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Updated: Jun 29, 2025

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

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Published on: July 25, 2013

Combining Rosetta Sequence Design with Protein Language Model Predictions Using Evolutionary Scale Modeling (ESM) as

Moritz Ertelt^1,2, Jens Meiler^1,2,3,4, Clara T Schoeder^1,2

¹Institute for Drug Discovery, University Leipzig Medicine Faculty, Liebigstr. 19, D-04103 Leipzig, Germany.

ACS Synthetic Biology

|April 3, 2024

Summary

This study integrates machine learning language models with protein design tools. The new method improves the design of stable and functional proteins by using evolutionary insights.

Keywords:

Rosetta computational protein design de novo proteins evolutionary fitness protein language model protein language models thermodynamic stability

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

Computational biology
Protein engineering
Machine learning in bioinformatics

Background:

Designing stable and functional proteins computationally is challenging due to difficulties in predicting protein dynamics and allostery.
Evolutionary information aids protein design by focusing on native-like sequences, enhancing stability and function.
Recent protein language models excel at predicting mutation effects, but initial assessments showed lower scores for designed sequences.

Purpose of the Study:

To enhance computational protein design by integrating predictions from protein language models into existing design protocols.
To improve the stability and functionality of computationally designed proteins by leveraging machine learning insights.

Main Methods:

Incorporated the Evolutionary Scale Modeling (ESM) language model into the Rosetta protein design energy function.
Developed a new metric to restrain the energy function during design using ESM predictions.
Evaluated the performance of the modified Rosetta protocol by assessing language model scores, sequence recovery, and Rosetta energy.

Main Results:

Sequences designed with the integrated approach achieved higher language model scores compared to standard Rosetta designs.
The new method maintained similar sequence recovery rates.
A minor decrease in fitness, as evaluated by Rosetta energy, was observed for the designed sequences.

Conclusions:

Combining protein language models with the Rosetta design toolbox offers a powerful approach for designing more stable and functional proteins.
This integration leverages the predictive power of machine learning and the established framework of protein design software.
The developed method represents a significant step forward in the field of computational protein sequence design.