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Protein Organization

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 Organization

Protein Organization

Protein and Protein Structure

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...

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Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Conservation of Protein Domains Over Different Proteins

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain. Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...

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Related Experiment Video

Updated: Dec 30, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Improved protein structure prediction using potentials from deep learning.

Andrew W Senior¹, Richard Evans², John Jumper²

¹DeepMind, London, UK. andrewsenior@google.com.

|January 17, 2020

Summary

This summary is machine-generated.

AlphaFold uses neural networks to predict protein structures from amino acid sequences by estimating residue-pair distances. This method significantly advances protein structure prediction accuracy, aiding in understanding protein function.

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Last Updated: Dec 30, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Published on: January 26, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

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Area of Science:

Computational biology
Structural biology
Bioinformatics

Background:

Protein structure determination is crucial for understanding protein function.
Experimental methods for protein structure determination are challenging and time-consuming.
Leveraging genetic information, such as homologous sequence covariation, has improved structure prediction.

Purpose of the Study:

To develop a novel method for accurate protein structure prediction.
To improve upon existing protein structure prediction techniques using deep learning.

Main Methods:

A neural network was trained to predict distances between amino acid residue pairs.
A potential of mean force was constructed using predicted distance information.
Gradient descent optimization was employed to generate protein structures.

Main Results:

The AlphaFold system achieved high accuracy in protein structure prediction.
AlphaFold outperformed other methods in the Critical Assessment of Protein Structure Prediction (CASP13).
The method demonstrated effectiveness even for sequences with limited homologous data.

Conclusions:

AlphaFold represents a significant advancement in computational protein structure prediction.
The enhanced accuracy facilitates insights into protein function and malfunction.
This approach is particularly valuable for proteins lacking experimentally determined homologous structures.