RT Journal Article
SR Electronic
T1 Improved Peptide Docking with Privileged Knowledge Distillation using Deep Learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.12.01.569671
DO 10.1101/2023.12.01.569671
A1 Zhang, Zicong
A1 Verburgt, Jacob
A1 Kagaya, Yuki
A1 Christoffer, Charles
A1 Kihara, Daisuke
YR 2023
UL http://biorxiv.org/content/early/2023/12/04/2023.12.01.569671.abstract
AB Protein-peptide interactions play a key role in biological processes. Understanding the interactions that occur within a receptor-peptide complex can help in discovering and altering their biological functions. Various computational methods for modeling the structures of receptor-peptide complexes have been developed. Recently, accurate structure prediction enabled by deep learning methods has significantly advanced the field of structural biology. AlphaFold (AF) is among the top-performing structure prediction methods and has highly accurate structure modeling performance on single-chain targets. Shortly after the release of AlphaFold, AlphaFold-Multimer (AFM) was developed in a similar fashion as AF for prediction of protein complex structures. AFM has achieved competitive performance in modeling protein-peptide interactions compared to previous computational methods; however, still further improvement is needed. Here, we present DistPepFold, which improves protein-peptide complex docking using an AFM-based architecture through a privileged knowledge distillation approach. DistPepFold leverages a teacher model that uses native interaction information during training and transfers its knowledge to a student model through a teacher-student distillation process. We evaluated DistPepFold’s docking performance on two protein-peptide complex datasets and showed that DistPepFold outperforms AFM. Furthermore, we demonstrate that the student model was able to learn from the teacher model to make structural improvements based on AFM predictions.Competing Interest StatementThe authors have declared no competing interest.