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In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.<\/jats:p>","DOI":"10.1063\/5.0180691","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T10:51:12Z","timestamp":1713955872000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":31,"title":["Advanced computational approaches to understand protein aggregation"],"prefix":"10.1063","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1538-2622","authenticated-orcid":false,"given":"Deepshikha","family":"Ghosh","sequence":"first","affiliation":[{"name":"Department of Biological Sciences and Engineering, Indian Institute of Technology (IIT) Gandhinagar 1 , Palaj, Gujarat 382355, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0701-3173","authenticated-orcid":false,"given":"Anushka","family":"Biswas","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Indian Institute of Technology (IIT) Gandhinagar 2 , Palaj, Gujarat 382355, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7127-4744","authenticated-orcid":false,"given":"Mithun","family":"Radhakrishna","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, Indian Institute of Technology (IIT) Gandhinagar 2 , Palaj, Gujarat 382355, India"},{"name":"Center for Biomedical Engineering, Indian Institute of Technology (IIT) Gandhinagar 3 , Palaj, Gujarat 382355, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"317","published-online":{"date-parts":[[2024,4,24]]},"reference":[{"key":"2024042414510438000_c1","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1002\/cphc.201900904","article-title":"In silico evidence that protein unfolding is a precursor of protein aggregation","volume":"21","year":"2020","journal-title":"ChemPhysChem"},{"key":"2024042414510438000_c2","doi-asserted-by":"publisher","first-page":"6557","DOI":"10.1021\/cr500288y","article-title":"Introduction to intrinsically disordered proteins (IDPs)","volume":"114","year":"2014","journal-title":"Chem. 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