Making sense of disorder: Investigating intrinsically disordered proteins in the tardigrade proteome via a computational approach

Abstract

AbstractTardigrades, also known as water bears, are a phylum of microscopic metazoans with the extraordinary ability to endure environmental extremes. When threatened by suboptimal habitat conditions, these creatures enter a suspended animation-like state called cryptobiosis, in which metabolism is diminished, similar to hibernation. In this state, tardigrades benefit from enhanced extremotolerance, withstanding dehydration efficiently for years at a time in a type of cryptobiosis called anhydrobiosis. Recent studies have demonstrated that the tardigrade proteome is at the heart of cryptobiosis. Principally, intrinsically disordered proteins (IDPs) and tardigrade-specific intrinsically disordered proteins (TDPs) are known to help protect cell function in the absence of water. Importantly, TDPs have been successfully expressed in cells of other species experimentally, even protecting human tissue against stressin vitro. However, previous work has failed to address how to strategically identify TDPs in the tardigrade proteome holistically. The overarching purpose of this current study, consequently, was to generate a list of IDPs/TDPs associated with tardigrade cryptobiosis that are high-priority for further investigation. Firstly, a novel database containing 44,836 tardigrade proteins from 338 different species was constructed to consolidate and standardize publicly available data. Secondly, a support vector machine (SVM) was created to sort the newly constructed database entries on the binary basis of disorder (i.e., IDP versus non-IDP). Features of this model draw from disorder metrics and literature curation, correctly classifying 160 of the 171 training set proteins (~93.6%). Of the 5,415 putative IDPs/TDPs our SVM identified, we present 82 (30 having confident subclass prediction and 52 having experimental detection in previous studies). Subsequently, the role each protein might play in tardigrade resilience is discussed. By and large, this supervised machine learning classifier represents a promising new approach for identifying IDPs/TDPs, opening doors to harness the tardigrade’s remarkable faculties for biomaterial preservation, genetic engineering, astrobiological research, and ultimately, the benefit of humankind.