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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...

Structural Protein Function

Structural Protein Function

Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity. In bones and teeth, it mineralizes to...

Structural Protein Function

Structural Protein Function

Protein and Protein Structures

Protein and Protein Structures

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

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Related Experiment Video

Updated: Feb 8, 2026

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 Methods.

Samantha K Teixeira¹, Angélica N Lima², Pedro Túlio Resende-Lara^3,4

¹Laboratório de Genética e Cardiologia Molecular, Instituto do Coração, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil. samantha.teixeira@hc.fm.usp.br.

Advances in Experimental Medicine and Biology

|February 6, 2026

Summary

This summary is machine-generated.

Protein structure prediction uses computational biology to determine 3D protein structures from amino acid sequences. Recent deep learning models like AlphaFold2 achieve near-experimental accuracy, advancing drug discovery and enzyme engineering.

Keywords:

End-to-end models Free modeling Protein language models Template-based modeling

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Last Updated: Feb 8, 2026

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

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

Area of Science:

Computational Biology
Structural Biology
Bioinformatics

Background:

Protein structure prediction is crucial for understanding biological function, stemming from the protein folding problem.
Methodologies have evolved significantly over four decades, from template-based modeling to advanced deep learning.
The sequence determines the three-dimensional structure, a key relationship in molecular biology.

Purpose of the Study:

To explore the principles, advances, and impact of protein structure prediction methodologies.
To highlight the evolution from traditional methods to modern deep learning approaches.
To discuss the applications of these innovations in various biological research areas.

Main Methods:

Template-based modeling (TBM) using sequence homology and threading.
Free modeling (FM) employing physics-based principles for de novo structure prediction.
Advanced hybrid and end-to-end deep learning methods (e.g., AlphaFold2, RoseTTAFold) utilizing neural networks.

Main Results:

Deep learning methods, particularly end-to-end approaches, have achieved near-experimental accuracy in predicting atomic coordinates.
Protein language models learn sequence-structure-function relationships directly from sequences.
Innovations are revolutionizing structural biology and related fields.

Conclusions:

Modern protein structure prediction methods, especially deep learning, have transformed computational biology.
These advances enable a deeper understanding of the sequence-structure-function paradigm.
Applications span drug discovery, enzyme engineering, and disease research, underscoring the field's impact.