Inter-protein contact map generated only from intra-monomer by image inpainting

Abstract

Intra-protein contact or distance prediction has seen many progresses in recent years with the development of deep learning algorithms combined with direct coupling analysis. Some works try to extend these state-of-art intra-protein methods to inter-protein. However, it’s difficult to build high-quality joint multiple sequence alignments (MSAs) for protein dimers, especially for heterodimers. Here we propose a generative adversarial network (GAN) based method, Protein Dimer Image Inpainting (PDII), to predict inter-protein contact map (CM) solely from monomer structures without any co-evolution information. PDII can learn intrinsic contact patterns with local coherence and global consistency for several cases. It’s robust to monomer structure quality as our method is only based on joint CMs and doesn’t need precise structure information. Furthermore, PDII not only works well on bound monomers but also on unbound ones. When evaluating on 3Dcomplex datasets, our method works better than DNCON-inter and RaptorX-ComplexContact. Tested on homodimer proteins with C2 symmetry type, PDII can make good predictions if homologous sequences are relatively small. Besides, it presents good patch accuracy on the MaSif-PPI dataset. In all, PDII is an effective way to predict contacts in dimer protein complexes and provide new understandings for interactions in protein complexes.