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 Combining Semantic Similarity and GO Enrichment for Computation of Functional Similarity

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.

scholarly journals Graph embeddings on gene ontology annotations for protein–protein interaction prediction

2020 ◽  
Vol 21 (S16) ◽  
Author(s):  
Xiaoshi Zhong ◽  
Jagath C. Rajapakse
Keyword(s):  
Gene Ontology ◽  
Protein Interaction ◽  
Structural Information ◽  
Experimental Results ◽  
Graph Embeddings ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Vector Representations ◽  
Ppi Prediction ◽  
Go Terms

Abstract Background Protein–protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consider missing and spurious interactions inherent in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a method that employs graph embeddings that learn vector representations from constructed Gene Ontology Annotation (GOA) graphs and then use embedded vectors to achieve the two tasks. Our method leverages on information from both term–term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. Results We compare our method with those methods that are based on information content (IC) and one method that is based on word embeddings, with experiments on three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods. Conclusion Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GOA graphs for our defined missing and spurious PPI tasks.

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

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 Investigating the Role of Large-Scale Domain Dynamics in Protein-Protein Interactions

2016 ◽  
Vol 3 ◽  
Author(s):  
Elise Delaforge ◽  
Sigrid Milles ◽  
Jie-rong Huang ◽  
Denis Bouvier ◽  
Malene Ringkjøbing Jensen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Protein Interactions ◽  
Domain Dynamics

scholarly journals FrustratometeR: an R-package to compute Local frustration in protein structures, point mutants and MD simulations

2020 ◽  
Author(s):  
Atilio O. Rausch ◽  
Maria I. Freiberger ◽  
Cesar O. Leonetti ◽  
Diego M. Luna ◽  
Leandro G. Radusky ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Structures ◽  
Md Simulations ◽  
R Package ◽  
Protein Protein Interactions ◽  
Large Scale Analysis ◽  
Functional Aspects ◽  
Catalytic Sites ◽  
Polypeptide Chains

Once folded natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and MD trajectories, allowing straightforward integration of local frustration analysis in to pipelines for protein structural analysis.Availability and implementation: https://github.com/proteinphysiologylab/frustratometeR

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