Attention-based convolutional neural networks for protein-protein interaction site prediction

Motivation: Protein-protein interactions are of great importance in the life cycles of living cells. Accurate prediction of the protein-protein interaction site (PPIs) from protein sequence improves our understanding of protein-protein interaction, contributes to the protein-protein docking and is crucial for drug design. However, practical experimental methods are costly and time-consuming so that many sequence-based computational methods have been developed. Most of those methods employ a sliding window approach, which utilize local neighbor information within a window size. However, they don't distinguish and use the effect of each individual neighboring residue at different position. Results: We propose a novel sequence-based deep learning method consisting of convolutional neural networks (CNNs) and attention mechanism to improve the performance of PPIs prediction. Our attention-based CNNs captures the different effect of each neighboring residue within a sliding window, and therefore making a better understanding of the local environment of target residue. We employ experiments on several public benchmark datasets. The experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art techniques. We also analyze the difference using various sliding window lengths and amino acid residue features combination. Availability and implementation: The source code can be obtained from https://github.com/biolushuai/attention-based-CNNs-for-PPIs-prediction Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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Attention-based Convolutional Neural Networks for Protein-Protein Interaction Site Prediction

10.1109/bibm52615.2021.9669435 ◽

2021 ◽

Author(s):

Shuai Lu ◽

Yuguang Li ◽

Xiaofei Nan ◽

Shoutao Zhang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Protein Interaction ◽

Interaction Site ◽

Site Prediction ◽

Protein Protein Interaction ◽

Interaction Site Prediction

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Protein–protein interaction sites prediction by ensemble random forests with synthetic minority oversampling technique

Bioinformatics ◽

10.1093/bioinformatics/bty995 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2395-2402 ◽

Cited By ~ 39

Author(s):

Xiaoying Wang ◽

Bin Yu ◽

Anjun Ma ◽

Cheng Chen ◽

Bingqiang Liu ◽

...

Keyword(s):

Ensemble Learning ◽

Protein Interaction ◽

Feature Selection Method ◽

Feature Space ◽

Supplementary Information ◽

Sequence Profile ◽

Protein Protein Interaction ◽

Interaction Sites ◽

Imbalance Problem ◽

Ppi Networks

Abstract Motivation The prediction of protein–protein interaction (PPI) sites is a key to mutation design, catalytic reaction and the reconstruction of PPI networks. It is a challenging task considering the significant abundant sequences and the imbalance issue in samples. Results A new ensemble learning-based method, Ensemble Learning of synthetic minority oversampling technique (SMOTE) for Unbalancing samples and RF algorithm (EL-SMURF), was proposed for PPI sites prediction in this study. The sequence profile feature and the residue evolution rates were combined for feature extraction of neighboring residues using a sliding window, and the SMOTE was applied to oversample interface residues in the feature space for the imbalance problem. The Multi-dimensional Scaling feature selection method was implemented to reduce feature redundancy and subset selection. Finally, the Random Forest classifiers were applied to build the ensemble learning model, and the optimal feature vectors were inserted into EL-SMURF to predict PPI sites. The performance validation of EL-SMURF on two independent validation datasets showed 77.1% and 77.7% accuracy, which were 6.2–15.7% and 6.1–18.9% higher than the other existing tools, respectively. Availability and implementation The source codes and data used in this study are publicly available at http://github.com/QUST-AIBBDRC/EL-SMURF/. Supplementary information Supplementary data are available at Bioinformatics online.

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Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

Bioinformatics ◽

10.1093/bioinformatics/btz328 ◽

2019 ◽

Vol 35 (14) ◽

pp. i305-i314 ◽

Cited By ~ 27

Author(s):

Muhao Chen ◽

Chelsea J -T Ju ◽

Guangyu Zhou ◽

Xuelu Chen ◽

Tianran Zhang ◽

...

Keyword(s):

Protein Interaction ◽

Mutual Influence ◽

State Of The Art ◽

Supplementary Information ◽

Interaction Type ◽

Interaction Prediction ◽

Protein Protein Interaction ◽

Protein Interaction Prediction ◽

Ppi Prediction ◽

Limited Coverage

AbstractMotivationSequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.ResultsWe present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.Availability and implementationThe implementation is available at https://github.com/muhaochen/seq_ppi.git.Supplementary informationSupplementary data are available at Bioinformatics online.

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Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging

Bioinformatics ◽

10.1093/bioinformatics/btz055 ◽

2019 ◽

Vol 35 (17) ◽

pp. 3208-3210 ◽

Cited By ~ 1

Author(s):

Yangzhen Wang ◽

Feng Su ◽

Shanshan Wang ◽

Chaojuan Yang ◽

Yonglu Tian ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Detection Methods ◽

Supplementary Information ◽

Imaging Data ◽

Two Photon ◽

Processing Power ◽

Fluctuation Method ◽

The Brain

Abstract Motivation Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters. Results We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users. Availability and implementation ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN. Supplementary information Supplementary data are available at Bioinformatics online.

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