Cancer Drivers, Protein Complex Prediction, and Crawling and Gliding Cells: the PLOS Comp Biol October Issue
Here are our highlights from October’s PLOS Computational Biology:
A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces
Until now, most efforts in cancer genomics have focused on identifying genes and pathways driving tumor development. Although this has been a success, there is still a poor understanding of why patients with the same affected driver genes may have different disease outcomes or drug responses. Adam Godzik and colleagues show how—by considering proteins as multifunctional factories instead of monolithic black boxes—it is possible to identify novel cancer driver genes and propose molecular hypotheses to explain such heterogeneity.
Qualitative and Quantitative Protein Complex Prediction Through Proteome-Wide Simulations
Most proteins are biologically active only when part of a complex with other proteins. Hence, to unravel biological functions of proteins, it is important to identify the type of complexes they can form. Attila Csikász-Nagy and colleagues propose an integrative computational approach able to predict protein complexes from existing data sources on protein-protein and domain-domain interactions and protein abundances.
Crawling and Gliding: A Computational Model for Shape-Driven Cell Migration
Cell migration is involved in vital processes like morphogenesis, regeneration and immune system responses, but can also play a central role in pathological processes like metastasis. Computational models have been successfully employed to explain how single cells migrate, but there are few models that implement realistic cell shapes in multicellular simulations. Ioana Niculescu and colleagues present a model that is able to reproduce two different types of motile cells—amoeboid and keratocyte-like cells.