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Related Concept Videos

Protein Organization01:13

Protein Organization

Overview
Overview of Protein Sorting and Transport01:45

Overview of Protein Sorting and Transport

Eukaryotic cells have different membrane-bound organelles with distinct protein requirements. The process by which proteins are targeted to a specific organelle is called protein sorting.
Protein sorting can be of two types: signal-based sorting and vesicle-based trafficking. In signal-based sorting, specific amino acid sequences called sorting signals target proteins to the proper location inside the cell either via gated transport or by protein translocation.  In gated transport, folded...
Nuclear Protein Sorting01:34

Nuclear Protein Sorting

Nuclear protein sorting is the selective trafficking of histones, polymerases, gene regulatory proteins into the nucleus and exporting RNAs and ribosomes to the cytosol. It is a tightly controlled process that regulates gene expression within a cell.
Proteins targeted to the nucleus carry nuclear localization signals or NLS recognized by import receptors in the cytosol. Similarly, proteins with nuclear export signals are recognized by export receptors. Import and export receptors are...
Directing Proteins to the Rough Endoplasmic Reticulum01:34

Directing Proteins to the Rough Endoplasmic Reticulum

The organelle-specific signaling sequences direct proteins synthesized in the cytosol to their final destination like ER, mitochondria, peroxisomes, etc. Some of the proteins directed to ER are then trafficked via vesicles to other organelles within the cell or the extracellular environment through the Golgi complex. For example, the rough ER synthesizes soluble proteins for transportation to the lysosomes or secretion out of the cell. It can also synthesize transmembrane proteins that can...
Protein Organization01:24

Protein Organization

Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence.
Subcellular Fractionation01:32

Subcellular Fractionation

The homogenate obtained after cell lysis contains various membrane-bound organelles that can be further separated into pure fractions by subcellular fractionation. These isolates are used to study specific cellular components, analyze localized protein activity, and are even employed in diagnostics. Fractionation is typically achieved using centrifugation methods, the most common being density-gradient and differential centrifugation.
Differential Centrifugation
Differential centrifugation is...

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A Review for Artificial Intelligence Based Protein Subcellular Localization.

Hanyu Xiao1, Yijin Zou2, Jieqiong Wang3

  • 1Department of Genetics, Cell Biology and Anatomy, College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA.

Biomolecules
|April 27, 2024
PubMed
Summary
This summary is machine-generated.

This study reviews artificial intelligence (AI) methods for predicting protein subcellular localization. AI offers a scalable solution to identify protein locations, aiding disease research and drug design.

Keywords:
artificial intelligencedeep learninggene ontologymachine learningprotein subcellular localizationsequence analysis

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Genomics

Background:

  • Accurate protein subcellular localization is crucial for biological function and understanding disease.
  • Traditional wet-lab methods are insufficient for the vast number of newly discovered proteins.
  • Mislocalized proteins are implicated in diseases like cancer and Alzheimer's.

Purpose of the Study:

  • To review recent advancements in artificial intelligence (AI) and machine learning (ML) for protein subcellular localization.
  • To categorize AI-based methods into sequence-based, knowledge-based, and image-based approaches.
  • To discuss current challenges and future research directions in this field.

Main Methods:

  • Review of AI/ML methodologies applied to protein subcellular localization.
  • Categorization of methods based on input data: protein sequence, existing biological knowledge, or imaging data.
  • Analysis of the strengths and limitations of each AI approach.

Main Results:

  • AI and deep learning methods have shown significant progress in predicting protein subcellular localization.
  • Sequence-based methods utilize protein sequence information for prediction.
  • Knowledge-based methods leverage biological databases and pathways.
  • Image-based methods analyze cellular images for localization patterns.

Conclusions:

  • AI-based approaches are essential for handling the scale of modern proteomics and genomics.
  • Further development is needed to address existing challenges in AI-driven protein localization.
  • Future research should focus on integrating diverse data types and improving model interpretability.