AligNet: Alignment of Protein-Protein Interaction Networks

Alberich, Ricardo; Alcalá, Adrià; Llabrés, Mercè; Rosselló, Francesc; Valiente, Gabriel

doi:10.48550/arxiv.1902.07107

Cited by 1 publication

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“…Even more worrisome, when trying to balance the objectives of aligning proteins using network topology vs. aligning proteins that have similar sequences, a negative correlation has been widely observed: topology-weighted network alignments are far less able to align functionally similar proteins than sequence-similarity-weighted network alignments. The connection between topological network similarity and functional similarity has been so tenuous for so long that many authors now refer explicitly to a "sequence-topology trade-off", effectively abandoning the promise of independently using the expected common network topology across species to uncover functional similarity [23][24][25][26][27][28] .…”

Section: Motivationmentioning

confidence: 99%

“…To our knowledge, in order to gain biological relevance, every other network alignment algorithm applied to PPI networks has had to guide the alignment with objective functions that include protein-pair sequence similarities, in order to encourage sequence-and thus functionally-similar protein pairs to align to each other. This fact has led to a widespread belief in a so-called "sequence-topology trade-off" 23,28 , so that virtually every alignment algorithm either imposes sequence-based restrictions [25][26][27][30][31][32][33][34][35][36][37] , or has a literal trade-off in an objective function of the form αT (a) + (1 − α)S(a), where a is an alignment, T (a) measures the topological similarity exposed by the alignment according to some topological measure, S(a) is an objective based on sequence similarity, and α is a balancing parameter that makes the trade-off explicit 18,23,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] . Virtually all methods that use the explicit trade-off have found that functional similarity is positively correlated with the weight given to sequence, and negatively correlated with the weight given to topology 15,23,35,38,40 .…”

Section: The Sequence-topology "Trade-off"mentioning

confidence: 99%

“…For example, via personal communication with the authors, we have verified that NATALIE 25 , NATALIE 2.0 26 , and PrimAlign 27 all require the use of pairwise sequence similarities. Several published alignment papers 23,24,28 explicitly state that they regard the use of sequence similarities as necessary, and in any case their topological objectives are usually some minor variant of EC and not worth considering independent of EC. We ran each of these algorithms on all 28 combinations of the 8 largest PPI networks from BioGRID version 3.4.156 from January 2018.…”

Section: Comparing Sana's Alignments To Those Of Competing Alignersmentioning

confidence: 99%

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On the current failure -- but bright future -- of topology-driven biological network alignment

Wang,

Chen,

Frederisy

et al. 2022

Preprint

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Since the function of a protein is defined by its interaction partners, and since we expect similar interaction patterns across species, the alignment of protein-protein interaction (PPI) networks between species, based on network topology alone, should uncover functionally related proteins across species. Surprisingly, despite the publication of more than fifty algorithms aimed at performing PPI network alignment, few have demonstrated a statistically significant link between network topology and functional similarity, and none have demonstrated that orthologs can be recovered using network topology alone. We find that the major contributing factors are to this surprising failure are: (i) edge densities in most currently available experimental PPI networks are demonstrably too low to expect topological network alignment to succeed; (ii) in the few cases where the edge densities are high enough, some measures of topological similarity easily uncover functionally similar proteins while others do not; and (iii) most network alignment algorithms to date perform poorly at optimizing even their own topological objective functions, hampering their ability to use topology effectively. We demonstrate that SANA-the Simulated Annealing Network Aligner-significantly outperforms existing aligners at optimizing their own objective functions, even achieving near-optimal solutions when optimal solution is known. We offer the first demonstration of global network alignments based on topology alone that align functionally similar proteins with p-values in some cases below 10 −300 . We predict that topological network alignment has a bright future as edge densities increase towards the value where good alignments become possible. We demonstrate that when enough common topology is present at high enough edge densities-for example in the recent, partly synthetic networks of the Integrated Interaction Database-topological network alignment easily recovers most orthologs, paving the way towards high-throughput functional prediction based on topology-driven network alignment.

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