Predicting protein-peptide binding sites with a deep convolutional neural network

Wardah, Wafaa; Dehzangi, Abdollah; Taherzadeh, Ghazaleh; Rashid, Mahmood A.; Khan, Mohammad G.M.; Tsunoda, Tatsuhiko; Sharma, Alok

doi:10.1016/j.jtbi.2020.110278

Cited by 37 publications

(29 citation statements)

References 45 publications

(48 reference statements)

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“…Several studies show that CNN has high recognition accuracy not only for image data but also for data in arbitrary two-dimensional format [9,10,11]. As shown in [10], CNN functioned well to classify time series matrices which consist of OpenPose's outputs.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Convolutional Neural Network based on Temporal Pose Features for Surgical Procedure Recognition

Kishi

Suzuki

Tsuyuki

et al. 2021

Stochastic Systems Theory and its Applications (SSS)

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To compensate for severe shortage of scrub nurses who support surgeons during surgery, Miyawaki et al. have developed a scrub nurse robot (SNR) system [1,2,3,4,5]. One of its current challenges is how to make the SNR recognize surgical procedures which compose a surgical operation and understand/predict surgeons' intentions.Therefore, in this paper, we propose a visual recognition system for surgeons' actions based on convolutional neural network (CNN). We developed a temporal pose feature (TPF) CNN, which is a method to recognize surgical procedures based on the body movements of a surgeon's stand-in during a simulated surgical operation. We used OpenPose to extract the pose feature vectors from every frame of the short videos filmed our simulated surgery. Besides, we used a matrix of which the pose vectors were chronologically ordered as the input of CNN by considering it as the pseudograyscale image.We show that the TPF CNN was more accurate in the objects of this study than the conventional LSTM, which is used to recognize time series data. The TPF CNN shows higher recognition accuracy with fewer training than LSTM. Our results suggest that surgeons' body movements may contain much information to be required for recognizing subtle differences in several types of surgical procedures.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Convolutional Neural Network based on Temporal Pose Features for Surgical Procedure Recognition

Kishi

Suzuki

Tsuyuki

et al. 2021

Stochastic Systems Theory and its Applications (SSS)

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“…Convolution neural networks (CNNs) model has been used in some bioinformatic tools for protein binding site prediction [44], protein-ligand scoring [45] and protein-compound affinity prediction [46]. In our method, the input of CNNs is the local information of the target antibody residue which can be represented as r i−w:i+w .…”

Section: Cnnsmentioning

confidence: 99%

A Sequence-based Antibody Paratope Prediction Model Through Combing Local-Global Information and Partner Features

Lü

Nan

et al. 2021

Preprint

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Antibodies are proteins which play a vital role in the immune system by recognizing and neutralizing antigens. The region on the antibody binds to the antigens, also known as paratope, mediates antibody-antigen interaction with high affinity and specificity. And the accurate prediction of those regions from antibody sequence contributes to the design of therapeutic antibodies and remains challenging. However, the experimental methods are time-consuming and expensive. In this article, we propose a sequence-based method for antibody paratope prediction by combing the local and global features of antibody sequence and global features of partner antigen sequence. For extracting local features, we use Convolution Neural Networks(CNNs) and a sliding window approach on antibody sequence. For extracting global features, we use Attention-based Bidirectional Long Short-Term Memory(Att-BLSTM) networks on antibody sequence. For extracting partner features, we employ Att-BLSTM on the partner antigen sequence as well. And then, all features are combined to predict antibody paratope by fully-connected networks. The experiments show that our proposed method achieves superior performance over the state-of-the-art sequenced-based antibody paratope prediction methods on benchmark datasets.

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“…Convolution neural networks(CNNs) model has been adapted to various bioinformatics tasks such as protein binding site prediction (Wardah et al, 2020), protein-ligand scoring (Ragoza et al, 2017) and protein-compound affinity prediction (Karimi et al, 2019). In PPIs prediction, the input of CNNs is a protein sequence represented as a matrix S. The CNNs model employs a convolutional operation on a sliding window of length w(w = 2n + 1) , where n could be any positive integer for the i-th target residue r i .…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks

Lü

Nan

et al. 2021

Preprint

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MotivationProtein-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.ResultsWe 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 sizes and amino acid residue features combination.AvailabilityThe source code can be obtained from https://github.com/biolushuai/attention-based-CNNs-for-PPIs-predictionContactiexfnan@zzu.edu.cn or zhangst@zzu.edu.cnSupplementary informationSupplementary data are available online.

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Predicting protein-peptide binding sites with a deep convolutional neural network

Cited by 37 publications

References 45 publications

Convolutional Neural Network based on Temporal Pose Features for Surgical Procedure Recognition

Convolutional Neural Network based on Temporal Pose Features for Surgical Procedure Recognition

A Sequence-based Antibody Paratope Prediction Model Through Combing Local-Global Information and Partner Features

Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks

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