Computational design of intrinsically disordered proteins
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866911156483915776 |
|---|---|
| author | Tesei, Giulio Pesce, Francesco Lindorff-Larsen, Kresten |
| author_facet | Tesei, Giulio Pesce, Francesco Lindorff-Larsen, Kresten |
| contents | Protein design has the potential to revolutionize biotechnology and medicine. While most efforts have focused on proteins with well-defined structures, increased recognition of the functional significance of intrinsically disordered regions, together with improvements in their modeling, has paved the way to their computational de novo design. This review summarizes recent advances in engineering intrinsically disordered regions with tailored conformational ensembles, molecular recognition, and phase behavior. We discuss challenges in combining models with predictive accuracy with scalable design workflows and outline emerging strategies that integrate knowledge-based, physics-based, and machine-learning approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12460 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computational design of intrinsically disordered proteins Tesei, Giulio Pesce, Francesco Lindorff-Larsen, Kresten Biomolecules Protein design has the potential to revolutionize biotechnology and medicine. While most efforts have focused on proteins with well-defined structures, increased recognition of the functional significance of intrinsically disordered regions, together with improvements in their modeling, has paved the way to their computational de novo design. This review summarizes recent advances in engineering intrinsically disordered regions with tailored conformational ensembles, molecular recognition, and phase behavior. We discuss challenges in combining models with predictive accuracy with scalable design workflows and outline emerging strategies that integrate knowledge-based, physics-based, and machine-learning approaches. |
| title | Computational design of intrinsically disordered proteins |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2509.12460 |