A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning.

Acidic transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that promote their dynamic behavior, "fuzzy" interactions, and target specificity. We screened a large set of random 30-mer peptides for AD function in yeast and trained a deep neural network (ADpred) on the AD-positive and -negative sequences. ADpred identifies known acidic ADs within transcription factors and accurately predicts the consequences of mutations. Our work reveals that strong acidic ADs contain multiple clusters of hydrophobic residues near acidic side chains, explaining why ADs often have a biased amino acid composition. ADs likely use a binding mechanism similar to avidity where a minimum number of weak dynamic interactions are required between activator and target to generate biologically relevant affinity and in vivo function. This mechanism explains the basis for fuzzy binding observed between acidic ADs and targets.

Behaviour of intrinsically disordered proteins in protein-protein complexes with an emphasis on fuzziness.

Intrinsically disordered proteins (IDPs) do not, by themselves, fold into a compact globular structure. They are extremely dynamic and flexible, and are typically involved in signalling and transduction of information through binding to other macromolecules. The reason for their existence may lie in their malleability, which enables them to bind several different partners with high specificity. In addition, their interactions with other macromolecules can be regulated by a variable amount of chemically diverse post-translational modifications. Four kinetically and energetically different types of complexes between an IDP and another macromolecule are reviewed: (1) simple two-state binding involving a single binding site, (2) avidity, (3) allovalency and (4) fuzzy binding; the last three involving more than one site. Finally, a qualitative definition of fuzzy binding is suggested, examples are provided, and its distinction to allovalency and avidity is highlighted and discussed.

Protein Binding: A Fuzzy Concept.

Our understanding of protein binding interactions has matured significantly over the last few years, largely as a result of trying to make sense of the binding interactions of intrinsically disordered proteins. Here, we bring together some disparate ideas that have largely developed independently, and show that they can be linked into a coherent picture that provides insight into quantitative aspects of protein interactions, in particular that transient protein interactions are often optimised for speed, rather than tight binding.

The MDMX Acidic Domain Uses Allovalency to Bind Both p53 and MDMX.

Autoinhibition of p53 binding to MDMX requires two short-linear motifs (SLiMs) containing adjacent tryptophan (WW) and tryptophan-phenylalanine (WF) residues. NMR spectroscopy was used to show the WW and WF motifs directly compete for the p53 binding site on MDMX and circular dichroism spectroscopy was used to show the WW motif becomes helical when it is bound to the p53 binding domain (p53BD) of MDMX. Binding studies using isothermal titration calorimetry showed the WW motif is a stronger inhibitor of p53 binding than the WF motif when they are both tethered to p53BD by the natural disordered linker. We also investigated how the WW and WF motifs interact with the DNA binding domain (DBD) of p53. Both motifs bind independently to similar sites on DBD that overlap the DNA binding site. Taken together our work defines a model for complex formation between MDMX and p53 where a pair of disordered SLiMs bind overlapping sites on both proteins.