Community Detection for Decoy Selection in Template-free Protein Structure Prediction

Significant efforts are devoted to resolving biologically-active structures in wet and dry laboratories. In particular, due to hardware and algorithmic innovations, computational methods can now obtain thousands of structures that populate the structure space of a protein of interest. With such advances, attention turns to organizing computed structures to extract the underlying organization of the structure space in service of highlighting biologically-active structural states. In this paper we report on the promise of leveraging community detection methods, designed originally to detect communities in social networks, to organize protein structure spaces probed in silico. We report on a principled comparison of such methods along several metrics and on proteins of diverse folds and lengths. More importantly, we present a rigorous evaluation in the context of decoy selection in template-free protein structure prediction. The presented results make the case that network-based community detection methods warrant further investigation to advance analysis of protein structure spaces for automated selection of biologically-active structures.

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