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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 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...

Protein and Protein Structures

Protein and Protein Structures

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...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

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Updated: Jun 17, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

AI-Driven Deep Learning Techniques in Protein Structure Prediction.

Lingtao Chen¹, Qiaomu Li¹, Kazi Fahim Ahmad Nasif¹

¹College of Computing and Software Engineering, Kennesaw State University, Marietta, GA 30060, USA.

International Journal of Molecular Sciences

|August 10, 2024

Summary

This summary is machine-generated.

This review explores computational protein structure prediction, highlighting advancements from traditional methods to cutting-edge artificial intelligence (AI) models like AlphaFold. It assesses AI

Keywords:

AlphaFold artificial intelligence bioinformatics computational methods deep learning healthcare machine learning protein modeling protein structure transformer

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

Computational biology
Structural bioinformatics
Artificial intelligence in life sciences

Background:

Protein structure prediction is crucial for understanding protein function and behavior.
Traditional methods include homology modeling, ab initio modeling, and threading.
The field has rapidly advanced with the integration of artificial intelligence (AI).

Purpose of the Study:

To provide a comprehensive review of computational models for protein structure prediction.
To cover the evolution from established protein modeling techniques to state-of-the-art AI frameworks.
To discuss the performance and applications of these models.

Main Methods:

Review of established protein modeling techniques (homology, ab initio, threading).
Analysis of deep learning-based AI models (AlphaFold, RoseTTAFold, ProteinBERT).
Comparison of model performance using CASP and CAMEO rankings and metrics (TM-score, GDT_TS, lDDT).

Main Results:

AI models, particularly deep learning approaches, have significantly improved protein structure prediction accuracy.
Established frameworks have integrated AI techniques, enhancing their capabilities.
Performance comparisons show substantial progress in prediction accuracy via CASP and CAMEO evaluations.

Conclusions:

AI has revolutionized protein structure prediction, achieving unprecedented accuracy.
Challenges remain in predicting dynamic protein behavior, conformational changes, and interactions.
Future research directions include exploring these complex dynamics and expanding applications in drug design and development.