Hybrid AI/physics pipeline for miniprotein binder prioritization: application to the BRD3 ET domain

Jokent Gaza; Monica J. Roth; Gaetano T Montelione; Alberto Pérez

doi:10.1039/d5cc05032d

January 1, 2025Chemical CommunicationsOpen Access

Hybrid AI/physics pipeline for miniprotein binder prioritization: application to the BRD3 ET domain

View Full Paper

Ask AI

Bookmark

Share

Key Points

To improve the efficiency of binder candidate selection in protein design using AI techniques.
Utilized a hybrid AI and physics-driven approach for binder generation and evaluation.
Applied the pipeline specifically to the BRD3 ET protein domain for prioritization of miniprotein binders.
Identified robust methods for prioritizing protein binders.
Demonstrated the pipeline's effectiveness in generating viable candidate binders.

Implication

AI-based design prioritizes binder candidates for BRD3 ET domain, suggesting efficient selection in protein engineering.

Abstract

AI-based protein design can rapidly generate thousands of candidate binders, but most fail to fold or bind productively, creating a critical need for robust prioritization.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Gaza et al. (2025) studied this question.

synapsesocial.com/papers/6925572cc0ce034ddc35a78a https://doi.org/10.1039/d5cc05032d

View Full Paper

Ask AI

Bookmark

Share

Hybrid AI/physics pipeline for miniprotein binder prioritization: application to the BRD3 ET domain

Key Points

Implication

Abstract

Discussion

Cite This Study

Hybrid AI/physics pipeline for miniprotein binder prioritization: application to the BRD3 ET domain

Key Points

Implication

Abstract

Discussion

Cite This Study

Keep reading

Keep reading