A Comprehensive Review of Harnessing Bioinformatics in Biochemistry: A New Era of Data-Driven Discoveries and Applications
Abstract
The integration of bioinformatics into biochemistry has ushered in a new era of scientific discovery, leveraging computational power and big data to uncover molecular mechanisms, predict molecular interactions, and accelerate the development of therapeutics. This review explores the advancements in bioinformatics tools and techniques that are transforming biochemistry. By discussing key applications, such as protein structure prediction, genomic data analysis, and systems biology, this paper highlights the significant contributions of bioinformatics in biochemistry and its potential for future applications in personalized medicine, drug discovery, and disease modeling. A key factor in the advancement of biochemistry, bioinformatics has become a transformative field at the nexus of biology, computer science, and statistics. Using tools and methods from genomics, proteomics, drug discovery, and systems biology, this review examines how bioinformatics might be integrated into the study of biochemical processes. The study of multi-omics data, the use of machine learning techniques to find molecular patterns and biological insights, and the application of computational modeling for protein structure prediction are important subjects. The paper also looks at the difficulties in analyzing biological data on a big scale, such as problems with data quality, reproducibility, and the requirement for interdisciplinary cooperation. As new technologies like artificial intelligence and quantum computing become available, bioinformatics has the potential to completely transform our knowledge of biological systems and speed up the identification of new biomarkers and treatment targets. This era of data-driven science promises to enhance human health through advancements in personalized medicine and innovative solutions to complex biochemical challenges.
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A. McLennan. Molecular biology. Garland Science. 2013. https://books.google.co.id/books?hl=id&lr=&id=oS3FwmcRm-gC.
A. M. Lesk. Introduction to bioinformatics. Oxford university press. 2019. https://books.google.co.id/books?hl=id&lr=&id=sCCWDwAAQBAJ.
S. F. Altschul, W. Gish, W. Miller, E. W. Myers, D. J. Lipman, “Basic Local Alignment Search Tool,” Journal of Molecular Biology, vol. 215, no. 3, pp. 403-410, 1990, https://doi.org/10.1016/S0022-2836(05)80360-2.
X. Xia “Bioinformatics and drug discovery,” Current topics in medicinal chemistry, vol. 17, no. 15, pp. 1709-1726, 2017, https://doi.org/10.2174/1568026617666161116143440.
H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, ande P. E. Bourne, “The Protein Data Bank. Nucleic Acids Research, vol. 28, no. 1, pp. 235-242, 2000, https://doi.org/10.1093/nar/28.1.235.
Y. Shen and A. Sali, “Statistical potential for assessment and prediction of protein structures,” Protein Science, vol. 15, no. 11, pp. 2507-2524, 2006, https://doi.org/10.1110/ps.062416606.
J. Jumper et al., “Highly accurate protein structure prediction with AlphaFold,” nature, vol. 596, no. 7873, pp. 583-589, 2021, https://doi.org/10.1038/s41586-021-03819-2.
M. Torrisi, G. Pollastri and Q. Le, “Deep learning methods in protein structure prediction,” Computational and structural biotechnology journal, vol. 18, pp. 1301-1310, 2020, https://doi.org/10.1016/j.csbj.2019.12.011.
A. Vlahou, F. Magni, H. Mischak, and J. Zoidakis. Integration of omics approaches and systems biology for clinical applications. John Wiley & Sons. 2018. https://books.google.co.id/books?hl=id&lr=&id=Tg1MDwAAQBAJ.
Z. Cai, R. C. Poulos, J. Liu, and Q. Zhong, “Machine learning for multi-omics data integration in cancer,” Iscience, vol. 25 no. 2, 2022, https://doi.org/10.1016/j.isci.2022.103798.
C. Fotis, A. Antoranz, D. Hatziavramidis, T. Sakellaropoulos, and L. G. Alexopoulos, “Network-based technologies for early drug discovery,” Drug discovery today, vol. 23, no. 3, pp. 626-635, 2018, https://doi.org/10.1016/j.drudis.2017.12.001.
A. Chroni and S. Kumar, “Tumors are evolutionary island-like ecosystems,” Genome Biology and Evolution, vol. 13, no. 12, p. evab276, 2021, https://doi.org/10.1093/gbe/evab276.
A. C. Wallace, R. A. Laskowski, and J. M. Thornton, “LIGPLOT: A program to generate schematic diagrams of protein-ligand interactions,” Protein Engineering, vol. 8, no. 2, pp. 127-134, 1995, https://doi.org/10.1093/protein/8.2.127.
P. Horton, K. J. Park, T. Obayashi, N. Fujita, H. Harada, C. J. Adams-Collier, and K. Nakai, "WoLF PSORT: Protein localization predictor,” Nucleic Acids Research, no. 35(Web Server issue), pp. W585-W587, 2017, https://doi.org/10.1093/nar/gkm259.
P. K. Mukherjee et al., “Development of Ayurveda–tradition to trend,” Journal of ethnopharmacology, vol. 197, pp. 10-24, 2017, https://doi.org/10.1016/j.jep.2016.09.024.
M. G. Agidew “Phytochemical analysis of some selected traditional medicinal plants in Ethiopia,” Bulletin of the National Research Centre, vol. 46, no. 1, p. 87, 2022, https://doi.org/10.1186/s42269-022-00770-8.
N. M. Patel and R. Desai, “Phytochemical constituents and their biological activities in Ayurvedic herbs,” Pharmacognosy Journal, 11(5), 1085-1095, 2019, https://doi.org/10.5530/pj.2019.11.180.
S. Singh, D. B. Singh, S. Singh, R. Shukla, P. W. Ramteke, and K. Misra, “Exploring medicinal plant legacy for drug discovery in post-genomic era,” Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, vol. 89, pp. 1141-1151, 2019, https://doi.org/10.1007/s40011-018-1013-x.
A. L. Barabási and Z. N. Oltvai, “Network biology: Understanding the cell’s functional organization,” Nature Reviews Genetics, vol. 5, no. 2, pp. 101-113, 2004, https://doi.org/10.1038/nrg1272.
T. Cheng, Q. Li, Y. Wang, and S. H. Bryant, “Structure-based virtual screening for drug discovery: A problem-centric review,” American Journal of Pharmacogenomics, vol. 12, no. 4, pp. 223-239, 2012, https://doi.org/10.1208/s12248-012-9322-0.
M. H. Rahman et al., “A network-based bioinformatics approach to identify molecular biomarkers for type 2 diabetes that are linked to the progression of neurological diseases,” International journal of environmental research and public health, vol. 17, no. 3, p. 1035, 2020, https://doi.org/10.3390/ijerph17031035.
R. Sharan and T. Ideker, “Network-based prediction of protein function,” Trends in Biotechnology, vol. 26, no. 11, pp. 514-521, 2008, https://doi.org/10.1038/msb4100129.
Z. Ahmed, “Multi-omics strategies for personalized and predictive medicine: past, current, and future translational opportunities,” Emerging topics in life sciences, vol. 6, no. 2, pp. 215-225, 2022, https://doi.org/10.1042/ETLS20210244.
G. M. Morris and M. Lim-Wilby, “Molecular docking,” Molecular Modeling of Proteins, vol. 13, pp. 365-382, 2008, https://doi.org/10.1007/978-1-59745-177-2_19.
Y. Xiao, M. Bi, H. Guo, and M. Li, “Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis,” EBioMedicine, vol. 79, 2022, https://doi.org/10.1016/j.ebiom.2022.104001.
G. Sunil Krishnan, A. Joshi, and V. Kaushik, “Bioinformatics in personalized medicine,” Advances in Bioinformatics, pp. 303-315, 2021, https://doi.org/10.1007/978-981-33-6191-1_15.
S. K. Wooller, G. Benstead-Hume, X. Chen, Y. Ali, and F. M. Pearl, F. M. (2017). Bioinformatics in translational drug discovery. Bioscience reports, 37(4), BSR20160180, 2017, https://doi.org/10.1042/BSR20160180.
B. Vogelstein and K. W. Kinzler, “Cancer genome landscapes,” Science, vol. 339, no. 6127, pp. 1546-1558, 2015, https://doi.org/10.1126/science.1235122.
A. Mardinoglu, J. Boren, U. Smith, M. Uhlen, and J. Nielsen, “Systems biology in hepatology: approaches and applications,” Nature Reviews Gastroenterology & Hepatology, vol. 15, no. 6, pp. 365-377, 2018, https://doi.org/10.1038/s41575-018-0007-8.
A. D. Baxevanis, G. D. Bader, and D. S. Wishart. Bioinformatics. John Wiley & Sons. 2020. https://books.google.co.id/books?hl=id&lr=&id=OuHNDwAAQBAJ.
K. Prince, S. Sasidharan, N. Nag, T. Tripathi, and P. Saudagar, “ Integration of spectroscopic and computational data to analyze protein structure, function, folding, and dynamics,” In Advanced spectroscopic methods to study biomolecular structure and dynamics, pp. 483-502, 2023, https://doi.org/10.1016/B978-0-323-99127-8.00018-0.
Y. Duarte, V. Márquez‐Miranda, M. J. Miossec, and F. González‐Nilo, “Integration of target discovery, drug discovery and drug delivery: a review on computational strategies. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, 11(4), e1554, 2019, https://doi.org/10.1002/wnan.1554.
S. Paliwal, A. Sharma, S. Jain, and S. Sharma, “Machine learning and deep learning in bioinformatics,” In Bioinformatics and Computational Biology, pp. 63-74, 2023, https://doi.org/10.1201/9781003331247-7.
Y. Tsuchiya, Y. Yamamori, and K. Tomii, “Protein–protein interaction prediction methods: from docking-based to AI-based approaches,” Biophysical Reviews, vol. 14, no. 6, pp. 1341-1348, 2022, https://doi.org/10.1007/s12551-022-01032-7.
V. Kanakaveti, A. Shanmugam, C. Ramakrishnan, P. Anoosha, R. Sakthivel, S. K. Rayala, and M. M. Gromiha, “Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery,” Advances in protein chemistry and structural biology, vol. 121, pp. 25-47, 2020, https://doi.org/10.1016/bs.apcsb.2019.11.013.
F. Meissner, J. Geddes-McAlister, M. Mann, and M. Bantscheff, “The emerging role of mass spectrometry-based proteomics in drug discovery,” Nature Reviews Drug Discovery, vol. 21, no. 9, pp. 637-654, 2022, https://doi.org/10.1038/s41573-022-00409-3.
C. Angione “Human systems biology and metabolic modelling: a review—from disease metabolism to precision medicine,” BioMed research international, vol. 2019, no. 1, pp. 8304260, 2019, https://doi.org/10.1155/2019/8304260.
J. Chen and G. Coppola, “Bioinformatics and genomic databases,” Handbook of Clinical Neurology, vol. 147, pp. 75-92, 2018, https://doi.org/10.1016/B978-0-444-63233-3.00007-5.
M. J. Jiménez‐Santos, S. García‐Martín, C. Fustero‐Torre, T. Di Domenico, G. Gómez‐López, and F. Al‐Shahrour, “Bioinformatics roadmap for therapy selection in cancer genomics,” Molecular Oncology, vol. 16, no. 21, pp. 3881-3908, 2022, https://doi.org/10.1002/1878-0261.13286.
M. A. Wörheide, J. Krumsiek, G. Kastenmüller, and M. Arnold, “Multi-omics integration in biomedical research–A metabolomics-centric review,” Analytica chimica acta, vol. 1141, pp. 144-162, 2021, https://doi.org/10.1016/j.aca.2020.10.038.
DOI: https://doi.org/10.59247/csol.v3i1.168
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