Xiao, H.; Zou, Y.; Wang, J.; Wan, S. A Review for Artificial-Intelligence-Based Protein Subcellular Localization. Biomolecules2024, 14, 409.
Xiao, H.; Zou, Y.; Wang, J.; Wan, S. A Review for Artificial-Intelligence-Based Protein Subcellular Localization. Biomolecules 2024, 14, 409.
Xiao, H.; Zou, Y.; Wang, J.; Wan, S. A Review for Artificial-Intelligence-Based Protein Subcellular Localization. Biomolecules2024, 14, 409.
Xiao, H.; Zou, Y.; Wang, J.; Wan, S. A Review for Artificial-Intelligence-Based Protein Subcellular Localization. Biomolecules 2024, 14, 409.
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 subcel-lular 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.
Keywords
Protein Subcellular Localization; Machine Learning; Deep Learning; Artificial Intelligence; Gene Ontology; Sequence Analysis
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
