scholarly journals Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction

2021 ◽  
Author(s):  
Xiaodi Yang ◽  
Shiping Yang ◽  
Xianyi Lian ◽  
Stefan Wuchty ◽  
Ziding Zhang
Keyword(s):  
Learning Strategies ◽  
Transfer Learning ◽  
Fine Tuning ◽  
Virus Protein ◽  
Sequence Profile ◽  
Human Virus ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Multi Scale ◽  
Protein Interaction Prediction

AbstractTo predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron (MLP). Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two types of transfer learning methods (i.e., ‘frozen’ type and ‘fine-tuning’ type) that reliably predict interactions in a target human-virus domain based on training in a source human-virus domain, by retraining CNN layers. Our transfer learning strategies can effectively apply prior knowledge transfer from large source dataset/task to small target dataset/task to improve prediction performance. Finally, we utilize the ‘frozen’ type of transfer learning to predict human-SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions. Source code and datasets are available at https://github.com/XiaodiYangCAU/TransPPI/.

scholarly journals A multitask transfer learning framework for the prediction of virus-human protein–protein interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Thi Ngan Dong ◽  
Graham Brogden ◽  
Gisa Gerold ◽  
Megha Khosla
Keyword(s):  
Transfer Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Sequences ◽  
Human Protein ◽  
Interaction Patterns ◽  
Protein Protein Interactions ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction

Abstract Background Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein–protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses. Results We developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein–protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein–protein interaction prediction model. Conclusions Our approach achieved competitive results on 13 benchmark datasets and the case study for the SARS-CoV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein–protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer.

scholarly journals Multifaceted protein–protein interaction prediction based on Siamese residual RCNN

2019 ◽  
Vol 35 (14) ◽  
pp. i305-i314 ◽  
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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