scholarly journals ScanNet: An interpretable geometric deep learning model for structure-based protein binding site prediction

2021 ◽  
Author(s):  
Jérôme Tubiana ◽  
Dina Schneidman-Duhovny ◽  
Haim Wolfson
Keyword(s):  
Deep Learning ◽  
Small Molecules ◽  
Binding Sites ◽  
Comparative Modeling ◽  
Learning Model ◽  
Functional Sites ◽  
Site Prediction ◽  
Interpretable Model ◽  
Deep Learning Model

Abstract Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies sheds light on its function in vivo. Currently, two classes of methods prevail: Machine Learning (ML) models built on top of handcrafted features and comparative modeling. They are respectively limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy - including for unseen protein folds - and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful, and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available from http://bioinfo3d.cs.tau.ac.il/ScanNet/

scholarly journals ScanNet: An interpretable geometric deep learning model for structure-based protein binding site prediction

2021 ◽  
Author(s):  
Jérôme Tubiana ◽  
Dina Schneidman-Duhovny ◽  
Haim J. Wolfson
Keyword(s):  
Deep Learning ◽  
Small Molecules ◽  
Binding Sites ◽  
Comparative Modeling ◽  
Learning Model ◽  
Functional Sites ◽  
Site Prediction ◽  
Interpretable Model ◽  
Deep Learning Model

Predicting the functional sites of a protein from its structure, such as the binding sites of small molecules, other proteins or antibodies sheds light on its function in vivo. Currently, two classes of methods prevail: Machine Learning (ML) models built on top of handcrafted features and comparative modeling. They are respectively limited by the expressivity of the handcrafted features and the availability of similar proteins. Here, we introduce ScanNet, an end-to-end, interpretable geometric deep learning model that learns features directly from 3D structures. ScanNet builds representations of atoms and amino acids based on the spatio-chemical arrangement of their neighbors. We train ScanNet for detecting protein-protein and protein-antibody binding sites, demonstrate its accuracy - including for unseen protein folds - and interpret the filters learned. Finally, we predict epitopes of the SARS-CoV-2 spike protein, validating known antigenic regions and predicting previously uncharacterized ones. Overall, ScanNet is a versatile, powerful, and interpretable model suitable for functional site prediction tasks. A webserver for ScanNet is available from http://bioinfo3d.cs.tau.ac.il/ScanNet/

scholarly journals DeepHBV: A deep learning model to predict hepatitis B virus (HBV) integration sites.

2021 ◽  
Author(s):  
Canbiao Wu ◽  
Xiaofang Guo ◽  
Mengyuan Li ◽  
Xiayu Fu ◽  
Zeliang Hou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Binding Sites ◽  
Learning Model ◽  
Validation Dataset ◽  
B Virus ◽  
Integration Sites ◽  
Deep Learning Model ◽  
Pan Cancer

Hepatitis B virus (HBV) is one of the main causes for viral hepatitis and liver cancer. Previous studies showed HBV can integrate into host genome and further promote malignant transformation. In this study, we developed an attention-based deep learning model DeepHBV to predict HBV integration sites by learning local genomic features automatically. We trained and tested DeepHBV using the HBV integration sites data from dsVIS database. Initially, DeepHBV showed AUROC of 0.6363 and AUPR of 0.5471 on the dataset. Adding repeat peaks and TCGA Pan Cancer peaks can significantly improve the model performance, with an AUROC of 0.8378 and 0.9430 and an AUPR of 0.7535 and 0.9310, respectively. On independent validation dataset of HBV integration sites from VISDB, DeepHBV with HBV integration sequences plus TCGA Pan Cancer (AUROC of 0.7603 and AUPR of 0.6189) performed better than HBV integration sequences plus repeat peaks (AUROC of 0.6657 and AUPR of 0.5737). Next, we found the transcriptional factor binding sites (TFBS) were significantly enriched near genomic positions that were paid attention to by convolution neural network. The binding sites of AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra and Foxo3 were highlighted by DeepHBV attention mechanism in both dsVIS dataset and VISDB dataset, revealing the HBV integration preference. In summary, DeepHBV is a robust and explainable deep learning model not only for the prediction of HBV integration sites but also for further mechanism study of HBV induced cancer.

scholarly journals DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Canbiao Wu ◽  
Xiaofang Guo ◽  
Mengyuan Li ◽  
Jingxian Shen ◽  
Xiayu Fu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Binding Sites ◽  
Integration Site ◽  
Learning Model ◽  
Genomic Features ◽  
B Virus ◽  
Integration Sites ◽  
Deep Learning Model

Abstract Background The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the virus-promoted malignant transformation. Results An attention-based deep learning model, DeepHBV, was developed to predict HBV integration sites. By learning local genomic features automatically, DeepHBV was trained and tested using HBV integration site data from the dsVIS database. Initially, DeepHBV showed an AUROC of 0.6363 and an AUPR of 0.5471 for the dataset. The integration of genomic features of repeat peaks and TCGA Pan-Cancer peaks significantly improved model performance, with AUROCs of 0.8378 and 0.9430 and AUPRs of 0.7535 and 0.9310, respectively. The transcription factor binding sites (TFBS) were significantly enriched near the genomic positions that were considered. The binding sites of the AR-halfsite, Arnt, Atf1, bHLHE40, bHLHE41, BMAL1, CLOCK, c-Myc, COUP-TFII, E2A, EBF1, Erra, and Foxo3 were highlighted by DeepHBV in both the dsVIS and VISDB datasets, revealing a novel integration preference for HBV. Conclusions DeepHBV is a useful tool for predicting HBV integration sites, revealing novel insights into HBV integration-related carcinogenesis.

Deep learning model predicts water interaction sites on the surface of proteins using limited-resolution data

2020 ◽  
Vol 56 (98) ◽  
pp. 15454-15457
Author(s):  
Jan Zaucha ◽  
Charlotte A. Softley ◽  
Michael Sattler ◽  
Dmitrij Frishman ◽  
Grzegorz M. Popowicz
Keyword(s):  
Deep Learning ◽  
Binding Sites ◽  
Learning Model ◽  
Water Binding ◽  
Interaction Sites ◽  
Water Interaction ◽  
Deep Learning Model ◽  
Resolution Data

Deep learning model ‘hotWater’ scans the surface of proteins to identify the most likely water binding sites.

scholarly journals DeepDISE: DNA Binding Site Prediction Using a Deep Learning Method

2021 ◽  
Vol 22 (11) ◽  
pp. 5510
Author(s):  
Samuel Godfrey Hendrix ◽  
Kuan Y. Chang ◽  
Zeezoo Ryu ◽  
Zhong-Ru Xie
Keyword(s):  
Deep Learning ◽  
Dna Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Protein Structures ◽  
Learning Model ◽  
Atom Type ◽  
Dna Binding Sites ◽  
Dna Binding Site ◽  
Deep Learning Model

It is essential for future research to develop a new, reliable prediction method of DNA binding sites because DNA binding sites on DNA-binding proteins provide critical clues about protein function and drug discovery. However, the current prediction methods of DNA binding sites have relatively poor accuracy. Using 3D coordinates and the atom-type of surface protein atom as the input, we trained and tested a deep learning model to predict how likely a voxel on the protein surface is to be a DNA-binding site. Based on three different evaluation datasets, the results show that our model not only outperforms several previous methods on two commonly used datasets, but also demonstrates its robust performance to be consistent among the three datasets. The visualized prediction outcomes show that the binding sites are also mostly located in correct regions. We successfully built a deep learning model to predict the DNA binding sites on target proteins. It demonstrates that 3D protein structures plus atom-type information on protein surfaces can be used to predict the potential binding sites on a protein. This approach should be further extended to develop the binding sites of other important biological molecules.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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