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The allosteric drugs bind to allosteric sites, induce changes in the target protein's active site, and modulate its function with high selectivity, reduced adverse effects, and low toxicity. While identifying allosteric sites is costly and labor\u2010intensive with experimental approaches, computational methods utilizing three\u2010dimensional protein structures offer a cost\u2010effective solution for discovering potential allosteric sites and predicting the effects of ligand binding. This study evaluates the effectiveness of two network models, the residue interaction network (RIN) model and the mixed coarse\u2010grained anisotropic network model (mcgANM), in identifying putative allosteric regions, predicting the structural response of the protein to ligand binding, and elucidating allosteric mechanisms while maintaining computational efficiency. The SARS\u2010CoV\u20102 main protease (M\n                    <jats:sup>pro<\/jats:sup>\n                    ) is employed as an allosteric protein model due to a rich experimental and computational data available since the COVID\u201019 pandemic. The findings of the methods are assessed with statistical analysis, all\u2010atom molecular dynamics simulations, and other elastic network models, namely Essential Site Scanning Analysis and Gaussian Network Model using a dataset of 15 ligand\u2010bound and 4 ligand\u2010free structures. RIN predicted the known drug binding sites of M\n                    <jats:sup>pro<\/jats:sup>\n                    with high statistics, up to 80.0% sensitivity, 89.7% specificity, 29.6% precision, and 89.2% accuracy. RIN suggested an allosteric mechanism of M\n                    <jats:sup>pro<\/jats:sup>\n                    that facilitates the allosteric communication of the allosteric and active sites through residue fluctuations. RIN was able to decompose the enzyme structure to dynamic domains, showing the organization of structural components to form a functional viral protease. mcgANM suggested the changes in residue fluctuations after ligand binding. The findings underscore the utility of the network models in advancing allosteric drug design.\n                  <\/jats:p>","DOI":"10.1002\/prot.70122","type":"journal-article","created":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:55:22Z","timestamp":1771048522000},"page":"1341-1355","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Computationally Efficient Network Models Successfully Predict Allosteric Sites of\n                    <scp>SARS<\/scp>\n                    \u2010\n                    <scp>CoV<\/scp>\n                    \u20102 Main Protease and Reveal Its Dynamic Allostery"],"prefix":"10.1002","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3257-1097","authenticated-orcid":false,"given":"Merve","family":"Yuce","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering Istanbul Technical University  Istanbul 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