Intrinsic Disorder in Tetratricopeptide Repeat Proteins

Abstract

Among the realm of repeat containing proteins that commonly serve as “scaffolds” promoting protein-protein interactions, there is a family of proteins containing between 2 and 20 tetratricopeptide repeats (TPRs), which are functional motifs consisting of 34 amino acids. The most distinguishing feature of TPR domains is their ability to stack continuously one upon the other, with these stacked repeats being able to affect interaction with binding partners either sequentially or in combination. It is known that many repeat-containing proteins are characterized by high levels of intrinsic disorder, and that many protein tandem repeats can be intrinsically disordered. Furthermore, it seems that TPR-containing proteins share many characteristics with hybrid proteins containing ordered domains and intrinsically disordered protein regions. However, there has not been a systematic analysis of the intrinsic disorder status of TPR proteins. To fill this gap, we analyzed 166 human TPR proteins to determine the degree to which proteins containing TPR motifs are affected by intrinsic disorder. Our analysis revealed that these proteins are characterized by different levels of intrinsic disorder and contain functional disordered regions that are utilized for protein-protein interactions and often serve as targets of various posttranslational modifications.

Intrinsic Disorder in Human Proteins Encoded by Core Duplicon Gene Families

“…This PPI network with an average node degree of 13.8, average local clustering coefficient of 0.871, expected number of edges of 49, and PPI enrichment p -value of <10 –16 includes 34 proteins/genes connected by 235 interactions. The presence of high levels of functional disorder in human RGPD4 is in line with the results of our global analysis of a set of human TPR proteins, where it was shown that these TPR proteins contain different levels of intrinsic disorder and possess functional IDPRs that are utilized in the PPIs and often serve as targets of various PTMs.…”

Rapid prediction and analysis of protein intrinsic disorder

“…However, with a few exceptions, 32,33 these servers are not designed with genome‐scale structural bioinformatics or comparative genomics/proteomics in mind. We previously developed a web‐crawler style disorder predictor for internal usage that was implemented into a tool we refer to as Disorder Spider (DiSpi) in our previous studies, see for example, refs 34–39 . DiSpi works by first aggregating disorder profiles from six well‐known disorder predictors: PONDR VLXT, 40 PONDR VL3, 41 PONDR VLS2B, 42 PONDR‐FIT, 43 IUPred‐Short, and IUPred‐Long 44 .…”

“…We previously developed a web-crawler style disorder predictor for internal usage that was implemented into a tool we refer to as Disorder Spider (DiSpi) in our previous studies, see for example, refs. [34][35][36][37][38][39] DiSpi works by first aggregating disorder profiles from six well-known disorder predictors: PONDR VLXT, 40 PONDR VL3, 41 PONDR VLS2B, 42 PONDR-FIT, 43 IUPred-Short, and IUPred-Long. 44 Then, a mean disorder profile (MDP) is computed along with the standard error.…”

Chaotic aging: Intrinsically disordered proteins in aging-related processes

“…The predicted percent of intrinsically disordered residues (PPIDR) is defined as the number of disordered residues divided by the length of the sequence. Based on their levels of intrinsic disorder, proteins are classified as highly disordered (MDS ≥ 0.5, PPIDR ≥ 30%), moderately disordered (0.25 ≤ MDS < 0.5, 10% ≤ PPIDR < 30%) and ordered (MDS < 0.25, PPIDR < 10%) [93]. The MDS is not directly related to PPIDR (in particular, at 100% PPIDR value of MDS can be anything in the range from 0.5 to 1); therefore, these two methods of protein intrinsic disorder evaluation should be analyzed using correlation.…”