Selection of GO-Based Semantic Similarity Measures through AMDE for Predicting Protein-Protein Interactions

Protein–protein interactions (PPIs) are involved in the majority of biological processes. Identification of PPIs is therefore one of the key aims of biological research. Although there are many databases of PPIs, many other unidentified PPIs could be buried in the biomedical literature. Therefore, automated identification of PPIs from biomedical literature repositories could be used to discover otherwise hidden interactions. Search engines, such as Google, have been successfully applied to measure the relatedness among words. Inspired by such approaches, we propose a novel method to identify PPIs through semantic similarity measures among protein mentions. We define six semantic similarity measures as features based on the page counts retrieved from the MEDLINE database. A machine learning classifier, Random Forest, is trained using the above features. The proposed approach achieve an averaged micro-F of 71.28% and an averaged macro-F of 64.03% over five PPI corpora, an improvement over the results of using only the conventional co-occurrence feature (averaged micro-F of 68.79% and an averaged macro-F of 60.49%). A relation-word reinforcement further improves the averaged micro-F to 71.3% and averaged macro-F to 65.12%. Comparing the results of the current work with other studies on the AIMed corpus (ranging from 77.58% to 85.1% in micro-F, 62.18% to 76.27% in macro-F), we show that the proposed approach achieves micro-F of 81.88% and macro-F of 64.01% without the use of sophisticated feature extraction. Finally, we manually examine the newly discovered PPI pairs based on a literature review, and the results suggest that our approach could extract novel protein–protein interactions.

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Combining Semantic Similarity and GO Enrichment for Computation of Functional Similarity

10.1101/155689 ◽

2017 ◽

Author(s):

Wenting Liu ◽

Jianjun Liu ◽

Jagath C. Rajapakse

Keyword(s):

Semantic Similarity ◽

Protein Interactions ◽

Similarity Measures ◽

Functional Similarity ◽

Protein Protein Interactions ◽

Go Enrichment ◽

Sequence Homologies ◽

Benchmark Datasets ◽

Novel Method ◽

Go Terms

AbstractFunctional similarity between genes is widely used in many bioinformatics applications including detecting molecular pathways, finding co-expressed genes, predicting protein-protein interactions, and prioritization of candidate genes. Methods evaluating functional similarity of genes are mostly based on semantic similarity of gene ontology (GO) terms. Though there are hundreds of functional similarity measures available in the literature, none of them considers the enrichment of the GO terms by the querying gene pair. We propose a novel method to incorporate GO enrichment into the existing functional similarity measures. Our experiments show that the inclusion of gene enrichment significantly improves the performance of 44 widely used functional similarity measures, especially in the prediction of sequence homologies, gene expression correlations, and protein-protein interactions.Software availabilityThe software (python code) and all the benchmark datasets evaluation (R script) are available at https://gitlab.com/liuwt/EnrichFunSim.

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One- Plus Two-Hybrid System for the Efficient Selection of Missense Mutant Alleles Defective in Protein–Protein Interactions

Methods in Molecular Biology - Two Hybrid Technologies ◽

10.1007/978-1-61779-455-1_12 ◽

2011 ◽

pp. 209-223 ◽

Cited By ~ 1

Author(s):

Ji Young Kim ◽

Ok Gu Park ◽

Young Chul Lee

Keyword(s):

Hybrid System ◽

Protein Interactions ◽

Protein Protein Interactions ◽

Missense Mutant ◽

Efficient Selection ◽

Two Hybrid ◽

Selection Of

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First person – Jia Yu and Pei-Ju Liao

Journal of Cell Science ◽

10.1242/jcs.259653 ◽

2021 ◽

Vol 134 (24) ◽

Keyword(s):

Protein Interactions ◽

Structural Model ◽

Early Career ◽

Drug Binding ◽

First Person ◽

Protein Protein Interactions ◽

Early Career Researchers ◽

Complex Simulation ◽

Drug Binding Sites ◽

Selection Of

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Jia Yu and Pei-Ju Liao are co-first authors on ‘ Structural model of human PORCN illuminates disease-associated variants and drug-binding sites’, published in JCS. Jia is a senior postdoc in the lab of David Virshup at Duke-NUS Medical School, Singapore, investigating Wnt secretion and signalling; in particular, how Wnt trafficking and secretion is regulated by two integral membrane proteins, porcupine and WLS. Pei-Ju is a research assistant in the same lab, investigating protein–protein interactions in the systems biology of signalling pathways using protein structure modelling and protein complex simulation.

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

BMC Genomics ◽

10.1186/s12864-019-6272-2 ◽

2019 ◽

Vol 20 (S9) ◽

Cited By ~ 3

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.

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Pseudokinase drug intervention: a potentially poisoned chalice

Biochemical Society Transactions ◽

10.1042/bst20130078 ◽

2013 ◽

Vol 41 (4) ◽

pp. 1083-1088 ◽

Cited By ~ 10

Author(s):

Jeroen Claus ◽

Angus J.M. Cameron ◽

Peter J. Parker

Keyword(s):

Protein Interactions ◽

Drug Targets ◽

Binding Pocket ◽

Atp Binding ◽

Potential Drug ◽

Protein Protein Interactions ◽

Drug Intervention ◽

Potential Impact ◽

Potential Drug Targets ◽

Selection Of

Pseudokinases, the catalytically impaired component of the kinome, have recently been found to share more properties with active kinases than previously thought. In many pseudokinases, ATP binding and even some activity is preserved, highlighting these proteins as potential drug targets. In both active kinases and pseudokinases, binding of ATP or drugs in the nucleotide-binding pocket can stabilize specific conformations required for activity and protein–protein interactions. We discuss the implications of locking particular conformations in a selection of (pseudo)kinases and the dual potential impact on the druggability of these proteins.

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