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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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Published on: July 25, 2013

Rosetta:MSF: a modular framework for multi-state computational protein design.

Patrick Löffler¹, Samuel Schmitz¹, Enrico Hupfeld¹

¹Institute of Biophysics and Physical Biochemistry, University of Regensburg, Regensburg, Germany.

Plos Computational Biology

|June 13, 2017

Summary

This summary is machine-generated.

Computational protein design (CPD) using Rosetta

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Area of Science:

Protein engineering
Computational biology
Biochemistry

Background:

Computational protein design (CPD) uses algorithms for protein modeling.
Rosetta software suite offers protocols for protein engineering tasks.
Single-state design optimizes sequences based on one conformation.

Purpose of the Study:

To develop a multi-state framework (MSF) for Rosetta.
To enable simultaneous assessment of multiple protein states for complex design objectives.
To improve the performance of CPD by incorporating multi-state design.

Main Methods:

Developed the multi-state framework (MSF) for Rosetta.
Implemented and utilized two existing Rosetta protocols within MSF.
Applied MSF to a ligand-binding benchmark and de novo enzyme design.

Main Results:

Multi-state design showed a 15% higher performance than single-state design on a ligand-binding benchmark.
Successfully designed nine de novo retro-aldolases using MSF.
All designed enzyme variants exhibited measurable catalytic activity.

Conclusions:

The multi-state framework (MSF) enhances Rosetta's capabilities for complex protein design.
Multi-state design is a successful strategy for engineering novel enzymes with desired functions.
This approach significantly improves the success rate of computational protein engineering.