scholarly journals Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Fei Yuan ◽  
Yu-Hang Zhang ◽  
Sibao Wan ◽  
ShaoPeng Wang ◽  
Xiang-Yin Kong
Keyword(s):  
Pancreatic Cancer ◽  
Candidate Genes ◽  
Shortest Path ◽  
Protein Interactions ◽  
Large Scale ◽  
Permutation Test ◽  
Computational Method ◽  
Large Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction

Pancreatic cancer (PC) is a highly malignant tumor derived from pancreas tissue and is one of the leading causes of death from cancer. Its molecular mechanism has been partially revealed by validating its oncogenes and tumor suppressor genes; however, the available data remain insufficient for medical workers to design effective treatments. Large-scale identification of PC-related genes can promote studies on PC. In this study, we propose a computational method for mining new candidate PC-related genes. A large network was constructed using protein-protein interaction information, and a shortest path approach was applied to mine new candidate genes based on validated PC-related genes. In addition, a permutation test was adopted to further select key candidate genes. Finally, for all discovered candidate genes, the likelihood that the genes are novel PC-related genes is discussed based on their currently known functions.

scholarly journals Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Fei Yuan ◽  
You Zhou ◽  
Meng Wang ◽  
Jing Yang ◽  
Kai Wu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Candidate Genes ◽  
Shortest Path ◽  
Protein Interactions ◽  
The Body ◽  
Computational Method ◽  
Human Reproduction ◽  
Hybrid Network ◽  
Prostate Cancer Cells ◽  
Protein Protein Interactions

Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Li ◽  
Zheng Wang ◽  
Li-Ping Li ◽  
Zhu-Hong You ◽  
Wen-Zhun Huang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Sequence ◽  
Large Scale ◽  
False Positive Rate ◽  
Computational Method ◽  
Evolutionary Information ◽  
Local Alignment ◽  
Protein Interaction Data ◽  
Sequence Information ◽  
Protein Protein Interactions

AbstractVarious biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

On the structure of protein–protein interaction networks

2003 ◽  
Vol 31 (6) ◽  
pp. 1491-1496 ◽  
Author(s):  
A. Thomas ◽  
R. Cannings ◽  
N.A.M. Monk ◽  
C. Cannings
Keyword(s):  
Power Law ◽  
Protein Interactions ◽  
Large Scale ◽  
Underlying Structure ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Protein Protein Interaction ◽  
Human Proteins ◽  
Approximate Power ◽  
Two Hybrid

We present a simple model for the underlying structure of protein–protein pairwise interaction graphs that is based on the way in which proteins attach to each other in experiments such as yeast two-hybrid assays. We show that data on the interactions of human proteins lend support to this model. The frequency of the number of connections per protein under this model does not follow a power law, in contrast to the reported behaviour of data from large-scale yeast two-hybrid screens of yeast protein–protein interactions. Sampling sub-graphs from the underlying graphs generated with our model, in a way analogous to the sampling performed in large-scale yeast two-hybrid searches, gives degree distributions that differ subtly from the power law and that fit the observed data better than the power law itself. Our results show that the observation of approximate power law behaviour in a sampled sub-graph does not imply that the underlying graph follows a power law.

scholarly journals Identifying Novel Candidate Genes Related to Apoptosis from a Protein-Protein Interaction Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Baoman Wang ◽  
Fei Yuan ◽  
Xiangyin Kong ◽  
Lan-Dian Hu ◽  
Yu-Dong Cai
Keyword(s):  
Cell Death ◽  
Candidate Genes ◽  
Protein Interaction ◽  
Permutation Test ◽  
Interaction Network ◽  
Weighted Graph ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Underlying Mechanisms ◽  
Multicellular Organisms

Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.

scholarly journals Development of a cell-free split-luciferase biochemical assay as a tool for screening for inhibitors of challenging protein-protein interaction targets

2020 ◽  
Vol 5 ◽  
pp. 20
Author(s):  
Rachel Cooley ◽  
Neesha Kara ◽  
Ning Sze Hui ◽  
Jonathan Tart ◽  
Chloë Roustan ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Drug Screening ◽  
Protein Interactions ◽  
Mammalian Cells ◽  
Large Scale ◽  
Cost Effective ◽  
Biochemical Assay ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Phosphoinositide 3 Kinase

Targeting the interaction of proteins with weak binding affinities or low solubility represents a particular challenge for drug screening. The NanoLucâ ® Binary Technology (NanoBiTâ ®) was originally developed to detect protein-protein interactions in live mammalian cells. Here we report the successful translation of the NanoBit cellular assay into a biochemical, cell-free format using mammalian cell lysates. We show that the assay is suitable for the detection of both strong and weak protein interactions such as those involving the binding of RAS oncoproteins to either RAF or phosphoinositide 3-kinase (PI3K) effectors respectively, and that it is also effective for the study of poorly soluble protein domains such as the RAS binding domain of PI3K. Furthermore, the RAS interaction assay is sensitive and responds to both strong and weak RAS inhibitors. Our data show that the assay is robust, reproducible, cost-effective, and can be adapted for small and large-scale screening approaches. The NanoBit Biochemical Assay offers an attractive tool for drug screening against challenging protein-protein interaction targets, including the interaction of RAS with PI3K.

DNN-PPI: A LARGE-SCALE PREDICTION OF PROTEIN–PROTEIN INTERACTIONS BASED ON DEEP NEURAL NETWORKS

2019 ◽  
Vol 27 (01) ◽  
pp. 1-18
Author(s):  
YUANMIAO GUI ◽  
RUJING WANG ◽  
YUANYUAN WEI ◽  
XUE WANG
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Area Under The Curve ◽  
Computing Methods ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Prediction ◽  
Auto Covariance ◽  
Fold Cross Validation

Protein–protein interaction (PPI) is very important for various biological processes and has given rise to a series of prediction-computing methods. In spite of different computing methods in relation to PPI prediction, PPI network projects fail to perform on a large scale. Aiming at ensuring that PPI can be predicted effectively, we used a deep neural network (DNN) for the study of PPI prediction that is based on an amino acid sequence. We present a novel DNN-PPI model with an auto covariance (AC) descriptor and a conjoint triad (CT) descriptor for the prediction of PPI that is based only on the protein sequence information. The 10-fold cross-validation indicated that the best DNN-PPI model with CT achieved 97.65% accuracy, 98.96% recall and a 98.51% area under the curve (AUC). The model exhibits a prediction accuracy of 94.20–97.10% for other external datasets. All of these suggest the high validity of the proposed algorithm in relation to various species.

scholarly journals Development of a cell-free split-luciferase biochemical assay as a tool for screening for inhibitors of challenging protein-protein interaction targets

2020 ◽  
Vol 5 ◽  
pp. 20
Author(s):  
Rachel Cooley ◽  
Neesha Kara ◽  
Ning Sze Hui ◽  
Jonathan Tart ◽  
Chloë Roustan ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Drug Screening ◽  
Protein Interactions ◽  
Mammalian Cells ◽  
Large Scale ◽  
Cost Effective ◽  
Biochemical Assay ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Phosphoinositide 3 Kinase

Targeting the interaction of proteins with weak binding affinities or low solubility represents a particular challenge for drug screening. The NanoLuc ® Binary Technology (NanoBiT ®) was originally developed to detect protein-protein interactions in live mammalian cells. Here we report the successful translation of the NanoBit cellular assay into a biochemical, cell-free format using mammalian cell lysates. We show that the assay is suitable for the detection of both strong and weak protein interactions such as those involving the binding of RAS oncoproteins to either RAF or phosphoinositide 3-kinase (PI3K) effectors respectively, and that it is also effective for the study of poorly soluble protein domains such as the RAS binding domain of PI3K. Furthermore, the RAS interaction assay is sensitive and responds to both strong and weak RAS inhibitors. Our data show that the assay is robust, reproducible, cost-effective, and can be adapted for small and large-scale screening approaches. The NanoBit Biochemical Assay offers an attractive tool for drug screening against challenging protein-protein interaction targets, including the interaction of RAS with PI3K.

scholarly journals Flexible web-based integration of distributed large-scale human protein interaction maps

2007 ◽  
Vol 4 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Gautam Chaurasia ◽  
Yasir Iqbal ◽  
Christian Hänig ◽  
Hanspeter Herzel ◽  
Erich E. Wanker ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Large Scale ◽  
Human Interaction ◽  
Human Protein ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Protein Interactions ◽  
Human Interactome ◽  
Protein Protein Interaction

Summary Protein-protein interactions constitute the backbone of many molecular processes. This has motivated the recent construction of several large-scale human protein-protein interaction maps [1-10]. Although these maps clearly offer a wealth of information, their use is challenging: complexity, rapid growth, and fragmentation of interaction data hamper their usability. To overcome these hurdles, we have developed a publicly accessible database termed UniHI (Unified Human Interactome) for integration of human protein-protein interaction data. This database is designed to provide biomedical researchers a common platform for exploring previously disconnected human interaction maps. UniHI offers researchers flexible integrated tools for accessing comprehensive information about the human interactome. Several features included in the UniHI allow users to perform various types of network-oriented and functional analysis. At present, UniHI contains over 160,000 distinct interactions between 17,000 unique proteins from ten major interaction maps derived by both computational and experimental approaches [1-10]. Here we describe the details of the implementation and maintenance of UniHI and discuss the challenges that have to be addressed for a successful integration of interaction data.

scholarly journals Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Ravindra Kumar ◽  
Sabindra K. Samal ◽  
Samapika Routray ◽  
Rupesh Dash ◽  
Anshuman Dixit
Keyword(s):  
Oral Cancer ◽  
Candidate Genes ◽  
Pathway Analysis ◽  
Protein Interactions ◽  
Target Genes ◽  
Human Cancer ◽  
Centrality Measures ◽  
Protein Protein Interactions ◽  
Tissue Samples ◽  
Protein Protein Interaction

Abstract In the recent years, bioinformatics methods have been reported with a high degree of success for candidate gene identification. In this milieu, we have used an integrated bioinformatics approach assimilating information from gene ontologies (GO), protein–protein interaction (PPI) and network analysis to predict candidate genes related to oral squamous cell carcinoma (OSCC). A total of 40973 PPIs were considered for 4704 cancer-related genes to construct human cancer gene network (HCGN). The importance of each node was measured in HCGN by ten different centrality measures. We have shown that the top ranking genes are related to a significantly higher number of diseases as compared to other genes in HCGN. A total of 39 candidate oral cancer target genes were predicted by combining top ranked genes and the genes corresponding to significantly enriched oral cancer related GO terms. Initial verification using literature and available experimental data indicated that 29 genes were related with OSCC. A detailed pathway analysis led us to propose a role for the selected candidate genes in the invasion and metastasis in OSCC. We further validated our predictions using immunohistochemistry (IHC) and found that the gene FLNA was upregulated while the genes ARRB1 and HTT were downregulated in the OSCC tissue samples.

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