pyProGA-A PyMOL plugin for protein residue network analysis

Abstract

The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.

Fig 1. Basic overview of pyProGA functionality.

Input requirements are on the left, data structure in the centre and various analytical and output options are shown. The subsystem analysis requires the same kind of files to be loaded for the monomers as was for the super-system (dimer).

Fig 2. Subunits of the network.

The PRN of the supersystem consisting of monomers A and B and the protein-protein interface (PPI): G = G_A ∪ G_B ∪ G_PPI. G_A and G_B contain all nodes and edges within protein A and B, respectively. a) G_PPI contains vertices from both monomers, but only those vertices, which have at least one edge connecting it to a vertex from the other monomer. The edges in G_PPI are only those which connect the subgraphs G_A and G_B (dashed lines) and no edges within the subgraphs G_A, G_B. Hence, G_PPI is a bipartite graph. Part b) shows how blocks in the adjacency matrix A correspond to parts of G and the adjacency matrix A_PPI of G_PPI (circles indicating edges in G_PPI).

Fig 3. Network Differential Analysis (NDA).

The protein structure coloured to represent the magnitude of the centrality for each residuum. The PyMOL colour palette rainbow is used (red colour for high centrality, blue for low values). In a) $\Delta C_k^{E_{\text{tot}}}$ is shown and in b) we show $\Delta C_k^{\text{eff}}$ results. Selected high scoring residues are labelled.

Fig 4. PPI analysis in pyProGA.

a) The attractive G_PPI. Green monomer A, blue B. Thickness of edge corresponds to interaction strength, colour to character; red for prevailing electrostatic, blue for dispersion, see [85]. b) First four most dominant principal coordinates (interaction motifs in the PPI) as identified by the SVD analysis of G_PPI. Colour coded assignment of residues to PC. Factor f^(r) is defined by Eq 3. c) 3D-SPIE plot helps to identify strongest attractive and repulsive interactions between monomers A and B. More details in S1 File.

Fig 5. NDA predictions and experimental data.

The NDA $\Delta C_k^{\text{eff}}$ scores for the residues in the UL141…TRAIL-R2 complex based on an FMO/DFTB calculation. The top panel shows the scores of residues in both proteins (the border is depicted by the dashed line) and the bottom is a detailed plot for the residues of the TRAIL-R2 protein. The table contains experimental SPR (surface plasmon resonance) data published elsewhere [87]. Specific site mutations in the TRAIL-R2 protein to alanine (so called alanine scan), resulted in altered stability of the complex. The NDA bars corresponding to the residues that were mutated in the SPR experiments are labelled.