Atomevo: a web server combining protein modelling, docking, molecular dynamic simulation and MMPBSA analysis of Candida antarctica lipase B (CalB) fusion protein

Abstract

Although current computational biology software is available and has prompted the development of enzyme–substrates simulation, they are difficult to install and inconvenient to use. This makes the time-consuming and error-prone process. By far there is still a lack of a complete tool which can provide a one-stop service for the enzyme–substrates simulation process. Hence, in this study, several computational biology software was extended development and integrated as a website toolbox named Atomevo. The Atomevo is a free web server providing a user-friendly interface for enzyme–substrates simulation: (1) protein homologous modeling; (2) parallel docking module of Autodock Vina 1.2; (3) automatic modeling builder for Gromacs molecular dynamics simulation package; and (4) Molecular Mechanics/Poisson–Boltzmann Surface Area (MMPBSA) analysis module for receptor–ligand binding affinity analysis. We officially launched the web server and provided instructions through a case for the design and simulation of Candida antarctica lipase B (CalB) fusion protein called Maltose Binding Protein—Thioredoxin A—Candida antarctica lipase B (MBP-TrxA-CalB).

Keywords

Biofuel / Molecular dynamics simulation / Docking / MMPBSA / Web Server

References

[1] Baek M, DiMaio F, Anishchenko I, . Accurate prediction of protein structures and interactions using a three-track neural network. Science, 2021, 373: 871-876.

[2] Berendsen HJC, van der Spoel D, van Drunen R. GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun, 1995, 91: 43-56.

[3] Bhachoo J, Beuming T. Schueler-Furman O, London N. Investigating protein–peptide interactions using the schrödinger computational suite. Modeling peptide–protein interactions: methods and protocols, 2017, New York: Springer.

[4] Eberhardt J, Santos-Martins D, Tillack AF, . AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. J Chem Inf Model, 2021, 61: 3891-3898.

[5] Fatma T, Zafar Z, Fatima S, . Computational assessment of Botrytis cinerea lipase for biofuel production. Catalysts, 2021

[6] Grosdidier A, Zoete V, Michielin O. SwissDock, a protein-small molecule docking web service based on EADock DSS. Nucleic Acids Res, 2011, 39: W270-W277.

[7] Jumper J, Evans R, Pritzel A, . Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596: 583-589.

[8] Kumari R, Kumar R, Lynn A. g_mmpbsa—a GROMACS tool for high-throughput MM-PBSA calculations. J Chem Inf Model, 2014, 54: 1951-1962.

[9] Liu K, Chen X, Zhong Y, . Rational design of a highly efficient catalytic system for the production of PAPS from ATP and its application in the synthesis of chondroitin sulfate. Biotechnol Bioeng, 2021, 118: 4503-4515.

[10] Manta B, Boyd D, Berkmen M, . Disulfide bond formation in the periplasm of Escherichia coli. EcoSal plus, 2019

[11] McGregor MJ, Luo Z, Jiang X. Huang Z. Virtual screening in drug discovery. Drug discovery research: new frontiers in the post-genomic era, 2007, New York: Wiley.

[12] Nelson MT, Humphrey W, Gursoy A, . NAMD: a parallel, object-oriented molecular dynamics program. The Int J Supercomput Appl High Perform Comput, 1996, 10: 251-268.

[13] O'Boyle NM, Banck M, James CA, . Open Babel: an open chemical toolbox. J Cheminformat, 2011

[14] Phillips JC, Hardy DJ, Maia JDC, . Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys , 2020, 10(1063/5): 0014475.

[15] Rester U. From virtuality to reality—virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. Curr Opin Drug Discov Devel, 2008, 11: 559-568.

[16] Salentin S, Schreiber S, Haupt VJ, . Current opinion in drug discovery & developmentPLIP: fully automated protein–ligand interaction profiler. Nucleic Acids Res, 2015, 43: W443-W447.

[17] Thompson AP, Aktulga HM, Berger R, . LAMMPS—a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput Phys Commun, 2022

[18] Valentine D, Zaslavsky I, Richard S, et al (2021) EarthCube data discovery studio: a gateway into geoscience data discovery and exploration with Jupyter notebooks. In: Concurrency and computation: practice and experience. https://doi.org/10.1002/cpe.6086.

[19] Wang R, Lai L, Wang S. Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput Aided Mol Des, 2002, 16: 11-26.

[20] Wang Z, Sun H, Yao X, . Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys, 2016, 18: 12964-12975.

[21] Xu J, Zhou H, Yu H, . Computational design of highly stable and soluble alcohol dehydrogenase for NADPH regeneration. Bioresourc Bioprocess, 2021, 8: 12.

[22] Zhang X, Peng Y, Zhao J, . Bacterial cytochrome P450-catalyzed regio- and stereoselective steroid hydroxylation enabled by directed evolution and rational design. Bioresourc Bioprocess, 2020, 7: 2.

Funding

Guangdong Basic and Applied Basic Research Foundation(2019A1515110621)

The Project of Department of Education of Guangdong Province(2017KQNCX217)

The High-Level Talent Start-Up Research Project of Foshan University(GG07016)