Presentation Information
[2Biocat-11-KL]Development of Protein Redesign Tools for Accelerated Enzyme Applications
○Shogo Nakano1 (1. University of Shizuoka (Japan))
Keywords:
protein redesign,enzyme engineering,algorithm
Enzymes are increasingly recognized as next-generation biocatalysts that complement conventional chemical catalysts. However, two major challenges must be addressed to enable their practical application: (i) identification of enzymes with high potential for specific applications, and (ii) optimization of enzymatic properties through targeted mutagenesis.
To overcome these challenges, we have developed two complementary computational frameworks: iAnglerNet and GAOptimizer1/GArNet2. iAnglerNet is a sequence-clustering algorithm that leverages coevolving residues identified through a combination of statistical coupling analysis and network theory, treating these residues as functional sequence motifs. When integrated with ancestral sequence reconstruction, this approach enables the identification of promising enzyme candidates from thousands of homologous sequences, including L-amino acid oxidases3, sortase Es4, 5, and L-tryptophan synthases6.
GAOptimizer/GArNet is a structure-based rational protein redesign platform that integrates genetic algorithms with network theory. It generates multi-point mutants by introducing evolutionarily allowed mutations derived from homologous sequences, selected according to defined fitness functions. Application of GAOptimizer/GArNet to three distinct enzymes enabled the generation of highly functional variants through the introduction of dozens of mutations, demonstrating the effectiveness of the platform in enhancing enzymatic properties. Both GAOptimizer and GArNet are available at the following URL: https://doi.org/10.5281/zenodo.10208126 (GAOptimizer) and https://doi.org/10.5281/zenodo.18454473(GArNet).
In this presentation, we will describe the underlying algorithms of these tools and showcase their application in the design of novel biocatalysts.
[Reference]
1. Ozawa et al., Cell Rep. Phys. Sci. 5, 101758, (2024)
2. Ozawa et al., J. Chem. Inf. Model., 65, 6331-6340, (2025)
3. S. Nakano et al., ACS Catal., 9, 10152-10158, (2019)
4. A. Miyata et al., ACS Catal., 14, 3514-3523 (2024)
5. R. Koshiba et al., Bioconj. Chem., 37, 341-350, (2026)
6. M. Ohata et al., ChemCatChem, 22, e01027, (2025)
To overcome these challenges, we have developed two complementary computational frameworks: iAnglerNet and GAOptimizer1/GArNet2. iAnglerNet is a sequence-clustering algorithm that leverages coevolving residues identified through a combination of statistical coupling analysis and network theory, treating these residues as functional sequence motifs. When integrated with ancestral sequence reconstruction, this approach enables the identification of promising enzyme candidates from thousands of homologous sequences, including L-amino acid oxidases3, sortase Es4, 5, and L-tryptophan synthases6.
GAOptimizer/GArNet is a structure-based rational protein redesign platform that integrates genetic algorithms with network theory. It generates multi-point mutants by introducing evolutionarily allowed mutations derived from homologous sequences, selected according to defined fitness functions. Application of GAOptimizer/GArNet to three distinct enzymes enabled the generation of highly functional variants through the introduction of dozens of mutations, demonstrating the effectiveness of the platform in enhancing enzymatic properties. Both GAOptimizer and GArNet are available at the following URL: https://doi.org/10.5281/zenodo.10208126 (GAOptimizer) and https://doi.org/10.5281/zenodo.18454473(GArNet).
In this presentation, we will describe the underlying algorithms of these tools and showcase their application in the design of novel biocatalysts.
[Reference]
1. Ozawa et al., Cell Rep. Phys. Sci. 5, 101758, (2024)
2. Ozawa et al., J. Chem. Inf. Model., 65, 6331-6340, (2025)
3. S. Nakano et al., ACS Catal., 9, 10152-10158, (2019)
4. A. Miyata et al., ACS Catal., 14, 3514-3523 (2024)
5. R. Koshiba et al., Bioconj. Chem., 37, 341-350, (2026)
6. M. Ohata et al., ChemCatChem, 22, e01027, (2025)
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