Back

LEVELNET: diving into the multiple layers of protein-protein physical interaction networks

MOHSENI BEHBAHANI, Y.; SAIGHI, P.; CORSI, F.; LAINE, E.; CARBONE, A.

2021-08-02 bioinformatics
10.1101/2021.07.31.453756 bioRxiv
Show abstract

AO_SCPLOWBSTRACTC_SCPLOWPhysical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in the cell and in what manner relies on partial, noisy, and highly heterogeneous data. Thus, there is a need for methods comprehensively describing and organising such data. LEVELNET is a versatile and interactive tool for visualising, exploring and comparing protein-protein interaction (PPI) networks inferred from different types of evidence. LEVELNET helps to break down the complexity of PPI networks by representing them as multilayered graphs and by facilitating the direct comparison of their subnetworks toward biological interpretation. It focuses primarily on the protein chains whose 3D structures are available in the Protein Data Bank. We showcase some potential applications, such as investigating the structural evidence supporting PPIs associated to specific biological processes, assessing the co-localisation of interaction partners, comparing the PPI networks obtained through computational experiments versus homology transfer, and creating PPI benchmarks with desired properties. Availability: LEVELNET is freely available to the community at http://www.lcqb.upmc.fr/levelnet/.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.