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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...

Conservation of Protein Domains

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Protein Families

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Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

Updated: Jul 9, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

Published on: July 16, 2017

STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning.

Likun Yang¹, Xiaoli Liang¹, Na Zhang¹

¹Asymchem Life Science (Tianjin) Co., Ltd, Tianjin 300457, P. R. China.

|December 4, 2023

Summary

This summary is machine-generated.

We developed STAR, a web tool using artificial intelligence and Bayesian optimization to recommend protein sequences. This system aids researchers in protein engineering, making advanced machine learning accessible without coding skills.

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Area of Science:

Biotechnology
Protein Engineering
Artificial Intelligence in Life Sciences

Background:

Protein engineering drives innovation in agriculture, food, and pharmaceuticals.
Directed evolution combined with AI is a powerful R&D approach.
Machine learning application in protein engineering requires specialized skills.

Purpose of the Study:

To develop an accessible web-based system for protein sequence recommendation.
To simplify the application of AI and machine learning in protein engineering.
To enhance the success rate of protein sequence design.

Main Methods:

Developed STAR (Sequence recommendaTion via ARtificial intelligence), a web-based system.
Utilized Bayesian optimization as the core recommendation engine.
Incorporated a regression model for filtering and an in silico-directed evolution approach.

Main Results:

STAR provides a user-friendly platform for protein sequence recommendation.
The system integrates advanced AI techniques, including Bayesian optimization and regression models.
In silico-directed evolution expands the search space for novel protein sequences.

Conclusions:

STAR democratizes the use of AI in protein engineering.
The system facilitates the discovery of improved protein sequences for various applications.
Web accessibility (https://www.FindProteinStar.com/) lowers the barrier for researchers.