AlloGen: Conformation-Selective Protein Design

In this AI Research Roundup episode, Alex discusses the paper: 'AlloGen: Conformation-Selective Binder Generation with Differential State Scoring' Traditional protein binder design focuses on binding affinity but fails to address conformational selectivity, which is critical for allosteric targets like kinases and GPCRs. To solve this, the researchers introduce AlloGen, a modular framework that decouples backbone generation from a learned state-selectivity scorer called Q-theta. Q-theta is an SE(3)-invariant interface graph transformer trained to recognize specific interface geometries and impose conformational discrimination. Because it is fully differentiable, AlloGen can integrate with any backbone generator as a passive reranker or active gradient-based guide. Experimental validation on calmodulin demonstrates that AlloGen successfully generates de novo peptides that bind desired target states with high selectivity. Paper URL: https://arxiv.org/pdf/2606.05474 #AI #MachineLearning #DeepLearning #ProteinDesign #StructuralBiology #GenerativeModels #Bioinformatics

Видео AlloGen: Conformation-Selective Protein Design канала AI Research Roundup