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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 polypeptide...

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.
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Protein Networks

Protein Networks

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Mechanical Protein Function

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Related Experiment Video

Updated: Jul 6, 2026

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

AI-Driven Protein Research: From Prediction to Design.

Guodong Min¹, Huan Peng²

¹School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.

Methods in Molecular Biology (Clifton, N.J.)

|July 4, 2026

Summary

Artificial intelligence (AI) is revolutionizing protein science by enabling faster prediction, annotation, and design of protein structures and functions. This evolution from early methods to advanced protein language models (PLMs) accelerates biomedical and biotechnological applications.

Keywords:

Artificial intelligence Generative design Molecular docking Protein language model Protein structure prediction

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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

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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:

Molecular Biology
Computational Biology
Biotechnology

Background:

Proteins are essential for biological processes, but predicting their structure and function from sequence is challenging.
Traditional methods like X-ray crystallography and cryo-electron microscopy are costly and slow, limiting proteome-wide analysis.
Advances in AI and deep learning, coupled with large sequence databases, offer new solutions.

Purpose of the Study:

To review the evolution of AI-driven methods in protein research.
To highlight key conceptual advances and their impact.
To discuss translational implications for medicine and biotechnology.

Main Methods:

Review of AI and deep learning applications in protein science.
Tracing the development from coevolutionary analysis to protein language models (PLMs).
Discussion of generative design and functional modeling techniques.

Main Results:

AI has significantly accelerated protein prediction, annotation, and design.
Protein language models (PLMs) represent a major breakthrough in understanding protein sequence-function relationships.
Emerging AI approaches enable de novo protein design and functional prediction.

Conclusions:

AI is transforming protein research, overcoming limitations of traditional methods.
These advancements hold significant promise for biomedical science and biotechnology.
The future of protein science is increasingly intertwined with AI-driven innovation.