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) for a benchmark dataset with corresponding nuclear magnetic resonance (NMR)-determined conformational ensembles. De novo structure predictions were accompanied by structural comparison analyses to assess prediction accuracy. We found that the prediction of conformational ensembles of peptides with AF2 varied in accuracy versus NMR data, with average root-mean-square deviation (RMSD) among structured regions under 2.5 [A] and average root-mean-square fluctuation (RMSF) differences under 1.5 [A] for the entire set of 557 peptides. Our results reveal notable capabilities of AF2-based structural conformation prediction for peptides but also highlight considerable limitations, underscoring the necessity for interpretation discretion and the need for improved conformational ensemble prediction approaches.