AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

An outlook on structural biology after AlphaFold: tools, limits and perspectives / Rosignoli, Serena; Pacelli, Maddalena; Manganiello, Francesca; Paiardini, Alessandro. - In: FEBS OPEN BIO. - ISSN 2211-5463. - 2024:(2024), pp. 1-21. [10.1002/2211-5463.13902]

An outlook on structural biology after AlphaFold: tools, limits and perspectives

Rosignoli, Serena;Pacelli, Maddalena;Manganiello, Francesca;Paiardini, Alessandro
Ultimo
2024

Abstract

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.
2024
AlphaFold; machine learning; structural bioinformatics; structure prediction
01 Pubblicazione su rivista::01g Articolo di rassegna (Review)
An outlook on structural biology after AlphaFold: tools, limits and perspectives / Rosignoli, Serena; Pacelli, Maddalena; Manganiello, Francesca; Paiardini, Alessandro. - In: FEBS OPEN BIO. - ISSN 2211-5463. - 2024:(2024), pp. 1-21. [10.1002/2211-5463.13902]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1720302
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