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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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Gillani M, Pollastri G. SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks. Int J Mol Sci 2024; 25:5440. [PMID: 38791479 PMCID: PMC11121631 DOI: 10.3390/ijms25105440] [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: 04/05/2024] [Revised: 05/09/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024] Open
Abstract
The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein's function. It can potentially provide information about protein-protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention.
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Affiliation(s)
- Maryam Gillani
- School of Computer Science, University College Dublin (UCD), D04 V1W8 Dublin, Ireland;
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Waury K, Gogishvili D, Nieuwland R, Chatterjee M, Teunissen CE, Abeln S. Proteome encoded determinants of protein sorting into extracellular vesicles. JOURNAL OF EXTRACELLULAR BIOLOGY 2024; 3:e120. [PMID: 38938677 PMCID: PMC11080751 DOI: 10.1002/jex2.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 09/13/2023] [Accepted: 10/05/2023] [Indexed: 06/29/2024]
Abstract
Extracellular vesicles (EVs) are membranous structures released by cells into the extracellular space and are thought to be involved in cell-to-cell communication. While EVs and their cargo are promising biomarker candidates, sorting mechanisms of proteins to EVs remain unclear. In this study, we ask if it is possible to determine EV association based on the protein sequence. Additionally, we ask what the most important determinants are for EV association. We answer these questions with explainable AI models, using human proteome data from EV databases to train and validate the model. It is essential to correct the datasets for contaminants introduced by coarse EV isolation workflows and for experimental bias caused by mass spectrometry. In this study, we show that it is indeed possible to predict EV association from the protein sequence: a simple sequence-based model for predicting EV proteins achieved an area under the curve of 0.77 ± 0.01, which increased further to 0.84 ± 0.00 when incorporating curated post-translational modification (PTM) annotations. Feature analysis shows that EV-associated proteins are stable, polar, and structured with low isoelectric point compared to non-EV proteins. PTM annotations emerged as the most important features for correct classification; specifically, palmitoylation is one of the most prevalent EV sorting mechanisms for unique proteins. Palmitoylation and nitrosylation sites are especially prevalent in EV proteins that are determined by very strict isolation protocols, indicating they could potentially serve as quality control criteria for future studies. This computational study offers an effective sequence-based predictor of EV associated proteins with extensive characterisation of the human EV proteome that can explain for individual proteins which factors contribute to their EV association.
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Affiliation(s)
- Katharina Waury
- Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Dea Gogishvili
- Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Rienk Nieuwland
- Laboratory of Experimental Clinical Chemistry, Department of Clinical Chemistry, Amsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Vesicle Observation Centre, Amsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
| | | | - Charlotte E. Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMCVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Sanne Abeln
- Department of Computer ScienceVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Centrum Wiskunde & InformaticaAmsterdamThe Netherlands
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Li J, Zou Q, Yuan L. A review from biological mapping to computation-based subcellular localization. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 32:507-521. [PMID: 37215152 PMCID: PMC10192651 DOI: 10.1016/j.omtn.2023.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.
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Affiliation(s)
- Jing Li
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 1 Chengdian Road, Quzhou, Zhejiang 324000, China
- School of Biomedical Sciences, University of Hong Kong, Hong Kong, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, 1 Chengdian Road, Quzhou, Zhejiang 324000, China
| | - Lei Yuan
- Department of Hepatobiliary Surgery, Quzhou People's Hospital, 100 Minjiang Main Road, Quzhou, Zhejiang 324000, China
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Broni E, Miller WA. Computational Analysis Predicts Correlations among Amino Acids in SARS-CoV-2 Proteomes. Biomedicines 2023; 11:512. [PMID: 36831052 PMCID: PMC9953644 DOI: 10.3390/biomedicines11020512] [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: 01/16/2023] [Revised: 02/03/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious global challenge requiring urgent and permanent therapeutic solutions. These solutions can only be engineered if the patterns and rate of mutations of the virus can be elucidated. Predicting mutations and the structure of proteins based on these mutations have become necessary for early drug and vaccine design purposes in anticipation of future viral mutations. The amino acid composition (AAC) of proteomes and individual viral proteins provide avenues for exploitation since AACs have been previously used to predict structure, shape and evolutionary rates. Herein, the frequency of amino acid residues found in 1637 complete proteomes belonging to 11 SARS-CoV-2 variants/lineages were analyzed. Leucine is the most abundant amino acid residue in the SARS-CoV-2 with an average AAC of 9.658% while tryptophan had the least abundance of 1.11%. The AAC and ranking of lysine and glycine varied in the proteome. For some variants, glycine had higher frequency and AAC than lysine and vice versa in other variants. Tryptophan was also observed to be the most intolerant to mutation in the various proteomes for the variants used. A correlogram revealed a very strong correlation of 0.999992 between B.1.525 (Eta) and B.1.526 (Iota) variants. Furthermore, isoleucine and threonine were observed to have a very strong negative correlation of -0.912, while cysteine and isoleucine had a very strong positive correlation of 0.835 at p < 0.001. Shapiro-Wilk normality test revealed that AAC values for all the amino acid residues except methionine showed no evidence of non-normality at p < 0.05. Thus, AACs of SARS-CoV-2 variants can be predicted using probability and z-scores. AACs may be beneficial in classifying viral strains, predicting viral disease types, members of protein families, protein interactions and for diagnostic purposes. They may also be used as a feature along with other crucial factors in machine-learning based algorithms to predict viral mutations. These mutation-predicting algorithms may help in developing effective therapeutics and vaccines for SARS-CoV-2.
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Affiliation(s)
- Emmanuel Broni
- Department of Medicine, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USA
| | - Whelton A. Miller
- Department of Medicine, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USA
- Department of Molecular Pharmacology & Neuroscience, Loyola University Medical Center, Loyola University Chicago, Maywood, IL 60153, USA
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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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de la Fuente M, Novo M. Understanding Diversity, Evolution, and Structure of Small Heat Shock Proteins in Annelida Through in Silico Analyses. Front Physiol 2022; 13:817272. [PMID: 35530508 PMCID: PMC9075518 DOI: 10.3389/fphys.2022.817272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/22/2022] [Indexed: 12/04/2022] Open
Abstract
Small heat shock proteins (sHsps) are oligomeric stress proteins characterized by an α-crystallin domain (ACD). These proteins are localized in different subcellular compartments and play critical roles in the stress physiology of tissues, organs, and whole multicellular eukaryotes. They are ubiquitous proteins found in all living organisms, from bacteria to mammals, but they have never been studied in annelids. Here, a data set of 23 species spanning the annelid tree of life, including mostly transcriptomes but also two genomes, was interrogated and 228 novel putative sHsps were identified and manually curated. The analysis revealed very high protein diversity and showed that a significant number of sHsps have a particular dimeric architecture consisting of two tandemly repeated ACDs. The phylogenetic analysis distinguished three main clusters, two of them containing both monomeric sHsps, and ACDs located downstream in the dimeric sHsps, and the other one comprising the upstream ACDs from those dimeric forms. Our results support an evolutionary history of these proteins based on duplication events prior to the Spiralia split. Monomeric sHsps 76) were further divided into five subclusters. Physicochemical properties, subcellular location predictions, and sequence conservation analyses provided insights into the differentiating elements of these putative functional groups. Strikingly, three of those subclusters included sHsps with features typical of metazoans, while the other two presented characteristics resembling non-metazoan proteins. This study provides a solid background for further research on the diversity, evolution, and function in the family of the sHsps. The characterized annelid sHsps are disclosed as essential for improving our understanding of this important family of proteins and their pleotropic functions. The features and the great diversity of annelid sHsps position them as potential powerful molecular biomarkers of environmental stress for acting as prognostic tool in a diverse range of environments.
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Affiliation(s)
- Mercedes de la Fuente
- Departamento de Ciencias y Técnicas Fisicoquímicas, Universidad Nacional de Educación a Distancia (UNED), Las Rozas, Spain
- *Correspondence: Mercedes de la Fuente,
| | - Marta Novo
- Faculty of Biology, Biodiversity, Ecology and Evolution Department, Complutense University of Madrid, Madrid, Spain
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Wang G, Zhai YJ, Xue ZZ, Xu YY. Improving Protein Subcellular Location Classification by Incorporating Three-Dimensional Structure Information. Biomolecules 2021; 11:1607. [PMID: 34827605 PMCID: PMC8615982 DOI: 10.3390/biom11111607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/27/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
The subcellular locations of proteins are closely related to their functions. In the past few decades, the application of machine learning algorithms to predict protein subcellular locations has been an important topic in proteomics. However, most studies in this field used only amino acid sequences as the data source. Only a few works focused on other protein data types. For example, three-dimensional structures, which contain far more functional protein information than sequences, remain to be explored. In this work, we extracted various handcrafted features to describe the protein structures from physical, chemical, and topological aspects, as well as the learned features obtained by deep neural networks. We then used these features to classify the protein subcellular locations. Our experimental results demonstrated that some of these structural features have a certain effect on the protein location classification, and can help improve the performance of sequence-based location predictors. Our method provides a new view for the analysis of protein spatial distribution, and is anticipated to be used in revealing the relationships between protein structures and functions.
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Affiliation(s)
- Ge Wang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; (G.W.); (Z.-Z.X.)
- 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 510515, China
| | - Yu-Jia Zhai
- Guangzhou Women and Children’s Medical Center, Department of Pharmacy, Guangzhou Medical University, Guangzhou 510623, China;
| | - Zhen-Zhen Xue
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; (G.W.); (Z.-Z.X.)
- 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 510515, China
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ying-Ying Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; (G.W.); (Z.-Z.X.)
- 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 510515, China
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Jiang Y, Wang D, Wang W, Xu D. Computational methods for protein localization prediction. Comput Struct Biotechnol J 2021; 19:5834-5844. [PMID: 34765098 PMCID: PMC8564054 DOI: 10.1016/j.csbj.2021.10.023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 10/12/2021] [Accepted: 10/13/2021] [Indexed: 12/16/2022] Open
Abstract
The accurate annotation of protein localization is crucial in understanding protein function in tandem with a broad range of applications such as pathological analysis and drug design. Since most proteins do not have experimentally-determined localization information, the computational prediction of protein localization has been an active research area for more than two decades. In particular, recent machine-learning advancements have fueled the development of new methods in protein localization prediction. In this review paper, we first categorize the main features and algorithms used for protein localization prediction. Then, we summarize a list of protein localization prediction tools in terms of their coverage, characteristics, and accessibility to help users find suitable tools based on their needs. Next, we evaluate some of these tools on a benchmark dataset. Finally, we provide an outlook on the future exploration of protein localization methods.
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Affiliation(s)
- Yuexu Jiang
- Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Duolin Wang
- Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Weiwei Wang
- Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, MO, USA
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mRNA Trafficking in the Nervous System: A Key Mechanism of the Involvement of Activity-Regulated Cytoskeleton-Associated Protein (Arc) in Synaptic Plasticity. Neural Plast 2021; 2021:3468795. [PMID: 34603440 PMCID: PMC8486535 DOI: 10.1155/2021/3468795] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/15/2021] [Accepted: 09/07/2021] [Indexed: 12/18/2022] Open
Abstract
Synaptic activity mediates information storage and memory consolidation in the brain and requires a fast de novo synthesis of mRNAs in the nucleus and proteins in synapses. Intracellular localization of a protein can be achieved by mRNA trafficking and localized translation. Activity-regulated cytoskeleton-associated protein (Arc) is a master regulator of synaptic plasticity and plays an important role in controlling large signaling networks implicated in learning, memory consolidation, and behavior. Transcription of the Arc gene may be induced by a short behavioral event, resulting in synaptic activation. Arc mRNA is exported into the cytoplasm and can be trafficked into the dendrite of an activated synapse where it is docked and translated. The structure of Arc is similar to the viral GAG (group-specific antigen) protein, and phylogenic analysis suggests that Arc may originate from the family of Ty3/Gypsy retrotransposons. Therefore, Arc might evolve through “domestication” of retroviruses. Arc can form a capsid-like structure that encapsulates a retrovirus-like sentence in the 3′-UTR (untranslated region) of Arc mRNA. Such complex can be loaded into extracellular vesicles and transported to other neurons or muscle cells carrying not only genetic information but also regulatory signals within neuronal networks. Therefore, Arc mRNA inter- and intramolecular trafficking is essential for the modulation of synaptic activity required for memory consolidation and cognitive functions. Recent studies with single-molecule imaging in live neurons confirmed and extended the role of Arc mRNA trafficking in synaptic plasticity.
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Wattanapornprom W, Thammarongtham C, Hongsthong A, Lertampaiporn S. Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization. Life (Basel) 2021; 11:life11040293. [PMID: 33808227 PMCID: PMC8066735 DOI: 10.3390/life11040293] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/16/2021] [Accepted: 03/25/2021] [Indexed: 12/17/2022] Open
Abstract
The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus, cytoplasm, and mitochondria) is generally much greater than that in other compartments (vacuole, peroxisome, Golgi, and cell wall). Therefore, the problem of imbalanced data usually arises. Therefore, we propose an ensemble machine learning method based on average voting among heterogeneous classifiers. We first extracted various types of features suitable for each type of protein localization to form a total of 479 feature spaces. Then, feature selection methods were used to reduce the dimensions of the features into smaller informative feature subsets. This reduced feature subset was then used to train/build three different individual models. In the process of combining the three distinct classifier models, we used an average voting approach to combine the results of these three different classifiers that we constructed to return the final probability prediction. The method could predict subcellular localizations in both single- and multilabel locations, based on the voting probability. Experimental results indicated that the proposed ensemble method could achieve correct classification with an overall accuracy of 84.58% for 11 compartments, on the basis of the testing dataset.
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Affiliation(s)
- Warin Wattanapornprom
- Applied Computer Science Program, Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand;
| | - Chinae Thammarongtham
- Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut’s University of Technology Thonburi, Tha Kham, Bang Khun Thian, Bangkok 10150, Thailand; (C.T.); (A.H.)
| | - Apiradee Hongsthong
- Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut’s University of Technology Thonburi, Tha Kham, Bang Khun Thian, Bangkok 10150, Thailand; (C.T.); (A.H.)
| | - Supatcha Lertampaiporn
- Biochemical Engineering and Systems Biology Research Group, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency at King Mongkut’s University of Technology Thonburi, Tha Kham, Bang Khun Thian, Bangkok 10150, Thailand; (C.T.); (A.H.)
- Correspondence:
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