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Molecular dynamics-driven exploration of peptides targeting SARS-CoV-2,with special attention on ACE2,S protein,M^(pro),and PL^(pro):A review

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BIOCELL 2023年第8期47卷 1727-1742页

作者： mohamad zulkeflee sabri JOANNA BOJARSKA FAI-CHU WONG TSUN-THAI CHAI Green Chemistry and Sustainable Technology Cluster Bioengineering SectionUniversiti Kuala LumpurMalaysian Institute of Chemical and Bioengineering Technology(UniKL MICET)Alor Gajah78000Malaysia Institute of General and Ecological Chemistry Faculty of ChemistryLodz University of TechnologyLodz90-924Poland Department of Chemical Science Faculty of ScienceUniversiti Tunku Abdul RahmanKampar31900Malaysia Center for Agriculture and Food Research Universiti Tunku Abdul RahmanKampar31900Malaysia

Molecular dynamics(MD)simulation is a computational technique that analyzes the movement of a system of particles over a given *** can provide detailed information about the fluctuations and conformational changes of biomolecules at the atomic level over *** recent years,MD has been widely applied to the discovery of peptides and peptide-like molecules that may serve as severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)*** review summarizes recent advances in such explorations,focusing on four protein targets:angiotensin-converting enzyme 2(ACE2),spike protein(S protein),main protease(M^(pro)),and papain-like protease(PL^(pro)).These four proteins are important druggable targets of SARS-CoV-2 because of their roles in viral entry,maturation,and infectivity of the virus.A review of the literature revealed that ACE2,S protein,and M^(pro) have received more attention in MD research than PL^(pro).Inhibitors of the four targets identified by MD simulations included peptides derived from food and other bioresources,peptides designed using the targets as templates,and peptide-like molecules retrieved from *** of the inhibitors have yet to be validated in experimental assays for ***,the role of MD simulation as an efficient tool in the early stages of anti-SARS-CoV-2 drug discovery agents has been demonstrated.

关键词： COVID-19 Anti-SARS-CoV-2 agent Receptor binding domain Computational screening Drug discovery

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An Efficient Arrhythmia Detection Using Autocorrelation and Statistical Approach

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Journal of Computer and Communications 2018年第10期6卷 63-81页

作者： mohamad sabri Bin Sinal Eiji Kamioka Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan

Computational electrocardiogram (ECG) analysis is one of the most crucial topics in cardiovascular research domain especially in identifying abnormalities of heart condition through cardiac arrhythmia symptom. There are many existing works focusing on recognizing the abnormalities condition through arrhythmia symptom, however, the detection rate is still unsatisfied. Arrhythmia consists of more than 14 various types of symptoms. Therefore, most of the existing research found it difficult to classify the entire symptom and maintain the overall accuracy especially in long hour data. In this study, a new mechanism to overcome this issue is proposed: A combination between Autocorrelation methods with K-Nearest Neighbor (KNN) classifier method is introduced to accurately and robustly detect 14 types of Arrhythmia symptom regardless of the origin of the symptom in a long hour data. Moreover, variability analysis based on periodic autocorrelation result is proposed and used for classification procedure. 1 minute and 12 hours duration data was chosen to compare and signify the most suitable time duration to detect Arrhythmia symptom. In addition, an analytical result and discussion is done to provide justification behind each tendency of Arrhythmia and Normal Sinus symptom in autocorrelation result. As the result of proposed method performance evaluation, it was revealed that the accuracy of 95.5% in discriminating Arrhythmia from Normal Sinus data is achieved. Furthermore, it was confirmed that utilizing autocorrelation result in long hour data can help to generalize abnormalities characteristic of heart condition like Arrhythmia symptom. It is concluded that the proposed method can be useful to diagnose abnormalities of heart condition at any stage.

关键词： Autocorrelation Analysis Arrhythmia Diagnosis KNN Classifier Autocorrelation Function Heart Disease Diagnosis Computational Heart Disease Diagnosis

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The Qatari Environment Friendly Web Portal

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通讯和计算机（中英文版） 2011年第7期8卷 536-544页

作者： Jihad M. AlJa＇am Abusin Rahama Abdlah A. sabri mohamad H. Abbasi Department of Computer Science and Engineering College of Engineering Qatar University Doha 2713 Qatar

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