Identifying critical residues of a protein using meaningfully-thresholded Random Geometric Graphs
This work addresses the identification of critical residues for protein function, which is important for biological research, but appears incremental as it builds on existing RGG techniques with new thresholding and criticality measures.
The authors tackled the problem of identifying critical residues in a protein by developing three methods based on Random Geometric Graphs (RGGs) using molecular dynamics simulations, and compared these methods against experimentally-ascertained critical residues, though no concrete numbers on performance were provided.
Identification of critical residues of a protein is actively pursued, since such residues are essential for protein function. We present three ways of recognising critical residues of an example protein, the evolution of which is tracked via molecular dynamical simulations. Our methods are based on learning a Random Geometric Graph (RGG) variable, where the state variable of each of 156 residues, is attached to a node of this graph, with the RGG learnt using the matrix of correlations between state variables of each residue-pair. Given the categorical nature of the state variable, correlation between a residue pair is computed using Cramer's V. We advance an organic thresholding to learn an RGG, and compare results against extant thresholding techniques, when parametrising criticality as the nodal degree in the learnt RGG. Secondly, we develop a criticality measure by ranking the computed differences between the posterior probability of the full graph variable defined on all 156 residues, and that of the graph with all but one residue omitted. A third parametrisation of criticality informs on the dynamical variation of nodal degrees as the protein evolves during the simulation. Finally, we compare results obtained with the three distinct criticality parameters, against experimentally-ascertained critical residues.