Abstract
During the last years, the increasing number of DNA sequencing and protein mutagenesis studies has generated a large amount of variation data published in the biomedical literature. The collection of such data has been essential for the development and assessment of tools predicting the impact of protein variants at functional and structural levels. Nevertheless, the collection of manually curated data from literature is a highly time consuming and costly process that requires domain experts. In particular, the development of methods for predicting the effect of amino acid variants on protein stability relies on the thermodynamic data extracted from literature. In the past, such data were deposited in the ProTherm database, which however is no longer maintained since 2013. For facilitating the collection of protein thermodynamic data from literature, we developed the semi-automatic tool ThermoScan. ThermoScan is a text mining approach for the identification of relevant thermodynamic data on protein stability from full-text articles. The method relies on a regular expression searching for groups of words, including the most common conceptual words appearing in experimental studies on protein stability, several thermodynamic variables, and their units of measure. ThermoScan analyzes full-text articles from the PubMed Central Open Access subset and calculates an empiric score that allows the identification of manuscripts reporting thermodynamic data on protein stability. The method was optimized on a set of publications included in the ProTherm database, and tested on a new curated set of articles, manually selected for presence of thermodynamic data. The results show that ThermoScan returns accurate predictions and outperforms recently developed text-mining algorithms based on the analysis of publication abstracts. Availability: The ThermoScan server is freely accessible online at https://folding.biofold.org/thermoscan. The ThermoScan python code and the Google Chrome extension for submitting visualized PMC web pages to the ThermoScan server are available at https://github.com/biofold/ThermoScan.
Highlights
A key aspect for characterizing the relationship between genotype and phenotype is the study of the impact of amino acid variants on protein function and structure (Thusberg and Vihinen, 2009; Compiani and Capriotti, 2013)
We present the results achieved by ThermoScan in the selection of manuscripts reporting experimental protein thermodynamic data from PubMed
The method based on the maximum score achieved 3% higher accuracy (Q2) and 5% higher Matthews correlation coefficient (MCC)
Summary
A key aspect for characterizing the relationship between genotype and phenotype is the study of the impact of amino acid variants on protein function and structure (Thusberg and Vihinen, 2009; Compiani and Capriotti, 2013) To address this task, several tools for predicting the effect of variants on protein stability have been developed (Sanavia et al, 2020). Text-mining tools are used in daily life science research activity to improve web search (Ananiadou et al, 2010) and facilitate the database curation process (Yeh et al, 2003; Wei et al, 2012; Karp, 2016) In this context, we developed ThermoScan, a new method for facilitating the collection and curation of thermodynamic data. We evaluated the performance of ThermoScan in the detection of thermodynamic data in comparison with two existing web-server tools for documents classification (Fontaine et al, 2009; Simon et al, 2019)
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