Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs

Linear motifs have an integral role in dynamic cell functions, including cell signaling. However, due to their small size, low complexity, and frequent mutations, identifying novel functional motifs poses a challenge. Viruses rely extensively on the molecular mimicry of cellular linear motifs. In this study, we apply systematic motif prediction combined with functional filters to identify human linear motifs convergently evolved also in viral proteins. We observe an increase in the sensitivity of motif prediction and improved enrichment in known instances. We identify >7,300 non-redundant motif instances at various confidence levels, 99 of which are supported by all functional and structural filters. Overall, we provide a pipeline to improve the identification of functional linear motifs from interactomics datasets and a comprehensive catalog of putative human motifs that can contribute to our understanding of the human domain-linear motif code and the associated mechanisms of viral interference.

Automated summary

Linear motifs play a crucial role in cell functions, but their identification has been challenging. This study uses systematic motif prediction and functional filters to identify viral protein motifs that have evolved convergently with human motifs. The findings improve motif prediction sensitivity and provide a comprehensive catalog of potential human motifs that contribute to understanding the human domain-linear motif code and viral interference mechanisms.