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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1002/jcc.26759
|2 doi
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|a pubmed25n1109.xml
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|a (DE-627)NLM332795268
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|a (NLM)34741467
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Vankayala, Sai L
|e verfasserin
|4 aut
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| 245 |
1 |
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|a CIFDock
|b A novel CHARMM-based flexible receptor-flexible ligand docking protocol
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Completed 07.03.2022
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|a Date Revised 03.02.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2021 Wiley Periodicals LLC.
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|a Docking studies play a critical role in the current workflow of drug discovery. However, limitations may often arise through factors including inadequate ligand sampling, a lack of protein flexibility, scoring function inadequacies (e.g., due to metals, co-factors, etc.), and difficulty in retaining explicit water molecules. Herein, we present a novel CHARMM-based induced fit docking (CIFDock) workflow that can circumvent these limitations by employing all-atom force fields coupled to enhanced sampling molecular dynamics procedures. Self-guided Langevin dynamics simulations are used to effectively sample relevant ligand conformations, side chain orientations, crystal water positions, and active site residue motion. Protein flexibility is further enhanced by dynamic sampling of side chain orientations using an expandable rotamer library. Steps in the procedure consisting of fixing individual components (e.g., the ligand) while sampling the other components (e.g., the residues in the active site of the protein) allow for the complex to adapt to conformational changes. Ultimately, all components of the complex-the protein, ligand, and waters-are sampled simultaneously and unrestrained with SGLD to capture any induced fit effects. This modular flexible docking procedure is automated using CHARMM scripting, interfaced with SLURM array processing, and parallelized to use the desired number of processors. We validated the CIFDock procedure by performing cross-docking studies using a data set comprised of 21 pharmaceutically relevant proteins. Five variants of the CHARMM-based SWISSDOCK scoring functions were created to quantify the results of the final generated poses. Results obtained were comparable to, or in some cases improved upon, commercial docking program data
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|a Journal Article
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|a Research Support, N.I.H., Extramural
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 650 |
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4 |
|a Research Support, U.S. Gov't, Non-P.H.S.
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| 650 |
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4 |
|a CHARMM
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| 650 |
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4 |
|a clustering
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| 650 |
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4 |
|a force field
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| 650 |
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4 |
|a induced fit docking
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| 650 |
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4 |
|a ligand sampling
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| 650 |
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7 |
|a Ligands
|2 NLM
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| 650 |
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7 |
|a Proteins
|2 NLM
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| 650 |
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7 |
|a Water
|2 NLM
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| 650 |
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7 |
|a 059QF0KO0R
|2 NLM
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| 700 |
1 |
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|a Warrensford, Luke C
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pittman, Amanda R
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pollard, Benjamin C
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kearns, Fiona L
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Larkin, Joseph D
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Woodcock, H Lee
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 43(2022), 2 vom: 15. Jan., Seite 84-95
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnas
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| 773 |
1 |
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|g volume:43
|g year:2022
|g number:2
|g day:15
|g month:01
|g pages:84-95
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|u http://dx.doi.org/10.1002/jcc.26759
|3 Volltext
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