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Updated: Oct 1, 2025

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

Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design.

Ben E Clifton¹, Dan Kozome¹, Paola Laurino¹

¹Protein Engineering and Evolution Unit, Okinawa Institute of Science and Technology, 1919-1 Tancha, Onna, Okinawa 904-0495, Japan.

|March 4, 2022

Summary

Leveraging vast protein sequence data aids protein engineering. Advanced methods like ancestral reconstruction and machine learning accelerate the design of stable, functional proteins for various applications.

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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

Published on: August 9, 2019

Area of Science:

Protein Engineering
Computational Biology
Bioinformatics

Background:

Sequence databases have grown exponentially, providing rich evolutionary information.
Understanding sequence-function relationships is key for protein optimization.
Traditional protein engineering methods are being augmented by data-driven approaches.

Purpose of the Study:

To review the utility of protein sequence data in engineering and design.
To highlight recent advances in leveraging sequence information for protein optimization.
To discuss the potential of these methods for industrial and biomedical applications.

Main Methods:

Ancestral sequence reconstruction for enhanced protein stability and function.
Structure-based computational protein design guided by sequence data for multipoint mutants.
Unsupervised and semisupervised machine learning using unlabeled sequence data.

Main Results:

Ancestral proteins exhibit increased thermostability and multifunctionality.
Sequence data effectively guides the design of complex, multi-mutant proteins.
Machine learning generates diverse, functional protein sequences in unexplored sequence space.

Conclusions:

Protein sequence data is a powerful resource for accelerating protein engineering.
Integrating evolutionary and computational methods enhances the design of stable and functional proteins.
These approaches hold significant promise for advancing protein engineering in diverse fields.