the drug molecule Trazodone displayed a loss of crucial interaction with Leu347, which was essential to inhibit the kinase activity of PAK1. The molecular orbital and electrostatic potential analyses elucidated the reactivity and strong complementarity potentials of the drug molecules in the binding pocket of PAK1. Therefore, the CADD-based reposition efforts, reported in this work, helped in the successful identification of new PAK1 inhibitors that requires further investigation by in vitro analysis.

Computational identification of 2,4-disubstituted amino-pyrimidines as L858R/T790M-EGFR double mutant inhibitors using pharmacophore mapping, molecular docking, binding free energy calculation, DFT study and molecular dynamic simulation

Pharmacophore modelling studies have been performed for a series of 2,4-disubstituted-pyrimidines derivatives as EGFR L858R/T790M tyrosine kinase inhibitors. The high scoring AARR.15 hypothesis was selected as the best pharmacophore model with the highest survival score of 3.436 having two hydrogen bond acceptors and two aromatic ring features. Pharmacophore-based virtual screening followed by structure-based yielded the six molecules (ZINC17013227, ZINC17013215, ZINC9573324, ZINC9573445, ZINC24023331 and ZINC17013503) from the ZINC database with significant in silico predicted activity and strong binding affinity towords the EGFR L858R/T790M tyrosine kinase. In silico toxicity and cytochrome profiling indicates that all the 06 virtually screened compounds were substrate/inhibitors of the CYP-3A4 metabolizing enzyme and were non-carcinogenic and devoid of Ames mutagenesis. Density functional theory (DFT) and molecular dynamic (MD) simulation further validated the obtained hits.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00113-x.

A systematic review of modeling and simulation approaches in designing targeted treatment technologies for Leukemia Cancer in low and middle income countries

Virtual experimentation is a widely used approach for predicting systems behaviour especially in situations where resources for physical experiments are very limited. For example, targeted treatment inside the human body is particularly challenging, and as such, modeling and simulation is utilised to aid planning before a specific treatment is administered. In such approaches, precise treatment, as it is the case in radiotherapy, is used to administer a maximum dose to the infected regions while minimizing the effect on normal tissue. Complicated cancers such as leukemia present even greater challenges due to their presentation in liquid form and not being localised in one area. As such, science has led to the development of targeted drug delivery, where the infected cells can be specifically targeted anywhere in the body. Despite the great prospects and advances of these modeling and simulation tools in the design and delivery of targeted drugs, their use by Low and Middle Income Countries (LMICs) researchers and clinicians is still very limited. This paper therefore reviews the modeling and simulation approaches for leukemia treatment using nanoparticles as an example for virtual experimentation. A systematic review from various databases was carried out for studies that involved cancer treatment approaches through modeling and simulation with emphasis to data collected from LMICs. Results indicated that whereas there is an increasing trend in the use of modeling and simulation approaches, their uptake in LMICs is still limited. According to the review data collected, there is a clear need to employ these tools as key approaches for the planning of targeted drug treatment approaches.

Modelling studies reveal the importance of the C-terminal inter motif loop of NSP1 as a promising target site for drug discovery and screening of potential phytochemicals to combat SARS-CoV-2

COVID-19 pandemic causative SARS-CoV-2 coronavirus is still rapid in progression and transmission even after a year. Understanding the viral transmission and impeding the replication process within human cells are considered as the vital point to control and overcome COVID-19 infection. Non-structural Protein 1, one among the proteins initially produced upon viral entry into human cells, instantly binds with the human ribosome and inhibit the host translation process by preventing the mRNA attachment. However, the formation of NSP1 bound Ribosome complex does not affect the viral replication process. NSP1 plays an indispensable role in modulating the host gene expression and completely steals the host cellular machinery. The full-length structure of NSP1 is essential for the activity in the host cell and importantly the loop connecting N and C-terminal domains are reported to play a role in ribosome binding. Due to the unavailability of the experimentally determined full-length structure of NSP1, we have modelled the complete structure using comparative modelling and the stability and conformational behaviour of the modelled structure was evaluated through molecular dynamics simulation. Interestingly, the present study reveals the significance of the inter motif loop to serves as a potential binding site for drug discovery experiments. Further, we have screened the phytochemicals from medicinal plant sources since they were used for several hundred years that minimizes the traditional drug development time. Among the 5638 phytochemicals screened against the functionally associated binding site of NSP1, the best five phytochemicals shown high docking score of −9.63 to −8.75 kcal/mol were further evaluated through molecular dynamics simulations to understand the binding affinity and stability of the complex. Prime MM-GBSA analysis gave the relative binding free energies for the top five compounds (dihydromyricetin, 10-demethylcephaeline, dihydroquercetin, pseudolycorine and tricetin) in the range of −45.17 kcal/mol to −37.23 kcal/mol, indicating its binding efficacy in the predicted binding site of NSP1. The density functional theory calculations were performed for the selected five phytochemicals to determine the complex stability and chemical reactivity. Thus, the identified phytochemicals could further be used as effective anti-viral agents to overcome COVID-19 and as well as several other viral infections.

A Combined approach of Pharmacophore Modeling, QSAR Study, Molecular Docking and in silico ADME/Tox prediction of 4-Arylthio & 4- Aryloxy-3- Iodopyridine-2(1H)-one analogs to identify potential Reverse Transcriptase inhibitor: Anti-HIV agents

BACKGROUND

Reverse transcriptase is an important therapeutic target to treat AIDS caused by the Human Immunodeficiency Virus (HIV). Despite many effective anti-HIV drugs, reverse transcriptase (RT) inhibitors remain the cornerstone of the drug regimen to treat AIDS. In the present work, we have expedited the use of different computational modules and presented an easy, cost-effective and high throughput screening method to identify potential reverse transcriptase inhibitors.

METHODS

A congeneric series of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one analogs having anti-HIV activity were subjected to structure-based 2D, 3D QSAR, Pharmacophore Modeling, and Molecular Docking to elucidate the structural properties required for the design of potent HIV-RT inhibitors. Prediction of preliminary Pharmacokinetic and the Drug Likeliness profile was performed for these compounds by in silico ADME study.

RESULTS

The 2D and 3D- QSAR models were developed by correlating two and three-dimensional descriptors with activity (pIC50) by sphere exclusion method and k-nearest neighbor molecular field analysis approach, respectively. The significant 2D- QSAR model developed by Partial Least Square associated with the Sphere Exclusion method (PLS-SE) having r2 and q2 values 0.9509 and 0.8038 respectively. The 3D-QSAR model by Step Wise variable selection method (SW-kNN MFA) is more significant which has a cross-validated squared correlation coefficient q2= 0.8509 and a non-cross-validated correlation coefficient pred_r2= 0.8102. The pharmacophore hypothesis was developed which comprised 5 features includes 3 aliphatic regions (Ala), 1 H-bond donor (HDr) and 1 H-bond acceptor (HAc). Docking studies of the selected inhibitors with the active site of reverse transcriptase enzyme showed hydrogen bond and π - π interaction with LYS-101, LYS-103, TYR- 181, TYR-188 and TRP-229 residues present at the active site. All the candidates with good bioavailability and ADMET drug likeliness properties.

CONCLUSION

The results of the present work provide more useful information and important structural insights for the discovery, design of novel and potent reverse transcriptase inhibitors with high therapeutic windows in the future.

Atomistic simulation on flavonoids derivatives as potential inhibitors of bacterial gyrase of Staphylococcus aureus

The bacterial DNA gyrase is an attractive target to identify the novel antibacterial agents. The flavonoid derivatives possess various biological activities such as antimicrobial, anti-inflammatory and anticancer activities. The aim of present study is to identify the potential molecule from flavonoid derivatives against Staphylococcus aureus using atomistic simulation namely Molecular Docking, Quantum Chemical and Molecular Dynamics. The molecules Cpd58, Cpd65 and Cpd70 are identified as potential molecules through molecular docking approaches by exploring through the N - H…O hydrogen bonding interactions with Asn31 and Glu35 of Gyrase B. To confirm the intramolecular charge transfer in the flavonoid derivatives, Frontier Molecular Orbital (FMO) calculation was performed at M06/6-31g(d) level in gas phase. The lowest HOMO-LUMO gap was calculated for Cpd58, Cpd65 and Cpd70 among the selected compounds used in this study. Molecular dynamics simulation were carried out for Cpd58 and Cpd70 for a time period of 50 ns and found to be stable throughout the analysis. Therefore, the identified compounds are found to be a potent inhibitor for GyrB of S. aureus that can be validated by experimental studies. Communicated by Ramaswamy H. Sarma.

Investigation of the effect of different linker chemotypes on the inhibition of histone deacetylases (HDACs)

Histone Deacetylases (HDACs) are among the most attractive and interesting targets in anticancer drug discovery. The clinical relevance of HDAC inhibitors (HDACIs) is testified by four FDA-approved drugs for cancer treatment. However, one of the main drawbacks of these drugs resides in the lack of selectivity against the different HDAC isoforms, resulting in severe side effects. Thus, the identification of selective HDACIs represents an exciting challenge for medicinal chemists. HDACIs are composed of a cap group, a linker region, and a metal-binding group interacting with the catalytic zinc ion. While the cap group has been extensively investigated, less information is available about the effect of the linker on isoform selectivity. To this aim, in this work, we explored novel linker chemotypes to direct isoform selectivity. A small library of 25 hydroxamic acids with hitherto unexplored linker chemotypes was prepared. In vitro tests demonstrated that, depending on the linker type, some candidates selectively inhibit HDAC1 over HDAC6 isoform or vice versa. Docking calculations were performed to rationalize the effect of the novel linker chemotypes on biologic activity. Moreover, four compounds were able to increase the levels of acetylation of histone H3 or tubulin. These compounds were also assayed in breast cancer MCF7 cells to test their antiproliferative effect. Three compounds showed a significant reduction of cancer proliferation, representing valuable starting points for further optimization.

PTML Modeling for Alzheimer’s Disease: Design and Prediction of Virtual Multi-Target Inhibitors of GSK3B, HDAC1, and HDAC6

Background:

Alzheimer’s disease is characterized by a progressive pattern of cognitive and functional impairment, which ultimately leads to death. Computational approaches have played an important role in the context of drug discovery for anti-Alzheimer's therapies. However, most of the computational models reported to date have been focused on only one protein associated with Alzheimer's, while relying on small datasets of structurally related molecules.

Objective:

We introduce the first model combining perturbation theory and machine learning based on artificial neural networks (PTML-ANN) for simultaneous prediction and design of inhibitors of three Alzheimer’s disease-related proteins, namely glycogen synthase kinase 3 beta (GSK3B), histone deacetylase 1 (HDAC1), and histone deacetylase 6 (HDAC6).

Methods:

The PTML-ANN model was obtained from a dataset retrieved from ChEMBL, and it relied on a classification approach to predict chemicals as active or inactive.

Results:

The PTML-ANN model displayed sensitivity and specificity higher than 85% in both training and test sets. The physicochemical and structural interpretation of the molecular descriptors in the model permitted the direct extraction of fragments suggested to favorably contribute to enhancing the multitarget inhibitory activity. Based on this information, we assembled ten molecules from several fragments with positive contributions. Seven of these molecules were predicted as triple target inhibitors while the remaining three were predicted as dual-target inhibitors. The estimated physicochemical properties of the designed molecules complied with Lipinski’s rule of five and its variants.

Conclusion:

This work opens new horizons toward the design of multi-target inhibitors for anti- Alzheimer's therapies.

Molecular Structure, Binding Affinity, and Biological Activity in the Epigenome

Development of valid structure–activity relationships (SARs) is a key to the elucidation of pathomechanisms of epigenetic diseases and the development of efficient, new drugs. The present review is based on selected methodologies and applications supplying molecular structure, binding affinity and biological activity data for the development of new SARs. An emphasis is placed on emerging trends and permanent challenges of new discoveries of SARs in the context of proteins as epigenetic drug targets. The review gives a brief overview and classification of the molecular background of epigenetic changes, and surveys both experimental and theoretical approaches in the field. Besides the results of sophisticated, cutting edge techniques such as cryo-electron microscopy, protein crystallography, and isothermal titration calorimetry, examples of frequently used assays and fast screening techniques are also selected. The review features how different experimental methods and theoretical approaches complement each other and result in valid SARs of the epigenome.

Exploring the inhibitory activity of valproic acid against the HDAC family using an MMGBSA approach

Valproic acid (VPA) is a compound currently used in clinical practice for the treatment of epilepsy as well as bipolar and mood disorders. VPA targets histone deacetylases (HDACs), which participate in the removal of acetyl groups from lysine in several proteins, regulating a wide variety of functions within the organism. An imbalance or malfunction of these enzymes is associated with the development and progression of several diseases, such as cancer and neurodegenerative diseases. HDACs are divided into four classes, but VPA only targets Class I (HDAC1-3 and 8) and Class IIa (HDAC4-5, 7 and 9) HDACs; however, structural and energetic information regarding the manner by which VPA inhibits these HDACs is lacking. Here, the structural and energetic features that determine this recognition were studied using molecular docking and molecular dynamics (MD) simulation. It was found that VPA reaches the catalytic site in HDAC1-3 and 7, whereas in HDAC6, VPA only reaches the catalytic tunnel. In HDAC4, VPA was bound adjacent to L1 and L2, a zone that participates in corepressor binding, and in HDAC8, VPA was bound to the hydrophobic active site channel (HASC), in line with previous reports.

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Background:

Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation.

Results:

Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile.

Conclusion:

In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

Small molecule HDAC inhibitors: Promising agents for breast cancer treatment

Breast cancer, a heterogeneous disease, is the most frequently diagnosed cancer and the second leading cause of cancer-related death among women worldwide. Recently, epigenetic abnormalities have emerged as an important hallmark of cancer development and progression. Given that histone deacetylases (HDACs) are crucial to chromatin remodeling and epigenetics, their inhibitors have become promising potential anticancer drugs for research. Here we reviewed the mechanism and classification of histone deacetylases (HDACs), association between HDACs and breast cancer, classification and structure-activity relationship (SAR) of HDACIs, pharmacokinetic and toxicological properties of the HDACIs, and registered clinical studies for breast cancer treatment. In conclusion, HDACIs have shown desirable effects on breast cancer, especially when they are used in combination with other anticancer agents. In the coming future, more multicenter and randomized Phase III studies are expected to be conducted pushing promising new therapies closer to the market. In addition, the design and synthesis of novel HDACIs are also needed.

From GROMACS to LAMMPS: GRO2LAM : A converter for molecular dynamics software

Atomistic simulations have progressively attracted attention in the study of physical-chemical properties of innovative nanomaterials. GROMACS and LAMMPS are currently the most widespread open-source software for molecular dynamics simulations thanks to their good flexibility, numerous functionalities and responsive community support. Nevertheless, the very different formats adopted for input and output files are limiting the possibility to transfer GROMACS simulations to LAMMPS. In this article, we present GRO2LAM, a modular and open-source Python 2.7 code for rapidly translating input files and parameters from GROMACS to LAMMPS format. The robustness of the tool has been assessed by comparing the simulation results obtained by GROMACS and LAMMPS, after the format conversion by GRO2LAM. Specifically, three nanoscale configurations of interest in both engineering and biomedical fields are studied, namely a carbon nanotube, an iron oxide nanoparticle, and a protein immersed in water. In perspective, GRO2LAM may be the first step to achieve a full interoperability between molecular dynamics software. This would allow to easily exploit their complementary potentialities and post-processing functionalities. Moreover, GRO2LAM could facilitate the cross-check of simulation results, guaranteeing the reproducibility of molecular dynamics models and testing their robustness. Graphical Abstract GRO2LAM, a modular and open-source Python code for rapidly translating input files and parameters from GROMACS to LAMMPS format.

Insights from the Molecular modeling, docking analysis of illicit drugs and Bomb Compounds with Honey Bee Odorant Binding Proteins (OBPs)

Analysis of honeybee PBPs is of interest in the development of Biosensor applications. We described the predicted binding of 19 such compounds with 43-honey bee OBPs using molecular modeling, docking and phylogenetic analysis. Therefore, training the honeybees using preferred compounds formulate the bees to identify the illicit drugs and bomb compounds. Consequently, high docking score produced complex such OBP16-N-Phenyl-2-Napthalamine (-12.25k/mol), 3BJH-Crack Cocaine (-11.75k/mol), OBP10-Methadone (- 11.71k/mol), 1TUJ-Dronobinal Cannabis (-11.66k/mol), OBP13-Plasticizer (-11.27k/mol) and OBP24-Ecstasy (-10.89 k/mol) can be used to identify the compounds using biosensor application. The chemical reaction of the compounds for olfactory sensory was analyzed using DFT (Density Functional Theory) studies. Some of these compounds show high binding OBPs across distant phylogeny.

Insights into structural features of HDAC1 and its selectivity inhibition elucidated by Molecular dynamic simulation and Molecular Docking

Histone deacetylases (HDACs) are a family of proteins whose main function is the removal of acetyl groups from lysine residues located on histone and non-histone substrates, which regulates gene transcription and other activities in cells. HDAC1 dysfunction has been implicated in cancer development and progression; thus, its inhibition has emerged as a new therapeutic strategy. Two additional metal binding sites (Site 1 and Site 2) in HDACs have been described that are primarily occupied by potassium ions, suggesting a possible structural role that affects HDAC activity. In this work, we explored the structural role of potassium ions in Site 1 and Site 2 and how they affect the interactions of compounds with high affinities for HDAC1 (AC1OCG0B, Chlamydocin, Dacinostat and Quisinostat) and SAHA (a pan-inhibitor) using molecular docking and molecular dynamics (MD) simulations in concert with a Molecular-Mechanics-Generalized-Born-Surface-Area (MMGBSA) approach. Four models were generated: one with a potassium ion (K⁺) in both sites (HDAC1_k), a second with K⁺ only at site 1 (HDAC1_ks1), a third with K⁺ only at site 2 (HDAC1_ks2) and a fourth with no K⁺ (HDAC1_wk). We found that the presence or absence of K⁺ not only impacted the structural flexibility of HDAC1, but also its molecular recognition, consistent with experimental findings. These results could therefore be useful for further structure-based drug design studies addressing new HDAC1 inhibitors.