High‐throughput discovery of functional disordered regions: investigation of transactivation domains

Abstract

Over 40% of proteins in any eukaryotic genome encode intrinsically disordered regions (IDRs) that do not adopt defined tertiary structures. Certain IDRs perform critical functions, but discovering them is non‐trivial as the biological context determines their function. We present IDR‐Screen, a framework to discover functional IDRs in a high‐throughput manner by simultaneously assaying large numbers of DNA sequences that code for short disordered sequences. Functionality‐conferring patterns in their protein sequence are inferred through statistical learning. Using yeast HSF1 transcription factor‐based assay, we discovered IDRs that function as transactivation domains (TADs) by screening a random sequence library and a designed library consisting of variants of 13 diverse TADs. Using machine learning, we find that segments devoid of positively charged residues but with redundant short sequence patterns of negatively charged and aromatic residues are a generic feature for TAD functionality. We anticipate that investigating defined sequence libraries using IDR‐Screen for specific functions can facilitate discovering novel and functional regions of the disordered proteome as well as understand the impact of natural and disease variants in disordered segments.

Proteome-wide signatures of function in highly diverged intrinsically disordered regions

“…For example, we found IDRs that show constraint on glycine and arginine content, but these may reflect the real constraint on planar-pi interactions and are not fully captured by either of these features. In the future, we could exhaustively search for protein sequence features that best explain the evolutionary patterns as was done for features of activation domains that explain reporter activity (Ravarani et al, 2018).…”

From prioritisation to understanding: mechanistic predictions of variant effects

“…In this context, experimental developments in large‐scale mutagenesis (Staller et al , ), phage display (Davey et al , ; Sundell et al , ), ligand foot‐printing and mass spectrometry (preprint: Parker et al , ) are beginning to provide insights into functional elements within disordered regions. Alongside these experimental approaches, hybrid experimental–computational approaches such as IDR‐Screen that exploit machine learning (Ravarani et al , ) will allow for new ways of uncovering mechanistic interpretation of mutations. Lastly, a more principled construction of multiple sequence alignments from which conservation is inferred can also improve predictions.…”

A high-throughput screen for transcription activation domains reveals their sequence characteristics and permits reliable prediction by deep learning

“…For example, while AD sequences can be moderately conserved in closely related orthologs , no common sequence motif has been found when comparing ADs from different transcription factors. In addition, small-scale screens for ADs using random sequences of varying length fused to a DNA binding domain found that ~1% of these sequences encoded AD function (Abedi et al, 2001;Erkine et al, 2002;Ma and Ptashne, 1987a;Ravarani et al, 2018;Ruden et al, 1991). Finally, prior work analyzing natural and synthetic ADs have found several sequence features that correlate with AD function including intrinsic disorder, the presence of acidic, hydrophobic, and aromatic residues, low sequence complexity, net negative charge (or lack of positive charge) and, in some cases, alpha helix propensity (Brzovic et al, 2011;Dyson and Wright, 2016;Erkina et al, 2016;Ma and Ptashne, 1987a;Ravarani et al, 2018).…”