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Gillani M, Pollastri G. Protein subcellular localization prediction tools. Comput Struct Biotechnol J 2024; 23:1796-1807. [PMID: 38707539 PMCID: PMC11066471 DOI: 10.1016/j.csbj.2024.04.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.
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Affiliation(s)
- Maryam Gillani
- School of Computer Science, University College Dublin (UCD), Dublin, D04 V1W8, Ireland
| | - Gianluca Pollastri
- School of Computer Science, University College Dublin (UCD), Dublin, D04 V1W8, Ireland
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2
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Wen JW, Zhang HL, Du PF. Vislocas: Vision transformers for identifying protein subcellular mis-localization signatures of different cancer subtypes from immunohistochemistry images. Comput Biol Med 2024; 174:108392. [PMID: 38608321 DOI: 10.1016/j.compbiomed.2024.108392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/22/2024] [Accepted: 04/01/2024] [Indexed: 04/14/2024]
Abstract
Proteins must be sorted to specific subcellular compartments to perform their functions. Abnormal protein subcellular localizations are related to many diseases. Although many efforts have been made in predicting protein subcellular localization from various static information, including sequences, structures and interactions, such static information cannot predict protein mis-localization events in diseases. On the contrary, the IHC (immunohistochemistry) images, which have been widely applied in clinical diagnosis, contains information that can be used to find protein mis-localization events in disease states. In this study, we create the Vislocas method, which is capable of finding mis-localized proteins from IHC images as markers of cancer subtypes. By combining CNNs and vision transformer encoders, Vislocas can automatically extract image features at both global and local level. Vislocas can be trained with full-sized IHC images from scratch. It is the first attempt to create an end-to-end IHC image-based protein subcellular location predictor. Vislocas achieved comparable or better performances than state-of-the-art methods. We applied Vislocas to find significant protein mis-localization events in different subtypes of glioma, melanoma and skin cancer. The mis-localized proteins, which were found purely from IHC images by Vislocas, are in consistency with clinical or experimental results in literatures. All codes of Vislocas have been deposited in a Github repository (https://github.com/JingwenWen99/Vislocas). All datasets of Vislocas have been deposited in Zenodo (https://zenodo.org/records/10632698).
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Affiliation(s)
- Jing-Wen Wen
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
| | - Han-Lin Zhang
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
| | - Pu-Feng Du
- College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China.
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3
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Zhu Z, Zhao S, Li J, Wang Y, Xu L, Jia Y, Li Z, Li W, Chen G, Wu X. Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease. Respir Res 2024; 25:167. [PMID: 38637823 PMCID: PMC11027407 DOI: 10.1186/s12931-024-02793-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 03/28/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). RESULTS The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. CONCLUSION We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. TRIAL REGISTRATION Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.
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Affiliation(s)
- Zecheng Zhu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Shunjin Zhao
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Lanxi Branch (Lanxi People's Hospital), Hangzhou, Zhejiang, China
| | - Jiahui Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuting Wang
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Luopiao Xu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yubing Jia
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zihan Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenyuan Li
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Gang Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Xifeng Wu
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
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4
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Bao LX, Luo ZM, Zhu XL, Xu YY. Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images. Med Biol Eng Comput 2024; 62:1105-1119. [PMID: 38150111 DOI: 10.1007/s11517-023-02985-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 11/28/2023] [Indexed: 12/28/2023]
Abstract
Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions.
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Affiliation(s)
- Lin-Xia Bao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China
| | - Zhuo-Ming Luo
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China
| | - Xi-Liang Zhu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China
| | - Ying-Ying Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Provincial Key Laboratory of Medical Imaging Processing, Southern Medical University, Guangzhou, 510515, China.
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510623, China.
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Xiao H, Zou Y, Wang J, Wan S. A Review for Artificial Intelligence Based Protein Subcellular Localization. Biomolecules 2024; 14:409. [PMID: 38672426 PMCID: PMC11048326 DOI: 10.3390/biom14040409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/21/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024] Open
Abstract
Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.
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Affiliation(s)
- Hanyu Xiao
- Department of Genetics, Cell Biology and Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Yijin Zou
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China;
| | - Jieqiong Wang
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Shibiao Wan
- Department of Genetics, Cell Biology and Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA;
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6
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Zou K, Wang S, Wang Z, Zou H, Yang F. Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence. SENSORS (BASEL, SWITZERLAND) 2023; 23:9014. [PMID: 38005402 PMCID: PMC10675401 DOI: 10.3390/s23229014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/29/2023] [Accepted: 11/01/2023] [Indexed: 11/26/2023]
Abstract
Protein is one of the primary biochemical macromolecular regulators in the compartmental cellular structure, and the subcellular locations of proteins can therefore provide information on the function of subcellular structures and physiological environments. Recently, data-driven systems have been developed to predict the subcellular location of proteins based on protein sequence, immunohistochemistry (IHC) images, or immunofluorescence (IF) images. However, the research on the fusion of multiple protein signals has received little attention. In this study, we developed a dual-signal computational protocol by incorporating IHC images into protein sequences to learn protein subcellular localization. Three major steps can be summarized as follows in this protocol: first, a benchmark database that includes 281 proteins sorted out from 4722 proteins of the Human Protein Atlas (HPA) and Swiss-Prot database, which is involved in the endoplasmic reticulum (ER), Golgi apparatus, cytosol, and nucleoplasm; second, discriminative feature operators were first employed to quantitate protein image-sequence samples that include IHC images and protein sequence; finally, the feature subspace of different protein signals is absorbed to construct multiple sub-classifiers via dimensionality reduction and binary relevance (BR), and multiple confidence derived from multiple sub-classifiers is adopted to decide subcellular location by the centralized voting mechanism at the decision layer. The experimental results indicated that the dual-signal model embedded IHC images and protein sequences outperformed the single-signal models with accuracy, precision, and recall of 75.41%, 80.38%, and 74.38%, respectively. It is enlightening for further research on protein subcellular location prediction under multi-signal fusion of protein.
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Affiliation(s)
- Kai Zou
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Simeng Wang
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Ziqian Wang
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Hongliang Zou
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
| | - Fan Yang
- School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330038, China
- Artificial Intelligence and Bioinformation Cognition Laboratory, Jiangxi Science and Technology Normal University, Nanchang 330038, China
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7
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Yang TH, Liao ZY, Yu YH, Hsia M. RDDL: A systematic ensemble pipeline tool that streamlines balancing training schemes to reduce the effects of data imbalance in rare-disease-related deep-learning applications. Comput Biol Chem 2023; 106:107929. [PMID: 37517206 DOI: 10.1016/j.compbiolchem.2023.107929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 04/19/2023] [Accepted: 07/14/2023] [Indexed: 08/01/2023]
Abstract
Identifying lowly prevalent diseases, or rare diseases, in their early stages is key to disease treatment in the medical field. Deep learning techniques now provide promising tools for this purpose. Nevertheless, the low prevalence of rare diseases entangles the proper application of deep networks for disease identification due to the severe class-imbalance issue. In the past decades, some balancing methods have been studied to handle the data-imbalance issue. The bad news is that it is verified that none of these methods guarantees superior performance to others. This performance variation causes the need to formulate a systematic pipeline with a comprehensive software tool for enhancing deep-learning applications in rare disease identification. We reviewed the existing balancing schemes and summarized a systematic deep ensemble pipeline with a constructed tool called RDDL for handling the data imbalance issue. Through two real case studies, we showed that rare disease identification could be boosted with this systematic RDDL pipeline tool by lessening the data imbalance problem during model training. The RDDL pipeline tool is available at https://github.com/cobisLab/RDDL/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Biomedical Engineering, National Cheng Kung University, No. 1, University Road, Tainan 701, Taiwan; Medical Device Innovation Center, National Cheng Kung University, Tainan City 701, Taiwan.
| | - Zhan-Yi Liao
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Yu-Huai Yu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
| | - Min Hsia
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan.
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8
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Watanabe N, Kuriya Y, Murata M, Yamamoto M, Shimizu M, Araki M. Different Recognition of Protein Features Depending on Deep Learning Models: A Case Study of Aromatic Decarboxylase UbiD. BIOLOGY 2023; 12:795. [PMID: 37372080 DOI: 10.3390/biology12060795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/17/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023]
Abstract
The number of unannotated protein sequences is explosively increasing due to genome sequence technology. A more comprehensive understanding of protein functions for protein annotation requires the discovery of new features that cannot be captured from conventional methods. Deep learning can extract important features from input data and predict protein functions based on the features. Here, protein feature vectors generated by 3 deep learning models are analyzed using Integrated Gradients to explore important features of amino acid sites. As a case study, prediction and feature extraction models for UbiD enzymes were built using these models. The important amino acid residues extracted from the models were different from secondary structures, conserved regions and active sites of known UbiD information. Interestingly, the different amino acid residues within UbiD sequences were regarded as important factors depending on the type of models and sequences. The Transformer models focused on more specific regions than the other models. These results suggest that each deep learning model understands protein features with different aspects from existing knowledge and has the potential to discover new laws of protein functions. This study will help to extract new protein features for the other protein annotations.
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Affiliation(s)
- Naoki Watanabe
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
| | - Yuki Kuriya
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
| | - Masahiro Murata
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan
| | - Masaki Yamamoto
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
| | - Masayuki Shimizu
- Bacchus Bio Innovation Co., Ltd., 6-3-7 Minatojima minami-machi, Kobe 650-0047, Japan
| | - Michihiro Araki
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 3-17 Senrioka-shinmachi, Settsu 566-0002, Japan
- Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada-Ku, Kobe 657-8501, Japan
- Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
- National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shinmachi, Suita 564-8565, Japan
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9
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Nithiyanandam S, Sangaraju VK, Manavalan B, Lee G. Computational prediction of protein folding rate using structural parameters and network centrality measures. Comput Biol Med 2023; 155:106436. [PMID: 36848800 DOI: 10.1016/j.compbiomed.2022.106436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/28/2022] [Accepted: 12/13/2022] [Indexed: 02/17/2023]
Abstract
Protein folding is a complex physicochemical process whereby a polymer of amino acids samples numerous conformations in its unfolded state before settling on an essentially unique native three-dimensional (3D) structure. To understand this process, several theoretical studies have used a set of 3D structures, identified different structural parameters, and analyzed their relationships using the natural logarithmic protein folding rate (ln(kf)). Unfortunately, these structural parameters are specific to a small set of proteins that are not capable of accurately predicting ln(kf) for both two-state (TS) and non-two-state (NTS) proteins. To overcome the limitations of the statistical approach, a few machine learning (ML)-based models have been proposed using limited training data. However, none of these methods can explain plausible folding mechanisms. In this study, we evaluated the predictive capabilities of ten different ML algorithms using eight different structural parameters and five different network centrality measures based on newly constructed datasets. In comparison to the other nine regressors, support vector machine was found to be the most appropriate for predicting ln(kf) with mean absolute differences of 1.856, 1.55, and 1.745 for the TS, NTS, and combined datasets, respectively. Furthermore, combining structural parameters and network centrality measures improves the prediction performance compared to individual parameters, indicating that multiple factors are involved in the folding process.
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Affiliation(s)
- Saraswathy Nithiyanandam
- Department of Molecular Science and Technology, Ajou University, 206 World Cup-ro, Suwon, 16499, South Korea
| | - Vinoth Kumar Sangaraju
- Department of Physiology, Ajou University School of Medicine, 206 World Cup-ro, Suwon, 16499, South Korea
| | - Balachandran Manavalan
- Department of Physiology, Ajou University School of Medicine, 206 World Cup-ro, Suwon, 16499, South Korea.
| | - Gwang Lee
- Department of Molecular Science and Technology, Ajou University, 206 World Cup-ro, Suwon, 16499, South Korea; Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon, 16419, Gyeonggi-do, South Korea.
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10
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Zhang X, Wei L, Ye X, Zhang K, Teng S, Li Z, Jin J, Kim MJ, Sakurai T, Cui L, Manavalan B, Wei L. SiameseCPP: a sequence-based Siamese network to predict cell-penetrating peptides by contrastive learning. Brief Bioinform 2023; 24:6958502. [PMID: 36562719 DOI: 10.1093/bib/bbac545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/07/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Cell-penetrating peptides (CPPs) have received considerable attention as a means of transporting pharmacologically active molecules into living cells without damaging the cell membrane, and thus hold great promise as future therapeutics. Recently, several machine learning-based algorithms have been proposed for predicting CPPs. However, most existing predictive methods do not consider the agreement (disagreement) between similar (dissimilar) CPPs and depend heavily on expert knowledge-based handcrafted features. RESULTS In this study, we present SiameseCPP, a novel deep learning framework for automated CPPs prediction. SiameseCPP learns discriminative representations of CPPs based on a well-pretrained model and a Siamese neural network consisting of a transformer and gated recurrent units. Contrastive learning is used for the first time to build a CPP predictive model. Comprehensive experiments demonstrate that our proposed SiameseCPP is superior to existing baseline models for predicting CPPs. Moreover, SiameseCPP also achieves good performance on other functional peptide datasets, exhibiting satisfactory generalization ability.
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Affiliation(s)
- Xin Zhang
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Lesong Wei
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Kai Zhang
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Saisai Teng
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Zhongshen Li
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Junru Jin
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Min Jae Kim
- Department of integrative Biotechnology, College of Biotechnology & Bioengineering, Sungkyunkwan University, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan
| | - Lizhen Cui
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | - Balachandran Manavalan
- Department of integrative Biotechnology, College of Biotechnology & Bioengineering, Sungkyunkwan University, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Republic of Korea
| | - Leyi Wei
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
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11
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Cardoen B, Wong T, Alan P, Lee S, Matsubara JA, Nabi IR, Hamarneh G. SPECHT: Self-tuning Plausibility based object detection Enables quantification of Conflict in Heterogeneous multi-scale microscopy. PLoS One 2022; 17:e0276726. [PMID: 36580473 PMCID: PMC9799313 DOI: 10.1371/journal.pone.0276726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/12/2022] [Indexed: 12/30/2022] Open
Abstract
Identification of small objects in fluorescence microscopy is a non-trivial task burdened by parameter-sensitive algorithms, for which there is a clear need for an approach that adapts dynamically to changing imaging conditions. Here, we introduce an adaptive object detection method that, given a microscopy image and an image level label, uses kurtosis-based matching of the distribution of the image differential to express operator intent in terms of recall or precision. We show how a theoretical upper bound of the statistical distance in feature space enables application of belief theory to obtain statistical support for each detected object, capturing those aspects of the image that support the label, and to what extent. We validate our method on 2 datasets: distinguishing sub-diffraction limit caveolae and scaffold by stimulated emission depletion (STED) super-resolution microscopy; and detecting amyloid-β deposits in confocal microscopy retinal cross-sections of neuropathologically confirmed Alzheimer's disease donor tissue. Our results are consistent with biological ground truth and with previous subcellular object classification results, and add insight into more nuanced class transition dynamics. We illustrate the novel application of belief theory to object detection in heterogeneous microscopy datasets and the quantification of conflict of evidence in a joint belief function. By applying our method successfully to diffraction-limited confocal imaging of tissue sections and super-resolution microscopy of subcellular structures, we demonstrate multi-scale applicability.
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Affiliation(s)
- Ben Cardoen
- Medical Image Analysis Laboratory, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
- * E-mail: (BC); (IRN); (GH)
| | - Timothy Wong
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | - Parsa Alan
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
| | - Sieun Lee
- Department of Ophthalmology and Visual Sciences, Eye Care Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Joanne Aiko Matsubara
- Department of Ophthalmology and Visual Sciences, Eye Care Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ivan Robert Nabi
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, British Columbia, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
- * E-mail: (BC); (IRN); (GH)
| | - Ghassan Hamarneh
- Medical Image Analysis Laboratory, School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada
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Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells. Int J Mol Sci 2022; 23:ijms231810827. [PMID: 36142736 PMCID: PMC9504098 DOI: 10.3390/ijms231810827] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/10/2022] [Accepted: 09/13/2022] [Indexed: 11/23/2022] Open
Abstract
Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.
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Hu JX, Yang Y, Xu YY, Shen HB. GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images. Bioinformatics 2022; 38:4941-4948. [DOI: 10.1093/bioinformatics/btac634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/07/2022] [Accepted: 09/15/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Motivation
Recognition of protein subcellular distribution patterns and identification of location biomarker proteins in cancer tissues are important for understanding protein functions and related diseases. Immunohistochemical (IHC) images enable visualizing the distribution of proteins at the tissue level, providing an important resource for the protein localization studies. In the past decades, several image-based protein subcellular location prediction methods have been developed, but the prediction accuracies still have much space to improve due to the complexity of protein patterns resulting from multi-label proteins and variation of location patterns across cell types or states.
Results
Here, we propose a multi-label multi-instance model based on deep graph convolutional neural networks, GraphLoc, to recognize protein subcellular location patterns. GraphLoc builds a graph of multiple IHC images for one protein, learns protein-level representations by graph convolutions, and predicts multi-label information by a dynamic threshold method. Our results show that GraphLoc is a promising model for image-based protein subcellular location prediction with model interpretability. Furthermore, we apply GraphLoc to the identification of candidate location biomarkers and potential members for protein networks. A large portion of the predicted results have supporting evidence from the existing literatures and the new candidates also provide guidance for further experimental screening.
Availability
The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/GraphLoc.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jin-Xian Hu
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing , Ministry of Education of China, Shanghai 200240, China
| | - Yang Yang
- Shanghai Jiao Tong University Department of Computer Science and Engineering, Center for Brain-Like Computing and Machine Intelligence, , Shanghai 200240, China
| | - Ying-Ying Xu
- Southern Medical University School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, , Guangzhou 510515, China
- Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University , Guangzhou 510515, China
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing , Ministry of Education of China, Shanghai 200240, China
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Nakai K, Wei L. Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics. FRONTIERS IN BIOINFORMATICS 2022; 2:910531. [PMID: 36304291 PMCID: PMC9580943 DOI: 10.3389/fbinf.2022.910531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.
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Affiliation(s)
- Kenta Nakai
- Institute of Medical Science, The University of Tokyo, Minato-Ku, Japan
- *Correspondence: Kenta Nakai,
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China
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