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Frontiers | Molecular Docking and Dynamics Studies to Explore Effective Inhibitory Peptides Against the Spike Receptor Binding Domain of SARS-CoV-2

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic due to the high transmission and mortality rate of this virus. The world health and economic sectors have been severely affected by this deadly virus, exacerbated by the lack of sufficient efficient vaccines. The design of effective drug candidates and their rapid development is necessary to combat this virus. In this study, we selected 23 antimicrobial peptides from the literature and predicted their structure using PEP-FOLD 3.5. In addition, we docked them to the SARS-CoV-2 spike protein receptor-binding domain (RBD) to study their capability to inhibit the RBD, which plays a significant role in virus binding, fusion and entry into the host cell. We used several docking programs including HDOCK, HPEPDOCK, ClusPro, and HawkDock to calculate the binding energy of the protein-peptide complexes. We identified four peptides with high binding free energy and docking scores. The docking results ....

Saudi Arabia , United States , San Diego , J Neuroimmune Pharmacol , Albiol Matanic , Dermatophagoides Pteronyssinus , Silico Pharmacol , Van Doremalen , Raihan Uddin , Acta Tropica , Toxinpred Gupta , Binding Free Energy Calculation , Accelrys Inc , Institute Of India , Taif University , Protein Docking Energy , Free Energy Decomposition For The Ras , Protein Data Bank , Taif University Researchers , Amber Force Field , State Institute , Hidden Markov Model , Protein Data , Discovery Studio , Particle Mesh Ewald , Supplementary Figure ,

"Ensemble learning for remaining fatigue life prediction of structures " by S. Z. Feng, X. Han et al.

An effective approach is proposed to predict the remaining fatigue life (RFL) of structures with stochastic parameters. The extended finite element method (XFEM) was firstly used to produce a large amount of datasets associated with structural responses and RFL. Then, a RFL prediction model was developed using the ensemble learning algorithm, which employed multiple machine-learning algorithms to learn useful degradation patterns of the structures from the XFEM datasets. Several numerical examples were investigated to evaluate the performance of proposed RFL prediction approach. The analysis results demonstrate that the ensemble learning is able to effectively predict the RFL of the structures with stochastic parameters. ....

Ensemble Learning , Genetic Algorithm , Emaining Fatigue Life , Tochastic Parameters ,

Argonne Captures 3 R&D 100 Awards for Innovative Technology

Nov. 5, 2021 Three technologies developed by researchers at the U.S. Department of Energy’s (DOE) Argonne National Laboratory and partner organizations ....

United States , Griffin Dube , Jerome Soumagne , Opeoluwa Owoyele , Galen Shipman , Ahmed Abdul Moiz , George Amvrosiadis , Dana Robinson , Franck Cappello , Robert Ross , Charles Cranor , Jon Calhoun , Pinaki Pal , Yuqing Zheng , Robert Latham , Chengming Zhang , Robert Underwood , Xin Liang , Tyler Reddy , Dingwen Tao , Ali Murat Gok , Shane Snyder , Cody Rivera , Kai Zhao , Janardhan Kodavasal , Robert Robey ,

Argonne and Parallel Works Inc. win FLC recognition for commercializing lab's machine learning-based design optimization software technology

Argonne and Parallel Works, Inc., won the Federal Laboratory Consortium’s Midwest Regional Award for Excellence in Technology Transfer for bringing Argonne’ ....

United States , University Of Illinois , Matthew Shaxted , Michela Wilde , Michael Wilde , Pinaki Pal , Greg Halder , Suresh Sunderrajan , Argonne Energy , Works Inc , International Journal Of Engine Research , Technology Commercialization Fund , Convergent Science Inc , Us Department Of Energy , Argonne Center , Renewable Energy , Laboratory Consortium , Vehicle Technologies Office , Transportation Research , Argonne National Laboratory , Office Of Energy Efficiency , Parallel Works , Federal Laboratory Consortium , Midwest Regional Award , Technology Transfer , Machine Learning ,

Argonne and Parallel Works Inc. win FLC recognition for commercializing lab's machine learning-based design optimization software technology

Argonne and Parallel Works Inc. win FLC recognition for commercializing lab's machine learning-based design optimization software technology
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United States , University Of Illinois , Matthew Shaxted , Michela Wilde , Michael Wilde , Pinaki Pal , Greg Halder , Suresh Sunderrajan , Argonne Energy , Works Inc , International Journal Of Engine Research , Technology Commercialization Fund , Convergent Science Inc , Us Department Of Energy , Argonne Center , Renewable Energy , Laboratory Consortium , Vehicle Technologies Office , Transportation Research , Argonne National Laboratory , Office Of Energy Efficiency , Parallel Works , Federal Laboratory Consortium , Midwest Regional Award , Technology Transfer , Machine Learning ,