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.

One 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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Motivation All molecular functions and biological processes are carried out by groups of proteins that interact to each other. Proteins interactions are modeled by simple networks called Protein-Protein Interaction Networks (PPINs) whose nodes are proteins and whose edges are the protein-protein interactions. PPINs are broadly accepted to model the protein’s functional relations, and their analysis has become a key ingredient in the study of protein functions. New proteins are collected every day from metaproteomic data, and their functional relations must be obtained with high-throughput technology. Retrieving protein-protein interaction data experimentally is a very high time-consuming and labor-intensive task. Consequently, in the last years, the biological community is looking for computational methods to correctly predict PPIs. Results We present here Prots2Net, a tool designed to predict the PPIs of a proteome or a metaproteome sample. Our prediction model is a multilayer perceptron neural network that uses protein sequence information only from the input proteins and interaction information from the STRING database. To train the model, Prots2Net explores the PPIs retrieved from the STRING database of two selected species. The tests, reported here on the Yeast and the Human datasets, show that Prots2Net performs better than the previous prediction methods that used protein sequence information only. Therefore, considering the information of PPI data available on the STRING database improves the PPI prediction. Availability https://github.com/adriaalcala/prots2net Contact merce.llabres{at}uib.es

Motivation Several protein-protein interaction networks (PPIN) aligners have been developed during the last 15 years. One of their goals is to help the functional annotation of proteins and the prediction of protein-protein interactions. A correct aligner must preserve the network’s topology as well as the biological coherence. However, this is a trade-off that is hard to achieve. In addition, most aligners require a considerable effort to use in practice and many researchers must choose an aligner without the opportunity to previously compare the performance of different aligners. Results We developed PINAWeb, a user-friendly web-based tool to obtain and compare the results produced by the aligners: AligNet, HubAlign, L-GRAAL, PINALOG and SPINAL. PPINs can be uploaded either from the STRING database or from a user database. The source code of PINAWeb is freely available on GitHub to enable researchers to add other aligners, network databases or alignment score metrics. In addition, PINAWeb provides a report with the analysis for every alignment in terms of topological and functional information scores, as well as the visualization of the alignments’ comparison (agreement/differences) when more than one aligner are considered. Availability https://bioinfo.uib.es/~recerca/PINAWeb