scholarly journals A novel network aligner for the analysis of multiple protein-protein interaction networks

Author(s):  
Jing Chen ◽  
Jia Huang

The analysis of protein-protein interaction networks can transfer the knowledge of well-studied biological functions to functions that are not yet adequately investigated by constructing networks and extracting similar network structures in different species. Multiple network alignment can be used to find similar regions among multiple networks. In this paper, we introduce Accurate Combined Clustering Multiple Network Alignment (ACCMNA), which is a new and accurate multiple network alignment algorithm. It uses both topology and sequence similarity information. First, the importance of all the nodes is calculated according to the network structures. Second, the seed-and-extend framework is used to conduct an iterative search. In each iteration, a clustering method is combined to generate the alignment. Extensive experimental results show that ACCMNA outperformed the state-of-the-art algorithms in producing functionally consistent and topological conservation alignments within an acceptable running time.

Author(s):  
Vu Thi Ngoc Anh ◽  
Nguyen Trong Dong ◽  
Nguyen Vu Hoang Vuong ◽  
Dang Thanh Hai ◽  
Do Duc Dong

Aligning protein-protein interaction networks from different species is a useful mechanism for figuring out orthologous proteins, predicting/verifying protein unknown functions or constructing evolutionary relationships. The network alignment problem is proved to be NP-hard, requiring exponential-time algorithms, which is not feasible for the fast growth of biological data. In this paper, we present a novel protein-protein interaction global network alignment algorithm, which is enhanced with an extended large neighborhood search heuristics. Evaluated on benchmark datasets of yeast, fly, human and worm, the proposed algorithm outperforms state-of-the-art. Furthermore, the complexity of ours is polynomial, thus being scalable to large biological networks in practice. Keywords Heuristic, Protein-protein interaction networks, network alignment, neighborhood search References [1] R.L. Finley, R. Brent, Interaction mating reveals binary and ternary connections between drosophila cell cycle regulators. Proc. Natl Acad. Sci. USA. 91 (1994) 12980-12984.[2] R. Aebersold, M. Mann, Mass spectrometry-based proteomics, Nature. 422 (2003) 198-207.[3] C.S. Goh, F.R. Cohen, Co-evolutionary analysis reveals insights into protein-protein interactions, J. Mol. Biol. 324 (2002) 177-192.[4] J.D. Han et al, Evidence for dynamically organized modularity in the yeast proteinprotein interaction network, Nature. 430 (2004) 88-93.[5] G.D. Bader, C.W. Hogue, Analyzing yeast protein-protein interaction data obtained from different sources, Nat. Biotechnol. 20 (2002) 991-997.[6] H.B. Hunter et al, Evolutionary rate in the protein interaction network, Science. 296 (2002) 750-752.[7] J. Dutkowski, J. Tiuryn,J, Identification of functional modules from conserved ancestral protein-protein interactions, Bioinformatics. 23 (2007) i149-i158.[8] B.P. Kelley et al, Conserved pathways within bacteria and yeast as revealed by global protein network alignment, Proc. Natl Acad. Sci. USA. 100 (2003) 11394-11399.[9] O. Kuchaiev, N. Przˇ ulj, Integrative network alignment reveals large regions of global network similarity in yeast and human, Bioinformatics. 27 (2011) 1390-1396.[10] M. Remm et al, Automatic clustering of orthologs and in-paralogs from pairwise species comparisons, J. Mol. Biol. 314 (2001) 1041-1052. [11] L. Chindelevitch et al, Local optimization for global alignment of protein interaction networks, In: Pacific Symposium on Biocomputing, Hawaii, USA, 2010, pp. 123-132.[12] E. hmet, Aladağ, Cesim Erten, SPINAL: scalable protein interaction network alignment, Bioinformatics. Volume 29(7) (2013) 917-924. https://doi.org/10.1093/bioinformatics/btt071.[13] B.P. Kelley et al, Pathblast: a tool for alignment of protein interaction networks, Nucleic Acids Res. 32 (2004) 83-88.[14] R. Sharan et al, Conserved patterns of protein interaction in multiple species, Proc. Natl Acad. Sci. USA. 102 (2005) 1974-1979.[15] M. Koyuturk et al, Pairwise alignment of protein interaction networks, J. Comput. Biol. 13 (2006) 182-199.[16] M. Narayanan, R.M. Karp, Comparing protein interaction networks via a graph match-and-split algorithm, J. Comput. Biol. 14 (2007) 892-907.[17] J. Flannick et al, Graemlin: general and robust alignment of multiple large interaction networks, Genome Res. 16 (2006) 1169-1181.[18] R. Singh et al, Global alignment of multiple protein interaction networks. In: Pacific Symposium on Biocomputing, 2008, pp. 303-314.[19] M. Zaslavskiy et al, Global alignment of protein-protein interaction networks by graph matching methods, Bioinformatics. 25 (2009) 259-267.[20] L. Chindelevitch, Extracting information from biological networks. PhD Thesis, Department of Mathematics, Massachusetts Institute of Technology, Cambridge, 2010.[21] Do Duc Dong et al, An efficient algorithm for global alignment of protein-protein interaction networks, Proceeding of ATC15, 2015, pp. 332-336.[22] S. Ropke, D. Pisinger, An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows. Transportation Science. 40 (2006) 455-472. https:// doi.org/10.1287/trsc.1050.0135.[23] P. Shaw, A new local search algorithm providing high quality solutions to vehicle routing problems, Technical report, Department of Computer Science, University of Strathclyde, Scotland, 1997.[24] Roman Lutz, Adaptive Large Neighborhood Search, Bachelor thesis, Ulm University, 2014.[25] M.A. Trick, A linear relaxation heuristic for the generalized assignment prob-lem, Naval Research Logistics. 39 (1992) 137-151.[26] J.Y. Potvin, M. Rousseau, Parallel Route Building Algorithm for the Vehicle Routing and Scheduling Problem with Time Windows, European Journal of Operational Research. 66(3) (1993) pp. 331-340.[27] https://www.researchgate.net/figure/Network-alignment-a-A-dashed-arrow-from-a-node-i-V1-from-the-first-network-G1-V1-E_fig1_24017968[28] J.M. Peter, Van Laarhoven, H.L. Emile, Aarts. Simulated annealing. Springer, 1987.


2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Adrià Alcalá ◽  
Ricardo Alberich ◽  
Mercè Llabrés ◽  
Francesc Rosselló ◽  
Gabriel Valiente

Abstract Background All molecular functions and biological processes are carried out by groups of proteins that interact with each other. Metaproteomic data continuously generates new proteins whose molecular functions and relations must be discovered. A widely accepted structure to model functional relations between proteins are protein-protein interaction networks (PPIN), and their analysis and alignment has become a key ingredient in the study and prediction of protein-protein interactions, protein function, and evolutionary conserved assembly pathways of protein complexes. Several PPIN aligners have been proposed, but attaining the right balance between network topology and biological information is one of the most difficult and key points in the design of any PPIN alignment algorithm. Results Motivated by the challenge of well-balanced and efficient algorithms, we have designed and implemented AligNet, a parameter-free pairwise PPIN alignment algorithm aimed at bridging the gap between topologically efficient and biologically meaningful matchings. A comparison of the results obtained with AligNet and with the best aligners shows that AligNet achieves indeed a good balance between topological and biological matching. Conclusion In this paper we present AligNet, a new pairwise global PPIN aligner that produces biologically meaningful alignments, by achieving a good balance between structural matching and protein function conservation, and more efficient computations than state-of-the-art tools.


2019 ◽  
Author(s):  
R. Alberich ◽  
A. Alcalá ◽  
M. Llabrés ◽  
F. Rosselló ◽  
G. Valiente

AbstractOne of the most difficult problems difficult problem in systems biology is to discover protein-protein interactions as well as their associated functions. The analysis and alignment of protein-protein interaction networks (PPIN), which are the standard model to describe protein-protein interactions, has become a key ingredient to obtain functional orthologs as well as evolutionary conserved pathways and protein complexes. Several methods have been proposed to solve the PPIN alignment problem, aimed to match conserved subnetworks or functionally related proteins. However, the right balance between considering network topology and biological information is one of the most difficult and key points in any PPIN alignment algorithm which, unfortunately, remains unsolved. Therefore, in this work, we propose AligNet, a new method and software tool for the pairwise global alignment of PPIN that produces biologically meaningful alignments and more efficient computations than state-of-the-art methods and tools, by achieving a good balance between structural matching and protein function conservation as well as reasonable running times.


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