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Related Experiment Video

Updated: Jul 19, 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

Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models.

Yuchi Qiu¹, Guo-Wei Wei^1,2,3

¹Department of Mathematics, Michigan State University, East Lansing, 48824 MI, USA.

Briefings in Bioinformatics

|August 14, 2023

Summary

This summary is machine-generated.

Protein engineering uses machine learning (ML) and natural language processing (NLP) to navigate vast protein mutation possibilities. This review details methods combining topological data analysis (TDA) and AI for advanced protein design.

Keywords:

deep learning and machine learning protein engineering protein language models topological data analysis

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Last Updated: Jul 19, 2025

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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Published on: July 8, 2025

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Published on: November 3, 2011

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

Area of Science:

Biotechnology and computational biology.
Focuses on protein engineering applications.

Background:

Protein engineering offers revolutionary potential across diverse fields like drug discovery and antibody design.
Experimental methods are limited by the vast mutational space of proteins.

Approach:

Leverages large protein databases and machine learning (ML), including natural language processing (NLP).
Integrates topological data analysis (TDA) and AI-driven protein structure prediction (e.g., AlphaFold2).
Enables powerful structure-based ML-assisted protein engineering strategies.

Key Points:

ML models, especially NLP, accelerate protein engineering by analyzing protein databases.
TDA provides novel analytical tools for understanding complex protein data.
AI advancements like AlphaFold2 enhance structure-based design approaches.

Conclusions:

This review provides a systematic overview of TDA and NLP methodologies for protein engineering.
Aims to facilitate the development and application of advanced computational tools in protein design.
Highlights the synergy between AI, TDA, and NLP for future protein engineering advancements.