EMCBOW-GPCR: A method for identifying G-protein coupled receptors based on word embedding and wordbooks

Qiu, Wang-Ren; Lv, Zhe; Xiao, Xuan; Shuai, Shao; Lin, Hao

doi:10.1016/j.csbj.2021.08.044

Cited by 7 publications

(3 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, a new solution idea was proposed, which used the product of the average of the input context word vectors and the weights from the input layer to the hidden layer as input and the average of the context word vectors as output, and used the context in this way to predict the current word, which was the continuous bag-of-words model, or CBOW for short ( Mikolov et al, 2013 ). Based on CBOW model, we constructed two different words embedded in wordbooks for training model and the corresponding feature vectors were generated using them ( Qiu et al, 2021 ). The training process is as follows.…”

Section: Methodsmentioning

confidence: 99%

pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm

Qiu

2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Being a new type of widespread protein post-translational modifications discovered in recent years, succinylation plays a key role in protein conformational regulation and cellular function regulation. Numerous studies have shown that succinylation modifications are closely associated with the development of many diseases. In order to gain insight into the mechanism of succinylation, it is vital to identify lysine succinylation sites. However, experimental identification of succinylation sites is time-consuming and laborious, and traditional identification tools are unable to meet the rapid growth of datasets. Therefore, to solve this problem, we developed a new predictor named pSuc-FFSEA, which can predict succinylation sites in protein sequences by feature fusion and stacking ensemble algorithm. Specifically, the sequence information and physicochemical properties were first extracted using EBGW, One-Hot, continuous bag-of-words, chaos game representation, and AAF_DWT. Following that, feature selection was performed, which applied LASSO to select the optimal subset of features for the classifier, and then, stacking ensemble classifier was designed using two-layer stacking ensemble, selecting three classifiers, SVM, broad learning system and LightGBM classifier, as the base classifiers of the first layer, using logistic regression classifier as the meta classifier of the second layer. In order to further improve the model prediction accuracy and reduce the computational effort, bayesian optimization algorithm and grid search algorithm were utilized to optimize the hyperparameters of the classifier. Finally, the results of rigorous 10-fold cross-validation indicated our predictor showed excellent robustness and performed better than the previous prediction tools, which achieved an average prediction accuracy of 0.7773 ± 0.0120. Besides, for the convenience of the most experimental scientists, a user-friendly and comprehensive web-server for pSuc-FFSEA has been established at https://bio.cangmang.xyz/pSuc-FFSEA, by which one can easily obtain the expected data and results without going through the complicated mathematics.

show abstract

Section: Methodsmentioning

confidence: 99%

pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm

Qiu

2022

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

show abstract

“…AI models have been used to accurately distinguish GPCRs from non‐GPCRs and to further classify GPCRs into families, subfamilies, sub‐subfamilies and subtypes. The models use a variety of data for the input, including amino acid sequences or structural data derived from X‐ray crystallography, cryo‐electron microscopy (cryoEM) or molecular dynamics simulation experiments (Ao et al, 2020; Begum et al, 2020; Gu et al, 2020; Li et al, 2017; Liao et al, 2016; Naveed & Khan, 2012; Nie et al, 2015; Peng et al, 2010; Qiu et al, 2021; Wang et al, 2022; Zia‐Ur‐Rehman & Khan, 2012). A machine learning model, trained using the helical and loop information from X‐ray crystal structures, can distinguish active and inactive states of class A GPCRs (Bemister‐Buffington et al, 2020).…”

Section: At What Stages Can Ai Be Employed To Accelerate the Gpcr Dru...mentioning

confidence: 99%

The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery

Nguyen

Koh

et al. 2023

British J Pharmacology

View full text Add to dashboard Cite

The application of artificial intelligence (AI) approaches to drug discovery for G protein‐coupled receptors (GPCRs) is a rapidly expanding area. Artificial intelligence can be used at multiple stages during the drug discovery process, from aiding our understanding of the fundamental actions of GPCRs to the discovery of new ligand‐GPCR interactions or the prediction of clinical responses. Here, we provide an overview of the concepts behind artificial intelligence, including the subfields of machine learning and deep learning. We summarise the published applications of artificial intelligence to different stages of the GPCR drug discovery process. Finally, we reflect on the benefits and limitations of artificial intelligence and share our vision for the exciting potential for further development of applications to aid GPCR drug discovery. In addition to making the drug discovery process “faster, smarter and cheaper,” we anticipate that the application of artificial intelligence will create exciting new opportunities for GPCR drug discovery.

show abstract

“…Molecular dynamics (MD) simulations that stand on the static crystal structure can predict atomic-level motion and capture the dynamic information of conformational transitions [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] . As a result of computation and algorithmic promotion, MD simulations have become an important source of complementary information in crystallography and a primary tool for mechanism research [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] , [62] , [63] , [64] , [65] .…”

Section: Introductionmentioning

confidence: 99%

Delineating the activation mechanism and conformational landscape of a class B G protein-coupled receptor glucagon receptor

Wang

Liang

et al. 2022

Computational and Structural Biotechnology Journal

View full text Add to dashboard Cite

show abstract

EMCBOW-GPCR: A method for identifying G-protein coupled receptors based on word embedding and wordbooks

Abstract: Graphical abstract

Cited by 7 publications

References 57 publications

pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm

pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm

The application of artificial intelligence to accelerate G protein‐coupled receptor drug discovery

Delineating the activation mechanism and conformational landscape of a class B G protein-coupled receptor glucagon receptor

Contact Info

Product

Resources

About