High-throughput prediction of peptide structural conformations with AlphaFold2.

ABSTRACT: Protein structure prediction via artificial intelligence/machine learning (AI/ML) approaches has sparked substantial research interest in structural biology and adjacent disciplines. More recently, AlphaFold2 (AF2) has been adapted for the prediction of multiple structural conformations-beyond the original scope of predicting single-state structures. This is accomplished by using multiple random seeds and subsampling the multiple sequence alignment (MSA). Research using this novel approach has focused on proteins (typically 50 residues in length or greater), while multi-conformation prediction of shorter peptides has not yet been explored in this context. Here, we report AF2-based structural conformation prediction of a total of 557 peptides (ranging in length from 10 to 40 residues

SUBMITTER: Ille AM 

PROVIDER: S-EPMC11642853 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature