PRIGSA2: Improved version of protein repeat identification by graph spectral analysis

Tandemly repeated structural motifs in proteins form highly stable structural folds and provide multiplebinding sites associated with diverse functional roles. The tertiary structure and function of these proteins aredetermined by the type and copy number of the repeating units. Each repeat type exhibits a unique pattern ofintra- and inter-repeat unit interactions that is well-captured by the topological features in the network representation of protein structures. Here we present an improved version of our graph based algorithm, PRIGSA,with structure-based validation and filtering steps incorporated for accurate detection of tandem structuralrepeats. The algorithm integrates available knowledge on repeat families with de novo prediction to detectrepeats in single monomer chains as well as in multimeric protein complexes. Three levels of performanceevaluation are presented: comparison with state-of-the-art algorithms on benchmark dataset of repeat and nonrepeat proteins, accuracy in the detection of members of 13 known repeat families reported in UniProt andexecution on the complete Protein Data Bank to show its ability to identify previously uncharacterizedproteins. A *3-fold increase in the coverage of the members of 13 known families and 3408 noveluncharacterized structural repeat proteins are identified on executing it on PDB. PRIGSA2 is available at http://bioinf.iiit.ac.in/PRIGSA2/.

Analysis of core–periphery organization in protein contact networks reveals groups of structurally and functionally critical residues.

The representation of proteins as networks of interacting amino acids, referred to as protein contact networks (PCN), and their subsequent analyses using graph theoretic tools, can provide novel insights into the key functional roles of specific groups of residues. We have characterized the networks corresponding to the native states of 66 proteins (belonging to different families) in terms of their core–periphery organization. The resulting hierarchical classification of the amino acid constituents of a protein arranges the residues into successive layers – having higher core order – with increasing connection density, ranging from a sparsely linked periphery to a densely intra-connected core (distinct from the earlier concept of protein core defined in terms of the three-dimensional geometry of the native state, which has least solvent accessibility). Our results show that residues in the inner cores are more conserved than those at the periphery. Underlining the functional importance of the network core, we see that the receptor sites for known ligand molecules of most proteins occur in the innermost core. Furthermore, the association of residues with structural pockets and cavities in binding or active sites increases with the core order. From mutation sensitivity analysis, we show that the probability of deleterious or intolerant mutations also increases with the core order. We also show that stabilization centre residues are in the innermost cores, suggesting that the network core is critically important in maintaining the structural stability of the protein. A publicly available Web resource for performing core–periphery analysis of any protein whose native state is known has been made available by us at http://www.imsc.res.in/ ~sitabhra/proteinKcore/index.html.