Graphlet signature-based scoring method to estimate protein–ligand binding affinity

Abstract

Over the years, various computational methodologies have been developed to understand and quantify receptor–ligand interactions. Protein–ligand interactions can also be explained in the form of a network and its properties. The ligand binding at the protein-active site is stabilized by formation of new interactions like hydrogen bond, hydrophobic and ionic. These non-covalent interactions when considered as links cause non-isomorphic sub-graphs in the residue interaction network. This study aims to investigate the relationship between these induced sub-graphs and ligand activity. Graphlet signature-based analysis of networks has been applied in various biological problems; the focus of this work is to analyse protein–ligand interactions in terms of neighbourhood connectivity and to develop a method in which the information from residue interaction networks, i.e. graphlet signatures, can be applied to quantify ligand affinity. A scoring method was developed, which depicts the variability in signatures adopted by different amino acids during inhibitor binding, and was termed as GSUS (graphlet signature uniqueness score). The score is specific for every individual inhibitor. Two well-known drug targets, COX-2 and CA-II and their inhibitors, were considered to assess the method. Residue interaction networks of COX-2 and CA-II with their respective inhibitors were used. Only hydrogen bond network was considered to calculate GSUS and quantify protein–ligand interaction in terms of graphlet signatures. The correlation of the GSUS with pIC50 was consistent in both proteins and better in comparison to the Autodock results. The GSUS scoring method was better in activity prediction of molecules with similar structure and diverse activity and vice versa. This study can be a major platform in developing approaches that can be used alone or together with existing methods to predict ligand affinity from protein–ligand complexes.

Exploring the structure and function of temporal networks with dynamic graphlets

“…, 2014 ), alignment ( Hsieh and Sze, 2014 ; Kuchaiev et al , 2010 ; Milenković et al , 2010b ; Saraph and Milenković, 2014 ), clustering ( Hulovatyy et al , 2014a ; Solava et al , 2012 ) or de-noising ( Hulovatyy et al , 2014b ), as well as for various application problems in computational biology, such as studying human aging ( Faisal et al , 2014 ; Faisal and Milenković, 2014 ), cancer ( Ho et al , 2010 ; Milenković et al , 2010a ) and other diseases ( Wang et al , 2014 ), pathogenicity ( Milenković et al , 2011 ; Solava et al. , 2012 ) or receptor–ligand interactions ( Singh et al , 2014 ).…”

From homogeneous to heterogeneous network alignment via colored graphlets

“…Intuitively, two nodes from different homogeneous networks are topologically similar if their extended neighborhoods are similar. This idea can be quantified with homogeneous graphlets (small -typically up to 5-node -connected subgraphs), which have been been extensively studied in homogeneous network analysis [39,64,55,24,54,15,62,51]. For each node, for each graphlet, one counts how many times the given node touches each node symmetry group, or node orbit, in the given graphlet (e.g., in a 3-node path, the nodes at the end of the path are symmetric to each other and are thus in the same orbit, but they are distinct from the node in the middle, which is thus in a separate orbit).…”

Fleet

“…Network motif detection and counting is now an indispensable tool in network analysis [30,34]. The distribution of motif counts in a network, as well as the number of motifs that a node takes part in, help characterize the roles of networks and nodes [33], an idea that has been used in numerous applications in networking, web and social network analysis, and computational biology [8,18,43,46,50]. Butterfly Counting.…”