An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

Cystic fibrosis (CF) is the most common lethal inherited disease among Caucasians in North America and a significant portion of Europe. The disease arises from one of many mutations in the gene encoding the cystic fibrosis transmembrane conductance regulator, or CFTR. The most common disease-associated allele, F508del, along with several other mutations affect the folding, transport, and stability of CFTR as it transits from the endoplasmic reticulum (ER) to the plasma membrane, where it functions primarily as a chloride channel. Early data demonstrated that F508del CFTR is selected for ER associated degradation (ERAD), a pathway in which misfolded proteins are recognized by ER-associated molecular chaperones, ubiquitinated, and delivered to the proteasome for degradation. Later studies showed that F508del CFTR that is rescued from ERAD and folds can alternatively be selected for enhanced endocytosis and lysosomal degradation. A number of other disease-causing mutations in CFTR also undergo these events. Fortunately, pharmacological modulators of CFTR biogenesis can repair CFTR, permitting its folding, escape from ERAD, and function at the cell surface. In this article, we review the many cellular checkpoints that monitor CFTR biogenesis, discuss the emergence of effective treatments for CF, and highlight future areas of research on the proteostatic control of CFTR.

C-terminus of Hsc/p70-Interacting Protein (CHIP) is a homodimeric E3 ubiquitin ligase. Each CHIP monomer consists of a tetratricopeptide-repeat (TPR), helix-turn-helix (HH), and U-box domain. In contrast to nearly all homodimeric proteins, CHIP is asymmetric. To uncover the origins of asymmetry, we performed molecular dynamics simulations of dimer assembly. We determined that a CHIP monomer is most stable when the HH domain has an extended helix that supports intra-monomer TPR-U-box interaction, blocking the E2-binding surface of the U-box. We also discovered that monomers first dimerize symmetrically through their HH domains, which then triggers U-box dimerization. This brings the extended helices into close proximity, including a repulsive stretch of positively charged residues. Unable to smoothly unwind, this conflict bends the helices until the helix of one protomer breaks to relieve the repulsion. The abrupt snapping of the helix forces the C-terminal residues of the other protomer to disrupt that protomer's TPR-U-box tight binding interface, swiftly exposing and activating one of the E2 binding sites. Mutagenesis and biochemical experiments confirm that C-terminal residues are necessary both to maintain CHIP stability and function. This novel mechanism indicates how a ubiquitin ligase maintains an inactive monomeric form that rapidly activates only after asymmetric assembly.Generally, the lowest energy state of protein assembly is symmetrical, whereas asymmetry is associated with energy frustration and structural instability 1 . However, symmetry is not evolutionarily constrained, and many oligomeric enzymes are known to be asymmetric with only half-of-sites active 2-5 . In the majority of such oligomers, asymmetry results from conformational changes triggered by ligand binding 2,6,7 . Another mechanism to fold and activate asymmetric dimers requires that one of the ligand-binding sites is deformed 3,6,7 . However, very few of the vast number of known homo-multimeric proteins assemble into asymmetric structures 6 . Among the exceptions are the C-terminus of Hsc/p70-Interacting Protein (CHIP) 8 , an E3 ubiquitin ligase that associates with cyoplasmic Hsp70 and Hsp90 chaperones, and Hikeshi 9 , a nuclear import protein that also binds Hsp70. Unveiling the mechanism of symmetry breaking in homo-oligomers will shed light on new principles of folding and assembly for this important class of proteins.The CHIP homodimer consists of three domains: tetratricopeptide repeat (TPR), helix-turn-helix (HH), and U-box 8 (Fig. 1). CHIP targets misfolded, chaperone-bound substrates for proteasomal degradation by transferring ubiquitin from a compatible E2 ubiquitin conjugating-enzyme, which associates with the U-box domain, to a lysine residue on the target protein 10, 11 . The crystal structure of murine CHIP (CHIP, PDB: 2C2L), which differs from human CHIP by one residue at the N-terminus, showcases an asymmetric homodimer in which only one U-box domain has an accessible E2-binding surface. The E2 binding site/U-box in t...

SummarySystems biology seeks to understand how normal and disease protein networks respond when specific interactions are disrupted. A first step towards this goal is identifying the molecular target(s) of bioactive compounds. Here, we hypothesize that inhibitory drugs should produce network-level effects similar to silencing the inhibited gene and show that drug-protein interactions are encoded in mRNA expression profile correlations. We use machine learning to classify correlations between drug-and knockdown-induced expression signatures and enrich our predictions through a structure-based screen.Interactions manifest both as direct correlations between drug and target knockdowns, and as indirect correlations with up/downstream knockdowns. Cross-validation on 152 FDA-approved drugs and 3104 potential targets achieved top 10/100 prediction accuracies of 26/41%. We apply our method to 1680 bioactive compounds and experimentally validate five previously unknown interactions. Our pipeline can accelerate drug discovery by matching existing compounds to new therapeutic targets while informing on network and multi-target effects.

Ubiquitin (Ub) plays critical roles in myriad protein degradation and signaling networks in the cell. We report herein Ub mimetics based on backbones that blend natural and artificial amino acid units. The variants were prepared by a modular route based on native chemical ligation. Biological assays show that some are enzymatically polymerized onto protein substrates, and that the resulting Ub tags are recognized for downstream pathways. These results advance the size and complexity of folded proteins mimicked by artificial backbones and expand the functional scope of such agents.