Short loop motif profiling of protein interaction networks in acute myeloid leukaemia

Recent advances in biotechnologies for genomics and proteomics have expanded our understanding of biological components which play crucial roles in complex mechanisms related to cancer. However, it is still challenging to extract from the available knowledge reliable targets to use in a translational setting. The reasons for this are manifold, but essentially distilling real biological signal from heterogeneous ″big data″ collections is the major hurdle. Here, we aim to establish an in-silico pipeline to explore mutations and their effects on protein-protein interactions, with a focus on acute myeloid leukaemia (AML), one of the most common blood cancers with the highest mortality rate. Our method, based on cyclic interactions of a small number of proteins topologically linked in the network (short loop network motifs), highlights specific protein-protein interactions (PPIs) and their functions in AML when compared with other leukaemias. We also developed a new property named ′short loop commonality′ to measure indirect PPIs occurring via common short loop interactions. This new method detects ″modules″ of PPI networks (PPINs) enriched with common biological functions which have proteins that contain mutation hotspots. We further perform 3D structural modelling to extract atomistic details, which shows that such hotspots map to PPI interfaces as well as active sites. Thus, our study proposes a framework for the macroscopic and microscopic investigation of PPINs, their relation to cancers, and highlights important functional modules in the network to be exploited in targeted drug screening.