Ryanodine Receptors: Current Progress and Future Perspective

2022 ◽  
Vol 28 (1) ◽  
pp. 1-1
Author(s):  
Qing Yang ◽  
Zhiguang Yuchi
Keyword(s):  
Ryanodine Receptors ◽  
Future Perspective

FUTURE PERSPECTIVE OF PLANT BIOTECHNOLOGY: A REVIEW

2020 ◽  
Vol 1 (1) ◽  
pp. 36-41
Author(s):  
Gaurav Ranabhat ◽  
Ashmita Dhakal ◽  
Saurav Ranabhat ◽  
Ananta Dhakal ◽  
Rakshya Aryal
Keyword(s):  
Insect Pest ◽  
Ornamental Plants ◽  
Herbicide Tolerance ◽  
Plant Biotechnology ◽  
Future Perspective ◽  
New Development ◽  
Recent Developments ◽  
Modern Biotechnology ◽  
Stable Yield ◽  
As Stress

Modern biotechnology enables an organism to produce a totally new product which the organism does not or cannot produce normally through the incorporation of the technology of ‘Genetic engineering’. Biotechnology shows its technical merits and new development prospects in breeding of new plants varieties with high and stable yield, good quality, as well as stress tolerance and resistance. Some of the most prevailing problems faced in agricultural ecosystems could be solved with the introduction of transgenic crops incorporated with traits for insect pest resistance, herbicide tolerance and resistance to viral diseases. Plant biotechnology has gained importance in the recent past for increasing the quality and quantity of agricultural, horticultural, ornamental plants, and in manipulating the plants for improved agronomic performance. Recent developments in the genome sequencing will have far reaching implications for future agriculture. From this study, we can know that the developing world adopts these fast-changing technologies soon and harness their unprecedented potential for the future benefit of human being.

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

2019 ◽  
Vol 20 (3) ◽  
pp. 170-176 ◽  
Author(s):  
Zhongyan Li ◽  
Qingqing Miao ◽  
Fugang Yan ◽  
Yang Meng ◽  
Peng Zhou
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Peptide Binding ◽  
Machine Learning Techniques ◽  
Future Perspective ◽  
Peptide Design ◽  
Human Interactome ◽  
Therapeutic Peptide ◽  
Peptide Affinity ◽  
Peptide Binding Affinity

Background:Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.Methods:We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.Results:Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.Conclusion:There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

Current Status and Future Perspective for Research on Medicinal Plants with Anticancerous Activity and Minimum Cytotoxic Value

2019 ◽  
Vol 20 (12) ◽  
pp. 1227-1243
Author(s):  
Hina Qamar ◽  
Sumbul Rehman ◽  
D.K. Chauhan
Keyword(s):  
Side Effects ◽  
Medicinal Plants ◽  
Anticancer Agents ◽  
Drug Targets ◽  
Psidium Guajava ◽  
Current Status ◽  
Future Perspective ◽  
Wide Range

Cancer is the second leading cause of morbidity and mortality worldwide. Although chemotherapy and radiotherapy enhance the survival rate of cancerous patients but they have several acute toxic effects. Therefore, there is a need to search for new anticancer agents having better efficacy and lesser side effects. In this regard, herbal treatment is found to be a safe method for treating and preventing cancer. Here, an attempt has been made to screen some less explored medicinal plants like Ammania baccifera, Asclepias curassavica, Azadarichta indica, Butea monosperma, Croton tiglium, Hedera nepalensis, Jatropha curcas, Momordica charantia, Moringa oleifera, Psidium guajava, etc. having potent anticancer activity with minimum cytotoxic value (IC50 >3μM) and lesser or negligible toxicity. They are rich in active phytochemicals with a wide range of drug targets. In this study, these medicinal plants were evaluated for dose-dependent cytotoxicological studies via in vitro MTT assay and in vivo tumor models along with some more plants which are reported to have IC50 value in the range of 0.019-0.528 mg/ml. The findings indicate that these plants inhibit tumor growth by their antiproliferative, pro-apoptotic, anti-metastatic and anti-angiogenic molecular targets. They are widely used because of their easy availability, affordable price and having no or sometimes minimal side effects. This review provides a baseline for the discovery of anticancer drugs from medicinal plants having minimum cytotoxic value with minimal side effects and establishment of their analogues for the welfare of mankind.

Drug repurposing using similarity-based target prediction, docking studies and scaffold hopping of lefamulin

2020 ◽  
Vol 17 ◽  
Author(s):  
Shikha Sharma ◽  
Shweta Sharma ◽  
Vaishali Pathak ◽  
Parwinder Kaur ◽  
Rajesh Kumar Singh
Keyword(s):  
In Silico ◽  
Target Prediction ◽  
Drug Repurposing ◽  
Docking Studies ◽  
Target Protein ◽  
Future Perspective ◽  
Antibacterial Drug ◽  
Scaffold Hopping ◽  
Target Proteins ◽  
Renin Inhibitors

Aim: To investigate and validate the potential target proteins for drug repurposing of newly FDA approved antibacterial drug. Background: Drug repurposing is the process of assigning indications for drugs other than the one(s) that they were initially developed for. Discovery of entirely new indications from already approved drugs is highly lucrative as it minimizes the pipeline of the drug development process by reducing time and cost. In silico driven technologies made it possible to analyze molecules for different target proteins which are not yet explored. Objective: To analyze possible targets proteins for drug repurposing of lefamulin and their validation. Also, in silico prediction of novel scaffolds from lefamulin has been performed for assisting medicinal chemists in future drug design. Methods: A similarity-based prediction tool was employed for predicting target protein and further investigated using docking studies on PDB ID: 2V16. Besides, various in silico tools were employed for prediction of novel scaffolds from lefamulin using scaffold hopping technique followed by evaluation with various in silico parameters viz., ADME, synthetic accessibility and PAINS. Results: Based on the similarity and target prediction studies, renin is found as the most probable target protein for lefamulin. Further, validation studies using docking of lefamulin revealed the significant interactions of lefamulin with the binding pocket of the target protein. Also, three novel scaffolds were predicted using scaffold hopping technique and found to be in the limit to reduce the chances of drug failure in the physiological system during the last stage approval process. Conclusion: To encapsulate the future perspective, lefamulin may assist in the development of the renin inhibitors and, also three possible novel scaffolds with good pharmacokinetic profile can be developed into both as renin inhibitors and for bacterial infections.

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