Virtual screening of the inhibitors targeting at the viral protein 40 of Ebola virus

In-silico evaluation of Azadirachta indica-derived Daucosterol against key viral proteins of Ebolavirus using ML and MD simulations approach

Ebola virus disease (EVD) is an acute life-threatening disease caused by highly pathogenic Ebolavirus (EBOV), with reported case fatality rates reaching 90%. There have been numerous EBOV outbreaks and epidemics since the first outbreak was reported in Africa in 1976. Despite the approval of three vaccines and two monoclonal antibody therapies by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) for the treatment of EVD the urgent need for alternative therapeutic strategies persists. In the present study, we screened a library of 235 phytocompounds derived from Azadirachta indica against the key EBOV viral protein 24 (VP24), VP30, VP35 and VP40 through a random forest-based machine learning model with an accuracy of 84.5%. Initially, 48 compounds were identified as active, and subsequent toxicity assessment refined the selection to a promising candidate, daucosterol. Molecular docking studies indicated that daucosterol exhibited significant binding affinity to all four viral proteins. Subsequent validation through molecular dynamics simulations confirmed the stability of daucosterol protein complexes. These results imply that daucosterol acts as a potential multitarget inhibitor against EBOV proteins and could serve as a promising lead compound for future therapeutic development against EVD.

Identification of novel inhibitors against VP40 protein of Marburg virus by integrating molecular modeling and dynamics approaches

Marburg virus (MV) is a highly etiological agent of haemorrhagic fever in humans and has spread across the world. Its outbreaks caused a 23-90% human death rate. However, there are currently no authorized preventive or curative measures yet. VP40 is the MV matrix protein, which builds protein shell underneath the viral envelope and confers hallmark filamentous. VP40 alone is able to induce assembly and budding of filamentous virus-like particles (VLPs), which resemble authentic virions. As a result, this research is credited with clarifying the function of VP40 and leading to the discovery of new therapeutic targets effective in combating MV disease (MVD). Virtual screening, molecular docking and molecular dynamics (MD) simulation were used to find the putative active chemicals based on a 3D pharmacophore model of the protein's active site cavity. Initially, andrographidine-C, a potent inhibitor was selected for the development of the pharmacophore model. Later, a library of 30,000 compounds along with the andrographidine-C was docked against VP40 protein. Three best hits including avanafil, diuvaretin and macrourone were subjected to further MD simulation analysis, as these compounds had better binding affinities as compared to andrographidine-C. Furthermore, throughout the 100 ns simulations, the back bone of VP40 protein in presence of avanafil, diuvaretin and macrourone remained stable which was further validated by MM-PBSA analysis. Additionally, all of these compounds depict maximum drug-like properties. The predicted drugs based on the ligand, avanafil, diuvaretin and macrourone could be exploited and developed as an alternative or complementary therapy for the treatment of MVD.

Investigation into in silico and in vitro approaches for inhibitors targeting MCM10 in Leishmania donovani: a comprehensive study

Visceral Leishmaniasis (VL), the second neglected tropical disease caused by various Leishmania species, presents a significant public health challenge due to limited treatment options and the absence of vaccines. The agent responsible for visceral leishmaniasis, also referred to as "black fever" in India, is Leishmania donovani. This study focuses on L. donovani Minichromosome maintenance 10 (LdMcm10), a crucial protein in the DNA replication machinery, as a potential therapeutic target in Leishmania therapy using in silico and in vitro approaches. We employed bioinformatics tools, molecular docking, and molecular dynamics simulations to predict potential inhibitors against the target protein. The research revealed that the target protein lacks homologues in the host, emphasizing its potential as a drug target. Ligands from the DrugBank database were screened against LdMcm10 using PyRx software. The top three compounds, namely suramin, vapreotide, and pasireotide, exhibiting the best docking scores, underwent further investigation through molecular dynamic simulation and in vitro analysis. The observed structural dynamics suggested that LdMcm10-ligand complexes maintained consistent binding throughout the 300 ns simulation period, with minimal variations in their backbone. These findings suggest that these three compounds hold promise as potential lead compounds for developing new drugs against leishmaniasis. In vitro experiments also demonstrated a dose-dependent reduction in L. donovani viability for suramin, vapreotide, and pasireotide, with computed IC50 values providing quantitative metrics of their anti-leishmanial efficacy. The research offers a comprehensive understanding of LdMcm10 as a drug target and provides a foundation for further investigations and clinical exploration, ultimately advancing drug discovery strategies for leishmaniasis treatment.

Elucidating the functional impact of G137V and G144R variants in Maroteaux Lamy's Syndrome by Molecular Dynamics Simulation

Mucopolysaccharidoses VI (Maroteaux Lamy syndrome) is a metabolic disorder due to the loss of enzyme activity of N-acetyl galactosamine-4-sulphatase arising from mutations in the ARSB gene. The mutated ARSB is the origin for the accumulation of GAGs within the lysosome leading to severe growth deformities, causing lysosomal storage disease. The main focus of this study is to identify the deleterious variants by applying bioinformatics tools to predict the conservation, pathogenicity, stability, and effect of the ARSB variants. We examined 170 missense variants, of which G137V and G144R were the resultant variants predicted detrimental to the progression of the disease. The native along with G137V and G144R structures were fixed as the receptors and subjected to Molecular docking with the small molecule Odiparcil to analyze the binding efficiency and the varied interactions of the receptors towards the drug. The interaction resulted in similar docking scores of - 7.3 kcal/mol indicating effective binding and consistent interactions of the drug with residues CYS117, GLN118, THR182, and GLN517 for native, along with G137V and G144R structures. Molecular Dynamics were conducted to validate the stability and flexibility of the native and variant structures on ligand binding. The overall study indicates that the drug has similar therapeutic towards the native and variant based on the higher binding affinity and also the complexes show stability with an average of 0.2 nm RMS value. This can aid in the future development therapeutics for the Maroteaux Lamy syndrome.

Elucidating the mutational impact in causing Niemann-Pick disease type C: an in silico approach

Niemann-Pick disease type C is a rare autosomal recessive of lysosomal storage disorder characterized by impaired intracellular lipid transport and has a tendency to accumulate the fatty acids and glycosphingolipids in a variety of neurovisceral tissues. This work includes computational tools to deciphere the mutational effect in NPC protein. The study initiated with the collection of 471 missense mutations from various databases, which were then analyzed using computational tools. The mutations (G549V, F703S, Q775P and L1244P) were said to be disease associated, altering the biophysical properties, in highly conserved regions and reduces the stability using several in silico methods and were subjected to molecular docking analysis. To analyze the ligand (Itraconazole: a small molecule of antifungal drug class, which is known to inhibit cholesterol export from lysosomes) activity Molecular docking study was performed for all the complex proteins. The average binding affinity was taken and found to be -10.76 kcal/mol (native) and -11.06 kcal/mol (Q775P was located in transmembrane region IV which impacts the sterol-sensing domain of the NPC1 protein and associated with a severe infantile neurological form). Finally, molecular dynamic simulation was performed in duplicate and trajectories were built for the backbone of the RMSD, RMSF, the number of intramolecular hydrogen bonds, the radius of gyration and the SSE percent for both the complex proteins. This work contributes to understand the effectiveness and may provide an insight on the stability of the drug with the complex variant structures.Communicated by Ramaswamy H. Sarma.

Revealing the structural and molecular interaction landscape of the favipiravir-RTP and SARS-CoV-2 RdRp complex through integrative bioinformatics: Insights for developing potent drugs targeting SARS-CoV-2 and other viruses

BACKGROUND

The global research community has made considerable progress in therapeutic and vaccine research during the COVID-19 pandemic. Several therapeutics have been repurposed for the treatment of COVID-19. One such compound is, favipiravir, which was approved for the treatment of influenza viruses, including drug-resistant influenza. Despite the limited information on its molecular activity, clinical trials have attempted to determine the effectiveness of favipiravir in patients with mild to moderate COVID-19. Here, we report the structural and molecular interaction landscape of the macromolecular complex of favipiravir-RTP and SARS-CoV-2 RdRp with the RNA chain.

METHODS

Integrative bioinformatics was used to reveal the structural and molecular interaction landscapes of two macromolecular complexes retrieved from RCSB PDB.

RESULTS

We analyzed the interactive residues, H-bonds, and interaction interfaces to evaluate the structural and molecular interaction landscapes of the two macromolecular complexes. We found seven and six H-bonds in the first and second interaction landscapes, respectively. The maximum bond length is 3.79 Å. In the hydrophobic interactions, five residues (Asp618, Asp760, Thr687, Asp623, and Val557) were associated with the first complex and two residues (Lys73 and Tyr217) were associated with the second complex. The mobilities, collective motion, and B-factor of the two macromolecular complexes were analyzed. Finally, we developed different models, including trees, clusters, and heat maps of antiviral molecules, to evaluate the therapeutic status of favipiravir as an antiviral drug.

CONCLUSIONS

The results revealed the structural and molecular interaction landscape of the binding mode of favipiravir with the nsp7-nsp8-nsp12-RNA SARS-CoV-2 RdRp complex. Our findings can help future researchers in understanding the mechanism underlying viral action and guide the design of nucleotide analogs that mimic favipiravir and exhibit greater potency as antiviral drugs against SARS-CoV-2 and other infectious viruses. Thus, our work can help in preparing for future epidemics and pandemics.

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors

The Ebola virus (EBOV) is still highly infectious and causes severe hemorrhagic fevers in primates. However, there are no regulatorily approved drugs against the Ebola virus disease (EVD). The highly virulent and lethal nature of EVD highlights the need to develop therapeutic agents. Viral protein 40 kDa (VP40), the most abundantly expressed protein during infection, coordinates the assembly, budding, and release of viral particles into the host cell. It also regulates viral transcription and RNA replication. This study sought to identify small molecules that could potentially inhibit the VP40 protein by targeting the N-terminal domain using an in silico approach. The statistical quality of AutoDock Vina’s capacity to discriminate between inhibitors and decoys was determined, and an area under the curve of the receiver operating characteristic (AUC-ROC) curve of 0.791 was obtained. A total of 29,519 natural-product-derived compounds from Chinese and African sources as well as 2738 approved drugs were successfully screened against VP40. Using a threshold of −8 kcal/mol, a total of 7, 11, 163, and 30 compounds from the AfroDb, Northern African Natural Products Database (NANPDB), traditional Chinese medicine (TCM), and approved drugs libraries, respectively, were obtained after molecular docking. A biological activity prediction of the lead compounds suggested their potential antiviral properties. In addition, random-forest- and support-vector-machine-based algorithms predicted the compounds to be anti-Ebola with IC50 values in the micromolar range (less than 25 μM). A total of 42 natural-product-derived compounds were identified as potential EBOV inhibitors with desirable ADMET profiles, comprising 1, 2, and 39 compounds from NANPDB (2-hydroxyseneganolide), AfroDb (ZINC000034518176 and ZINC000095485942), and TCM, respectively. A total of 23 approved drugs, including doramectin, glecaprevir, velpatasvir, ledipasvir, avermectin B1, nafarelin acetate, danoprevir, eltrombopag, lanatoside C, and glycyrrhizin, among others, were also predicted to have potential anti-EBOV activity and can be further explored so that they may be repurposed for EVD treatment. Molecular dynamics simulations coupled with molecular mechanics Poisson–Boltzmann surface area calculations corroborated the stability and good binding affinities of the complexes (−46.97 to −118.9 kJ/mol). The potential lead compounds may have the potential to be developed as anti-EBOV drugs after experimental testing.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies

BACKGROUND

Porcine reproductive and respiratory syndrome respiratory sickness in weaned and growing pigs, as well as sow reproductive failure, and its infection is regarded as one of the most serious swine illnesses worldwide. Given the current lack of an effective treatment, in this study, we identified natural compounds capable of inhibiting non-structural protein 4 (Nsp4) of the virus, which is involved in their replication and pathogenesis.

RESULTS

We screened natural compounds (n = 97,999) obtained from the ZINC database against Nsp4 and selected the top 10 compounds for analysing protein-ligand interactions and physicochemical properties. The five compounds demonstrating strong binding affinity were then subjected to molecular dynamics simulations (100 ns) and binding free energy calculations. Based on analysis, we identified four possible lead compounds that represent potentially effective drug-like inhibitors.

CONCLUSIONS

These methods identified that these natural compounds are capable of inhibiting Nsp4 and possibly effective as antiviral therapeutics against PRRSV.

Mathematical analysis for the effect of voluntary vaccination on the propagation of Corona virus pandemic

In this manuscript, a new nonlinear model for the rapidly spreading Corona virus disease (COVID-19) is developed. We incorporate an additional class of vaccinated humans which ascertains the impact of vaccination strategy for susceptible humans. A complete mathematical analysis of this model is conducted to predict the dynamics of Corona virus in the population. The analysis proves the effectiveness of vaccination strategy employed and helps public health services to control or to reduce the burden of corona virus pandemic. We first prove the existence and uniqueness and then boundedness and positivity of solutions. Threshold parameter for the vaccination model is computed analytically. Stability of the proposed model at fixed points is investigated analytically with the help of threshold parameter to examine epidemiological relevance of the pandemic. We apply LaSalle's invariance principle from the theory of Lyapunov function to prove the global stability of both the equilibria. Two well known numerical techniques namely Runge-Kutta method of order 4 (RK4), and the Non-Standard Finite Difference (NSFD) method are employed to solve the system of ODE's and to validate our obtained theoretical results. For different coverage levels of voluntary vaccination, we explored a complete quantitative analysis of the model. To draw our conclusions, the effect of proposed vaccination on threshold parameter is studied numerically. It is claimed that Corona virus disease could be eradicated faster if a human community selfishly adopts mandatory vaccination measures at various coverage levels with proper awareness. Finally, we have executed the joint variability of all classes to understand the effect of vaccination strategy on a disease dynamics.

Computational Study on Potential Novel Anti-Ebola Virus Protein VP35 Natural Compounds

Ebola virus (EBOV) is one of the most lethal pathogens that can infect humans. The Ebola viral protein VP35 (EBOV VP35) inhibits host IFN-α/β production by interfering with host immune responses to viral invasion and is thus considered as a plausible drug target. The aim of this study was to identify potential novel lead compounds against EBOV VP35 using computational techniques in drug discovery. The 3D structure of the EBOV VP35 with PDB ID: 3FKE was used for molecular docking studies. An integrated library of 7675 African natural product was pre-filtered using ADMET risk, with a threshold of 7 and, as a result, 1470 ligands were obtained for the downstream molecular docking using AutoDock Vina, after an energy minimization of the protein via GROMACS. Five known inhibitors, namely, amodiaquine, chloroquine, gossypetin, taxifolin and EGCG were used as standard control compounds for this study. The area under the curve (AUC) value, evaluating the docking protocol obtained from the receiver operating characteristic (ROC) curve, generated was 0.72, which was considered to be acceptable. The four identified potential lead compounds of NANPDB4048, NANPDB2412, ZINC000095486250 and NANPDB2476 had binding affinities of −8.2, −8.2, −8.1 and −8.0 kcal/mol, respectively, and were predicted to possess desirable antiviral activity including the inhibition of RNA synthesis and membrane permeability, with the probable activity (Pa) being greater than the probable inactivity (Pi) values. The predicted anti-EBOV inhibition efficiency values (IC₅₀), found using a random forest classifier, ranged from 3.35 to 11.99 μM, while the Ki values ranged from 0.97 to 1.37 μM. The compounds NANPDB4048 and NANPDB2412 had the lowest binding energy of −8.2 kcal/mol, implying a higher binding affinity to EBOV VP35 which was greater than those of the known inhibitors. The compounds were predicted to possess a low toxicity risk and to possess reasonably good pharmacological profiles. Molecular dynamics (MD) simulations of the protein–ligand complexes, lasting 50 ns, and molecular mechanisms Poisson-Boltzmann surface area (MM-PBSA) calculations corroborated the binding affinities of the identified compounds and identified novel critical interacting residues. The antiviral potential of the molecules could be confirmed experimentally, while the scaffolds could be optimized for the design of future novel anti-EBOV chemotherapeutics.

A computational overview on phylogenetic characterization, pathogenic mutations, and drug targets for Ebola virus disease

The World Health Organization declared Ebola virus disease (EVD) as the major outbreak in the 20th century. EVD was first identified in 1976 in South Sudan and the Democratic Republic of the Congo. EVD was transmitted from infected fruit bats to humans via contact with infected animal body fluids. The Ebola virus (EBOV) has a genome size of ∼18,959 bp. It encodes seven distinct proteins: nucleoprotein (NP), glycoprotein (GP), viral proteins VP24, VP30, VP35, matrix protein VP40, and polymerase L is considered a prime target for potential antiviral strategies. The current US FDA-approved anti-EVD vaccine, ERVERBO, and the other equally effective anti-EBOV combinations of three fully human monoclonal antibodies such as REGN-EB3, primarily target the envelope glycoprotein. This work elaborates on the EBOV's phylogenetic structure and the crucial mutations associated with viral pathogenicity.

Potential neutralizing antibodies discovered for novel corona virus using machine learning

The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.

Potential neutralizing antibodies discovered for novel corona virus using machine learning

The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.

A Mechanistic Probe into the Dual Inhibition of T. cruzi Glucokinase and Hexokinase in Chagas Disease Treatment - A Stone Killing Two Birds?

Glucokinase (GLK) and Hexokinase (HK) have been characterized as essential targets in Trypanosoma cruzi (Tc)-mediated infection. A recent study reported the propensity of the concomitant inhibition of TcGLK and TcHK by compounds GLK2-003 and GLK2-004, thereby presenting an efficient approach in Chagas disease treatment. We investigated this possibility using atomic and molecular scaling methods. Sequence alignment of TcGLK and TcHK revealed that both proteins shared approximately 33.3 % homology in their glucose/inhibitor binding sites. The total binding free energies of GLK2-003 and GLK2-004 were favorable in both proteins. PRO92 and THR185 were pivotal to the binding and stabilization of the ligands in TcGLK, likewise their conserved counterparts, PRO163 and THR237 in TcHK. Both compounds also induced a similar pattern of perturbations in both TcGLK and TcHK secondary structure. Findings from this study therefore provide insights into the underlying mechanisms of dual inhibition exhibited by the compounds. These results can pave way to discover and optimize novel dual Tc inhibitors with favorable pharmacokinetics properties eventuating in the mitigation of Chagas disease.

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.

In silico approach of naringin as potent phosphatase and tensin homolog (PTEN) protein agonist against prostate cancer

Prostate cancer (PC) is one of the major impediments affecting men, which leads approximately 31,620 deaths in both developing and developed countries. Although some chemotherapy drugs have been reported for prostate cancer, they are not effective due to the lack of safety, efficacy and low selectivity. Hence, the novel alternative anticancer agents with remarkable effect are highly appreciable. Natural plants contain several bio-active compounds which have been traditionally used for the various medical treatments. Particularly, naringin is a natural bio-active compound commonly found in the citrus fruits, which have shown numerous biological activities. Phosphatase and tensin homolog (PTEN) is a tumor suppressor gene, which activates both lipid phosphates and protein phosphates. The PTEN gene is negative regulator of PI3K/AKT/mTOR pathways, since, this signaling pathway play an essential role in the cell survival, proliferation and migration. In the present in silico investigation, structure based virtual screening, molecular docking, molecular dynamics simulation and Adsorption, Distribution, Metabolism, Excretion (ADME) prediction were employed to determine the binding affinity, stability and drug likeness properties of top ranked screened compounds and naringin, respectively. The results revealed that the complex has good molecular interactions, binding stability (peak between 0.3 and 0.4 nm) and no violations in the Lipinski Rule of 5 in naringin, but the screened compounds violated the drug likeness properties. From the in silico analyses, it is identified that naringin compound might assist in the development of novel therapeutic candidate against prostate cancer.Communicated by Ramaswamy H. Sarma.

Applications of machine-learning methods for the discovery of NDM-1 inhibitors

The emergence of New Delhi metal beta-lactamase (NDM-1)-producing bacteria and their worldwide spread pose great challenges for the treatment of drug-resistant bacterial infections. These bacteria can hydrolyze most β-lactam antibacterials. Unfortunately, there are no clinically useful NDM-1 inhibitors. In the current work, we manually collected NDM-1 inhibitors reported in the past decade and established the first NDM-1 inhibitor database. Four machine-learning models were constructed using the structural and property characteristics of the collected compounds as input training set to discover potential NDM-1 inhibitors. In order to distinguish between high active inhibitors and putative positive drugs, a three-classification strategy was introduced in our study. In detail, the commonly used positive and negative divisions are converted into strongly active, weakly active, and inactive. The accuracy of the best prediction model designed based on this strategy reached 90.5%, compared with 69.14% achieved by the traditional docking-based virtual screening method. Consequently, the best model was used to virtually screen a natural product library. The safety of the selected compounds was analyzed by the ADMET prediction model based on machine learning. Seven novel NDM-1 inhibitors were identified, which will provide valuable clues for the discovery of NDM-1 inhibitors.

In Silico Studies against Viral Sexually Transmitted Diseases

Sexually Transmitted Diseases (STDs) refer to a variety of clinical syndromes and infections caused by pathogens that can be acquired and transmitted through sexual activity. Among STDs widely reported in the literature, viral sexual diseases have been increasing in a number of cases globally. This emphasizes the need for prevention and treatment. Among the methods widely used in drug planning are Computer-Aided Drug Design (CADD) studies and molecular docking which have the objective of investigating molecular interactions between two molecules to better understand the three -dimensional structural characteristics of the compounds. This review will discuss molecular docking studies applied to viral STDs, such as Ebola virus, Herpes virus and HIV, and reveal promising new drug candidates with high levels of specificity to their respective targets.

Computer-Assisted Drug Virtual Screening Based on the Natural Product Databases

BACKGROUND

Computer-assisted drug virtual screening models the process of drug screening through computer simulation technology, by docking small molecules in some of the databases to a certain protein target. There are many kinds of small molecules databases available for drug screening, including natural product databases.

METHODS

Plants have been used as a source of medication for millennia. About 80% of drugs were either natural products or related analogues by 1990, and many natural products are biologically active and have favorable absorption, distribution, metabolization, excretion, and toxicology.

RESULTS

In this paper, we review the natural product databases' contributions to drug discovery based on virtual screening, focusing particularly on the introductions of plant natural products, microorganism natural product, Traditional Chinese medicine databases, as well as natural product toxicity prediction databases.

CONCLUSION

We highlight the applications of these databases in many fields of virtual screening, and attempt to forecast the importance of the natural product database in next-generation drug discovery.

A novel small molecule A2A adenosine receptor agonist, indirubin-3′-monoxime, alleviates lipid-induced inflammation and insulin resistance in 3T3-L1 adipocytes

Saturated free fatty acid-induced adipocyte inflammation plays a pivotal role in implementing insulin resistance and type 2 diabetes. Recent reports suggest A2A adenosine receptor (A2AAR) could be an attractive choice to counteract adipocyte inflammation and insulin resistance. Thus, an effective A2AAR agonist devoid of any toxicity is highly appealing. Here, we report that indirubin-3′-monoxime (I3M), a derivative of the bisindole alkaloid indirubin, efficiently binds and activates A2AAR which leads to the attenuation of lipid-induced adipocyte inflammation and insulin resistance. Using a combination of in silico virtual screening of potential anti-diabetic candidates and in vitro study on insulin-resistant model of 3T3-L1 adipocytes, we determined I3M through A2AAR activation markedly prevents lipid-induced impairment of the insulin signaling pathway in adipocytes without any toxic effects. While I3M restrains lipid-induced adipocyte inflammation by inhibiting NF-κB dependent pro-inflammatory cytokines expression, it also augments cAMP-mediated CREB activation and anti-inflammatory state in adipocytes. However, these attributes were compromised when cells were pretreated with the A2AAR antagonist, SCH 58261 or siRNA mediated knockdown of A2AAR. I3M, therefore, could be a valuable option to intervene adipocyte inflammation and thus showing promise for the management of insulin resistance and type 2 diabetes.

In silico screening of sugar alcohol compounds to inhibit viral matrix protein VP40 of Ebola virus

Ebola virus is a virulent pathogen that causes highly lethal hemorrhagic fever in human and non-human species. The rapid growth of this virus infection has made the scenario increasingly complicated to control the disease. Receptor viral matrix protein (VP40) is highly responsible for the replication and budding of progeny virus. The binding of RNA to VP40 could be the crucial factor for the successful lifecycle of the Ebola virus. In this study, we aimed to identify the potential drug that could inhibit VP40. Sugar alcohols were enrich with antiviral properties used to inhibit VP40. Virtual screening analysis was perform for the 48 sugar alcohol compounds, of which the following three compounds show the best binding affinity: Sorbitol, Mannitol and Galactitol. To understand the perfect binding orientation and the strength of non-bonded interactions, individual molecular docking studies were perform for the best hits. Further molecular dynamics studies were conduct to analyze the efficacy between the protein-ligand complexes and it was identify that Sorbitol obtains the highest efficacy. The best-screened compounds obtained drug-like property and were less toxic, which could be use as a potential lead compound to develop anti-Ebola drugs.

Does Size Really Matter? Probing the Efficacy of Structural Reduction in the Optimization of Bioderived Compounds - A Computational "Proof-of-Concept"

Over the years, numerous synthetic approaches have been utilized in drug design to improve the pharmacological properties of naturally derived compounds and most importantly, minimize toxic effects associated with their transition to drugs. The reduction of complex bioderived compounds to simpler bioactive fragments has been identified as a viable strategy to develop lead compounds with improved activities and minimal toxicities. Although this ‘reductive’ strategy has been widely exemplified, underlying biological events remain unresolved, hence the unanswered question remains how does the fragmentation of a natural compound improve its bioactivity and reduce toxicities? Herein, using a combinatorial approach, we initialize a computational “proof-of- concept” to expound the differential pharmacological and antagonistic activities of a natural compound, Anguinomycin D, and its synthetic fragment, SB640 towards Exportin Chromosome Region Maintenance 1 (CRM1). Interestingly, our findings revealed that in comparison with the parent compound, SB640 exhibited improved pharmacological attributes, while toxicities and off-target activities were relatively minimal. Moreover, we observed that the reduced size of SB640 allowed ‘deep access’ at the Nuclear Export Signals (NES) binding groove of CRM1, which favored optimal and proximal positioning towards crucial residues while the presence of the long polyketide tail in Anguinomycin D constrained its burial at the hydrophobic groove. Furthermore, with regards to their antagonistic functions, structural inactivation (rigidity) was more pronounced in CRM1 when bound by SB640 as compared to Anguinomycin D. These findings provide essential insights that portray synthetic fragmentation of natural compounds as a feasible approach towards the discovery of potential leads in disease treatment.

Antiviral Agents Against Ebola Virus Infection: Repositioning Old Drugs and Finding Novel Small Molecules

Ebola virus (EBOV) causes a deadly hemorrhagic syndrome in humans with mortality rate up to 90%. First reported in Zaire in 1976, EBOV outbreaks showed a fluctuating trend during time and fora long period it was considered a tragic disease confined to the isolated regions of the African continent where the EBOV fear was perpetuated among the poor communities. The extreme severity of the recent 2014-16 EBOV outbreak in terms of fatality rate and rapid spread out of Africa led to the understanding that EBOV is a global health risk and highlights the necessity to find countermeasures against it. In the recent years, several small molecules have been shown to display in vitro and in vivo efficacy against EBOV and some of them have advanced into clinical trials. In addition, also existing drugs have been tested for their anti-EBOV activity and were shown to be promising candidates. However, despite the constant effort addressed to identify anti-EBOV therapeutics, no approved drugs are available against EBOV yet. In this chapter, we describe the main EBOV life cycle steps, providing a detailed picture of the druggable viral and host targets that have been explored so far by different technologies. We then summarize the small molecules, nucleic acid oligomers, and antibody-based therapies reported to have an effect either in in silico, or in biochemical and cell-based assays or in animal models and clinical trials, listing them according to their demonstrated or putative mechanism of action.

Across the blood-brain barrier: Neurotherapeutic screening and characterization of naringenin as a novel CRMP-2 inhibitor in the treatment of Alzheimer's disease using bioinformatics and computational tools

The discovery and developmental processes of CNS drugs have been limited by the inability of potential drug molecules to pass through the blood-brain barrier (BBB). This presents a significant setback in the treatment of neurodegenerative disorders such as Alzheimer's disease (AD), hence the need for compounds that can adhere strictly to the selective criteria of suitable CNS drugs. Collapsin response mediator protein-2 (CRMP-2) has been recently identified as a viable target in neurotherapeutics due to its involvement in the etiology of AD. As shown in previous studies, Naringenin (NAR), a small molecule derivative of Drynaria rhizome (DR) extract, specifically binds CRMP-2 and reduces its phosphorylation. This was shown to facilitate axonal regrowth, with improvement in cognition and learning. Herein, we report the first account of the use of cheminformatics techniques to define the CNS drug-suitability of NAR using selective criteria, coupled with the prediction of possible biological activities and toxicities. Also, we evaluated the mechanistic activity of NAR by modeling its molecular interaction with human CRMP-2 (hCRMP-2). Physicochemical analyses revealed the suitability of NAR as a CNS drug and its ability to transverse the BBB. Possible neurogenic, anti-carcinogenic and cardioprotective activities were also predicted. NAR exhibited favorable binding to CRMP-2 and formed strong bonds with active site residues, which accounts for its stabilization and affinity. Moreover, NAR induced notable conformational changes in CRMP-2, an occurrence that could possibly disrupt kinase-mediated phosphorylation. These findings will aid in the optimization of NAR and improve its neurotherapeutic activities in the treatment of AD.

A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment

Ebola virus disease (EVD) is a deadly global public health threat, with no currently approved treatments. Traditional drug discovery and development is too expensive and inefficient to react quickly to the threat. We review published research studies that utilize computational approaches to find or develop drugs that target the Ebola virus and synthesize its results. A variety of hypothesized and/or novel treatments are reported to have potential anti-Ebola activity. Approaches that utilize multi-targeting/polypharmacology have the most promise in treating EVD.

A Molecular Docking and Dynamics Approach to Screen Potent Inhibitors Against Fosfomycin Resistant Enzyme in Clinical Klebsiella pneumoniae

Klebsiella pneumoniae, BA6753 was cultured from a patient in the Clinical Microbiology Laboratory of Christian Medical College. K. pneumoniae, BA6753 has a multidrug resistance plasmid encoding novel FosA variant-7, fosfomycin resistance enzyme. Minimal side effects and a wide range of bactericidal activity of fosfomycin have resulted in its expanded clinical use that prompts the rise of fosfomycin-resistant strains. At present, there are no effective inhibitors available to conflict the FosA-medicated fosfomycin resistance. To develop effective FosA inhibitors, it is crucial to understand the structural and dynamic properties of resistance enzymes. Hence, the present study focuses on the identification of potent inhibitors that can effectively bind to the fosfomycin resistance enzyme, thus predispose the target to inactivate by the second antibiotic. Initially, a series of active compounds were screened against the resistant enzyme, and the binding affinities were confirmed using docking simulation analysis. For efficient activity, the binding affinity of the resistance enzyme ought to be high with the inhibitor than the fosfomycin drug. Consequently, the enzyme-ligand complex which showed higher binding affinity than the fosfomycin was employed for subsequent analysis. The stability of the top scoring enzyme-ligand complex was further validated using molecular dynamics simulation studies. On the whole, we presume that the compound 19583672 demonstrates a higher binding affinity for the resistance enzyme comparing to other compounds and fosfomycin. We believe that further enhancement of the lead compound can serve as a potential inhibitor against resistance enzyme in drug discovery process. J. Cell. Biochem. 118: 4088-4094, 2017. © 2017 Wiley Periodicals, Inc.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Ebola virus: A gap in drug design and discovery - experimental and computational perspective

The Ebola virus, formally known as the Ebola hemorrhagic fever, is an acute viral syndrome causing sporadic outbreaks that have ravaged West Africa. Due to its extreme virulence and highly transmissible nature, Ebola has been classified as a category A bioweapon organism. Only recently have vaccine or drug regimens for the Ebola virus been developed, including Zmapp and peptides. In addition, existing drugs which have been repurposed toward anti-Ebola virus activity have been re-examined and are seen to be promising candidates toward combating Ebola. Drug development involving computational tools has been widely employed toward target-based drug design. Screening large libraries have greatly stimulated research toward effective anti-Ebola virus drug regimens. Current emphasis has been placed on the investigation of host proteins and druggable viral targets. There is a huge gap in the literature regarding guidelines in the discovery of Ebola virus inhibitors, which may be due to the lack of information on the Ebola drug targets, binding sites, and mechanism of action of the virus. This review focuses on Ebola virus inhibitors, drugs which could be repurposed to combat the Ebola virus, computational methods which study drug-target interactions as well as providing further insight into the mode of action of the Ebola virus.

Integrated Computational Approach for Virtual Hit Identification against Ebola Viral Proteins VP35 and VP40

The Ebola virus (EBOV) has been recognised for nearly 40 years, with the most recent EBOV outbreak being in West Africa, where it created a humanitarian crisis. Mortalities reported up to 30 March 2016 totalled 11,307. However, up until now, EBOV drugs have been far from achieving regulatory (FDA) approval. It is therefore essential to identify parent compounds that have the potential to be developed into effective drugs. Studies on Ebola viral proteins have shown that some can elicit an immunological response in mice, and these are now considered essential components of a vaccine designed to protect against Ebola haemorrhagic fever. The current study focuses on chemoinformatic approaches to identify virtual hits against Ebola viral proteins (VP35 and VP40), including protein binding site prediction, drug-likeness, pharmacokinetic and pharmacodynamic properties, metabolic site prediction, and molecular docking. Retrospective validation was performed using a database of non-active compounds, and early enrichment of EBOV actives at different false positive rates was calculated. Homology modelling and subsequent superimposition of binding site residues on other strains of EBOV were carried out to check residual conformations, and hence to confirm the efficacy of potential compounds. As a mechanism for artefactual inhibition of proteins through non-specific compounds, virtual hits were assessed for their aggregator potential compared with previously reported aggregators. These systematic studies have indicated that a few compounds may be effective inhibitors of EBOV replication and therefore might have the potential to be developed as anti-EBOV drugs after subsequent testing and validation in experiments in vivo.

Molecular docking based screening of compounds against VP40 from Ebola virus

Ebola virus causes severe and often fatal hemorrhagic fevers in humans. The 2014 Ebola epidemic affected multiple countries. The virus matrix protein (VP40) plays a central role in virus assembly and budding. Since there is no FDA-approved vaccine or medicine against Ebola viral infection, discovering new compounds with different binding patterns against it is required. Therefore, we aim to identify small molecules that target the Arg 134 RNA binding and active site of VP40 protein. 1800 molecules were retrieved from PubChem compound database based on Structure Similarity and Conformers of pyrimidine-2, 4-dione. Molecular docking approach using Lamarckian Genetic Algorithm was carried out to find the potent inhibitors for VP40 based on calculated ligand-protein pairwise interaction energies. The grid maps representing the protein were calculated using auto grid and grid size was set to 60*60*60 points with grid spacing of 0.375 Ǻ. Ten independent docking runs were carried out for each ligand and results were clustered according to the 1.0 Ǻ RMSD criteria. The post-docking analysis showed that binding energies ranged from -8.87 to 0.6 Kcal/mol. We report 7 molecules, which showed promising ADMET results, LD-50, as well as H-bond interaction in the binding pocket. The small molecules discovered could act as potential inhibitors for VP40 and could interfere with virus assembly and budding process.