Dataset Information

Prediction of Leishmania major Key Proteins Via Topological Analysis of Protein-Protein Interaction Network.

ABSTRACT:

Background

Although leishmaniasis is regarded as a public health problem, no effective vaccine or decisive treatment has been introduced for this disease. Therefore, representing novel therapeutic proteins is essential. Protein-protein Interaction network analysis is a suitable tool to discover novel drug targets for leishmania major. To this aim, gene and protein expression data is used for instructing protein network and the key proteins are highlighted.

Materials and methods

In this computational and bioinformatics study, the protein/gene expression data related to leishmania major were studied, and 252 candidate proteins were extracted. Then, the protein networks of these proteins were explored and visualized by using String database and Cytoscape software. Finally, clustering and gene ontology were performed by MCODE and PANTHER databases, respectively.

Results

Based on gene ontology analysis, most of the leishmania major proteins were located in cell compartments and membrane. Catalytic activity and binding were regarded as the relevant molecular functions and metabolic and cellular processes were the significant biological process. In this network analysis, UB-EP52, EF-2, chaperonin, Hsp70.4, Hsp60, tubulin alpha and beta chain, and ENOL and LACK were introduced as hub-bottleneck proteins. Based on clustering analysis, Lmjf.32.3270, ENOL and Lmjf.13.0290 were determined as seed proteins in each cluster.

Conclusion

The results indicated that hub proteins play a significant role in pathogenesis and life cycle of leishmania major. Further studies of hubs will provide a better understanding of leishmaniasis mechanisms. Finally, these key hub proteins could be a suitable and helpful potential for drug targets and treating leishmaniasis by considering their validation.

SUBMITTER: Amiri Dashatan N

PROVIDER: S-EPMC8344062 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe 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.ResultsWe 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.ConclusionWe 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 .

Project description:Maintenance of the physiological morphologies of different types of cells and tissues is essential for the normal functioning of each system in the human body. Dynamic variations in cell and tissue morphologies depend on accurate adjustments of the cytoskeletal system. The cytoskeletal system in the glomerulus plays a key role in the normal process of kidney filtration. To enhance the understanding of the possible roles of the cytoskeleton in glomerular diseases, we constructed the Glomerular Cytoskeleton Network (GCNet), which shows the protein-protein interaction network in the glomerulus, and identified several possible key cytoskeletal components involved in glomerular diseases. In this study, genes/proteins annotated to the cytoskeleton were detected by Gene Ontology analysis, and glomerulus-enriched genes were selected from nine available glomerular expression datasets. Then, the GCNet was generated by combining these two sets of information. To predict the possible key cytoskeleton components in glomerular diseases, we then examined the common regulation of the genes in GCNet in the context of five glomerular diseases based on their transcriptomic data. As a result, twenty-one cytoskeleton components as potential candidate were highlighted for consistently down- or up-regulating in all five glomerular diseases. And then, these candidates were examined in relation to existing known glomerular diseases and genes to determine their possible functions and interactions. In addition, the mRNA levels of these candidates were also validated in a puromycin aminonucleoside(PAN) induced rat nephropathy model and were also matched with existing Diabetic Nephropathy (DN) transcriptomic data. As a result, there are 15 of 21 candidates in PAN induced nephropathy model were consistent with our predication and also 12 of 21 candidates were matched with differentially expressed genes in the DN transcriptomic data. By providing a novel interaction network and prediction, GCNet contributes to improving the understanding of normal glomerular function and will be useful for detecting target cytoskeleton molecules of interest that may be involved in glomerular diseases in future studies.