Semantic Similarity-based Validation of Human Protein-Protein Interactions

2006 ◽  
Author(s):  
Xiang Guo ◽  
Hai Hu ◽  
M.N. Liebman ◽  
C.D. Shriver
Keyword(s):  
Semantic Similarity ◽  
Protein Interactions ◽  
Human Protein ◽  
Protein Protein Interactions

scholarly journals GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Xiaoshi Zhong ◽  
Rama Kaalia ◽  
Jagath C. Rajapakse
Keyword(s):  
Information Content ◽  
Semantic Similarity ◽  
Protein Interactions ◽  
Large Scale ◽  
Functional Similarity ◽  
Experimental Results ◽  
Graph Embeddings ◽  
Protein Protein Interactions ◽  
Vector Representations ◽  
Go Terms

Abstract Background Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO terms; recently some research exploited word embeddings to learn vector representations for GO terms from a large-scale corpus. In this paper, we proposed a novel method, named GO2Vec, that exploits graph embeddings to learn vector representations for GO terms from GO graph. GO2Vec combines the information from both GO graph and GO annotations, and its learned vectors can be applied to a variety of bioinformatics applications, such as calculating functional similarity between proteins and predicting protein-protein interactions. Results We conducted two kinds of experiments to evaluate the quality of GO2Vec: (1) functional similarity between proteins on the Collaborative Evaluation of GO-based Semantic Similarity Measures (CESSM) dataset and (2) prediction of protein-protein interactions on the Yeast and Human datasets from the STRING database. Experimental results demonstrate the effectiveness of GO2Vec over the information content-based measures and the word embedding-based measures. Conclusion Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GO and GOA graphs. Our results also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins.

scholarly journals Visualizing Human Protein‐Protein Interactions and Subcellular Localizations on Cell Images Through CellMap

2020 ◽  
Vol 69 (1) ◽  
Author(s):  
Christian Dallago ◽  
Tatyana Goldberg ◽  
Miguel Angel Andrade‐Navarro ◽  
Gregorio Alanis‐Lobato ◽  
Burkhard Rost
Keyword(s):  
Protein Interactions ◽  
Human Protein ◽  
Protein Protein Interactions

scholarly journals Inferring high-confidence human protein-protein interactions

2012 ◽  
Vol 13 (1) ◽  
pp. 79 ◽  
Author(s):  
Xueping Yu ◽  
Anders Wallqvist ◽  
Jaques Reifman
Keyword(s):  
Protein Interactions ◽  
Human Protein ◽  
Protein Protein Interactions ◽  
High Confidence

scholarly journals Predictions of Protein-Protein Interactions in Schistosoma Mansoni

2017 ◽  
Author(s):  
Javona White Bear ◽  
James H. McKerrow
Keyword(s):  
Schistosoma Mansoni ◽  
Language Processing ◽  
Protein Interactions ◽  
Species Interactions ◽  
Human Protein ◽  
Comparative Approach ◽  
Application Framework ◽  
Protein Protein Interactions ◽  
Protease Inhibition ◽  
Human Proteins

AbstractBackgroundSchistosoma mansoni invasion of the human host involves a variety of cross-species protein-protein interactions. The pathogen expresses a diverse arsenal of proteins that facilitate the breach of physical and biochemical barriers present in skin, evasion of the immune system, and digestion of human hemoglobin, allowing schistosomes to reside in the host for years. However, only a small number of specific interactions between S. mansoni and human proteins have been identified. We present and apply a protocol that generates testable predictions of S. mansoni-human protein interactions.MethodsIn this study, we first predict S. mansoni-human protein interactions based on similarity to known protein complexes. Putative interactions were then scored and assessed using several contextual filters, including the use of annotation automatically derived from literature using a simple natural language processing methodology. Our method predicted 7 out of the 10 previously known cross-species interactions.ConclusionsSeveral predictions that warrant experimental follow-up were presented and discussed, including interactions involving potential vaccine candidate antigens, protease inhibition, and immune evasion. The application framework provides an integrated methodology for investigation of host-pathogen interactions and an extensive source of orthogonal data for experimental analysis. We have made the predictions available online for community perusal.Author SummaryThe S. mansoni parasite is the etiological agent of the disease Schistomiasis. However, protein-protein interactions have been experimentally characterized that relate to pathogenesis and establishment of infection. As with many pathogens, the understanding of these interactions is a key component for the development of new vaccines. In this project, we have applied a computational whole-genome comparative approach to aid in the prediction of interactions between S. mansoni and human proteins and to identify important proteins involved in infection. The results of applying this method recapitulate several previously characterized interactions, as well as suggest additional ones as potential therapeutic targets.

scholarly journals Neuropsychiatric Symptoms of COVID-19 Explained by SARS-CoV-2 Proteins’ Mimicry of Human Protein Interactions

2021 ◽  
Vol 15 ◽  
Author(s):  
Hale Yapici-Eser ◽  
Yunus Emre Koroglu ◽  
Ozgur Oztop-Cakmak ◽  
Ozlem Keskin ◽  
Attila Gursoy ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Clinical Symptoms ◽  
Neuropsychiatric Symptoms ◽  
Data Bank ◽  
In Vitro Studies ◽  
Human Protein ◽  
Protein Protein Interactions ◽  
Human Proteins

The first clinical symptoms focused on the presentation of coronavirus disease 2019 (COVID-19) have been respiratory failure, however, accumulating evidence also points to its presentation with neuropsychiatric symptoms, the exact mechanisms of which are not well known. By using a computational methodology, we aimed to explain the molecular paths of COVID-19 associated neuropsychiatric symptoms, based on the mimicry of the human protein interactions with SARS-CoV-2 proteins.Methods: Available 11 of the 29 SARS-CoV-2 proteins’ structures have been extracted from Protein Data Bank. HMI-PRED (Host-Microbe Interaction PREDiction), a recently developed web server for structural PREDiction of protein-protein interactions (PPIs) between host and any microbial species, was used to find the “interface mimicry” through which the microbial proteins hijack host binding surfaces. Classification of the found interactions was conducted using the PANTHER Classification System.Results: Predicted Human-SARS-CoV-2 protein interactions have been extensively compared with the literature. Based on the analysis of the molecular functions, cellular localizations and pathways related to human proteins, SARS-CoV-2 proteins are found to possibly interact with human proteins linked to synaptic vesicle trafficking, endocytosis, axonal transport, neurotransmission, growth factors, mitochondrial and blood-brain barrier elements, in addition to its peripheral interactions with proteins linked to thrombosis, inflammation and metabolic control.Conclusion: SARS-CoV-2-human protein interactions may lead to the development of delirium, psychosis, seizures, encephalitis, stroke, sensory impairments, peripheral nerve diseases, and autoimmune disorders. Our findings are also supported by the previous in vivo and in vitro studies from other viruses. Further in vivo and in vitro studies using the proteins that are pointed here, could pave new targets both for avoiding and reversing neuropsychiatric presentations.

scholarly journals A semantic similarity based methodology for predicting protein-protein interactions: Evaluation with P53-interacting kinases

2020 ◽  
Vol 111 ◽  
pp. 103579
Author(s):  
Steven Cox ◽  
Xialan Dong ◽  
Ruhi Rai ◽  
Laura Christopherson ◽  
Weifan Zheng ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Protein Interactions ◽  
Protein Protein Interactions
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