Geometry based general prediction model of protein-peptide binding affinities

摘要

Protein-peptide interactions are the promising targets for potential protein drugs due to their critical role in many signal pathways with its small interaction interface. As a significant step in virtual screening in peptide drug discovery, binding affinity prediction is still a unsolved problem compared with achievements in docking. Most of current binding affinity prediction models are limited to a specific domain and is thus not applicable to many receptor domains that have few or no affinity data. To address this issue, domain independent prediction models are strongly needed. Traditional energy-based affinity prediction models are domain-independent, but still cannot give satisfactory results while impeded by its high computational cost. In this paper, we proposed a geometry shape based affinity prediction model. We evaluated our model using cross-validation on a non-redundant dataset of 336 complexes and and an external datasest: 592 human SH3 domain compelxes. Our experiments showed that our model is fast and achieved higher accuracy than the energy based models. Our model could be a useful affinity prediction tool for peptide docking and virtual screening in peptide drug discovery.