AlphaFold-based peptide structure prediction: Opportunities, limitations, and future directions.

Area of Science:

• Computational Biology
• Structural Biology
• Drug Discovery

Background:

• Peptide structure prediction is vital for drug development but challenged by conformational flexibility.
• The AlphaFold model series has significantly improved computational structure prediction accuracy.
• Current models often provide static conformations, potentially missing functionally relevant, low-probability states.

Purpose of the Study:

• To review advances in the AlphaFold series for peptide studies and applications.
• To discuss the strengths and limitations of AlphaFold in peptide structure and binding prediction.
• To explore integrated computational strategies for enhanced peptide drug design.

Main Methods:

• Review of AlphaFold model series advancements (AlphaFold2, AlphaFold-Multimer, AlphaFold3).
• Analysis of AlphaFold's geometric reasoning and confidence metrics (invariant point attention, ipTM score).
• Synthesis of studies combining AlphaFold with molecular dynamics, free energy calculations, and ensemble sampling.

Main Results:

• AlphaFold achieves high accuracy in predicting monomeric peptide structures and multi-chain complexes.
• Limitations include capturing conformational dynamics, transient interactions, and chemical modifications.
• Integrated approaches enhance accuracy by representing the dynamic nature of peptide-drug interactions.

Conclusions:

• AlphaFold is a central platform for structure-guided peptide drug design.
• Complementary methods are needed to bridge static predictions with peptide dynamics.
• Enhanced strategies improve lead identification and optimization for peptide therapeutics.