Genome-wide comparison and identification of myosin gene family in Arabidopsis thaliana and Helianthus annuus.

Myosins are essential components of organelle trafficking in all the eukaryotic cells. Myosin driven movement plays a vital role in the development of pollen tubes, root hairs and root tips of flowering plants. The present research characterized the myosin genes in Arabidopsis thaliana and Helianthus annuus by using different computational tools. We discovered a total of 50 myosin genes and their splice variants in both pant species. Phylogenetic analysis indicated that myosin genes were divided into four subclasses. Chromosomal location revealed that myosin genes were located on all five chromosomes in A. thaliana, whereas they were present on nine chromosomes in H. annuus. Conserved motifs showed that conserved regions were closely similar within subgroups. Gene structure analysis showed that Atmyosin2.2 and Atmyosin2.3 had the highest number of introns/exons. Gene ontology analysis indicated that myosin genes were involved in vesicle transport along actin filament and cytoskeleton trafficking. Expression analysis showed that expression of myosin genes was higher during the flowering stage as compared to the seedling and budding stages. Tissue specific expression indicated that HanMYOSIN11.2, HanMYOSIN16.2 were highly expressed in stamen, whereas HanMYOSIN 2.2, HanMYOSIN 12.1 and HanMYOSIN 17.1 showed higher expression in nectary. This study enhance our understanding the function of myosins in plant development, and forms the basis for future research about the comparative genomics of plant myosin in other crop plants.

iPhosS(Deep)-PseAAC: Identification of Phosphoserine Sites in Proteins Using Deep Learning on General Pseudo Amino Acid Compositions.

Among all the PTMs, the protein phosphorylation is pivotal for various pathological and physiological processes. About 30 percent of eukaryotic proteins undergo the phosphorylation modification, leading to various changes in conformation, function, stability, localization, and so forth. In eukaryotic proteins, phosphorylation occurs on serine (S), Threonine (T) and Tyrosine (Y) residues. Among these all, serine phosphorylation has its own importance as it is associated with various importance biological processes, including energy metabolism, signal transduction pathways, cell cycling, and apoptosis. Thus, its identification is important, however, the in vitro, ex vivo and in vivo identification can be laborious, time-taking and costly. There is a dire need of an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner. Herein, we propose a novel predictor for identification of Phosphoserine sites (PhosS) in proteins, by integrating the Chou's Pseudo Amino Acid Composition (PseAAC) with deep features. We used well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications. Among different DNNs, the best score is shown by Covolutional Neural Network based model which renders CNN based prediction model the best for Phosphoserine prediction. Based on these results, it is concluded that the proposed model can help to identify PhosS sites in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

Virtual Screening of Phytochemicals by Targeting HR1 Domain of SARS-CoV-2 S Protein: Molecular Docking, Molecular Dynamics Simulations, and DFT Studies.

The recent COVID-19 pandemic has impacted nearly the whole world due to its high morbidity and mortality rate. Thus, scientists around the globe are working to find potent drugs and designing an effective vaccine against COVID-19. Phytochemicals from medicinal plants are known to have a long history for the treatment of various pathogens and infections; thus, keeping this in mind, this study was performed to explore the potential of different phytochemicals as candidate inhibitors of the HR1 domain in SARS-CoV-2 spike protein by using computer-aided drug discovery methods. Initially, the pharmacological assessment was performed to study the drug-likeness properties of the phytochemicals for their safe human administration. Suitable compounds were subjected to molecular docking to screen strongly binding phytochemicals with HR1 while the stability of ligand binding was analyzed using molecular dynamics simulations. Quantum computation-based density functional theory (DFT) analysis was constituted to analyze the reactivity of these compounds with the receptor. Through analysis, 108 phytochemicals passed the pharmacological assessment and upon docking of these 108 phytochemicals, 36 were screened passing a threshold of -8.5 kcal/mol. After analyzing stability and reactivity, 5 phytochemicals, i.e., SilybinC, Isopomiferin, Lycopene, SilydianinB, and Silydianin are identified as novel and potent candidates for the inhibition of HR1 domain in SARS-CoV-2 spike protein. Based on these results, it is concluded that these compounds can play an important role in the design and development of a drug against COVID-19, after an exhaustive in vitro and in vivo examination of these compounds, in future.

Biological perspective of thiazolide derivatives against Mpro and MTase of SARS-CoV-2: Molecular docking, DFT and MD simulation investigations.

Humans around the globe have been severely affected by SARS-CoV-2 and no treatment has yet been authorized for the treatment of this severe condition brought by COVID-19. Here, an in silico research was executed to elucidate the inhibitory potential of selected thiazolides derivatives against SARS-CoV-2 Protease (Mpro) and Methyltransferase (MTase). Based on the analysis; 4 compounds were discovered to have efficacious and remarkable results against the proteins of the interest. Primarily, results obtained through this study not only allude these compounds as potential inhibitors but also pave the way for in vivo and in vitro validation of these compounds.

iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features.

DNA replication is one of the specific processes to be considered in all the living organisms, specifically eukaryotes. The prevalence of DNA replication is significant for an evolutionary transition at the beginning of life. DNA replication proteins are those proteins which support the process of replication and are also reported to be important in drug design and discovery. This information depicts that DNA replication proteins have a very important role in human bodies, however, to study their mechanism, their identification is necessary. Thus, it is a very important task but, in any case, an experimental identification is time-consuming, highly-costly and laborious. To cope with this issue, a computational methodology is required for prediction of these proteins, however, no prior method exists. This study comprehends the construction of novel prediction model to serve the proposed purpose. The prediction model is developed based on the artificial neural network by integrating the position relative features and sequence statistical moments in PseAAC for training neural networks. Highest overall accuracy has been achieved through tenfold cross-validation and Jackknife testing that was computed to be 96.22% and 98.56%, respectively. Our astonishing experimental results demonstrated that the proposed predictor surpass the existing models that can be served as a time and cost-effective stratagem for designing novel drugs to strike the contemporary bacterial infection.

Optimization of serine phosphorylation prediction in proteins by comparing human engineered features and deep representations.

Deep representations can be used to replace human-engineered representations, as such features are constrained by certain limitations. For the prediction of protein post-translation modifications (PTMs) sites, research community uses different feature extraction techniques applied on Pseudo amino acid compositions (PseAAC). Serine phosphorylation is one of the most important PTM as it is the most occurring, and is important for various biological functions. Creating efficient representations from large protein sequences, to predict PTM sites, is a time and resource intensive task. In this study we propose, implement and evaluate use of Deep learning to learn effective protein data representations from PseAAC to develop data driven PTM detection systems and compare the same with two human representations.. The comparisons are performed by training an xgboost based classifier using each representation. The best scores were achieved by RNN-LSTM based deep representation and CNN based representation with an accuracy score of 81.1% and 78.3% respectively. Human engineered representations scored 77.3% and 74.9% respectively. Based on these results, it is concluded that the deep features are promising feature engineering replacement to identify PhosS sites in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

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.

iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition.

Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association, cytoskeletal dynamics, and enzymatic inhibition/activation. Phosphohistidine (PhosH) has a key role in a number of biological processes, including central metabolism to signalling in eukaryotes and bacteria. Thus, identification of phosphohistidine sites in a protein sequence is crucial, and experimental identification can be expensive, time-taking, and laborious. To address this problem, here, we propose a novel computational model namely iPhosH-PseAAC for prediction of phosphohistidine sites in a given protein sequence using pseudo amino acid composition (PseAAC), statistical moments, and position relative features. The results of the proposed predictor are validated through self-consistency testing, 10-fold cross-validation, and jackknife testing. The self-consistency validation gave the 100 percent accuracy, whereas, for cross-validation, the accuracy achieved is 94.26 percent. Moreover, jackknife testing gave 97.07 percent accuracy for the proposed model. Thus, the proposed model iPhosH-PseAAC for prediction of iPhosH site has the great ability to predict the PhosH sites in given proteins.

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.

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.

ADVIT: Using the potentials of deep representations incorporated with grid-based features of dorsum vein patterns for human identification.

The identification of an individual is one of the main applications of forensic science, used in legal settings for deciding cases in courts of law. Different methods have been developed for the identification of a person, including fingerprints, DNA profiling, retina scan, facial features and many others. The reliable and accurate identification mainly relies upon substantial variability of structures and features of evidence corresponding to reference material. During the last decade, human identification through hand vein patterns has been focused in various studies and shown promising results. However, most of the reported methods require extensive human efforts for manual feature calculation. Herein, we propose a novel identification tool namely ADVIT for the identification of humans based on their dorsum veins pattern. The samples of the dorsum of the right or left hand were collected from 50 participants in the form of images. Initially, images were preprocessed and noise (in terms of hair on the skin and other details) was removed. Later on, the vein skeleton was extracted from the preprocessed images and a binary image of veins pattern (veins in the foreground and every other detail as background) was generated. Two different types of the feature were computed and based on these features, three different experiments were performed and the evaluation metrics were computed. Merging of hand-crafted grid-based features and deep representation from RESNET-50 showed maximum results in terms of Sensitivity (0.8803), Specificity (0.8890), Precision (0.8849), False Positive Rate (0.1074), Accuracy (0.8861), F1 Score (0.8817), and MCC (0.7636). These results depicted that the model is accurate and sensitive for identification through dorsum veins pattern. The proposed model can aid forensic scientists to identify perpetrator using hand images. ADVIT is freely available at (http://zeetu.org/advit.html).

DNA evidence in sexual assault cases in Pakistan.

Sexual assault is becoming a global epidemic, affecting close to a billion women throughout the world. This paper explores the challenges in the admissibility of DNA evidence in rape cases in Pakistan. Delays in the medical examination of victims, and improper collection and packaging of evidentiary material, compromise the probative biological evidence. In the last few years, existing laws have been amended to increase the utility of DNA evidence during criminal trials. However, various issues - for example lack of proper knowledge of DNA evidence by lawyers and judicial officers, inadequacies in existing laws and conflicting decisions of apex courts - can affect the admissibility of DNA evidence during criminal trials.

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.

ForeStatistics: A windows-based feature-rich software program for performing statistics in forensic DNA analysis, paternity and relationship testing.

Forensic science is one of the most modern and applied fields of science, today and comprises of various domains. These include Fingerprints analysis, Questioned document analysis, Forensic DNA and serology, Anthropometry, Cyber and Digital forensics, and many other fields. All these fields aid the process of decision making in the courts of law and legal settings; however, DNA profiling and its analyses are one of the most important aspects of forensic science today. In Forensic DNA analysis, the statistical calculations are very important to estimate the conclusiveness of DNA evidence in forensic cases; and to establish paternity and relatedness in civil and criminal matters. These statistics, when performed manually, leave a chance of error or ambiguity in the calculation, and are hectic and time-taking. Therefore, the computer-aided approaches are opted in forensics to perform DNA statistics calculations. Keeping its importance in mind, a highly accurate windows-based software program namely ForeStatistics is proposed in this study. ForeStatistics is rich in features such as DNA statistical calculations, DNA profile management and its matching. The software can estimate random match probabilities for single-source profiles, combined probability of inclusion for mixed profiles, paternity index of a disputed child in duo and trio cases, paternity of the disputed child when the alleged father is related to mother or biological father and relatedness in cases of grandparents/grandchild, avuncular relation and cousin. It is validated through different protocols and the validation of ForeStatistics depicts that it is highly accurate in terms of performing DNA statistics or DNA profile matching. Thus, it is concluded, that ForeStatistics has a great utility in the field of Forensic DNA analysis and can help DNA scientists, in performing various DNA related statistics, accurately and very efficiently. ForeStatistics can be downloaded freely from (http://zeetu.org/forestatistics.html).

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.

iProtease-PseAAC(2L): A two-layer predictor for identifying proteases and their types using Chou's 5-step-rule and general PseAAC.

Proteases are a type of enzymes, which perform the process of proteolysis. Proteolysis normally refers to protein and peptide degradation which is crucial for the survival, growth and wellbeing of a cell. Moreover, proteases have a strong association with therapeutics and drug development. The proteases are classified into five different types according to their nature and physiochemical characteristics. Mostly the methods used to differentiate protease from other proteins and identify their class requires a clinical test which is usually time-consuming and operator dependent. Herein, we report a classifier named iProtease-PseAAC (2L) for identifying proteases and their classes. The predictor is developed employing the flow of 5-step rule, initiating from the collection of benchmark dataset and terminating at the development of predictor. Rigorous verification and validation tests are performed and metrics are collected to calculate the authenticity of the trained model. The self-consistency validation gives the 98.32% accuracy, for cross-validation the accuracy is 90.71% and jackknife gives 96.07% accuracy. The average accuracy for level-2 i.e. protease classification is 95.77%. Based on the above-mentioned results, it is concluded that iProtease-PseAAC (2L) has the great ability to identify the proteases and their classes using a given protein sequence.

Three Major Phosphoacceptor Sites in HIV-1 Capsid Protein Enhances its Structural Stability and Resistance Against the Inhibitor: Explication Through Molecular Dynamics Simulation, Molecular Docking and DFT Analysis.

BACKGROUND: Human Immunodeficiency Virus 1 (HIV-1) is a lentivirus, which causes various HIV-associated infections. The HIV-1 core dissociation is essential for viral cDNA synthesis and phosphorylation of HIV-1 capsid protein (HIV-1 CA) plays an important role in it. OBJECTIVE: The aim of this study was to explicate the role of three phosphoserine sites i.e. Ser109, Ser149 and Ser178 in the structural stability of HIV-1 CA, and it's binding with GS-CA1, a novel potent inhibitor. METHODS: Eight complexes were analyzed and Molecular Dynamics (MD) simulations were performed to observe the stability of HIV-1 CA in the presence and absence of phosphorylation of serine residues at four different temperatures i.e. 300K, 325K, 340K and 350K, along with molecular docking and DFT analysis. RESULTS: The structures showed maximum stability in the presence of phosphorylated serine residue. However, GS-CA1 docked most strongly with the native structure of HIV-1 CA i.e. binding affinity was -8.5 kcal/mol (Ki = 0.579 µM). CONCLUSION: These results suggest that the phosphorylation of these three serine residues weakens the binding of GS-CA1 with CA and casts derogatory effect on inhibition potential of this inhibitor, but it supports the stability of HIV-1 CA structure that can enhance regulation and replication of HIV-1 in host cells.

An in silico investigation of phytochemicals as potential inhibitors against non-structural protein 1 from dengue virus 4

Dengue fever has emerged as a big threat to human health since the last decade owing to high morbidity with considerable mortalities. The proposed study aims at the in silico investigation of the inhibitory action against DENV4-NS1 of phytochemicals from two local medicinal plants of Pakistan. Non-Structural Protein 1 of Dengue Virus 4 (DENV4-NS1) is known to be involved in the replication and maturation of viron in the host cells. A total of 129 phytochemicals (50 from Tanacetum parthenium and 79 from Silybum marianum) were selected for this study. The tertiary structure of DENV4-NS1 was predicted based on homology modelling using Modeller 9.18 and the structural stability was evaluated using molecular dynamics simulations. Absorption, distribution, metabolism, excretion and toxicity (ADMET) along with the drug-likeness was also predicted for these phytochemicals using SwissADME and PreADMET servers. The results of ADMET and drug-likeness predictions exhibited that 54 phytochemicals i.e. 25 from Tanacetum parthenium and 29 from Silybum marianum showed effective druglikeness. These phytochemicals were docked against DENV4-NS1 using AutoDock Vina and 18 most suitable phytochemicals with binding affinities ≤ -6.0 kcal/mol were selected as potential inhibitors for DENV4-NS1. Proposed study also exploits the novel inhibitory action of Jaceidin, Centaureidin, Artecanin, Secotanaparthenolide, Artematin, Schizolaenone B, Isopomiferin, 6, 8-Diprenyleriodictyol, and Anthraxin against dengue virus. It is concluded that the screened 18 phytochemicals have strong inhibition potential against Dengue Virus 4.