Benchmarking AlphaFold3-like Methods for Protein-Peptide Complex Prediction

Protein-short peptide interactions are central mechanisms regulating signal transduction, chaperone functions, and drug targeting design. However, the precise prediction of their complex structures has long been constrained by the limited accuracy of traditional methods. The breakthrough of AlphaFold2 resolved the challenge of predicting protein monomer structures. Its derivative method, AlphaFold2-multimer, surpassed traditional docking approaches in predicting protein complexes, yet its accuracy remains suboptimal. AlphaFold3 and its replication models, such as Protenix, Chai-1, and Boltz-1, have further improved prediction accuracy for protein-protein interactions. To evaluate the performance of these methods in protein-short peptide structure prediction, this study conducted structural predictions and comprehensive comparisons on a benchmark dataset of protein-short peptide complexes. The results demonstrate that the new-generation models significantly increased the success rate under stringent criteria (Fnat≥ 0.8) to 70-80%, compared to AlphaFold2-multimer (53%), with Protenix achieving the highest accuracy at 80.8%. A multi-method combination strategy (e.g., combining AF3 with Protenix) enabled a high-quality prediction success rate of 89%, while covering 97% of cases under moderate criteria (Fnat≥ 0.5), highlighting synergistic advantages among methods. Notably, inter-model consistency analysis provided an efficient metric for selecting optimal predictions.