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Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

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KStable: A Computational Method for Predicting Protein Thermal Stability Changes by K-Star with Regular-mRMR Feature

Chi-Wei Chen^1,2, Kai-Po Chang^3,4, Cheng-Wei Ho²

¹Department of Computer Science and Engineering, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

We developed KStable, a rapid, sequence-based tool to predict protein thermostability changes due to mutations. It considers temperature and pH, overcoming limitations of existing methods for protein engineering and drug development.

Keywords:

feature selection hill-climbing algorithm machine learning protein thermostability single-site mutations

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Area of Science:

Biochemistry
Computational Biology
Protein Engineering

Background:

Protein thermostability is crucial for various applications, including drug development and protein engineering.
Current computational tools often require tertiary structure data or suffer from slow execution and limited prediction capabilities.
Existing sequence-based methods lack temperature and pH parameters, hindering accurate thermostability prediction.

Purpose of the Study:

To develop a rapid, sequence-based computational tool for predicting protein thermostability.
To incorporate temperature and pH as input parameters for more accurate predictions.
To enable large-scale mutation effect predictions on protein thermostability.

Main Methods:

Developed KStable, a sequence-based tool for predicting thermostability changes upon single-site mutations.
Utilized basis and minimal redundancy-maximal relevance (mRMR) features for model training.
Employed a regular-mRMR method to identify representative features and tested 58 classifiers.

Main Results:

KStable demonstrates computational rapidity and sequence-based prediction capabilities.
The tool successfully incorporates temperature and pH as input parameters.
Achieved an accuracy of 0.708 when evaluated on an independent test set.

Conclusions:

KStable offers an efficient and accurate solution for predicting protein thermostability.
The tool addresses key limitations of existing computational methods.
Facilitates advancements in protein engineering, structure determination, and drug development.