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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...

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...

Protein Folding

Protein Folding

Protein Folding

Protein Folding

Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...

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Related Experiment Video

Updated: Nov 4, 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

Protein Structure Prediction: Conventional and Deep Learning Perspectives.

V A Jisna¹, P B Jayaraj²

¹Department of Computer Science and Engineering, National Institute of Technology Calicut, Calicut, Kerala, 673601, India. jisna_p170107cs@nitc.ac.in.

The Protein Journal

|May 29, 2021

Summary

This summary is machine-generated.

Protein structure prediction is crucial for understanding protein function. This review covers conventional and deep learning methods, highlighting deep learning

Keywords:

Ab-initio CNN Deep learning Fragment-based approaches Homology modeling LSTM Protein structure prediction Proteins Self-supervised learning Template-based modeling Tertiary structure Threading Transformers

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

Computational Biology
Biochemistry
Bioinformatics

Background:

Protein structure prediction addresses the sequence-structure gap, a key challenge in computational biology.
Experimental methods for protein structure determination are time-consuming and complex.
Accurate protein structures are vital for understanding biological functions and for applications like drug discovery.

Purpose of the Study:

To review conventional and deep learning approaches for protein structure prediction.
To discuss the challenges and advancements in the field.
To highlight resources and architectures for protein structure prediction.

Main Methods:

Review of existing literature on protein structure prediction.
Comparison of traditional methods with machine learning and deep learning techniques.
Analysis of co-evolution based methods and their limitations.

Main Results:

Deep learning methods offer advanced feature extraction from protein sequence data.
Deep learning approaches show promise, especially for proteins with limited homologous sequences.
Conventional methods often rely on experimental data or homology modeling.

Conclusions:

Deep learning is revolutionizing protein structure prediction.
Accurate prediction facilitates drug design and understanding molecular interactions.
Publicly available datasets and architectures are crucial for advancing the field.