Comparative Analysis of Deep Learning-Based Algorithms for Peptide Structure Prediction

While of primary importance in both the biomedical and therapeutic fields, peptides suffer from a relative lack of dedicated tools to predict efficiently and accurately their 3D structures despite being a crucial step in understanding their physio-pathological function or designing new drugs. In recent years, deep-learning methods have enabled a major breakthrough for the protein 3D structure prediction approaches, allowing to predict protein 3D structures with a near-experimental accuracy for nearly any protein sequence. This present study aims at confronting some of these new methods (AlphaFold2, RoseTTAFold2, and ESMFold) for the peptides' 3D structure prediction problem and evaluating their performance. All methods produced high-quality results, but their overall performance is lower as compared to the prediction of protein 3D structures. We also identified a few structural features that impede the ability to produce high-quality peptide structure predictions. These findings point out the discrepancy that still exists between the protein and peptide 3D structure prediction methods and underline a few cases where the generated peptide structures should be used very cautiously.