ai-science-alphafold-protein-design

Name: ai-science-alphafold-protein-design
Rating: 5 (3 reviews)
Author: Pavel-Kravchenko

Design e Frontend

Author: Pavel-Kravchenko

AlphaFold2/3 architecture, confidence metrics (pLDDT/PAE), structure fetching, and protein design ranking workflows.

3stars

Updated 3 months ago

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AlphaFold and Protein Design

Key Gotchas

AF2 does not predict dynamics — single static conformation, typically most stable
High pLDDT ≠ biologically correct — disordered regions can predict high pLDDT if they resemble structured training data
PAE is directional: PAE[i,j] asks "if residue i is placed correctly, how confident about j?" — not symmetric
AF2 vs AF3: AF2 is for single proteins/homomers. AF3 handles protein-DNA, protein-RNA, protein-ligand, modified resid

[Description truncada. Veja o README completo no GitHub.]

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