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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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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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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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Tiwari S, Vaish S, Singh N, Basantani M, Bhargava A. Genome-wide identification and characterization of glutathione S-transferase gene family in quinoa ( Chenopodium quinoa Willd.). 3 Biotech 2023; 13:230. [PMID: 37309406 PMCID: PMC10257622 DOI: 10.1007/s13205-023-03659-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/01/2023] [Indexed: 06/14/2023] Open
Abstract
The present investigation was envisaged for large scale in-silico genome wide identification and characterization of glutathione S-transferases (GSTs) in Chenopodium quinoa. In this study, a total of 120 GST genes (CqGSTs) were identified and divided into 11 classes of which tau and phi were highest in numbers. The average protein length of protein was found to be 279.06 with their corresponding average molecular weight of 31,819.4 kDa. The subcellular localization analysis results showed that proteins were centrally localized in the cytoplasm followed by chloroplast, mitochondria and plastids. Structural analysis revealed the presence of 2 -14 exons in CqGST genes. Most of the proteins possessed two exon one intron organization. MEME analysis identified 15 significantly conserved motifs with a width of 6-50 amino acids. Motifs 1, 3, 2, 5, 6, 8, 9 and 13 were found specifically in tau class family; motifs 3, 4, 5, 6, 7 and 9 were found in phi class gene family, while motifs 3, 4, 13 and 14 were found in metaxin class. Multiple sequence alignment revealed highly conserved N-terminus with active site serine (Ser; S) or cysteine (Cys; C) residue for the activation of GSH binding and GST catalytic activity. The gene loci were found to be unevenly distributed across 18 different chromosomes with a maximum of 17 genes located on chromosome number 7. Dominance of alpha helix was followed by coil, extended strand and beta turns. Gene duplication analysis revealed that segmental duplication and purifying type selection were highest in number and found to be main source of expansion of GST gene family. Cis acting regulatory elements analysis showed the presence of 21 different elements involved in stress, hormone and light response and cellular development. The evolutionary relationship of CqGST proteins carried out using maximum likelihood method revealed that all the tau and phi class GSTs were closely associated with those of G. max, O. sativa and A. thaliana. Molecular docking of GST molecules with the fungicide metalaxyl showed that the CqGSTF1 had the lowest binding energy. The comprehensive study of CqGST gene family in quinoa provides groundwork for further functional analysis of CqGST genes in the species at molecular level and has potential applications in plant breeding.
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Affiliation(s)
- Shivani Tiwari
- Department of Botany, School of Life Sciences, Mahatma Gandhi Central University, Motihari, Bihar 845401 India
| | - Swati Vaish
- Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Lucknow-Deva Road, Barabanki, Uttar Pradesh 225003 India
| | - Nootan Singh
- Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Lucknow-Deva Road, Barabanki, Uttar Pradesh 225003 India
| | - Mahesh Basantani
- Experiome Biotech Private Limited, Vibhuti Khand, Gomti Nagar, Lucknow, Uttar Pradesh 226010 India
| | - Atul Bhargava
- Department of Botany, School of Life Sciences, Mahatma Gandhi Central University, Motihari, Bihar 845401 India
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Zhang T, Gu J, Wang Z, Wu C, Liang Y, Shi X. Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network. Interdiscip Sci 2022; 14:937-946. [PMID: 35713780 DOI: 10.1007/s12539-022-00529-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 05/12/2022] [Accepted: 05/17/2022] [Indexed: 06/15/2023]
Abstract
Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https://github.com/GoodGuy398/GraphLoc . The proposed GraphLoc model consists of three parts. The first part is a graph convolutional network (GCN) module, which utilizes the predicted contact maps to construct protein graph, taking benefit of protein information accordingly. The second part is the multi-head attention module, which learns the weighted contribution of different amino acids in different feature representation subspace, and weighted average the feature map across all amino acid nodes. The last part is a fully connected layer that maps the flatten graph representation vector to another vector with a category number dimension, followed by a softmax layer to predict the protein subcellular localization.
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Affiliation(s)
- Tianhao Zhang
- College of Computer Science and Technology, University of Jilin, Changchun, 130012, China
| | - Jiawei Gu
- College of Computer Science and Technology, University of Jilin, Changchun, 130012, China
| | - Zeyu Wang
- College of Computer Science and Technology, University of Jilin, Changchun, 130012, China
| | - Chunguo Wu
- College of Computer Science and Technology, University of Jilin, Changchun, 130012, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Changchun, 130012, China
| | - Yanchun Liang
- College of Computer Science and Technology, University of Jilin, Changchun, 130012, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Changchun, 130012, China
- School of Computer Science, Zhuhai College of Science and Technology, Zhuhai, 519041, China
| | - Xiaohu Shi
- College of Computer Science and Technology, University of Jilin, Changchun, 130012, China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Changchun, 130012, China.
- School of Computer Science, Zhuhai College of Science and Technology, Zhuhai, 519041, 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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