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Related Concept Videos

Protein Organization01:13

Protein Organization

Overview
Protein and Protein Structure02:15

Protein and Protein Structure

Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme can...
Protein Organization01:13

Protein Organization

Overview
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.
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.
Nucleic Acid Structure01:25

Nucleic Acid Structure

The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA has a double-helix structure. The...

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CIAA: Integrated Proteomics and Structural Modeling for Understanding Cysteine Reactivity with Iodoacetamide Alkyne.

Lisa M Boatner1,2, Jerome Eberhardt3, Flowreen Shikwana1,2

  • 1Biological Chemistry Department, David Geffen School of Medicine, UCLA, Los Angeles, California 90095, United States.

ACS Chemical Biology
|June 30, 2025
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Summary

We developed a new AI method, CIAA, to predict cysteine reactivity in proteins using structural features. This advances chemoproteomic analysis by identifying key drivers of cysteine function and drug targeting potential.

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Area of Science:

  • Proteomics
  • Computational Biology
  • Structural Biology

Background:

  • Cysteine residues are crucial for protein structure and function, acting as targets for drugs and chemical probes.
  • Chemoproteomic studies using probes like iodoacetamide alkyne (IAA) link cysteine reactivity to functionality, but coverage remains incomplete for low-abundance proteins.

Purpose of the Study:

  • To identify structural features that dictate cysteine reactivity toward IAA.
  • To develop a computational method for predicting proteome-wide cysteine reactivity.

Main Methods:

  • Integrated chemoproteomic reactivity data with structure-guided computational analysis.
  • Developed the Cysteine reactivity toward IodoAcetamide Alkyne (CIAA) method using a Random Forest model and 3D structural descriptors.
  • Trained and validated the CIAA model using existing and new chemoproteomic data and Protein Data Bank (PDB) crystal structures.

Main Results:

  • No single structural feature accurately predicts cysteine reactivity.
  • The CIAA method effectively incorporates 3D thiol microenvironment descriptors to assess reactivity.
  • Identified key reactivity drivers, including backbone hydrogen bond donor atoms.

Conclusions:

  • The CIAA method provides a robust foundation for AI-driven analysis of chemoproteomic data.
  • Highlights the need for improved computational prediction of cysteine reactivity and curated protein structure datasets.
  • Enhances understanding of cysteine functionality and potential for targeted drug development.