Functional characterization and comparative analysis of gene repression-mediating domains interacting with yeast pleiotropic corepressors Sin3, Cyc8 and Tup1

Transcriptional corepressors Sin3, Cyc8 and Tup1 are important for downregulation of gene expression by recruiting various histone deacetylases once they gain access to defined genomic locations by interaction with pathway-specific repressor proteins. In this work we systematically investigated whether 17 yeast repressor proteins (Cti6, Dal80, Fkh1, Gal80, Mig1, Mot3, Nrg1, Opi1, Rdr1, Rox1, Sko1, Ume6, Ure2, Xbp1, Yhp1, Yox1 and Whi5) representing several unrelated regulatory pathways are able to bind to Sin3, Cyc8 and Tup1. Our results show that paired amphipathic helices 1 and 2 (PAH1 and PAH2) of Sin3 are functionally redundant for some regulatory pathways. WD40 domains of Tup1 proved to be sufficient for interaction with repressor proteins. Using length variants of selected repressors, we mapped corepressor interaction domains (CIDs) in vitro and assayed gene repression in vivo. Systematic comparison of CID minimal sequences allowed us to define several related positional patterns of hydrophobic amino acids some of which could be confirmed as functionally supported by site-directed mutagenesis. Although structural predictions indicated that certain CIDs may be α-helical, most repression domains appear to be randomly structured and must be considered as intrinsically disordered regions (IDR) adopting a defined conformation only by interaction with a corepressor.

Simplifying the Design of Protein-Peptide Interaction Specificity with Sequence-Based Representations of Atomistic Models

Computationally designed peptides targeting protein-protein interaction interfaces are of great interest as reagents for biological research and potential therapeutics. In recent years, it has been shown that detailed structure-based calculations can, in favorable cases, describe relevant determinants of protein-peptide recognition. Yet, despite large increases in available computing power, such accurate modeling of the binding reaction is still largely outside the realm of protein design. The chief limitation is in the large sequence spaces generally involved in protein design problems, such that it is typically infeasible to apply expensive modeling techniques to score each sequence. Toward addressing this issue, we have previously shown that by explicitly evaluating the scores of a relatively small number of sequences, it is possible to synthesize a direct mapping between sequences and scores, such that the entire sequence space can be analyzed extremely rapidly. The associated method, called Cluster Expansion, has been used in a number of studies to design binding affinity and specificity. In this chapter, we provide instructions and guidance for applying this technique in the context of designing protein-peptide interactions to enable the use of more detailed and expensive scoring approaches than is typically possible.

Design of Specific Peptide-Protein Recognition

Selective targeting of protein-protein interactions in the cell is of great interest in biological research. Computational structure-based design of peptides to bind protein interaction interfaces could provide a potential means of generating such reagents. However, to avoid perturbing off-target interactions, methods that explicitly account for interaction specificity are needed. Further, as peptides often retain considerable flexibility upon association, their binding reaction is computationally demanding to model-a stark limitation for structure-based design. Here we present a protocol for designing peptides that selectively target a given peptide-binding domain, relative to a pre-specified set of possibly related domains. We recently used the method to design peptides that discriminate with high selectivity between two closely related PDZ domains. The framework accounts for the flexibility of the peptide in the binding site, but is efficient enough to quickly analyze trade-offs between affinity and selectivity, enabling the identification of optimal peptides.