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1,891 to 1,900 of 2,012 Results
NP177_Sol_ion.psf
Feb 10, 2022 - Replication Data for: Active Site Prediction of Phosphorylated SARS-CoV-2 N-Protein Using Molecular Simulation
application/x-font-linux-psf - 10.0 MB - MD5: c68d16f7ac1493fa10786a87eb894cbd
PSF file generated from simulation
NP198_Sol_ion.psf
Feb 10, 2022 - Replication Data for: Active Site Prediction of Phosphorylated SARS-CoV-2 N-Protein Using Molecular Simulation
application/x-font-linux-psf - 10.3 MB - MD5: 076ecc3b27dd548adc1cade52f1567ea
PSF file generated from simulation
N_protein_177.pdb
Feb 10, 2022 - Replication Data for: Active Site Prediction of Phosphorylated SARS-CoV-2 N-Protein Using Molecular Simulation
application/vnd.palm - 501.4 KB - MD5: b35989ae1c9580b8dd83e8da3691e4bc
Initial structure for NAMD
N-protein phi-psi.tab
Feb 10, 2022 - Replication Data for: Active Site Prediction of Phosphorylated SARS-CoV-2 N-Protein Using Molecular Simulation
Tabular Data - 20.1 MB - 846 Variables, 2001 Observations - UNF:6:waZeEVsr8IWiZWHJnN95kQ==
data matrix containing phi and psi angles
NPS177A_Sol_ion.pdb
Feb 10, 2022 - Replication Data for: Active Site Prediction of Phosphorylated SARS-CoV-2 N-Protein Using Molecular Simulation
application/vnd.palm - 7.6 MB - MD5: 0ac39e744fdc0f90b059e74f1952d795
Initial structure for NAMD
NPS177A_Sol_ion.psf
Feb 10, 2022 - Replication Data for: Active Site Prediction of Phosphorylated SARS-CoV-2 N-Protein Using Molecular Simulation
application/x-font-linux-psf - 10.3 MB - MD5: 73203ce493f3955cc711816496631935
PSF file from NAMD simulation
NP_wb_ion .namd
Feb 10, 2022 - Replication Data for: Active Site Prediction of Phosphorylated SARS-CoV-2 N-Protein Using Molecular Simulation
Unknown - 7.2 KB - MD5: 961b60c57fe3dd2620404a2dca8150cf
NAMD script
NP_wb_ion.psf
Feb 10, 2022 - Replication Data for: Active Site Prediction of Phosphorylated SARS-CoV-2 N-Protein Using Molecular Simulation
application/x-font-linux-psf - 10.3 MB - MD5: 36a4767163dcf5d7c2dda275f5144c6c
PSF file from NAMD simulation
Replication Data for: Optimizing Peptide Inhibitors of SARS-Cov-2 nsp10/nsp16 Methyltransferase Predicted through Molecular Simulation and Machine Learning
Jan 26, 2022
Jafri, Saleet, 2022, "Replication Data for: Optimizing Peptide Inhibitors of SARS-Cov-2 nsp10/nsp16 Methyltransferase Predicted through Molecular Simulation and Machine Learning", https://doi.org/10.13021/orc2020/OW4T3K, George Mason University Dataverse, V1
Scripts and files needed to reproduce manuscript results.
6ZPE.pdb
Jan 26, 2022 - Replication Data for: Optimizing Peptide Inhibitors of SARS-Cov-2 nsp10/nsp16 Methyltransferase Predicted through Molecular Simulation and Machine Learning
application/vnd.palm - 34.7 KB - MD5: 27dad315019a2081532dfbcb27ca9200
PDB structure file with initial structure
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