Repo: [AFSample2](https://github.com/wallnerlab/AFsample2)  
Paper:  [Improving AlphaFold2 Performance in Virtual Screens Targeting GPCRs by Enhancing Binding-Site Conformational Sampling](https://pubs.acs.org/doi/full/10.1021/acs.jcim.6c00034)[https://www.nature.com/articles/s42003-025-07791-9](https://www.nature.com/articles/s42003-025-07791-9?utm_source=chatgpt.com)

The AFSample2T paper is finally out, and it is a cool example of where AI protein modelling is heading.

Vanilla AlphaFold2 is great at predicting a likely protein structure, but it usually collapses toward one dominant conformation. For GPCR drug discovery, that can be limiting because the binding pocket is flexible, and small local changes can strongly affect docking and virtual screening results.

AFSample2T tackles this by using **targeted MSA masking around the binding site**. Instead of perturbing the whole protein, it selectively weakens the evolutionary signal near the pocket, pushing AF2 to sample more ligand-compatible GPCR conformations.

So the difference is basically:

**Vanilla AF2:** predicts one likely structure.  
**AFSample2T:** samples alternative binding-site conformations for better virtual screening.

That matters because drug discovery needs more than static structures. It needs useful receptor states.

AFSample2T is out: making AlphaFold2 more useful for GPCR virtual screening