NAIGO: An Improved Method to Align PPI Networks Based on Gene Ontology and Graphlets

With the development of high throughput technologies, there are more and more protein–protein interaction (PPI) networks available, which provide a need for efficient computational tools for network alignment. Network alignment is widely used to predict functions of certain proteins, identify conserved network modules, and study the evolutionary relationship across species or biological entities. However, network alignment is an NP-complete problem, and previous algorithms are usually slow or less accurate in aligning big networks like human vs. yeast. In this study, we proposed a fast yet accurate algorithm called Network Alignment by Integrating Biological Process (NAIGO). Specifically, we first divided the networks into subnets taking the advantage of known prior knowledge, such as gene ontology. For each subnet pair, we then developed a novel method to align them by considering both protein orthologous information and their local structural information. After that, we expanded the obtained local network alignments in a greedy manner. Taking the aligned pairs as seeds, we formulated the global network alignment problem as an assignment problem based on similarity matrix, which was solved by the Hungarian method. We applied NAIGO to align human and Saccharomyces cerevisiae S288c PPI network and compared the results with other popular methods like IsoRank, GRAAL, SANA, and NABEECO. As a result, our method outperformed the competitors by aligning more orthologous proteins or matched interactions. In addition, we found a few potential functional orthologous proteins such as RRM2B in human and DNA2 in S. cerevisiae S288c, which are related to DNA repair. We also identified a conserved subnet with six orthologous proteins EXO1, MSH3, MSH2, MLH1, MLH3, and MSH6, and six aligned interactions. All these proteins are associated with mismatch repair. Finally, we predicted a few proteins of S. cerevisiae S288c potentially involving in certain biological processes like autophagosome assembly.

[1]  Natasa Przulj,et al.  Integrative network alignment reveals large regions of global network similarity in yeast and human , 2011, Bioinform..

[2]  Bonnie Berger,et al.  IsoRankN: spectral methods for global alignment of multiple protein networks , 2009, Bioinform..

[3]  Sourav S. Bhowmick,et al.  DualAligner: a dual alignment-based strategy to align protein interaction networks , 2014, Bioinform..

[4]  Shi-Hua Zhang,et al.  Alignment of molecular networks by integer quadratic programming , 2007, Bioinform..

[5]  Jun Li,et al.  BinAligner: a heuristic method to align biological networks , 2013, BMC Bioinformatics.

[6]  Kristina Ban,et al.  Unified Alignment of Protein-Protein Interaction Networks , 2017, Scientific Reports.

[7]  E. Almaas.  Biological impacts and context of network theory , 2007, Journal of Experimental Biology.

[8]  Behnam Neyshabur,et al.  NETAL: a new graph-based method for global alignment of protein-protein interaction networks , 2013, Bioinform..

[9]  Wayne B. Hayes,et al.  SANA: simulated annealing far outperforms many other search algorithms for biological network alignment , 2017, Bioinform..

[10]  Tijana Milenkovic,et al.  MAGNA: Maximizing Accuracy in Global Network Alignment , 2013, Bioinform..

[11]  Jerzy Tiuryn,et al.  Identification of functional modules from conserved ancestral protein-protein interactions , 2007, ISMB/ECCB.

[12]  Jan Martens,et al.  NABEECO: biological network alignment with bee colony optimization algorithm , 2013, GECCO.

[13]  R. Lathrop.  The protein threading problem with sequence amino acid interaction preferences is NP-complete. , 1994, Protein engineering.

[14]  Han Zhao,et al.  Global Network Alignment in the Context of Aging , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[15]  Sandhya Rani,et al.  Human Protein Reference Database—2009 update , 2008, Nucleic Acids Res..

[16]  T. Ideker,et al.  Modeling cellular machinery through biological network comparison , 2006, Nature Biotechnology.

[17]  R. Karp,et al.  From the Cover : Conserved patterns of protein interaction in multiple species , 2005 .

[18]  R. Ozawa,et al.  A comprehensive two-hybrid analysis to explore the yeast protein interactome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Kara Dolinski,et al.  The BioGRID interaction database: 2015 update , 2014, Nucleic Acids Res..

[20]  Natasa Przulj,et al.  Biological network comparison using graphlet degree distribution , 2007, Bioinform..

[21]  O. Kuchaiev,et al.  Topological network alignment uncovers biological function and phylogeny , 2008, Journal of The Royal Society Interface.

[22]  Nigam H. Shah,et al.  Current progress in network research: toward reference networks for key model organisms , 2007, Briefings Bioinform..

[23]  Robert Patro,et al.  Global network alignment using multiscale spectral signatures , 2012, Bioinform..

[24]  Francis Bach,et al.  Global alignment of protein–protein interaction networks by graph matching methods , 2009, Bioinform..

[25]  Wayne Hayes,et al.  Optimal Network Alignment with Graphlet Degree Vectors , 2010, Cancer informatics.

[26]  Michael Lässig,et al.  From protein interactions to functional annotation: graph alignment in Herpes , 2007, BMC Systems Biology.

[27]  Antal F. Novak,et al.  networks Græmlin : General and robust alignment of multiple large interaction data , 2006 .

[28]  Sean R. Collins,et al.  Global landscape of protein complexes in the yeast Saccharomyces cerevisiae , 2006, Nature.

[29]  James R. Knight,et al.  A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae , 2000, Nature.

[30]  M. Cannataro,et al.  AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology , 2012, PloS one.

[31]  Roded Sharan,et al.  PathBLAST: a tool for alignment of protein interaction networks , 2004, Nucleic Acids Res..

[32]  Michael P H Stumpf,et al.  Complex networks and simple models in biology , 2005, Journal of The Royal Society Interface.

[33]  Vasant Honavar,et al.  Aligning Biomolecular Networks Using Modular Graph Kernels , 2009, WABI.

[34]  Richard M. Karp,et al.  Comparing Protein Interaction Networks via a Graph Match-and-Split Algorithm , 2007, J. Comput. Biol..

[35]  Pietro Hiram Guzzi,et al.  Improving the Robustness of Local Network Alignment: Design and Extensive Assessmentof a Markov Clustering-Based Approach , 2014, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[36]  A. Barabasi,et al.  Uncovering disease-disease relationships through the incomplete interactome , 2015, Science.

[37]  Johannes Berg,et al.  Cross-species analysis of biological networks by Bayesian alignment. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[38]  J. Hopfield,et al.  From molecular to modular cell biology , 1999, Nature.

[39]  Michael Lässig,et al.  Local graph alignment and motif search in biological networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[40]  Wojciech Szpankowski,et al.  Pairwise Alignment of Protein Interaction Networks , 2006, J. Comput. Biol..

[41]  Meng Xu,et al.  NetAlign: a web-based tool for comparison of protein interaction networks , 2006, Bioinform..

[42]  Bonnie Berger,et al.  Global alignment of multiple protein interaction networks with application to functional orthology detection , 2008, Proceedings of the National Academy of Sciences.

[43]  Gunnar W. Klau,et al.  A new graph-based method for pairwise global network alignment , 2009, BMC Bioinformatics.