Traditional research means, such as in vitro and in vivo models, have consistently been used by scientists to test hypotheses in biochemistry. Computational (in silico) methods have been increasingly devised and applied to testing and hypothesis development in biochemistry over the last decade. The aim of in silico methods is to analyze the quantitative aspects of scientific (big) data, whether these are stored in databases for large data or generated with the use of sophisticated modeling and simulation tools; to gain a fundamental understanding of numerous biochemical processes related, in particular, to large biological macromolecules by applying computational means to big biological data sets, and by computing biological system behavior. Computational methods used in biochemistry studies include proteomics-based bioinformatics, genome-wide mapping of protein-DNA interaction, as well as high-throughput mapping of the protein-protein interaction networks. Some of the vastly used molecular modeling and simulation techniques are Monte Carlo and Langevin (stochastic, Brownian) dynamics, statistical thermodynamics, molecular dynamics, continuum electrostatics, protein-ligand docking, protein-ligand affinity calculations, protein modeling techniques, and the protein folding process and enzyme action computer simulation. This paper presents a short review of two important methods used in the studies of biochemistry - protein-ligand docking and the prediction of protein-protein interaction networks.
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
3
Marko Karović, Boško Nikolić, Nikola Nedeljković, Marina Vesović, Miloš Nikolić
(2024)
Design of vonoprazan pyrazole derivatives as potential reversible inhibitors of gastric proton pump: An in silico molecular docking study
AFMN Biomedicine, 41(1)
10.5937/afmnai41-43298
Prasanna Kumar Rangarajan, Bharathi Mohan Gurusamy, Elakkiya Rajasekar, Srisurya Ippatapu Venkata, Spandana Chereddy
(2024)
Retroactive data structure for protein–protein interaction in lung cancer using Dijkstra algorithm
International Journal of Information Technology, 16(2)
10.1007/s41870-023-01557-4
John Noone, Robert G. Wallace, Keith D. Rochfort
(2023)
Protein Chromatography
Methods in Molecular Biology, 2699()
10.1007/978-1-0716-3362-5_15The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.