scholarly journals Deep learning for extracting protein-protein interactions from biomedical literature

2017 ◽  
Author(s):  
Yifan Peng ◽  
Zhiyong Lu
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Biomedical Literature ◽  
Protein Protein Interactions

scholarly journals A Hybrid Deep Learning Model for Protein–Protein Interactions Extraction from Biomedical Literature

2020 ◽  
Vol 10 (8) ◽  
pp. 2690 ◽  
Author(s):  
Changqin Quan ◽  
Zhiwei Luo ◽  
Song Wang
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Short Term Memory ◽  
Channel Model ◽  
Biomedical Literature ◽  
Hybrid Architecture ◽  
Protein Protein Interactions ◽  
Long Distance ◽  
Proposed Model ◽  
Bidirectional Lstm

The exponentially increasing size of biomedical literature and the limited ability of manual curators to discover protein–protein interactions (PPIs) in text has led to delays in keeping PPI databases updated with the current findings. The state-of-the-art text mining methods for PPI extraction are primarily based on deep learning (DL) models, and the performance of a DL-based method is mainly affected by the architecture of DL models and the feature embedding methods. In this study, we compared different architectures of DL models, including convolutional neural networks (CNN), long short-term memory (LSTM), and hybrid models, and proposed a hybrid architecture of a bidirectional LSTM+CNN model for PPI extraction. Pretrained word embedding and shortest dependency path (SDP) embedding are fed into a two-embedding channel model, such that the model is able to model long-distance contextual information and can capture the local features and structure information effectively. The experimental results showed that the proposed model is superior to the non-hybrid DL models, and the hybrid CNN+Bidirectional LSTM model works well for PPI extraction. The visualization and comparison of the hidden features learned by different DL models further confirmed the effectiveness of the proposed model.

Deep Learning in the Study of Protein-Related Interactions

2020 ◽  
Vol 27 (5) ◽  
pp. 359-369 ◽  
Author(s):  
Cheng Shi ◽  
Jiaxing Chen ◽  
Xinyue Kang ◽  
Guiling Zhao ◽  
Xingzhen Lao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Physiological Data ◽  
Great Promise ◽  
Complex Data ◽  
Protein Protein Interactions ◽  
Learning Patterns ◽  
Introductory Overview ◽  
Protein Research ◽  
Neural Network Theory

: Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In recent years, advances in biological and medical technology have provided us with explosive biological and physiological data, and deep learning-based algorithms have shown great promise in extracting features and learning patterns from complex data. At present, deep learning in protein research has emerged. In this review, we provide an introductory overview of the deep neural network theory and its unique properties. Mainly focused on the application of this technology in protein-related interactions prediction over the past five years, including protein-protein interactions prediction, protein-RNA\DNA, Protein– drug interactions prediction, and others. Finally, we discuss some of the challenges that deep learning currently faces.

Forms and consequences of incompatibility

2019 ◽  
pp. 20-48
Author(s):  
Geoffrey E. Hill
Keyword(s):  
Mitochondrial Dna ◽  
Electron Transport ◽  
Mitochondrial Dysfunction ◽  
Protein Interactions ◽  
Biomedical Literature ◽  
Nuclear Genes ◽  
Extensive Literature ◽  
Protein Protein Interactions ◽  
Growing Body ◽  
Evolutionary Consequences

To understand the evolutionary consequences of poor coadaptation of mitochondrial and nuclear genes, it is necessary to consider in molecular detail the manifestations of mitochondrial dysfunction. Most considerations of mitochondrial dysfunction resulting from mitonuclear incompatibilities focus on protein–protein interactions in the electron transport system, but the interactions of mitochondrial and nuclear genes in enabling the transcription, translation, and replication of mitochondrial DNA can play an equally important role in mitonuclear coevolution and coadaptation. This chapter reviews the extensive literature on how mitochondrial dysfunction is the cause of many inherited human diseases and explains how this biomedical literature connects to a rapidly growing body of research on the evolution and maintenance of coadaptation of mitochondrial and nuclear genes among non-human eukaryotes. The goal of the chapter is to establish the fundamental importance of coadaptation between co-functioning mitochondrial and nuclear genes.

Deep learning based prediction of reversible HAT/HDAC-specific lysine acetylation

2019 ◽  
Vol 21 (5) ◽  
pp. 1798-1805 ◽  
Author(s):  
Kai Yu ◽  
Qingfeng Zhang ◽  
Zekun Liu ◽  
Yimeng Du ◽  
Xinjiao Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Web Server ◽  
Data Sets ◽  
Lysine Acetylation ◽  
Protein Acetylation ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Protein Lysine Acetylation ◽  
Specific Regulation

Abstract Protein lysine acetylation regulation is an important molecular mechanism for regulating cellular processes and plays critical physiological and pathological roles in cancers and diseases. Although massive acetylation sites have been identified through experimental identification and high-throughput proteomics techniques, their enzyme-specific regulation remains largely unknown. Here, we developed the deep learning-based protein lysine acetylation modification prediction (Deep-PLA) software for histone acetyltransferase (HAT)/histone deacetylase (HDAC)-specific acetylation prediction based on deep learning. Experimentally identified substrates and sites of several HATs and HDACs were curated from the literature to generate enzyme-specific data sets. We integrated various protein sequence features with deep neural network and optimized the hyperparameters with particle swarm optimization, which achieved satisfactory performance. Through comparisons based on cross-validations and testing data sets, the model outperformed previous studies. Meanwhile, we found that protein–protein interactions could enrich enzyme-specific acetylation regulatory relations and visualized this information in the Deep-PLA web server. Furthermore, a cross-cancer analysis of acetylation-associated mutations revealed that acetylation regulation was intensively disrupted by mutations in cancers and heavily implicated in the regulation of cancer signaling. These prediction and analysis results might provide helpful information to reveal the regulatory mechanism of protein acetylation in various biological processes to promote the research on prognosis and treatment of cancers. Therefore, the Deep-PLA predictor and protein acetylation interaction networks could provide helpful information for studying the regulation of protein acetylation. The web server of Deep-PLA could be accessed at http://deeppla.cancerbio.info.

scholarly journals UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Theodosios Theodosiou ◽  
Nikolaos Papanikolaou ◽  
Maria Savvaki ◽  
Giulia Bonetto ◽  
Stella Maxouri ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Computational Prediction ◽  
Biomedical Literature ◽  
Protein Network ◽  
Protein Protein Interactions ◽  
High Coverage ◽  
Depth Study ◽  
Experimental Approaches ◽  
Wet Lab ◽  
User Friendly

Abstract The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/

scholarly journals HMNPPID—human malignant neoplasm protein–protein interaction database

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Qingqing Li ◽  
Zhihao Yang ◽  
Zhehuan Zhao ◽  
Ling Luo ◽  
Zhiheng Li ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Malignant Neoplasm ◽  
Biomedical Literature ◽  
Malignant Neoplasms ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Protein Interaction Database

Abstract Background Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. Results In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. Conclusions HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

scholarly journals Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling

2021 ◽  
Author(s):  
Jimin Pei ◽  
Jing Zhang ◽  
Qian Cong
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Large Scale ◽  
Protein Complexes ◽  
Mitochondrial Protein ◽  
Protein Structures ◽  
Complex Structures ◽  
Protein Protein Interactions ◽  
Learning Methods ◽  
Contact Probability

AbstractRecent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3-dimensional protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions and modeling protein complexes at the proteome level. We applied RoseTTAFold and AlphaFold2, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death, and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to the modeling of the complex structures by AlphaFold2, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most of the top ranked pairs with high contact probability were supported by known protein-protein interactions and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2, ECSIT-NDUFAF1 and COQ7-COQ9, among others. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in.

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