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Related Concept Videos

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...

Protein and Protein Structures

Protein and Protein Structures

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...

Protein Folding

Protein Folding

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Related Experiment Video

Updated: Nov 25, 2025

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

Published on: July 8, 2025

Deep Learning in Protein Structural Modeling and Design.

Wenhao Gao¹, Sai Pooja Mahajan¹, Jeremias Sulam²

¹Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Patterns (New York, N.Y.)

|December 18, 2020

Summary

This summary is machine-generated.

Deep learning is revolutionizing protein structural modeling and design by leveraging big data and computational power. This review explores advances and challenges in applying these AI techniques to predict protein structure and function.

Keywords:

deep generative model deep learning protein design protein folding representation learning

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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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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

Area of Science:

Computational Biology
Artificial Intelligence
Biochemistry

Background:

Deep learning is transforming scientific research, particularly in fields with large datasets like protein structural modeling.
Understanding protein structure and function is crucial for engineering biological systems.
Current challenges exist in predicting protein structure and designing proteins with specific functions.

Purpose of the Study:

To review recent advances in applying deep learning to protein structural modeling and design.
To dissect emerging deep learning approaches for protein structure prediction and engineering.
To highlight challenges and future directions in the field.

Main Methods:

Review of recent literature on deep learning applications in protein structural modeling.
Analysis of deep learning techniques used for predicting protein structure from sequence and evolutionary information.
Examination of deep learning for protein design and property prediction.

Main Results:

Deep learning methods are showing significant promise in predicting protein structure and enabling protein design.
The "sequence-structure-function" paradigm is central to these advancements.
Emerging deep learning approaches offer new possibilities for understanding and engineering proteins.

Conclusions:

Deep learning is a powerful tool for advancing protein structural modeling and design.
Further research is needed to address current challenges and fully realize the potential of AI in this field.
This review aims to bridge the knowledge gap between computational biologists and computer scientists.