لخّصلي
خدمة تلخيص النصوص العربية أونلاين،قم بتلخيص نصوصك بضغطة واحدة من خلال هذه الخدمة
نتيجة التلخيص (50%)
4.4.1 protein preparation
Firstly, we will choose the best crystal structure of PD-L1 from the protein data bank with (PDB ID: 8P64) based on its co-crystallized ligand, clear binding site, and appropriateness for docking validation (Sastry et al., 2013),For protein preparation to be performed, we will use the Protein Preparation Wizard tool within the Maestro software in order to prepare the protein appropriately for subsequent molecular docking and simulation procedures(Schrodinger, 2023;Sastry et al., 2013) .All the usual preparatory steps will be carried out: adding missing hydrogen atoms, adjusting protonation state at an approximate pH of 7.4 using PROPKA, assigning correct bond orders, removing and retaining necessary water molecules likely to take part in binding or retaining a presumed bound water molecule once analyzed into place, fixing up added missing side chain atoms (or geometry) after reconstruction with the Prime module, and optimizing the H-bond network.4.4 Molecular Docking
The Glide module incorporated in Maestro will be used for molecular docking (Friesner et al., 2004).Hierarchical VS protocol, HTVS (High-Throughput Virtual Screening), SP (Standard Precision) and XP (Extra Precision) will be followed.The priority on the docking scores and binding postures to be afforded upon compounds after docking should been assigned.(Friesner et al., 2004).A reference ligand present in the original crystal structure (co-crystallized ligand) will be re-docked into the binding site to assess and validate the docking protocol.(Halgren et al., 2004).Ionization states will be produced at pH 7 +- 2 and relevant tautomers and stereoisomers will be produced if necessary,The energy-minimized using the OPLS3e force field45 and keeping only heavy atoms at 150A MD cutoff structures will be used for molecular docking (Sastry et al., 2013).4.6 Molecular Dynamics Simulations
Molecular dynamics simulations will be performed to assess the stability of computationally selected PD-L1-ligand complexes.
النص الأصلي
- METHODOLOGY
We will use Maestro software from the Schrödinger suite, Release 2023, version 13.5, for all in silico work (Schrödinger, 2023).
4.1 protein preparation
Firstly, we will choose the best crystal structure of PD-L1 from the protein data bank with (PDB ID: 8P64) based on its co-crystallized ligand, clear binding site, and appropriateness for docking validation (Sastry et al., 2013),For protein preparation to be performed, we will use the Protein Preparation Wizard tool within the Maestro software in order to prepare the protein appropriately for subsequent molecular docking and simulation procedures(Schrödinger, 2023;Sastry et al., 2013) . All the usual preparatory steps will be carried out: adding missing hydrogen atoms, adjusting protonation state at an approximate pH of 7.4 using PROPKA, assigning correct bond orders, removing and retaining necessary water molecules likely to take part in binding or retaining a presumed bound water molecule once analyzed into place, fixing up added missing side chain atoms (or geometry) after reconstruction with the Prime module, and optimizing the H-bond network.
In the last stage, energy-constrained minimization was carried out by using an OPLS3e force field to achieve a stable protein structure for molecular docking.
4.2 Ligand Library Preparation
A library of natural compounds will be obtained from the PubChem database(Kim et al., 2019).Ligands will be prepared using the LigPrep module in Maestro (Schrödinger, 2023;Sastry et al., 2013).
Ionization states will be produced at pH 7 ± 2 and relevant tautomers and stereoisomers will be produced if necessary,The energy-minimized using the OPLS3e force field45 and keeping only heavy atoms at 150A MD cutoff structures will be used for molecular docking (Sastry et al., 2013).
4.3 Receptor Grid Generation
Receptor grid will be generated by Glide module of Maestro(Friesner et al., 2004). The grid will be centered at PD-L1 binding site, after the co-crystallized ligand of chosen protein structure. Grid box sizes are adjusted to fully include the binding pocket (Friesner et al., 2004).
4.4 Molecular Docking
The Glide module incorporated in Maestro will be used for molecular docking (Friesner et al., 2004).Hierarchical VS protocol, HTVS (High-Throughput Virtual Screening), SP (Standard Precision) and XP (Extra Precision) will be followed.
The priority on the docking scores and binding postures to be afforded upon compounds after docking should been assigned.(Friesner et al., 2004).A reference ligand present in the original crystal structure (co-crystallized ligand) will be re-docked into the binding site to assess and validate the docking protocol.(Halgren et al., 2004).
4.5 MM/GBSA Binding Free Energy Calculations
The MM/GBSA model will be used to calculate the binding free energy based on the docking results using Prime MM/GBSA module in Maestro (Li et al., 2011). The protein–ligand complexes generated by XP docking will be employed to calculate binding free energies. Docked compounds will be re-ranked and prioritized based on the results of MM/GBSA binding free energy calculations (Li et al., 2011).
4.6 Molecular Dynamics Simulations
Molecular dynamics simulations will be performed to assess the stability of computationally selected PD-L1-ligand complexes. Simulations will be conducted in the Maestro system using the Desmond module (Bowers et al., 2006). All systems are to be solvated with TIP3P water model and neutralized by the addition of counterions.
Simulations will be performed in the NPT ensemble, at 300 K and 1 atm with a total simulation time compatible with that of a GRP. Trajectory analysis will include RMSD and protein–ligand interaction monitoring.(Bowers et al., 2006).
4.7 ADMET Analysis
In silico ADMET profile of top ranked compounds will be screened for pharmacokinetic properties and toxicity profiles. Absorption, distribution, metabolism, excretion and toxicity based parameters of the compounds will be screened for drug-likeness (Pires et al., 2015).
تلخيص النصوص العربية والإنجليزية أونلاين
تلخيص النصوص آلياً
تلخيص النصوص العربية والإنجليزية اليا باستخدام الخوارزميات الإحصائية وترتيب وأهمية الجمل في النص
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