A review from biological mapping to computation-based subcellular localization

Li, Jing; Zou, Quan; Liu, Yuan

doi:10.1016/j.omtn.2023.04.015

Cited by 8 publications

(3 citation statements)

References 104 publications

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“…System-wide studies of RNA subcellular localization (e.g., mRNA [177]) have also paved the way for a more comprehensive analysis of the cellular dynamics [178,179], as proteins are usually transcribed by RNA molecules. Moreover, except for RNA transcripts for protein, other RNAs, like long noncoding RNAs (lncRNAs), may also be involved in many biological functions [180].…”

Section: Future Directionsmentioning

confidence: 99%

“…Moreover, except for RNA transcripts for protein, other RNAs, like long noncoding RNAs (lncRNAs), may also be involved in many biological functions [180]. Predicting their subcellular locations with AI-based methods [180] can significantly reduce costs and time expenditure, enabling the investigation of their functionalities with limited data [178]. In addition, common [181] and rare cellular-compartment-specific prediction models can be further explored [182].…”

Section: Future Directionsmentioning

confidence: 99%

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A Review for Artificial Intelligence Based Protein Subcellular Localization

Xiao,

Zou,

Wang

et al. 2024

Biomolecules

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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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Section: Future Directionsmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

A Review for Artificial Intelligence Based Protein Subcellular Localization

Xiao,

Zou,

Wang

et al. 2024

Biomolecules

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“…Accurate prediction of subcellular locations of viral proteins plays a critical role in comprehending their functions [1]. A precise understanding of the viral protein's location within host cells is paramount for drug discovery and design [2,3]. Additionally, the prediction of the subcellular location of viral proteins provides insights into their functions and interactions within the host cell, facilitating the development of antiviral strategies and the identification of appropriate vaccine targets [4].…”

Section: Introductionmentioning

confidence: 99%

E-MuLA: An Ensemble Multi-Localized Attention Feature Extraction Network for Viral Protein Subcellular Localization

Bakanina Kissanga,

Zulfiqar,

Gao

et al. 2024

Information

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Accurate prediction of subcellular localization of viral proteins is crucial for understanding their functions and developing effective antiviral drugs. However, this task poses a significant challenge, especially when relying on expensive and time-consuming classical biological experiments. In this study, we introduced a computational model called E-MuLA, based on a deep learning network that combines multiple local attention modules to enhance feature extraction from protein sequences. The superior performance of the E-MuLA has been demonstrated through extensive comparisons with LSTM, CNN, AdaBoost, decision trees, KNN, and other state-of-the-art methods. It is noteworthy that the E-MuLA achieved an accuracy of 94.87%, specificity of 98.81%, and sensitivity of 84.18%, indicating that E-MuLA has the potential to become an effective tool for predicting virus subcellular localization.

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Integrated Biochemical and Computational Methods for Deciphering RNA‐Processing Codes

Du,

Fan,

Zhou

2024

WIREs RNA

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RNA processing involves steps such as capping, splicing, polyadenylation, modification, and nuclear export. These steps are essential for transforming genetic information in DNA into proteins and contribute to RNA diversity and complexity. Many biochemical methods have been developed to profile and quantify RNAs, as well as to identify the interactions between RNAs and RNA‐binding proteins (RBPs), especially when coupled with high‐throughput sequencing technologies. With the rapid accumulation of diverse data, it is crucial to develop computational methods to convert the big data into biological knowledge. In particular, machine learning and deep learning models are commonly utilized to learn the rules or codes governing the transformation from DNA sequences to intriguing RNAs based on manually designed or automatically extracted features. When precise enough, the RNA codes can be incredibly useful for predicting RNA products, decoding the molecular mechanisms, forecasting the impact of disease variants on RNA processing events, and identifying driver mutations. In this review, we systematically summarize the biochemical and computational methods for deciphering five important RNA codes related to alternative splicing, alternative polyadenylation, RNA localization, RNA modifications, and RBP binding. For each code, we review the main types of experimental methods used to generate training data, as well as the key features, strategic model structures, and advantages of representative tools. We also discuss the challenges encountered in developing predictive models using large language models and extensive domain knowledge. Additionally, we highlight useful resources and propose ways to improve computational tools for studying RNA codes.

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A review from biological mapping to computation-based subcellular localization

Cited by 8 publications

References 104 publications

A Review for Artificial Intelligence Based Protein Subcellular Localization

A Review for Artificial Intelligence Based Protein Subcellular Localization

E-MuLA: An Ensemble Multi-Localized Attention Feature Extraction Network for Viral Protein Subcellular Localization

Integrated Biochemical and Computational Methods for Deciphering RNA‐Processing Codes

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