Geodesic
Software pipeline for predicting and analyzing the structure of the receptor–ligand complex
Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki, Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, Tome 164 (2022) no. 1, pp. 122-136 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice du chapitre de livre

It is of fundamental importance in pharmacology and theoretical biology to analyze the binding of ligands to receptors. A better understanding of this process and its outcomes can help predict the following: how the protein interacts with ligands; whether the ligand acts as an activator, inhibitor, or substrate; in which areas the ligand interacts best with the receptor. This article describes the potential of using a software pipeline for finding the most probable position of the ligand in the receptor. Such a pipeline involves a complex algorithm, from predicting the structure of the receptor to searching and analyzing the interaction of ligands with the receptor. The advantage of the software pipeline is that it allows numerous combinations of ligands and receptors to be analyzed at once. Possible interactions of the receptor–ligand complex are studied based on certain parameters: the energy of the affinity of the ligand for the receptor; the length and energy of the bond between the receptor and the ligand, both in the whole complex and between individual atoms. All characteristics can be automatically calculated by default under the specified optimal parameters. Alternatively, they can be set by the user, depending on the task. To illustrate how the software pipeline works, we analyzed the interaction of ligands with human acetylcholinesterase, a protein with the most studied active center. We confirmed that the software pipeline works correctly by comparing the obtained results with the experimental data on the binding of various ligands to human acetylcholinesterase. The pipeline code was published on GitHub (https://github.com/NastiaKozlova/stabilisation_complex-receptor-ligand).
Keywords: molecular docking, molecular dynamics, globular protein, ligand–receptor complex, software pipeline, acetylcholinesterase, NAMD.
Mots-clés : AutoDock, VMD 
@article{UZKU_2022_164_1_a6,
     author = {A. S. Kozlova and A. R. Mukhametgalieva and A. N. Fattakhova and N. I. Akberova},
     title = {Software pipeline for predicting and analyzing the structure of the receptor{\textendash}ligand complex},
     journal = {U\v{c}\"enye zapiski Kazanskogo universiteta. Seri\^a Fiziko-matemati\v{c}eskie nauki},
     pages = {122--136},
     year = {2022},
     volume = {164},
     number = {1},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/UZKU_2022_164_1_a6/}
}
TY  - JOUR
AU  - A. S. Kozlova
AU  - A. R. Mukhametgalieva
AU  - A. N. Fattakhova
AU  - N. I. Akberova
TI  - Software pipeline for predicting and analyzing the structure of the receptor–ligand complex
JO  - Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki
PY  - 2022
SP  - 122
EP  - 136
VL  - 164
IS  - 1
UR  - http://geodesic.mathdoc.fr/item/UZKU_2022_164_1_a6/
LA  - ru
ID  - UZKU_2022_164_1_a6
ER  - 
%0 Journal Article
%A A. S. Kozlova
%A A. R. Mukhametgalieva
%A A. N. Fattakhova
%A N. I. Akberova
%T Software pipeline for predicting and analyzing the structure of the receptor–ligand complex
%J Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki
%D 2022
%P 122-136
%V 164
%N 1
%U http://geodesic.mathdoc.fr/item/UZKU_2022_164_1_a6/
%G ru
%F UZKU_2022_164_1_a6
A. S. Kozlova; A. R. Mukhametgalieva; A. N. Fattakhova; N. I. Akberova. Software pipeline for predicting and analyzing the structure of the receptor–ligand complex. Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki, Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, Tome 164 (2022) no. 1, pp. 122-136. http://geodesic.mathdoc.fr/item/UZKU_2022_164_1_a6/

[1] Santos L. H.S., Ferreira R. S., Caffarena E. R., “Integrating molecular docking and molecular dynamics simulations”, Docking Screens for Drug Discovery, Methods in Molecular Biology, 2053, ed. de Azevedo Jr.W., Humana, N. Y., 2019, 13–34 | DOI

[2] Wang X., Kleerekoper Q., Revtovich A. V., Kang D., Kirienko N. V., “Identification and validation of a novel anti-virulent that binds to pyoverdine and inhibits its function”, Virulence, 1:1 (2020), 1293–1309 | DOI

[3] Li H., Jiang X., Shen X., Sun Y., Jiang N., Zeng J., Lin J., Yue L., Lai J., Li Y., Wu A., Wang L., Qin D., Huang F., Mei Q., Yang J., Wu J., “TMEA, a polyphenol in Sanguisorba officinalis, promotes thrombocytopoiesis by upregulating PI3K/Akt signaling”, Front. Cell Dev. Biol., 9 (2021), 708331, 20 pp. | DOI

[4] Karplus M., McCammon J. A., “Molecular dynamics simulations of biomolecules”, Nat. Struct. Mol. Biol., 9 (2002), 646–652 | DOI

[5] Doerr S., Harvey M. J., Noé F., De Fabritiis G., “HTMD: High-throughput molecular dynamics for molecular discovery”, J. Chem. Theory Comput., 12:4 (2016), 1845–1852 | DOI

[6] Buch I., Giorgino T., De Fabritiis G., “Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations”, Proc. Natl. Acad. Sci. U. S. A., 108:25 (2011), 10184–10189 | DOI

[7] Phillips J. C., Braun R., Wang W., Gumbart J., Tajkhorshid E., Villa E., Chipot C., Skeel R. D., Kalé L., Schulten K., “Scalable molecular dynamics with NAMD”, J. Comput. Chem., 26:16 (2005), 1781–1802 | DOI

[8] Huang J., MacKerell A. D. Jr., “CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data”, J. Comput. Chem., 34:25 (2013), 2135–2145 | DOI

[9] Grant B. J., Rodrigues A. P.C., ElSawy K. M., McCammon J. A., Caves L. S.D., “Bio3d: An R package for the comparative analysis of protein structures”, Bioinformatics, 22:21 (2006), 2695–2696 | DOI

[10] Humphrey W., Dalke A., Schulten K., “VMD: Visual molecular dynamics”, J. Mol. Graphics, 14:1 (1996), 33–38 | DOI

[11] Morris G. M., Huey R., Lindstrom W., Sanner M. F., Belew R. K., Goodsell D. S., Olson A. J., “AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility”, J. Comput. Chem., 30:16 (2009), 2785–2791 | DOI

[12] O'Boyle N. M., Banck M., James C. A., Morley C., Vandermeersch T., Hutchison G. R., “Open Babel: An open chemical toolbox”, J. Cheminf., 3 (2011), 33, 14 pp. | DOI

[13] Wickham H., ggplot2: Elegant Graphics for Data Analysis, Springer Cham, N. Y., 2016, XVI+260 pp. | DOI

[14] Ayupov R.Kh., Akberova N. I., Tarasov D. S., “Docking of pyridoxine derivatives in the active site of cholinesterases”, Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki, 153, no. 3, 2011, 107–118 (In Russian)

[15] Silman I., Sussman J. L., Acetylcholinesterase: How is structure related to function?, Chem.-Biol. Interact., 175:1–3 (2008), 3–10 | DOI

[16] Kim D. E., Chivian D., Baker D., “Protein structure prediction and analysis using the Robetta server”, Nucleic Acids Res., 32 (2004), W526–W531 | DOI

[17] Benkert P., Tosatto S. C., Schomburg D., “QMEAN: A comprehensive scoring function for model quality assessment”, Proteins, 71:1 (2008), 261–277 | DOI

[18] Dvir H., Silman I., Harel M., Rosenberry T. L., Sussman J. L., “Acetylcholinesterase: From 3D structure to function”, Chem.-Biol. Interact., 187:1–3 (2010), 10–22 | DOI

[19] Sussman J. L., Harel M., Frolow F., Oefner C., Goldman A., Toker L., Silman I., “Atomic structure of acetylcholinesterase from Torpedo californica: A prototypic acetylcholine-binding protein”, Science, 253:5022 (1991), 872–879 | DOI

[20] Mukhametgalieva A. R., Kozlova A. S., Akberova N. I., Fattakhova A. N., “Ligands affinity for regulatory sites of human acetylcholesterase and butyrylcholinesterase: A comparative bioinformatic analysis”, Uchenye Zapiski Kazanskogo Universiteta. Seriya Estestvennye Nauki, 163:1 (2021), 5–19 (In Russian) | DOI