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

Yuchi Qiu1, Guo-Wei Wei1,2,3

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

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Summary

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

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Area of Science:

  • Biotechnology and Bioinformatics
  • Protein Engineering and Design

Background:

  • Protein engineering offers revolutionary potential across diverse fields like drug discovery and antibody design.
  • The vast mutational space in proteins necessitates advanced computational approaches beyond traditional experimental methods.
  • Emerging AI tools are transforming the capabilities of protein engineering.

Approach:

  • This review systematically examines key methodological components for advanced protein engineering.
  • It highlights the integration of machine learning (ML), particularly natural language processing (NLP), with protein databases.
  • The review also incorporates advances in topological data analysis (TDA) and AI-based protein structure prediction (e.g., AlphaFold2).

Key Points:

  • Machine learning models, especially NLP, accelerate protein engineering by analyzing large protein databases.
  • Topological data analysis (TDA) provides novel methods for understanding complex protein data.
  • AI-driven protein structure prediction tools like AlphaFold2 enable more powerful, structure-based engineering strategies.

Conclusions:

  • The convergence of TDA, NLP, and AI structure prediction offers a powerful toolkit for protein engineering.
  • This systematic review provides a foundational resource for researchers in the field.
  • Facilitating future developments in ML-assisted protein engineering is a primary objective.