Category: Systems Biology

What is Perfume (Parfum or Extrait de Parfum)

Perfume, also known as Parfum or Extrait de Parfum, refers to the highest concentration of fragrance in a perfume product. It contains the most concentrated form of perfume oils, making it the most potent and long-lasting type of fragrance available.

Perfume typically has a fragrance oil concentration ranging from 15% to 30%, although the exact concentration may vary between brands and perfumes. It contains a high proportion of pure perfume essence, which is mixed with a base of alcohol or a carrier oil to create the final product.

Due to its high concentration, perfume is known for its luxurious and intense nature. It often requires only a small amount to create a long-lasting and noticeable fragrance on the skin. Perfume has a strong scent projection and can linger for many hours, typically lasting around 6 to 8 hours or even longer, depending on factors such as individual skin chemistry and environmental conditions.

Perfume is often considered the most expensive and prestigious form of fragrance due to its high concentration of fragrance oils. It is typically packaged in small bottles or vials and is meant to be applied sparingly to specific pulse points on the body for optimal diffusion and longevity.

While perfume is highly concentrated and long-lasting, it may not be suitable for those who prefer a more subtle or lighter fragrance. Other concentrations such as Eau de Parfum, Eau de Toilette, or Eau de Cologne offer lighter options for those who desire a more understated scent or prefer a different balance between fragrance intensity and longevity.

What are the different types of perfume?

Perfumes can be categorized into different types based on their concentration of fragrance oils. The concentration of oils determines the intensity and longevity of the fragrance. Here are the main types of perfumes based on concentration, listed in descending order of oil concentration:

Perfume (Parfum or Extrait de Parfum): Perfume has the highest concentration of fragrance oils, usually ranging from 15% to 30%. It contains a high proportion of pure perfume essence and is known for its luxurious and long-lasting nature. Perfume typically lasts for 6 to 8 hours or even longer on the skin.

Eau de Parfum (EDP): Eau de Parfum has a slightly lower concentration of fragrance oils compared to perfume, typically ranging from 10% to 20%. It is still a relatively potent and long-lasting fragrance option. Eau de Parfum can last between 4 to 6 hours on the skin.

Eau de Toilette (EDT): Eau de Toilette has a lower concentration of fragrance oils, usually ranging from 5% to 15%. It is lighter and more suitable for everyday wear. Eau de Toilette typically lasts between 2 to 4 hours on the skin.

Eau de Cologne (EDC): Eau de Cologne has a lower concentration of fragrance oils compared to the above types, usually ranging from 2% to 4%. It is often fresh and citrusy in nature. Eau de Cologne has a shorter longevity, usually lasting around 2 hours or less on the skin.

Splash or Aftershave: Splashes and aftershaves have the lowest concentration of fragrance oils, usually below 2%. They are often used for a quick refreshing effect and may not last very long on the skin. It’s important to note that the actual concentration of fragrance oils may vary between brands and perfumes, and there can be overlap between these categories. Additionally, the longevity of a perfume can also be influenced by other factors such as individual skin chemistry and environmental conditions.

Comparative modelling and structure based drug repurposing of PAX2 transcription factor for targeting acquired chemoresistance in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a pancreatic malignancy suffering from poor prognosis; the worst among all types of cancer. Chemotherapy, which is the standard regime for treatment in most cases, is often rendered useless as drug resistance quickly sets in after prolonged exposure to the drug. The implication of PAX2 transcription factor in regulating several ATP-binding cassette (ABC) transporter proteins that are responsible for the acquisition of drug resistance in PDAC makes it a potential target for treatment purposes. In this study, the 3D structure of PAX2 protein was modelled, and the response of key amino acids to perturbation were identified. Subsequently, kappadione, a vitamin K derivative, was found to bind efficiently to PAX2 with a binding energy of -9.819 kcal/mol. The efficacy of mechanism and mode of binding was studied by docking the protein with DNA in the presence and absence of the drug. The presence of kappadione disrupted DNA binding with key effector resides, preventing the DNA from coming into contact with the binding region essential for protein translation. By occupying the DNA binding region and replacing it with a ligand, the mechanism by which DNA interacts with PAX2 could be manipulated. Inhibition of PAX2-DNA binding using kappadione and other small molecules can prove to be beneficial for combating chemoresistance in PDAC, as proposed through in silico approaches. DOI:  https://doi.org/10.1080/07391102.2020.1742793

DeepOlf: Deep neural network based architecture for predicting odorants and their interacting Olfactory Receptors

Abstract:

Olfaction transduction mechanism is triggered by the binding of odorants to the specific olfactory receptors (OR’s) present in the nasal cavity. Different odorants stimulate different OR’s due to the difference in shape, physical and chemical properties. In this paper, a deep neural network architecture DeepOlf, based on molecular features and fingerprints of odorants and ORs, to predict whether a chemical compound is a potential odorant or not along with its interacting OR is proposed. Odorant identification and Odorant-OR interaction were modeled as a binary classification through multiple classifiers. The evaluation of these classifier’s performance showed that the deep-neural network framework not only fits data with better accuracy in comparison to other classical methods (SVM, RF, k-NN) but also able to predict odorant-OR interactions more accurately. To our knowledge, this study is the first realization of deep learning ideas for the problem of odorant and interacting OR prediction. The accuracy of DeepOlf was found to be 94.83% and 99.92 % for the prediction of odorants and Odorant- OR interactions respectively. Comparison of DeepOlf prediction with the existing SVM based prediction server, ODORactor, showed that better performance can be achieved with the proposed deep learning approach. The DeepOlf tool can be accessed at https://bioserver.iiita.ac.in/deepolf/.
Published in: IEEE/ACM Transactions on Computational Biology and Bioinformatics ( Early Access )
Date of Publication: 12 June 2020 

DeEPn: A deep neural network based tool for enzyme functional annotation

Authors: Rahul Semwal, Imlimaong Aier, Pankaj Tyagi, Pritish Kumar Varadwaj
With the advancement of high throughput techniques, the discovery rate of enzyme sequences has increased significantly in the recent past. All of these raw sequences are required to be precisely mapped to their respective functional attributes, which helps in deciphering their biological role. In the recent past, various prediction models have been proposed to predict the enzyme functional class; however, all of these models were able to quantify at most six functional enzyme classes (EC1 to EC6) out of existing seven functional classes, making these approaches inappropriate for handling enzymes corresponding to the seventh functional class (EC7). In this study, a Deep Neural Network-based approach, DeEPn, has been proposed, which can quantify enzymes corresponding to all seven functional classes with high precision and accuracy. The proposed model was compared with two recently developed tools, ECPred and SVM-Prot. The result demonstrated that DeEPn outperformed ECPred and SVM-Prot in terms of predictive quality. The DeEPn tool has been hosted as a web-based tool at https://bioserver.iiita.ac.in/DeEPn/.

Understanding the Mechanism of Cell Death in Gemcitabine Resistant Pancreatic Ductal Adenocarcinoma: A Systems Biology Approach

Background: Gemcitabine is the standard chemotherapeutic drug administered in advanced Pancreatic Ductal Adenocarcinoma (PDAC). However, due to drug resistance in PDAC patients, this treatment has become less effective. Over the years, clinical trials for the quest of finding novel compounds that can be used in combination with gemcitabine have met very little success.

Objective: To predict the driving factors behind pancreatic ductal adenocarcinoma, and to understand the effect of these components in the progression of the disease and their contribution to cell growth and proliferation.

Methods: With the help of systems biology approaches and using gene expression data, which is generally found in abundance, dysregulated elements in key signalling pathways were predicted. Prominent dysregulated elements were integrated into a model to simulate and study the effect of gemcitabine- induced hypoxia.

Results: In this study, several transcription factors in the form of key drivers of cancer-related genes were predicted with the help of CARNIVAL, and the effect of gemcitabine-induced hypoxia on the apoptosis pathway was shown to have an effect on the downstream elements of two primary pathway models; EGF/VEGF and TNF signalling pathway.

Conclusion: It was observed that EGF/VEGF signalling pathway played a major role in inducing drug resistance through cell growth, proliferation, and avoiding cell death. Targeting the major upstream components of this pathway could potentially lead to successful treatment.

Deciphering the Novel Target Genes Involved in the Epigenetics of Hepatocellular Carcinoma Using Graph Theory Approach

Background: Even after decades of research, cancer, by and large, remains a challenge and is one of the major causes of death worldwide. For a very long time, it was believed that cancer is simply an outcome of changes at the genetic level but today, it has become a well-established fact that both genetics and epigenetics work together resulting in the transformation of normal cells to cancerous cells.

Objective: In the present scenario, researchers are focusing on targeting epigenetic machinery. The main advantage of targeting epigenetic mechanisms is their reversibility. Thus, cells can be reprogrammed to their normal state. Graph theory is a powerful gift of mathematics which allows us to understand complex networks.

Methodology: In this study, graph theory was utilized for quantitative analysis of the epigenetic network of hepato-cellular carcinoma (HCC) and subsequently finding out the important vertices in the network thus obtained. Secondly, this network was utilized to locate novel targets for hepato-cellular carcinoma epigenetic therapy.

Results: The vertices represent the genes involved in the epigenetic mechanism of HCC. Topological parameters like clustering coefficient, eccentricity, degree, etc. have been evaluated for the assessment of the essentiality of the node in the epigenetic network.

Conclusion: The top ten novel epigenetic target genes involved in HCC reported in this study are cdk6, cdk4, cdkn2a, smad7, smad3, ccnd1, e2f1, sf3b1, ctnnb1, and tgfb1.

Exploration of interaction mechanism of tyrosol as a potent anti-inflammatory agent

Exploration of interaction mechanism of tyrosol as a potent anti-inflammatory agent

Tara Chand Yadav, Naresh Kumar, Utkarsh Raj, Nidhi Goel, Pritish Kumar Vardawaj, Ramasare Prasad & Vikas Pruthi (2019)Exploration of interaction mechanism of tyrosol as a potent anti-inflammatory agent, Journal of Biomolecular Structure and Dynamics, 

DOI: 10.1080/07391102.2019.1575283

Abstract: Drug discovery for a vigorous and feasible lead candidate is a challenging scientific mission as it requires expertise, experience, and huge investment. Natural products and their derivatives having structural diversity are renowned source of therapeutic agents since many years. Tyrosol (a natural phenylethanoid) has been extracted from olive oil, and its structure was confirmed by elemental analysis, FT-IR, FT-NMR, and single crystal X-ray crystallography. The conformational analysis for tyrosol geometry was performed by Gaussian 09 in terms of density functional theory. Validation of bond lengths and bond angles obtained experimentally as well as theoretically were performed with the help of curve fitting analysis, and values of correlation coefficient (R) obtained as 0.988 and 0.984, respectively. The charge transfer within the tyrosol molecule was confirmed by analysis of HOMO→LUMO molecular orbitals. In molecular docking with COX-2 (PDB ID: 5F1A), tyrosol was found to possess satisfactory binding affinity as compared to other NSAIDs (Aspirin, Ibuprofen, and Naproxen) and a COX-2 selective drug (Celecoxib). ADMET prediction, drug-likeness and bioactivity score altogether confirm the lead/drug like potential of tyrosol. Further investigation of simulation quality plot, RMSD and RMSF plots, ligands behavior plot as well as post simulation analysis manifest the consistency of 5F1A-tyrosol complex throughout the 20 ns molecular simulation process that signifies its compactness and stability within the receptor pocket.

 

PROcket, an Efficient Algorithm to Predict Protein Ligand Binding Site

PROcket, an Efficient Algorithm to Predict Protein Ligand Binding Site

Semwal R., Aier I., Varadwaj P.K., Antsiperov S. (2019) PROcket, an Efficient Algorithm to Predict Protein Ligand Binding Site. In: Rojas I., Valenzuela O., Rojas F., Ortuño F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science, vol 11465. Springer, Cham

DOI: https://doi.org/10.1007/978-3-030-17938-0_40

Abstract: To carry out functional annotation of proteins, the most crucial step is to identify the ligand binding site (LBS) information. Although several algorithms have been reported to identify the LBS, most have limited accuracy and efficiency while considering the number and type of geometrical and physio-chemical features used for such predictions. In this proposed work, a fast and accurate algorithm “PROcket” has been implemented and discussed. The algorithm uses grid-based approach to cluster the local residue neighbors that are present on the solvent accessible surface of proteins. Further with inclusion of selected physio-chemical properties and phylogenetically conserved residues, the algorithm enables accurate detection of the LBS. A comparative study with well-known tools; LIGSITE, LIGSITECS, PASS and CASTptool was performed to analyze the performance of our tool. A set of 48 ligand-bound protein structures from different families were used to compare the performance of the tools. The PROcket algorithm outperformed the existing methods in terms of quality and processing speed with 91% accuracy while considering top 3 rank pockets and 98% accuracy considering top 5 rank pockets.

Structure-based drug designing and identification of Woodfordia fruticosa inhibitors targeted against heat shock protein (HSP70-1) as suppressor for Imiquimod-induced psoriasis like skin inflammation in mice model

Structure-based drug designing and identification of Woodfordia fruticosa inhibitors targeted against heat shock protein (HSP70-1) as suppressor for Imiquimod-induced psoriasis like skin inflammation in mice model

Materials Science and Engineering: CVolume 95, 1 February 2019, Pages 57-71

DOI: https://doi.org/10.1016/j.msec.2018.10.061

 

Abstract: Heat shock proteins (HSPs) emerged as a therapeutic target and it was observed that inhibition of HSP70-1 plays a pivotal role in the management of psoriasis. In-silico investigation involving techniques like molecular docking and molecular dynamics (MD) simulation analysis was performed against HSP70-1. Further, anti-psoriatic activity of bioactive immunomodulatory compounds present in ethanolic extract of Woodfordia fruticosa flowers (Wffe) using combination of bioinformatics together with ethnopharmacological approach has been explored in this study. Myricetin (−8.024), Quercetin (−7.368) and Ellagic acid (−7.311) were the top three compounds with minimum energy levels as well as high therapeutic value/ADMET as compared to currently available marketed anti-psoriatic drug Tretinoin (−7.195). ADMET prediction was used to screen ligands for drug-likeness and efficacy. Further, biogenically Woodfordia fruticosa gold nanoparticles (WfAuNPs) were synthesized and characterized by UV–Visible Spectroscopy (UV–vis), Dynamic Light Scattering (DLS), Zeta Potential, X-Ray Diffraction (XRD) and High Resolution Transmission Electron Microscopy (HRTEM) techniques. Synthesized WfAuNPs observed in the size range of 10–20 nm and were used to develop WfAuNPs-Carbopol®934 ointment gel. Subsequently, the therapeutic efficacy of WfAuNPs-Carbopol® 934 was checked against 5% Imiquimod-induced psoriasis like skin inflammation. WfAuNPs-Carbopol® 934 was found to be exerting better therapeutic effect in reducing the mean DAI score (0.63 ± 0.08), serum cytokines (TNF-α, IL-22 and IL-23) levels along with reduced epidermal thickness, parakeratosis and marked decrease in the hyperproliferation of keratinocytes. Results of the study revealed that the WfAuNPs-Carbopol® 934 could be an effective alternative treatment for psoriasis in near future.