MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence

Mehdi Foroozandeh Shahraki; Shohreh Ariaeenejad; Fereshteh Fallah Atanaki; Behrouz Zolfaghari; Takeshi Koshiba; Kaveh Kavousi; Ghasem Hosseini Salekdeh

doi:10.3389/fmicb.2020.567863

MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence

Front Microbiol. 2020 Oct 23:11:567863. doi: 10.3389/fmicb.2020.567863. eCollection 2020.

Authors

Mehdi Foroozandeh Shahraki¹, Shohreh Ariaeenejad², Fereshteh Fallah Atanaki¹, Behrouz Zolfaghari³, Takeshi Koshiba⁴, Kaveh Kavousi¹, Ghasem Hosseini Salekdeh^{2

5}

Affiliations

¹ Laboratory of Complex Biological Systems and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
² Department of Systems and Synthetic Biology, Agricultural Biotechnology Research Institute of Iran, Agricultural Research Education and Extension Organization, Karaj, Iran.
³ Computer Science and Engineering Department, Indian Institute of Technology Guwahati, Guwahati, India.
⁴ Department of Mathematics, Faculty of Education and Integrated Arts and Sciences, Waseda University, Tokyo, Japan.
⁵ Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.

Abstract

As the availability of high-throughput metagenomic data is increasing, agile and accurate tools are required to analyze and exploit this valuable and plentiful resource. Cellulose-degrading enzymes have various applications, and finding appropriate cellulases for different purposes is becoming increasingly challenging. An in silico screening method for high-throughput data can be of great assistance when combined with the characterization of thermal and pH dependence. By this means, various metagenomic sources with high cellulolytic potentials can be explored. Using a sequence similarity-based annotation and an ensemble of supervised learning algorithms, this study aims to identify and characterize cellulolytic enzymes from a given high-throughput metagenomic data based on optimum temperature and pH. The prediction performance of MCIC (metagenome cellulase identification and characterization) was evaluated through multiple iterations of sixfold cross-validation tests. This tool was also implemented for a comparative analysis of four metagenomic sources to estimate their cellulolytic profile and capabilities. For experimental validation of MCIC's screening and prediction abilities, two identified enzymes from cattle rumen were subjected to cloning, expression, and characterization. To the best of our knowledge, this is the first time that a sequence-similarity based method is used alongside an ensemble machine learning model to identify and characterize cellulase enzymes from extensive metagenomic data. This study highlights the strength of machine learning techniques to predict enzymatic properties solely based on their sequence. MCIC is freely available as a python package and standalone toolkit for Windows and Linux-based operating systems with several functions to facilitate the screening and thermal and pH dependence prediction of cellulases.

Keywords: MCIC; cellulase; enzyme screening; machine learning; metagenomics; optimum pH; optimum temperature.