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Updated: Jun 3, 2025

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

Exploring Evolution to Uncover Insights Into Protein Mutational Stability.

Pauline Hermans^1,2, Matsvei Tsishyn^1,2, Martin Schwersensky^1,2

¹Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels 1050, Belgium.

Molecular Biology and Evolution

|January 9, 2025

Summary

Predicting protein stability changes from mutations is crucial. Surprisingly, simple evolutionary models combined with residue accessibility match complex methods, offering new insights for protein design and variant interpretation.

Keywords:

coevolution and epistasis protein evolution protein mutational landscape protein thermodynamic stability

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Last Updated: Jun 3, 2025

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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:

Biochemistry
Computational Biology
Evolutionary Biology

Background:

Protein thermodynamic stability is vital for protein design and interpreting genetic variations.
Evolutionary data, derived from homologous protein sequences, are commonly used to predict mutation effects on stability.
Deep mutational scanning provides extensive data to refine these prediction methods.

Purpose of the Study:

To investigate optimal methods for constructing multiple sequence alignments and extracting evolutionary information for protein stability prediction.
To evaluate the effectiveness of different evolutionary models, including independent-site and epistatic models.
To assess the contribution of structural features, like solvent accessibility, in combination with evolutionary data.

Main Methods:

Utilized large-scale deep mutational scanning stability data.
Constructed and analyzed various multiple sequence alignments.
Tested independent-site and complex epistatic evolutionary models.
Incorporated the relative solvent accessibility of mutated residues as a structural feature.

Main Results:

Independent-site evolutionary models demonstrated accuracy comparable to more complex epistatic models for stability prediction.
Complex epistatic models often yielded noisy couplings without significant predictive improvement over simpler models.
Combining evolutionary features with relative solvent accessibility achieved prediction accuracy similar to advanced machine learning predictors.

Conclusions:

Simple evolutionary models are effective for predicting mutation-induced stability changes.
Relative solvent accessibility is a valuable feature that, when combined with evolutionary data, enhances prediction accuracy.
Findings offer new perspectives on leveraging evolutionary information for improved protein stability prediction.