IHP-PING—generating integrated human protein–protein interaction networks on-the-fly

Abstract

Advances in high-throughput sequencing technologies have resulted in an exponential growth of publicly accessible biological datasets. In the ‘big data’ driven ‘post-genomic’ context, much work is being done to explore human protein–protein interactions (PPIs) for a systems level based analysis to uncover useful signals and gain more insights to advance current knowledge and answer specific biological and health questions. These PPIs are experimentally or computationally predicted, stored in different online databases and some of PPI resources are updated regularly. As with many biological datasets, such regular updates continuously render older PPI datasets potentially outdated. Moreover, while many of these interactions are shared between these online resources, each resource includes its own identified PPIs and none of these databases exhaustively contains all existing human PPI maps. In this context, it is essential to enable the integration of or combining interaction datasets from different resources, to generate a PPI map with increased coverage and confidence. To allow researchers to produce an integrated human PPI datasets in real-time, we introduce the integrated human protein–protein interaction network generator (IHP-PING) tool. IHP-PING is a flexible python package which generates a human PPI network from freely available online resources. This tool extracts and integrates heterogeneous PPI datasets to generate a unified PPI network, which is stored locally for further applications.

Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects

“…Figure 2 (solid blue line) shows that, as expected, the fraction of drug pairs with the positive Kendall τ increases with the increasing chemical similarity and reaches a value of 1.0 for the TC threshold of 0.6. The second metric is the drug action similarity computed as the Matthews correlation coefficient (MCC) [ 60 ] between target proteins in the protein-protein interaction (PPI) network from the IHP-PING dataset [ 61 ]. Similar to the TC, the fraction of drug pairs with the positive Kendall τ also increases with the increasing MCC reaching 1.0 for the MCC threshold of 0.6 ( Fig.…”

“…Similarity of the mechanism of action of two drugs is quantified with the MCC [ 60 ] computed for 19,968 proteins in the IHP-PING dataset [ 61 ] according to chemical-protein associations obtained from the STITCH database [ 62 ]: where is the number of proteins targeted by both drugs, is the number of proteins not targeted by any drug, is the number of proteins only targeted by the first drug, and is the number of proteins only targeted by the second drug.…”

SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy

“…The human PPI network was constructed using IHP-PING [ 32 ] that integrates interaction data from multiple databases, including StringDB [ 33 ], BioGRID [ 34 ], DIP [ 35 ], HPRD [ 36 ], IntAct [ 37 ], MINT [ 38 ], MPPI-MIPS [ 39 ], with functional information from UniProt [ 40 ]. Applying a threshold of ≥0.7, indicating a high level of confidence in the data [ 32 ], resulted in a PPI network comprising 18,997 nodes. Based on the drug target data from AZ-DREAM Challenges [ 20 ], 14,374 nodes are categorized as druggable and 4623 as non-druggable.…”

“…This choice is supported by the fact that the TC range of (0.95, 1) corresponds to full identity in terms of molecular similarity [ 46 ]. Molecular target similarity is computed as the Matthews correlation coefficient against 18,997 nodes in the PPI network [ 32 ]. Drug candidates for augmentation were selected at a DACS cutoff of ≥0.53, which ensures that the majority of substitute drugs exhibit similar pharmacological profiles to their parent molecules [ 45 ].…”

Investigations of Kidney Dysfunction-Related Gene Variants in Sickle Cell Disease Patients in Cameroon (Sub-Saharan Africa)

“…Three gene variants within this sub-network, namely APOL1 (G1)-rs73885319, as well as APOL1 (G2)-rs71785313, WNT7A-rs6795744, and SYPL2-rs12136063, have been identified to influence variation in renal dysfunction phenotypes in SCD patients. These variant genes are connected to NFKB1, identified to be essential or a hub based on the network centrality measures within the sub-network via some specific intermediate genes (Figure 6 and Table 4), following a small world property of human PPI network (Mazandu et al, 2020). This NFKB1 gene might indirectly influence extreme phenotype levels.…”