In silico targeting of non-structural 4B protein from dengue virus 4 with spiropyrazolopyridone: study of molecular dynamics simulation, ADMET and virtual screening

Evaluation of bacteriophage ϕ11 host recognition protein and its host-binding peptides for diagnosing/targeting Staphylococcus aureus infections

BACKGROUND

Evaluating the potential of using both synthetic and biological products as targeting agents for the diagnosis, imaging, and treatment of infections due to particularly antibiotic-resistant pathogens is important for controlling infections. This study examined the interaction between Gp45, a receptor-binding protein of the ϕ11 lysogenic phage, and its host Staphylococcus aureus (S. aureus), a common cause of nosocomial infections.

METHODS

Using molecular dynamics and docking simulations, this study identified the peptides that bind to S. aureus wall teichoic acids via Gp45. It compared the binding affinity of Gp45 and the two highest-scoring peptide sequences (P1 and P3) and their scrambled forms using microscopy, spectroscopy, and ELISA.

RESULTS

It was found that rGp45 (recombinant Gp45) and chemically synthesised P1 had a higher binding affinity for S. aureus compared with all other peptides, except for Escherichia coli. Furthermore, rGp45 had a capture efficiency of > 86%; P1 had a capture efficiency of > 64%.

CONCLUSION

These findings suggest that receptor-binding proteins such as rGp45, which provide a critical initiation of the phage life cycle for host adsorption, might play an important role in the diagnosis, imaging, and targeting of bacterial infections. Studying such proteins could accordingly enable the development of effective strategies for controlling infections.

In silico homology modeling of dengue virus non-structural 4B (NS4B) protein and its molecular docking studies using triterpenoids

BACKGROUND

Dengue fever has become a significant worldwide health concern, because of its high morbidity rate and the potential for an increase in mortality rates due to lack of adequate treatment. There is an immediate need for the development of effective medication for dengue fever.

METHODS

Homology modeling of dengue virus (DENV) non-structural 4B (NS4B) protein was performed by SWISS-MODEL to predict the 3D structure of the protein. Structure validation was conducted using PROSA, PROCHECK, Ramachandran plot, and VERIFY-3D. MOE software was used to find out the in-Silico inhibitory potential of the five triterpenoids against the DENV-NS4B protein.

RESULTS

The SWISS-MODEL was employed to predict the three-dimensional protein structure of the NS4B protein. Through molecular docking, it was found that the chosen triterpenoid NS4B protein had a high binding affinity interaction. It was observed that the NS4B protein binding energy for 15-oxoursolic acid, betulinic acid, ursolic acid, lupeol, and 3-o-acetylursolic acid were - 7.18, - 7.02, - 5.71, - 6.67 and - 8.00 kcal/mol, respectively.

CONCLUSIONS

NS4B protein could be a promising target which showed good interaction with tested triterpenoids which can be developed as a potential antiviral drug for controlling dengue virus pathogenesis by inhibiting viral replication. However, further investigations are necessary to validate and confirm their efficacy.

Computational Advancement towards the Identification of Natural Inhibitors for Dengue Virus: A Brief Review

Viral infectious illnesses represent a severe hazard to human health due to their widespread incidence worldwide. Among these ailments, the dengue virus (DENV) infection stands out. World Health Organization (WHO) estimates that DENV infection affects ~400 million people each year, with potentially fatal symptoms showing up in 1% of the cases. In several instances, academic and pharmaceutical researchers have conducted several pilot and clinical studies on a variety of topics, including viral epidemiology, structure and function analyses, infection source and route, therapeutic targets, vaccinations, and therapeutic drugs. Amongst Takeda, TAK-003, Sanofi, Dengvaxia®, and Butantan/NIH/Merck, Dengvaxia® (CYD-TDV) is the only licensed vaccination yet; however, the potential inhibitors are under development. The biology and evolution of DENVs are briefly discussed in this review, which also compiles the most recent studies on prospective antiviral targets and antiviral candidates. In conclusion, the triumphs and failures have influenced the development of anti-DENV medications, and the findings in this review article will stimulate more investigation.

Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features

Objective

The objective of this study is to propose a novel in-silico method called Hemolytic-Pred for identifying hemolytic proteins based on their sequences, using statistical moment-based features, along with position-relative and frequency-relative information.

Methods

Primary sequences were transformed into feature vectors using statistical and position-relative moment-based features. Varying machine learning algorithms were employed for classification. Computational models were rigorously evaluated using four different validation. The Hemolytic-Pred webserver is available for further analysis at http://ec2-54-160-229-10.compute-1.amazonaws.com/.

Results

XGBoost outperformed the other six classifiers with an accuracy value of 0.99, 0.98, 0.97, and 0.98 for self-consistency test, 10-fold cross-validation, Jackknife test, and independent set test, respectively. The proposed method with the XGBoost classifier is a workable and robust solution for predicting hemolytic proteins efficiently and accurately.

Conclusions

The proposed method of Hemolytic-Pred with XGBoost classifier is a reliable tool for the timely identification of hemolytic cells and diagnosis of various related severe disorders. The application of Hemolytic-Pred can yield profound benefits in the medical field.

iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions

Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou's Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D DominicCommunicated by Ramaswamy H. Sarma.

Targeting the DENV NS2B-NS3 protease with active antiviral phytocompounds: structure-based virtual screening, molecular docking and molecular dynamics simulation studies

Dengue fever has been a global health concern. Mitigation is a challenging problem due to non-availability of workable treatments. The most difficult objective is to design a perfect anti-dengue agent capable of inhibiting infections caused by all four serotypes. Various tactics have been employed in the past to discover dengue antivirals, including screening of chemical compounds against dengue virus enzymes. The objective of the current study is to investigate phytocompounds as anti-dengue remedies that target the non-structural 2B and non-structural 3 protease (NS2B-NS3pro), a possible therapeutic target for dengue fever. Initially, 300 + antiviral phytocompounds were collected from Duke's phytochemical and ethnobotanical database and 30 phytocompounds with anti-dengue properties were identified from previously reported studies, which were virtually screened against NS2B-NS3pro using molecular docking and toxicity evaluation. The top five most screened ligands were naringin, hesperidin, gossypol, maslinic acid and rhodiolin with binding affinities of - 8.7 kcal/mol, - 8.5 kcal/mol, - 8.5 kcal/mol, - 8.5 kcal/mol and - 8.1 kcal/mol, respectively. The finest docked compounds complexed with NS2B-NS3pro were subjected for molecular dynamics (MD) simulations and binding free energy estimations through molecular mechanics generalized born surface area-based calculations. The results of the study are intriguing in the context of computer-aided screening and the binding affinities of the phytocompounds, proposing maslinic acid (MAS) as a potent bioactive antiviral for the development of phytocompound-based anti-dengue agent.

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.

Virtual Screening-Based Identification of Potent DENV-3 RdRp Protease Inhibitors via In-House Usnic Acid Derivative Database

Dengue virus (DENV) is the causative agent of dengue fever, dengue hemorrhagic disease and dengue shock syndrome (DSS), transmitted predominantly in tropical and subtropical regions by Aedes aegypti. It infects millions of people and causes thousands of deaths each year, but there is no antiviral drug against DENV. Usnic acid lately piqued the interest of researchers for extraordinary biological characteristics, including antiviral activity. Based on high larvicidal activities against Aedes aegypti, this study aims to search usnic acid derivatives as novel anti-DENV agents through a combination of ligand-based and pharmacophore-based virtual screening. One hundred and sixteen (116) usnic acid derivatives were obtained from a database of 428 in-house usnic acid derivatives through pharmacophore filtering steps. Subsequent docking simulation on DENV-3 NS-5 RdRp afforded 41 compounds with a strong binding affinity towards the enzyme. The pharmacokinetics and drug likeness prediction resulted in seven hit compounds, which eventually undergo cytochrome P450 enzyme screening to obtain the lead compound, labelled as 362. In addition, molecular dynamic (MD) simulation of lead compound 362 was performed to verify the stability of the docked complex and the binding posture acquired in docking experiments. Overall, the lead compounds have shown a high fit value of pharmacophore, strong binding affinity towards RdRp enzyme, good pharmacokinetics, and drug likeness properties. The discovery of a new usnic acid derivative as a novel anti-DENV agent targeting RdRp could lead to further drug development and optimization to treat dengue.

In Silico Inhibition of BACE-1 by Selective Phytochemicals as Novel Potential Inhibitors: Molecular Docking and DFT Studies

BACKGROUND

Alzheimer's Disease (AD) has become the most common age-dependent disease of dementia. The trademark pathologies of AD are the presence of amyloid aggregates in neurofibrils. Recently phytochemicals being considered as potential inhibitors against various neurodegenerative, antifungal, antibacterial and antiviral diseases in human beings.

OBJECTIVE

This study targets the inhibition of BACE-1 by phytochemicals using in silico drug discovery analysis.

METHODS

A total of 3150 phytochemicals were collected from almost 25 different plants through literature assessment. The ADMET studies, molecular docking and density functional theory (DFT) based analysis were performed to analyze the potential inhibitory properties of these phytochemicals.

RESULTS

The ADMET and docking results exposed seven compounds that have high potential as an inhibitory agent against BACE-1 and show binding affinity >8.0 kcal/mol against BACE-1. They show binding affinity greater than those of various previously reported inhibitors of BACE-1. Furthermore, DFT based analysis has shown high reactivity for these seven phytochemicals in the binding pocket of BACE- 1, based on ELUMO, EHOMO and Kohn-Sham energy gap. All seven phytochemicals were testified (as compared to experimental ones) as novel inhibitors against BACE-1.

CONCLUSION

Out of seven phytochemicals, four were obtained from plant Glycyrrhiza glabra i.e. Shinflavanone, Glabrolide, Glabrol and PrenyllicoflavoneA, one from Huperzia serrate i.e. Macleanine, one from Uncaria rhynchophylla i.e. 3a-dihydro-cadambine and another one was from VolvalerelactoneB from plant Valeriana-officinalis. It is concluded that these phytochemicals are suitable candidates for drug/inhibitor against BACE-1, and can be administered to humans after experimental validation through in vitro and in vivo trials.

Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment

Glycosylation of proteins in eukaryote cells is an important and complicated post-translation modification due to its pivotal role and association with crucial physiological functions within most of the proteins. Identification of glycosylation sites in a polypeptide chain is not an easy task due to multiple impediments. Analytical identification of these sites is expensive and laborious. There is a dire need to develop a reliable computational method for precise determination of such sites which can help researchers to save time and effort. Herein, we propose a novel predictor namely iGlycoS-PseAAC by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) and relative/absolute position-based features. The self-consistency results show that the accuracy revealed by the model using the benchmark dataset for prediction of O-linked glycosylation having serine sites is 98.8 percent. The overall accuracy of predictor achieved through 10-fold cross validation by combining the positive and negative results is 97.2 percent. The overall accuracy achieved through Jackknife test is 96.195 percent by aggregating of all the prediction results. Thus the proposed predictor can help in predicting the O-linked glycosylated serine sites in an efficient and accurate way. The overall results show that the accuracy of the iGlycoS-PseAAC is higher than the existing tools.

Analysis of Inhibitor Binding Combined with Reactivity Studies to Discover the Potentially Inhibiting Phytochemicals Targeting Chikungunya Viral Replication

BACKGROUND

Chikungunya fever is a challenging threat to human health in various parts of the world nowadays. Many attempts have been made for developing an effective drug against this viral disease and no effective antiviral treatment has been developed to control the spread of the Chikungunya virus (CHIKV) in humans.

OBJECTIVE

This research is aimed at the discovery of potential inhibitors against this virus by employing computational techniques to study the interactions between non-structural proteins of Chikungunya virus and phytochemicals from plants.

METHODS

Four non-structural proteins were docked with 2035 phytochemicals from various plants. The ligands having binding energies ≥ -8.0 kcal/mol were considered as potential inhibitors for these proteins. ADMET studies were also performed to analyze different pharmacological properties of these docked compounds and to further analyze the reactivity of these phytochemicals against CHIKV, DFT analysis was carried out based on HOMO and LUMO energies.

RESULTS

By analyzing the binding energies, Ki, ADMET properties and band energy gaps, it was observed that 13 phytochemicals passed all the criteria to be a potent inhibitor against CHIKV in humans.

CONCLUSION

A total of 13 phytochemicals were identified as potent inhibiting candidates, which can be used against the Chikungunya virus.

Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule

Background:

Allergens are antigens that can stimulate an atopic type I human hypersensitivity reaction by an immunoglobulin E (IgE) reaction. Some proteins are naturally allergenic than others. The challenge for toxicologists is to identify properties that allow proteins to cause allergic sensitization and allergic diseases. The identification of allergen proteins is a very critical and pivotal task. The experimental identification of protein functions is a hectic, laborious and costly task; therefore, computer scientists have proposed various methods in the field of computational biology and bioinformatics using various data science approaches. Objectives: Herein, we report a novel predictor for the identification of allergen proteins.

Methods:

For feature extraction, statistical moments and various position-based features have been incorporated into Chou’s pseudo amino acid composition (PseAAC), and are used for training of a neural network.

Results:

The predictor is validated through 10-fold cross-validation and Jackknife testing, which gave 99.43% and 99.87% accurate results.

Conclusions:

Thus, the proposed predictor can help in predicting the Allergen proteins in an efficient and accurate way and can provide baseline data for the discovery of new drugs and biomarkers.

Phytochemicals from Selective Plants Have Promising Potential against SARS-CoV-2: Investigation and Corroboration through Molecular Docking, MD Simulations, and Quantum Computations

Coronaviruses have been reported previously due to their association with the severe acute respiratory syndrome (SARS). After SARS, these viruses were known to be causing Middle East respiratory syndrome (MERS) and caused 35% evanescence amid victims pursuing remedial care. Nowadays, beta coronaviruses, members of Coronaviridae, family order Nidovirales, have become subjects of great importance due to their latest pandemic originating from Wuhan, China. The virus named as human-SARS-like coronavirus-2 contains four structural as well as sixteen nonstructural proteins encoded by single-stranded ribonucleic acid of positive polarity. As there is no vaccine available to treat the infection caused by these viruses, there is a dire need for taking necessary steps against this virus. Herein, we have targeted two nonstructural proteins of SARS-CoV-2, namely, methyltransferase (nsp16) and helicase (nsp13), respectively, due to their substantial activity in viral pathogenesis. A total of 2035 compounds were analyzed for their pharmacokinetics and pharmacological properties. The screened 108 compounds were docked against both targeted proteins and were compared with previously reported known compounds. Compounds with high binding affinity were analyzed for their reactivity through DFT analysis, and binding was analyzed using molecular dynamics simulations. Through the analyses performed in this study, it is concluded that EryvarinM, Silydianin, Osajin, and Raddeanine can be considered potential inhibitors for MTase, while TomentodiplaconeB, Osajin, Sesquiterpene Glycoside, Rhamnetin, and Silydianin for helicase after these compounds are validated thoroughly using in vitro and in vivo protocols.

Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD

BACKGROUND

Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS.

OBJECTIVES

This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article.

CONCLUSION

By this thorough study, we have observed that in LBVS algorithms, Support Vector Machines (SVM) and Random Forest (RF) are those which are widely used due to high accuracy and efficiency. These virtual screening approaches have the potential to revolutionize the drug designing field. Also, we believe that the process flow presented in this study, named FP-CADD, can streamline the whole process of computer-aided drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.

Insights into the inhibitory potential of selective phytochemicals against Mpro of 2019-nCoV: a computer-aided study

At the end of December 2019, a novel strain of coronavirus, given the name of 2019-nCoV, emerged for exhibiting symptoms of severe acute respiratory syndrome. The virus is spreading rapidly in China and around the globe, affecting thousands of people leading to a pandemic. To control the mortality rate associated with the 2019-nCoV, prompt steps are needed. Until now there is no effective treatment or drug present to control its life-threatening effects in the humans. The scientist is struggling to find new inhibitors of this deadly virus. In this study, to identify the effective inhibitor candidates against the main protease (Mpro) of 2019-nCoV, computational approaches were adopted. Phytochemicals having immense medicinal properties as ligands were docked against the Mpro of 2019-nCoV to study their binding properties. ADMET and DFT analyses were also further carried out to analyze the potential of these phytochemicals as an effective inhibitor against Mpro of 2019-nCoV.

A Sequence-Based Predictor of Zika Virus Proteins Developed by Integration of PseAAC and Statistical Moments

BACKGROUND

ZIKV has been a well-known global threat, which hits almost all of the American countries and posed a serious threat to the entire globe in 2016. The first outbreak of ZIKV was reported in 2007 in the Pacific area, followed by another severe outbreak, which occurred in 2013/2014 and subsequently, ZIKV spread to all other Pacific islands. A broad spectrum of ZIKV associated neurological malformations in neonates and adults has driven this deadly virus into the limelight. Though tremendous efforts have been focused on understanding the molecular basis of ZIKV, the viral proteins of ZIKV have still not been studied extensively.

OBJECTIVES

Herein, we report the first and the novel predictor for the identification of ZIKV proteins.

METHODS

We have employed Chou's pseudo amino acid composition (PseAAC), statistical moments and various position-based features.

RESULTS

The predictor is validated through 10-fold cross-validation and Jackknife testing. In 10- fold cross-validation, 94.09% accuracy, 93.48% specificity, 94.20% sensitivity and 0.80 MCC were achieved while in Jackknife testing, 96.62% accuracy, 94.57% specificity, 97.00% sensitivity and 0.88 MCC were achieved.

CONCLUSION

Thus, ZIKVPred-PseAAC can help in predicting the ZIKV proteins efficiently and accurately and can provide baseline data for the discovery of new drugs and biomarkers against ZIKV.