- Titel
- Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as Restraint
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa2-1014710
- Quellenangabe
- ACS Synthetic Biology
Erscheinungsjahr: 2024
Jahrgang: 13
Heft: 4
Seiten: 1085-1092
E-ISSN: 2161-5063 - Erstveröffentlichung
- 2024
- Abstract (EN)
- Computational protein sequence design has the ambitious goal of modifying existing or creating new proteins; however, designing stable and functional proteins is challenging without predictability of protein dynamics and allostery. Informing protein design methods with evolutionary information limits the mutational space to more native-like sequences and results in increased stability while maintaining functions. Recently, language models, trained on millions of protein sequences, have shown impressive performance in predicting the effects of mutations. Assessing Rosetta-designed sequences with a language model showed scores that were worse than those of their original sequence. To inform Rosetta design protocols with language model predictions, we added a new metric to restrain the energy function during design using the Evolutionary Scale Modeling (ESM) model. The resulting sequences have better language model scores and similar sequence recovery, with only a minor decrease in the fitness as assessed by Rosetta energy. In conclusion, our work combines the strength of recent machine learning approaches with the Rosetta protein design toolbox.
- Andere Ausgabe
- Erstveröffentlichung
DOI: 10.1021/acssynbio.3c00753 - Freie Schlagwörter (EN)
- computational protein design, protein language models, Rosetta, evolutionary fitness, thermodynamic stability, de novo proteins, protein language model
- Klassifikation (DDC)
- 570
- 540
- Verlag
- American Chemical Society, Washington, D.C.
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:15-qucosa2-1014710
- Veröffentlichungsdatum Qucosa
- 13.01.2026
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0