Efficient link prediction in the protein–protein interaction network using topological information in a generative adversarial network machine learning model

Abstract

Background The investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates for future experimental validations. Reformulating the problem in the field of network theory, the set of proteins can be represented as the nodes of a network, while the interactions between them as the edges. The resulting protein–protein interaction (PPI) network enables the use of link prediction techniques in order to discover new probable connections. Therefore, here we aimed to offer a novel approach to the link prediction task in PPI networks, utilizing a generative machine learning model. Results We created a tool that consists of two modules, the data processing framework and the machine learning model. As data processing, we used a modified breadth-first search algorithm to traverse the network and extract induced subgraphs, which served as image-like input data for our model. As machine learning, an image-to-image translation inspired conditional generative adversarial network (cGAN) model utilizing Wasserstein distance-based loss improved with gradient penalty was used, taking the combined representation from the data processing as input, and training the generator to predict the probable unknown edges in the provided induced subgraphs. Our link prediction tool was evaluated on the protein–protein interaction networks of five different species from the STRING database by calculating the area under the receiver operating characteristic, the precision-recall curves and the normalized discounted cumulative gain (AUROC, AUPRC, NDCG, respectively). Test runs yielded the averaged results of AUROC = 0.915, AUPRC = 0.176 and NDCG = 0.763 on all investigated species. Conclusion We developed a software for the purpose of link prediction in PPI networks utilizing machine learning. The evaluation of our software serves as the first demonstration that a cGAN model, conditioned on raw topological features of the PPI network, is an applicable solution for the PPI prediction problem without requiring often unavailable molecular node attributes. The corresponding scripts are available at https://github.com/semmelweis-pharmacology/ppi_pred.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

“…The global advantage of methods based on machine learning is the processing of multidimensional and multivariate data from several omics or horizontal omics ( Das et al, 2020 ; Jamasb et al, 2021 ). Prediction of interactions is highly efficient ( Terayama et al, 2019 ; Balogh et al, 2022 ), but machine learning requires large computational resources and large datasets of good quality ( Hashemifar et al, 2018 ; Y. Wang et al, 2018b ).…”

Establishment of fingerprint and mechanism of anti‐myocardial ischemic effect of Syringa pinnatifolia

“…Using the PubChem database, all compounds were converted to standard Canonical SMILES format, and the SMILES format files were imported into SWISS (http://www.swisstargetprediction.ch/) and Superpred websites (http://prediction.charite.de/) and set the property ‘homo sapiens’ to predict the targets of the compounds. The SWISS online prediction platform selected the targets with the parameter Probability ≥0.6 in the prediction results for further analysis (Balogh et al, 2022). Superpred predicts the potential targets of unknown molecules by calculating the Tanimoto similarity of the molecule to over 300,000 known compounds in the server (Leem et al, 2022).…”

“…The SWISS online prediction platform selected the targets with the parameter Probability ≥0.6 in the prediction results for further analysis (Balogh et al, 2022). Superpred predicts the potential targets of unknown molecules by calculating the Tanimoto similarity of the molecule to over 300,000 known compounds in the server (Leem et al, 2022).…”

Assessment of community efforts to advance network-based prediction of protein–protein interactions

“…Conditional Generative Adversarial Network (cGAN): generative adversarial network performing image-to-image translation conditioned on either embedding (cGAN1) or raw information (cGAN2) of the network topology. U 57 2. Skip similarity Graph Neural Network (SkipGNN): receive neural messages from two-hop and immediate neighbors in the interaction network and non‐linearly transforms the messages.…”