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Molecular docking studies on the phytoconstituents as therapeutic leads against SARS-CoV-2

Tiwari Abhishek, Tiwari Varsha, Verma Navneet, Singh Anita, Kumar Manish, Saini Vipin, Sahoo Biswa Mohan, Kaushik Deepak, Verma Ravinder, Sagadevan Suresh

Polimery

2022

Vol. 67, nr 7-8

355---374

Because of the present pandemic researchers are seeking for phytocandidates that can inhibit or stop SARS-CoV-2. The main protease (Mpro) of SARS-CoV-2 and spike glycoprotein (S) are both suppressed by bioactive compounds found in plants that work by docking them together. The Mpro proteins 6LU7 (complex with an inhibitor N3) and 5C3N (space group C2221) were employed in docking research. PyRx and AutoDock Vina software were used as docking engine. 22 identified phytoconstituents were selected from IMPPAT, a manually curated database, on the basis of their antiviral effects. Docking studies showed that phytoconstituents β-amyrin (-8.4 kcal/mol), withaferin A (-8.3 kcal/mol), oleanolic acid (-7.8 kcal/mol), and patentiflorin A (-8.1 kcal/mol) had the best results against 5C3N Mpro protein whereas kuwanon L (-7.1 kcal/mol), β-amyrin (-6.9 kcal/mol), oleanolic acid (-6.8 kcal/mol), cucurbitacin D (-6.5 kcal/mol), and quercetin (-6.5 kcal/mol) against 6LU7 Mpro protein. All the compounds were examined for their ADMET characteristics using SwissDock. Present research reports that the phytoconstituents along with docking score will be helpful for future drug development against Covid-19.

W związku z pandemią prowadzone są badania mające na celu znalezienie fitosubstancji, które mogą hamować lub zatrzymywać rozwój SARS-CoV-2. Działanie głównych białek proteazy (Mpro) SARS-CoV-2 i glikoproteiny kolca (S) jest osłabiane przez związki bioaktywne występujące w roślinach poprzez proces dokowania. Do badań dokujących użyto białka Mpro 6LU7 (kompleks z inhibitorem N3) i 5C3N (grupa przestrzenna C2221). Jako silnik dokujący zastosowano PyRx i AutoDock Vina. Zidentyfikowano 22 fitoskładniki wybrane z bazy danych IMPPAT, z uwzględnieniem ich działania przeciwwirusowego. Najbardziej skuteczne w przypadku białka Mpro 5C3N okazały się fitoskładniki β-amyryna (-8,4 kcal/mol), witaferyna A (-8,3 kcal/mol), kwas oleanolowy (-7,8 kcal/mol) i patentifloryna A (-8,1 kcal/mol), a w przypadku białka Mpro 6LU7 kuwanon L (-7,1 kcal/mol), β-amyryna (-6,9 kcal/mol), kwas oleanolowy (-6,8 kcal/mol), kukurbitacyna D (-6,5 kcal/mol) i kwercetyna (-6,5 kcal/mol). Wszystkie substancje zbadano pod kątem ich właściwości ADMET przy użyciu SwissDock. Wykazano, że fitoskładniki mogą być pomocne w pracach nad lekami przeciwko Covid-19.

Applicability of machine learning techniques for multi-time step ahead runof forecasting

Bajirao Tarate Suryakant, Elbeltagi Ahmed, Kumar Manish, Pham Quoc Bao

Acta Geophysica

2022

Vol. 70, no 2

757--776

Precise and reliable runoff forecasting is crucial for water resources planning and management. The present study was conducted to test the applicability of different data-driven techniques including artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and M5P models for runoff forecasting for the lead time of 1 day and 2 days in the Koyna River basin, India. The best input variables for the development of the models were selected by applying the Gamma test (GT). Two different scenarios were considered to select the input variables for 2 days ahead runoff forecasting. In the first scenario, the output of 1 day ahead runoff (t+1) was not used as an input while it was also used as an input along with other input variables for the development of the models in the second scenario. For 2 days ahead runoff forecasting, the models developed by adopting the second scenario performed more accurately than that of the first scenario. The RF model performed the best for 1 day ahead runoff forecasting with root mean square error (RMSE), coefficient of efficiency (CE), correlation coefficient (r) and coefficient of determination (R2 ) values of 168.94 m3 /s, 0.67, 0.84 and 0.704, respectively, during the test period. For 2 days ahead runoff forecasting, RF and ANN models performed the best in the first and second scenario, respectively. In 2 days ahead runoff forecasting, RMSE, CE, r and R2 values were observed to be 169.72 m3 /s, 0.67, 0.84, 0.7023 and 148.55 m3 /s, 0.74, 0.87, 0.76 in the first and second scenarios, respectively, during the test period. Finally, the results revealed that the addition of 1 day ahead runoff forecast increased the forecast accuracy of 2 days ahead runoff forecasts. In addition, the dependability of the various models was determined using the uncertainty analysis.

To study of various security attacks against Biometric template in a generic Biometric Recognition System

Kumar Manish, Vaisla Kunwar Singh

Annals of Computer Science and Information Systems

2017

Vol. 10

235--240

Biometric recognitions system is a system which provides the reliable solution for user authentication in identity management systems. The motivation is that how we can use the effective biometric system in e-Governance service delivery with secure channel. So first we discuss the various security attacks that can be encounter during the enrollment and verification of biometric traits. We analyze various vulnerabilities of biometric system and study the various counter measures that can be present in biometric system in any way. There are many successful developments in the field of e-Authentication but the security is remain challenge in real world. This paper focus on the risks of biometric data at the time of enrollment and verification process. There are many types of attacks like finger print in biometric system by using fake fingers at the sensor could be a serious threat for unattended applications. In biometric system fingerprint matching is very big challenge to match the saved image with real time template with reduce the false match rate and false reject rate. We focus here to analyze the behavior of fingerprint verification because fingerprint verification is the very reliable personal e-Authentication mechanism.