Understanding the protein sequence and structural adaptation in extremophilic organisms through machine learning techniques

Abstract

Organisms thriving at extremes of environmental surroundings are called extremophiles. Depending upon the different extreme surrounding conditions, the extremophiles are further classified into different classes, for example, thermophiles and psychrophiles based upon extreme temperature conditions. Extremophilic proteins have paved their way into the workbench of many laboratories for possible industrial applications. Molecular analysis of extremophilic protein sequences can provide a wealth of information about their successful structural and functional adaptations to such extremes of environment. Understanding the protein sequence adaptation parameters will facilitate in providing a set of rules to create the rational design of extremophilic proteins as well as in understanding the conversion processes of a mesophilic protein into some other form of extremophilic protein. Advent of affordable next-generation sequencing technologies resulted in swift increase in the amount of fully sequenced genomes of extremophilic organisms, giving rise to a larger datasets that are sufficient enough to be mined to increase the extremophilic knowledgebase. Supervised machine learning algorithms combined with other computational statistical methods are useful in more accurate prediction of extremophilic proteins. In this chapter, we have described the roadmap for developing supervised machine learning–based prediction models followed by statistical analysis for inferring the molecular basis of extremophilic adaptation. © 2020 Elsevier Inc. All rights reserved.