AI-based Protein 3D Prediction using AlphaFold
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| Autores: | , |
|---|---|
| Formáid: | artículo original |
| Stádas: | Versión publicada |
| Fecha de Publicación: | 2026 |
| Cur Síos: | Three-dimensional protein prediction with a high approximation to the actual conformation is conceptually possible using mathematical models according to the Anfinsen dogma. Still, it is impractical due to the multiple conformations that add complexity due to the Levinthal paradox. One way to solve this puzzle is by using machine learning models based on structures that have already been elucidated using artificial intelligence. The AlphaFold program allows de novo predictions by using machine learning algorithms. This paper explains the tool’s components and the metrics used to interpret the results. It provides optional ways to access the program, along with a practical example to learn how to execute a prediction. It concludes with the principles of use and ethics of the tool. |
| País: | Portal de Revistas TEC |
| Institiúid: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Teanga: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/8492 |
| Rochtain Ar Líne: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/8492 |
| Palabra clave: | Plegamiento tridimensional de proteínas estructura proteica alineamiento múltiple biotecnología ciencias de la computación Three-dimensional protein folding protein structure multiple alignment biotechnology computer science |