An outlook on structural biology after AlphaFold: tools, limits and perspectives
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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | FEBS Open Bio |
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| Online Access: | https://doi.org/10.1002/2211-5463.13902 |
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| author | Serena Rosignoli Maddalena Pacelli Francesca Manganiello Alessandro Paiardini |
| author_facet | Serena Rosignoli Maddalena Pacelli Francesca Manganiello Alessandro Paiardini |
| author_sort | Serena Rosignoli |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-327498c18b264f1d8a5a9c7dab0c0980 |
| institution | Kabale University |
| issn | 2211-5463 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | FEBS Open Bio |
| spelling | doaj-art-327498c18b264f1d8a5a9c7dab0c09802025-02-03T10:59:30ZengWileyFEBS Open Bio2211-54632025-02-0115220222210.1002/2211-5463.13902An outlook on structural biology after AlphaFold: tools, limits and perspectivesSerena Rosignoli0Maddalena Pacelli1Francesca Manganiello2Alessandro Paiardini3Department of Biochemical sciences “A. Rossi Fanelli” Sapienza Università di Roma ItalyDepartment of Biochemical sciences “A. Rossi Fanelli” Sapienza Università di Roma ItalyDepartment of Biochemical sciences “A. Rossi Fanelli” Sapienza Università di Roma ItalyDepartment of Biochemical sciences “A. Rossi Fanelli” Sapienza Università di Roma ItalyAlphaFold 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.https://doi.org/10.1002/2211-5463.13902AlphaFoldmachine learningstructural bioinformaticsstructure prediction |
| spellingShingle | Serena Rosignoli Maddalena Pacelli Francesca Manganiello Alessandro Paiardini An outlook on structural biology after AlphaFold: tools, limits and perspectives FEBS Open Bio AlphaFold machine learning structural bioinformatics structure prediction |
| title | An outlook on structural biology after AlphaFold: tools, limits and perspectives |
| title_full | An outlook on structural biology after AlphaFold: tools, limits and perspectives |
| title_fullStr | An outlook on structural biology after AlphaFold: tools, limits and perspectives |
| title_full_unstemmed | An outlook on structural biology after AlphaFold: tools, limits and perspectives |
| title_short | An outlook on structural biology after AlphaFold: tools, limits and perspectives |
| title_sort | outlook on structural biology after alphafold tools limits and perspectives |
| topic | AlphaFold machine learning structural bioinformatics structure prediction |
| url | https://doi.org/10.1002/2211-5463.13902 |
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