Improvement in the screening performance of potential aryl hydrocarbon receptor ligands by using supervised machine learning

The aryl hydrocarbon receptor (AhR), which is a ligand-dependent transcription factor, plays a crucial role in the regulation of xenobiotic metabolism. There are a large number of artificial or natural molecules in the environment that can activate AhR. In this study, we developed a virtual screening procedure to identify potential ligands of AhR. One structure-based method and two ligand-based methods were used for the virtual screening procedure. The results showed that the precision rate (0.96) and recall rate (0.64) of our procedure were significantly higher than those of a procedure used in a previous study, which suggests that supervised machine learning techniques can greatly improve the performance of virtual screening. Moreover, a pesticide dataset including 777 frequently used pesticides was screened. Seventy-seven pesticides were identified as potential AhR ligands by all three screening methods, among which 12 have never been previously reported as AhR agonists. Two non-agonist AhR ligands and 14 of the 77 pesticides were randomly selected for testing by in vitro and in vivo assays. All 14 pesticides showed different degrees of AhR agonistic activity, and none of the two non-agonist AhR ligand pesticides showed AhR agonistic activity, which suggests that our procedure had good robustness. Four of the pesticides were reported as AhR agonists for the first time, suggesting that these pesticides may need further toxicity assessment. In general, our procedure is a rapid, powerful and computationally inexpensive tool for predicting chemicals with AhR agonistic activity, which could be useful for environmental risk prediction and management.

Identification of potential Mpro inhibitors for the treatment of COVID-19 by using systematic virtual screening approach

The Corona virus Disease (COVID-19) is caused because of novel coronavirus (SARS-CoV-2) pathogen detected in China for the first time, and from there it spread across the globe creating a worldwide pandemic of severe respiratory complications. The virus requires structural and non-structural proteins for its multiplication that are produced from polyproteins obtained by translation of its genomic RNA. These polyproteins are converted into structural and non-structural proteins mainly by the main protease (Mpro). A systematic screening of a drug library (having drugs and diagnostic agents which are approved by FDA or other world authorities) and the Asinex BioDesign library was carried out using pharmacophore and sequential conformational precision level filters using the Schrodinger Suite. From the screening of approved drug library, three antiviral agents ritonavir, nelfinavir and saquinavir were predicted to be the most potent Mpro inhibitors. Apart from these pralmorelin, iodixanol and iotrolan were also identified from the systematic screening. As iodixanol and iotrolan carry some limitations, structural modifications in them could lead to stable and safer antiviral agents. Screenings of Asinex BioDesign library resulted in 20 molecules exhibiting promising interactions with the target protein Mpro. They can broadly be categorized into four classes based on the nature of the scaffold, viz. disubstituted pyrazoles, cyclic amides, pyrrolidine-based compounds and miscellaneous derivatives. These could be used as potential molecules or hits for further drug development to obtain clinically useful therapeutic agents for the treatment of COVID-19.

Importance of glutamine 189 flexibility in SARS-CoV-2 main protease: Lesson learned from in silico virtual screening of ChEMBL database and molecular dynamics

The current global pandemic outbreak of COVID-19, caused by the SARS-CoV-2, strikes an invincible damage to both daily life and the global economy. WHO guidelines for COVID-19 clinical management includes infection control and prevention, social distancing and supportive care using supplemental oxygen and mechanical ventilator support. Currently, evolving researches and clinical reports regarding infected patients with SARS-CoV-2 suggest a potential list of repurposed drugs that may produce appropriate pharmacological therapeutic efficacies in treating COVID-19 infected patients. In this study, we performed virtual screening and evaluated the obtained results of US-FDA approved small molecular database library (302 drug molecule) against two important different protein targets in COVID-19. Best compounds in molecular docking were used as a training set for generation of two different pharmacophores. The obtained pharmacophores were employed for virtual screening of ChEMBL database. The filtered compounds were clustered using Finger print model to obtain two compounds that will be subjected to molecular docking simulations against the two targets. Compounds complexes with SARS-CoV-2 main protease and S-protein were studied using molecular dynamics (MD) simulation. MD simulation studies suggest the potential inhibitory activity of ChEMBL398869 against SARS-CoV-2 main protease and restress the importance of Gln189 flexibility in inhibitors recognition through increasing S2 subsite plasticity.

Pharmacoinformatics approach based identification of potential Nsp15 endoribonuclease modulators for SARS-CoV-2 inhibition

In the current study, a structure-based virtual screening paradigm was used to screen a small molecular database against the Non-structural protein 15 (Nsp15) endoribonuclease of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The SARS-CoV-2 is the causative agent of the recent outbreak of coronavirus disease 2019 (COVID-19) which left the entire world locked down inside the home. A multi-step molecular docking study was performed against antiviral specific compounds (~8722) collected from the Asinex antiviral database. The less or non-interacting molecules were wiped out sequentially in the molecular docking. Further, MM-GBSA based binding free energy was estimated for 26 compounds which shows a high affinity towards the Nsp15. The drug-likeness and pharmacokinetic parameters of all 26 compounds were explored, and five molecules were found to have an acceptable pharmacokinetic profile. Overall, the Glide-XP docking score and Prime-MM-GBSA binding free energy of the selected molecules were explained strong interaction potentiality towards the Nsp15 endoribonuclease. The dynamic behavior of each molecule with Nsp15 was assessed using conventional molecular dynamics (MD) simulation. The MD simulation information was strongly favors the Nsp15 and each identified ligand stability in dynamic condition. Finally, from the MD simulation trajectories, the binding free energy was estimated using the MM-PBSA method. Hence, the proposed final five molecules might be considered as potential Nsp15 modulators for SARS-CoV-2 inhibition.

Virtual Screening-Based Discovery of Potent Hypoglycemic Agents: In Silico, Chemical Synthesis and Biological Study

BACKGROUND

Dipeptidyl peptidase IV has been reported to be an important target for the development and discovery of new therapies for diabetes mellitus type II.

OBJECTIVE

The main aim of this study was to discover chemical entities that target the inhibition of DPP IV and feature potent hypoglycemic action.

METHODS

A structure-based virtual screening was applied to discover new hypoglycemic agents. Molecular docking was performed to compute the binding free energies. Molecular dynamics simulations were done to evaluate the binding stability of resulted hits.

RESULTS

Seven small non-peptide potential inhibitors of Dipeptidyl peptidase IV with 3-imino-4-(4- substituted phenyl)-1, 2, 5-thiadiazolidine-1,1-dioxide scaffold were discovered. The binding free energies ranged from -24.50 to -36.06 kJ/mol. Molecular dynamics simulations revealed high stability of all protein-ligand complexes with low root mean square deviation over 10 ns simulation time. The tested compounds expressed a significant reduction in blood glucose level up to 90% with excellent oral glucose tolerance test after 120 minutes of injection in a diabetes mellitus type II animal model. A promising release of insulin was observed with a potential hypoglycemic activity for all compounds.

CONCLUSION

The virtual screening was successful to discover potent hypoglycemic agents with drug-like properties that may need more consideration for future studies and development.

WIDOCK: a reactive docking protocol for virtual screening of covalent inhibitors

Here we present WIDOCK, a virtual screening protocol that supports the selection of diverse electrophiles as covalent inhibitors by incorporating ligand reactivity towards cysteine residues into AutoDock4. WIDOCK applies the reactive docking method (Backus et al. in Nature 534:570–574, 2016) and extends it into a virtual screening tool by introducing facile experimental or computational parametrization and a ligand focused evaluation scheme together with a retrospective and prospective validation against various therapeutically relevant targets. Parameters accounting for ligand reactivity are derived from experimental reaction kinetic data or alternatively from computed reaction barriers. The performance of this docking protocol was first evaluated by investigating compound series with diverse warhead chemotypes against KRAS^G12C, MurA and cathepsin B. In addition, WIDOCK was challenged on larger electrophilic libraries screened against OTUB2 and NUDT7. These retrospective analyses showed high sensitivity in retrieving experimental actives, by also leading to superior ROC curves, AUC values and better enrichments than the standard covalent docking tool available in AutoDock4 when compound collections with diverse warheads were investigated. Finally, we applied WIDOCK for the prospective identification of covalent human MAO-A inhibitors acting via a new mechanism by binding to Cys323. The inhibitory activity of several predicted compounds was experimentally confirmed and the labelling of Cys323 was proved by subsequent MS/MS measurements. These findings demonstrate the usefulness of WIDOCK as a warhead-sensitive, covalent virtual screening protocol.

Bisindolylmaleimide IX: A novel anti-SARS-CoV2 agent targeting viral main protease 3CLpro demonstrated by virtual screening pipeline and in-vitro validation assays

SARS-CoV-2, the virus that causes COVID-19 consists of several enzymes with essential functions within its proteome. Here, we focused on repurposing approved and investigational drugs/compounds. We targeted seven proteins with enzymatic activities known to be essential at different stages of the viral cycle including PLpro, 3CLpro, RdRP, Helicase, ExoN, NendoU, and 2'-O-MT. For virtual screening, energy minimization of a crystal structure of the modeled protein was carried out using the Protein Preparation Wizard (Schrodinger LLC 2020-1). Following active site selection based on data mining and COACH predictions, we performed a high-throughput virtual screen of drugs and investigational molecules (n = 5903). The screening was performed against viral targets using three sequential docking modes (i.e., HTVS, SP, and XP). Virtual screening identified ∼290 potential inhibitors based on the criteria of energy, docking parameters, ligand, and binding site strain and score. Drugs specific to each target protein were further analyzed for binding free energy perturbation by molecular mechanics (prime MM-GBSA) and pruning the hits to the top 32 candidates. The top lead from each target pool was further subjected to molecular dynamics simulation using the Desmond module. The resulting top eight hits were tested for their SARS-CoV-2 anti-viral activity in-vitro. Among these, a known inhibitor of protein kinase C isoforms, Bisindolylmaleimide IX (BIM IX), was found to be a potent inhibitor of SARS-CoV-2. Further, target validation through enzymatic assays confirmed 3CLpro to be the target. This is the first study that has showcased BIM IX as a COVID-19 inhibitor thereby validating our pipeline.

Optical bioelectronic nose of outstanding sensitivity and selectivity toward volatile organic compounds implemented with genetically engineered bacteriophage: Integrated study of multi-scale computational prediction and experimental validation

Genetic engineering of a bacteriophage is a promising way to develop a highly functional biosensor. Almost countless configurational degree of freedom in the manipulation, considerable uncertainty and cost involved with the approach, however, have been huddles for the objective. In this paper, we demonstrate rapidly responding optical biosensor with high selectivity toward gaseous explosives with genetically engineered phages. The sensors are equipped with peptide sequences in phages optimally interacting with the volatile organic compounds (VOCs) in visible light regime. To overcome the conventional issues, we use extensive utilization of empirical calculations to construct a large database of 8000 tripeptides and screen the best for electronic nose sensing performance toward nine VOCs belonging to three chemical classes. First-principles density functional theory (DFT) calculations unveil underlying correlations between the chemical affinity and optical property change on an electronic band structure level. The computational outcomes are validated by in vitro experimental design and testing of multiarray sensors using genetically modified phage implemented with five selected tripeptide sequences and wild-type phages. The classification success rates estimated from hierarchical cluster analysis are shown to be very consistent with the calculations. Our optical biosensor demonstrates a 1 ppb level of sensing resolution for explosive VOCs, which is a substantial improvement over conventional biosensor. The systematic interplay of big data-based computational prediction and in situ experimental validation can provide smart design principles for unconventionally outstanding biosensors.

In silico exploration of novel protease inhibitors against coronavirus 2019 (COVID-19)

The spread of SARS-CoV-2 has affected human health globally. Hence, it is necessary to rapidly find the drug-candidates that can be used to treat the infection. Since the main protease (M^pro) is the key protein in the virus's life cycle, M^pro is served as one of the critical targets of antiviral treatment. We employed virtual screening tools to search for new inhibitors to accelerate the drug discovery process. The hit compounds were subsequently docked into the active site of SARS-CoV-2 main protease and ranked by their binding energy. Furthermore, in-silico ADME studies were performed to probe for adoption with the standard ranges. Finally, molecular dynamics simulations were applied to study the protein-drug complex's fluctuation over time in an aqueous medium. This study indicates that the interaction energy of the top ten retrieved compounds with COVID-19 main protease is much higher than the interaction energy of some currently in use protease drugs such as ML188, nelfinavir, lopinavir, ritonavir, and α-ketoamide. Among the discovered compounds, Pubchem44326934 showed druglike properties and was further analyzed by MD and MM/PBSA approaches. Besides, the constant binding free energy over MD trajectories suggests a probable drug possessing antiviral properties. MD simulations demonstrate that GLU166 and GLN189 are the most important residues of M^pro, which interact with inhibitors.

In-silico protein-ligand docking studies against the estrogen protein of breast cancer using pharmacophore based virtual screening approaches

Breast cancer in woman is the most common cancer and in 2018 there were around 2 million new cases recorded. The maximum rate of breast cancer is reported in Belgium followed by Luxembourg. It is the second most general cancer, Lung cancer being the first. If the cancer tumor is located only in the breast, the survival rate would be 99%. If the tumor has wide to lymph nodes around the survival rate would be 85% and if the tumor had extend to distant parts, the survival rate would come down to 27%. Mammary gland is an important organ in mammals which has potential function to secrete, synthesize and deliver milk to the infants for nourishment, improvement and protection. Generally, cancer is named after the body part in which it originated; thus, breast cancer refers to the erratic development and proliferation of cells that originate in the breast tissue (7). There are some kinds of tumors that may grow within various areas of the breast. Most tumors are the outcome of benign (non-cancerous) alters within the breast. The estrogen receptors (ER) in ordinary and diseased states are significant for the improvement of relevant therapeutic strategies. Two main forms of ER exist, ERα and ERβ, which are encoded by separate genes. Estrogens play a central role in breast cancer improvement with ERα status being the mainly significant predictor of breast cancer prognosis. The potent lead molecule binding mode, residue-interaction patterns and docking energy were examined by molecular docking and binding free energy studies. The lead compounds and 3ERT complex structural stability and dynamic behavior were monitored by molecular dynamics analysis. The drug-likeness properties of lead compounds were predicted ADME analysis.

A comprehensive comparison of molecular feature representations for use in predictive modeling

Machine learning methods are commonly used for predicting molecular properties to accelerate material and drug design. An important part of this process is deciding how to represent the molecules. Typically, machine learning methods expect examples represented by vectors of values, and many methods for calculating molecular feature representations have been proposed. In this paper, we perform a comprehensive comparison of different molecular features, including traditional methods such as fingerprints and molecular descriptors, and recently proposed learnable representations based on neural networks. Feature representations are evaluated on 11 benchmark datasets, used for predicting properties and measures such as mutagenicity, melting points, activity, solubility, and IC50. Our experiments show that several molecular features work similarly well over all benchmark datasets. The ones that stand out most are Spectrophores, which give significantly worse performance than other features on most datasets. Molecular descriptors from the PaDEL library seem very well suited for predicting physical properties of molecules. Despite their simplicity, MACCS fingerprints performed very well overall. The results show that learnable representations achieve competitive performance compared to expert based representations. However, task-specific representations (graph convolutions and Weave methods) rarely offer any benefits, even though they are computationally more demanding. Lastly, combining different molecular feature representations typically does not give a noticeable improvement in performance compared to individual feature representations.

A probable means to an end: exploring P131 pharmacophoric scaffold to identify potential inhibitors of Cryptosporidium parvum inosine monophosphate dehydrogenase

Compound P131 has been established to inhibit Cryptosporidium parvum's inosine monophosphate dehydrogenase (CpIMPDH). Its inhibitory activity supersedes that of paromomycin, which is extensively used in treating cryptosporidiosis. Through the per-residue energy decomposition approach, crucial moieties of P131 were identified and subsequently adopted to create a pharmacophore model for virtual screening in the ZINC database. This search generated eight ADMET-compliant hits that were examined thoroughly to fit into the active site of CpIMPDH via molecular docking. Three compounds ZINC46542062, ZINC58646829, and ZINC89780094, with favorable docking scores of - 8.3 kcal/mol, - 8.2 kcal/mol, and - 7.5 kcal/mol, were selected. The potential inhibitory mechanism of these compounds was probed using molecular dynamics simulation and Molecular Mechanics Generalized Poisson Boltzmann Surface Area (MM/PBSA) analyses. Results revealed that one of the hits (ZINC46542062) exhibited a lower binding free energy of - 39.52 kcal/mol than P131, which had - 34.6 kcal/mol. Conformational perturbation induced by the binding of the identified hits to CpIMPDH was similar to P131, suggesting a similarity in inhibitory mechanisms. Also, in silico investigation of the properties of the hit compounds implied superior physicochemical properties with regards to their synthetic accessibility, lipophilicity, and number of hydrogen bond donors and acceptors in comparison with P131. ZINC46542062 was identified as a promising hit compound with the highest binding affinity to the target protein and favorable physicochemical and pharmacokinetic properties relative to P131. The identified compounds can serve as a basis for conducting further experimental investigations toward the development of anticryptosporidials, which can overcome the challenges of existing therapeutic options. Graphical abstract P131 and the identified compounds docked in the NAD⁺ binding site of Cryptosporidium parvum IMPDH.

Identification of potential inhibitors of protein-protein interaction useful to fight against Ebola and other highly pathogenic viruses

Despite the efforts to develop new treatments against Ebola virus (EBOV) there is currently no antiviral drug licensed to treat patients with Ebola virus disease (EVD). Therefore, there is still an urgent need to find new drugs to fight against EBOV. In order to do this, a virtual screening was done on the druggable interaction between the EBOV glycoprotein (GP) and the host receptor NPC1 with a subsequent selection of compounds for further validation. This screening led to the identification of new small organic molecules with potent inhibitory action against EBOV infection using lentiviral EBOV-GP-pseudotype viruses. Moreover, some of these compounds have shown their ability to interfere with the intracellular cholesterol transport receptor NPC1 using an ELISA-based assay. These preliminary results pave the way to hit to lead optimization programs that lead to successful candidates.

Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening

Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein's ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.

Molecular Scaffold Hopping via Holistic Molecular Representation

Molecular descriptors encode a variety of molecular representations for computer-assisted drug discovery. Here, we focus on the Weighted Holistic Atom Localization and Entity Shape (WHALES) descriptors, which were originally designed for scaffold hopping from natural products to synthetic molecules. WHALES descriptors capture molecular shape and partial charges simultaneously. We introduce the key aspects of the WHALES concept and provide a step-by-step guide on how to use these descriptors for virtual compound screening and scaffold hopping. The results presented can be reproduced by using the code freely available from URL: github.com/ETHmodlab/scaffold_hopping_whales .

Reverse Docking for the Identification of Molecular Targets of Anticancer Compounds

Molecular docking is a useful and powerful computational method for the identification of potential interactions between small molecules and pharmacological targets. In reverse docking, the ability of one or a few compounds to bind a large dataset of proteins is evaluated in silico. This strategy is useful for identifying molecular targets of orphan bioactive compounds, proposing new molecular mechanisms, finding alternative indications of drugs, or predicting drug toxicity. Herein, we describe a detailed reverse docking protocol for the identification of potential targets for 4-hydroxycoumarin (4-HC). Our results showed that RAC1 is a target of 4-HC, which partially explains the biological activities of 4-HC on cancer cells. The strategy reported here can be easily applied to other compounds and protein datasets.

Exploiting Water Dynamics for Pharmacophore Screening

Three-dimensional pharmacophore models have been proven extremely valuable in exploring novel chemical space through virtual screening. However, traditional pharmacophore-based approaches need ligand information and rely on static snapshots of highly dynamic systems. In this chapter, we describe PyRod, a novel tool to generate three-dimensional pharmacophore models based on water traces of a molecular dynamics simulation of an apo-protein.The protocol described herein was successfully applied for the discovery of novel drug-like inhibitors of West Nile virus NS2B-NS3 protease. By using this recent example, we highlight the key steps of the generation and validation of PyRod-derived pharmacophore models and their application for virtual screening.

Binding Mode Prediction and Virtual Screening Applications by Covalent Docking

The mechanism of action of covalent drugs involves the formation of a bond between their electrophilic warhead group and a nucleophilic residue of the protein target. The recent advances in covalent drug discovery have accelerated the development of computational tools for the design and characterization of covalent binders. Covalent docking algorithms can predict the binding mode of covalent ligands by modeling the bonds and interactions formed at the reaction site. Their scoring functions can estimate the relative binding affinity of ligands towards the target of interest, thus allowing virtual screening of compound libraries. However, most of the scoring schemes have no specific terms for the bond formation, and therefore it prevents the direct comparison of warheads with different intrinsic reactivity. Herein, we describe a protocol for the binding mode prediction of covalent ligands, a typical virtual screening of compound sets with a single warhead chemistry, and an alternative approach to screen libraries that include various warhead types, as applied in recently validated studies.

Negative Image-Based Screening: Rigid Docking Using Cavity Information

Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.

From Homology Modeling to the Hit Identification and Drug Repurposing: A Structure-Based Approach in the Discovery of Novel Potential Anti-Obesity Compounds

Although science and technology have progressed rapidly, de novo drug development has been a costly and time-consuming process over the past decades. In this scenario, drug repurposing has appeared as an alternative tool to accelerate the drug development process. Herein, we applied such an approach to the highly popular human Carbonic Anhydrase (hCA) VA drug target, that is involved in ureagenesis, gluconeogenesis, lipogenesis, and in the metabolism regulation. Albeit several hCA inhibitors have been designed and are currently in clinical use, serious drug interactions have been reported due to their poor selectivity. In this perspective, the drug repurposing approach could be a useful tool for investigating the drug promiscuity/polypharmacology profile. In this chapter, we describe a combination of virtual screening techniques and in vitro assays aimed to identify novel selective hCA VA inhibitors and to repurpose drugs known for other clinical indications.

Discovery of 2-(5-(quinolin-6-yl)-1,3,4-oxadiazol-2-yl)acetamide derivatives as novel PI3Kα inhibitors via docking-based virtual screening

PI3Kα is an attractive target for PIK3CA mutated malignant tumor and searching for lead compounds with novel scaffold is important for the development of PI3Kα inhibitors. Therefore, the strategy of docking-based virtual screening was performed to discovery potent inhibitors. The 4L2Y_A PI3Kα crystal structure was used as the model protein receptor due to its high docking reliability. After the multistep virtual screening protocol and biological evaluation, three hits were picked up and further similarity searching led to more potent 2-(5-(quinolin-6-yl)-1,3,4-oxadiazol-2-yl)acetamide derivatives ES-25 and ES-27. In addition, the primary SAR of these novel derivatives was discussed, which provide a basis for the further structural modification.

Tankyrase Inhibitor for Cardiac Tissue Regeneration: an In-silico Approach

Myocardial infarction causes heart tissue damages; therefore, using non-invasive methods to regenerate the heart tissue could be very helpful. Recent studies claimed that the inhibition of the Wnt signaling could promote cardiac remodeling and induce cardiac regeneration. Therefore, a tankyrase inhibitor to stabilize the AXIN and inhibit the Wnt/β-catenin signaling pathway will induce cardiac regeneration after injury. In this regard, virtual screening procedure, using molecular docking of 9127 FDA and world approved drugs, including herbal medicine, was done over the crystal structures of tankyrase 1 (TNKS1) and tankyrase 2 (TNKS2) catalytic poly (ADP-ribose) polymerase (PARP) domains with PDB ID: 2RF5 and 3KR7, respectively, to find potential small molecule inhibitors to regenerate injured heart tissue. Subsequently, molecular dynamics simulations were done to assess the stability of selected ligands phenothrin and ethyl rosinate in the binding pocket of TNKS1 and TNKS2 for 100 ns, respectively. Both compounds show suitable interaction in their binding pocket. The molecular dynamics simulation results confirm their stability. The binding free energy of complexes was carried out by the MM-PBSA method. ADME properties also indicate the potential of drug-likeness of both compounds. Taking together both drugs may be promising for inducing cardiac regeneration after injury. Nevertheless, clinical approval remains.

In silico derived small molecules targeting the finger-finger interaction between the histone lysine methyltransferase NSD1 and Nizp1 repressor

PHD fingers are small chromatin binding domains, that alone or in tandem work as versatile interaction platforms for diversified activities, ranging from the decoding of the modification status of histone tails to the specific recognition of non-histone proteins. They play a crucial role in their host protein as mutations thereof cause several human malignancies. Thus, PHD fingers are starting to be considered as valuable pharmacological targets. While inhibitors or chemical probes of the histone binding activity of PHD fingers are emerging, their druggability as non-histone interaction platform is still unexplored. In the current study, using a computational and experimental pipeline, we provide proof of concept that the tandem PHD finger of Nuclear receptor-binding SET (Su(var)3–9, Enhancer of zeste, Trithorax) domain protein 1 (PHD_VC5HCH_NSD1) is ligandable. Combining virtual screening of a small subset of the ZINC database (Zinc Drug Database, ZDD, 2924 molecules) to NMR binding assays and ITC measurements, we have identified Mitoxantrone dihydrochloride, Quinacrine dihydrochloride and Chloroquine diphosphate as the first molecules able to bind to PHD_VC5HCH_NSD1 and to reduce its documented interaction with the Zinc finger domain (C2HR_Nizp1) of the transcriptional repressor Nizp1 (NSD1-interacting Zn-finger protein). These results pave the way for the design of small molecules with improved effectiveness in inhibiting this finger-finger interaction.

The discovery of novel small molecule allosteric activators of aldehyde dehydrogenase 2

Aldehyde dehydrogenase 2 (ALDH2) plays important role in ethanol metabolism, and also serves as an important shield from the damage occurring under oxidative stress. A special inactive variant was found carried by 35-45% of East Asians. The variant carriers have recently been found at the higher risk for the diseases related to the damage occurring under oxidative stress, such as cardiovascular and cerebrovascular diseases. As a result, ALDH2 activators may potentially serve as a new class of therapeutics. Herein, N-benzylanilines were found as novel allosteric activators of ALDH2 by computational virtual screening using ligand-based and structure-based screening parallel screening strategy. Then a structural optimization was performed and has led to the discovery of the compound C6. It has good activity in vitro and in vivo, which could reduce infarct size by ∼70% in ischemic stroke rat models. This study provided good lead compounds for the further development of ALDH2 activators.

Multi-step molecular docking and dynamics simulation-based screening of large antiviral specific chemical libraries for identification of Nipah virus glycoprotein inhibitors

Nipah virus (NiV) infections are highly contagious and can cause severe febrile encephalitis. An outbreak of NiV infection has reported high mortality rates in Southeast Asian countries including Bangladesh, East Timor, Malaysia, Papua New Guinea, Vietnam, Cambodia, Indonesia, Madagascar, Philippines, Thailand and India. Considering the high risk for an epidemic outbreak, the World Health Organization (WHO) declared NiV as an emerging priority pathogen. However, there are no effective therapeutics or any FDA approved drugs available for the treatment of this infection. Among the known nine proteins of NiV, glycoprotein plays an important role in initiating the entry of viruses and attaching to the host cell receptors. Herein, three antiviral databases consisting of 79,892 chemical entities have been computationally screened against NiV glycoprotein (NiV-G). Particularly, multi-step molecular docking followed by extensive molecular binding interactions analyses, binding free energy estimation, in silico pharmacokinetics, synthetic accessibility and toxicity profile evaluations have been carried out for initial identification of potential NiV-G inhibitors. Further, molecular dynamics (MD) simulation has been performed to understand the dynamic properties of NiV-G protein-bound with proposed five inhibitors (G1-G5) and their interactions behavior, and any conformational changes in NiV-G protein during simulations. Moreover, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) based binding free energies (∆G) has been calculated from all MD simulation trajectories to understand the energy contribution of each proposed compound in maintaining and stabilizing the complex binding interactions with NiV-G protein. Proposed compounds showed high negative ∆G values ranging from -166.246 to -226.652 kJ/mol indicating a strong affinity towards the NiV-G protein.