Abstract
Proteins are intricate, dynamic structures, and small changes in their amino acid sequences can lead to large effects on their folding, stability and dynamics. To facilitate the further development and evaluation of methods to predict these changes, we have developed ThermoMutDB, a manually curated database containing >14,669 experimental data of thermodynamic parameters for wild type and mutant proteins. This represents an increase of 83% in unique mutations over previous databases and includes thermodynamic information on 204 new proteins. During manual curation we have also corrected annotation errors in previously curated entries. Associated with each entry, we have included information on the unfolding Gibbs free energy and melting temperature change, and have associated entries with available experimental structural information. ThermoMutDB supports users to contribute to new data points and programmatic access to the database via a RESTful API. ThermoMutDB is freely available at: http://biosig.unimelb.edu.au/thermomutdb.
Highlights
Protein thermodynamic stability is a fundamental property of proteins that significantly influences their structure, function, expression, and solubility
The development of computational approaches to tackle this have required large mutational datasets, in turn have been limited by the quantity and quality of data available
ThermoMutDB contains information of the protein, mutational information, experimental methods and conditions, thermodynamic parameters, derived data, and literature information
Summary
Protein thermodynamic stability is a fundamental property of proteins that significantly influences their structure, function, expression, and solubility. Small changes in the protein sequence can have significant consequences on their intricate structures, reflected in changes in their stability and ability to correctly fold [19]. This is often a significant consideration whenever considering a new mutation, whether in the context of protein engineering or variant characterisation [20,21]. The development of computational approaches to tackle this have required large mutational datasets, in turn have been limited by the quantity and quality of data available
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