ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfaces

Abstract

Background: Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these interactions is key for a myriad of applications including drug development, peptide design and identification of drug targets. The biological data deluge demands efficient and scalable methods to characterize and understand protein-protein interfaces. In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces. To assess the ability of ppiGReMLIN to point out relevant conserved substructures on protein-protein interfaces, we compared our results to experimentally determined patterns that are key for protein-protein interactions in 2 datasets of complexes, Serine-protease and BCL-2. Results: ppiGReMLIN was able to detect, in an automatic fashion, conserved structural arrangements that represent highly conserved interactions at the specificity binding pocket of trypsin and trypsin-like proteins from Serine-protease dataset. Also, for the BCL-2 dataset, our method pointed out conserved arrangements that include critical residue interactions within the conserved motif LXXXXD, pivotal to the binding specificity of BH3 domains of pro-apoptotic BCL-2 proteins towards apoptotic suppressors. Quantitatively, ppiGReMLIN was able to find all of the most relevant residues described in literature for our datasets, showing precision of at least 69% up to 100% and recall of 100%. Conclusions: ppiGReMLIN was able to find highly conserved structures on the interfaces of protein-protein complexes, with minimum support value of 60%, in datasets of similar proteins. We showed that the patterns automatically detected on protein interfaces by our method are in agreement with interaction patterns described in the literature.

Propedia: a database for protein–peptide identification based on a hybrid clustering algorithm

“…We formerly designed peptides manually, with the support of certain bioinformatic tools. Now we are investing in the development of automatic tools to support this process, such as ppiGReMLIN [ 21 ]. In this context, Propedia aims to deliver a comprehensive data set of experimental protein–peptide complexes organized in three types of clusters based on : (1) sequence similarity; (2) interface structure; and (3) protein–peptide binding site.…”

Frequent Pattern Mining in Continuous-time Temporal Networks

“…Frequent subgraph mining problem has attracted substantial attention in domains where the data can be represented as networks, such as in chemo-informatics [16], [17], [18], health informatics [19], [20], [21], [22], [23], public health [24], [25], [26], bioinformatics [27], [28], [29], social network analysis [30], [31], [32], computer vision [33], [34], [35], [36], [37], [38], and security [39], [40], [41], [42], [43]. The frequent subgraph mining in these discplines are either applied to a data set of small networks [44] or a data set of one large network [45].…”