Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network

A current key feature in drug-target network is that drugs often bind to multiple targets, known as polypharmacology or drug promiscuity. Recent literature has indicated that relatively small fragments in both drugs and targets are crucial in forming polypharmacology. We hypothesize that principles behind polypharmacology are embedded in paired fragments in molecular graphs and amino acid sequences of drug-target interactions. We developed a fast, scalable algorithm for mining significantly co-occurring subgraph-subsequence pairs from drug-target interactions. A noteworthy feature of our approach is to capture significant paired patterns of subgraph-subsequence, while patterns of either drugs or targets only have been considered in the literature so far. Significant substructure pairs allow the grouping of drug-target interactions into clusters, covering approximately 75% of interactions containing approved drugs. These clusters were highly exclusive to each other, being statistically significant and logically implying that each cluster corresponds to a distinguished type of polypharmacology. These exclusive clusters cannot be easily obtained by using either drug or target information only but are naturally found by highlighting significant substructure pairs in drug-target interactions. These results confirm the effectiveness of our method for interpreting polypharmacology in drug-target network.

[1]  W. Pearson.  Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms. , 1991, Genomics.

[2]  Edward M. Reingold,et al.  Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..

[3]  J. Wess.  G‐protein‐coupled receptors: molecular mechanisms involved in receptor activation and selectivity of G‐protein recognition , 1997, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[4]  Umeshwar Dayal,et al.  PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth , 2001, ICDE 2001.

[5]  Vladimir Batagelj,et al.  Pajek - Analysis and Visualization of Large Networks , 2004, Graph Drawing Software.

[6]  H. Cordell.  Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. , 2002, Human molecular genetics.

[7]  A. Hopkins,et al.  The druggable genome , 2002, Nature Reviews Drug Discovery.

[8]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[9]  Lei Shi,et al.  The binding site of aminergic G protein-coupled receptors: the transmembrane segments and second extracellular loop. , 2002, Annual review of pharmacology and toxicology.

[10]  Alex M Aronov,et al.  Toward a pharmacophore for kinase frequent hitters. , 2004, Journal of medicinal chemistry.

[11]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

[12]  T. Speed,et al.  GOstat: find statistically overrepresented Gene Ontologies within a group of genes. , 2004, Bioinformatics.

[13]  Michael Jünger,et al.  Graph Drawing Software , 2003, Graph Drawing Software.

[14]  Vladimir Batagelj,et al.  Pajek - Analysis and Visualization of Large Networks , 2001, Graph Drawing Software.

[15]  S. Lampel,et al.  The druggable genome: an update. , 2005, Drug discovery today.

[16]  S. Frantz.  Drug discovery: Playing dirty , 2005, Nature.

[17]  G. V. Paolini,et al.  Global mapping of pharmacological space , 2006, Nature Biotechnology.

[18]  Jiawei Han,et al.  Frequent pattern mining: current status and future directions , 2007, Data Mining and Knowledge Discovery.

[19]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[20]  Valérie Capra,et al.  The Highly Conserved DRY Motif of Class A G Protein-Coupled Receptors: Beyond the Ground State , 2007, Molecular Pharmacology.

[21]  R. Stevens,et al.  High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor. , 2007, Science.

[22]  A. Barabasi,et al.  Drug—target network , 2007, Nature Biotechnology.

[23]  Pierre Baldi,et al.  Bounds and Algorithms for Fast Exact Searches of Chemical Fingerprints in Linear and Sublinear Time , 2007, J. Chem. Inf. Model..

[24]  Richard Morphy,et al.  Fragments, network biology and designing multiple ligands. , 2007, Drug discovery today.

[25]  A. Hopkins.  Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[26]  K. Shokat,et al.  Targeted polypharmacology: Discovery of dual inhibitors of tyrosine and phosphoinositide kinases , 2008, Nature chemical biology.

[27]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.

[28]  C. Murray,et al.  The rise of fragment-based drug discovery. , 2009, Nature chemistry.

[29]  P. Hajduk,et al.  Rational approaches to targeted polypharmacology: creating and navigating protein-ligand interaction networks. , 2010, Current opinion in chemical biology.