Pooled screening for antiproliferative inhibitors of protein-protein interactions

Abstract

Protein-protein interactions (PPIs) are emerging as a promising new class of drug targets. Here, we present a novel high-throughput approach to screen inhibitors of PPIs in cells. We designed a library of 50,000 human peptide binding motifs and used a pooled lentiviral system to express them intracellularly and screen for their effects on cell proliferation. We thereby identified inhibitors that drastically reduced the viability of a pancreas cancer line (RWP1) while leaving a control line virtually unaffected. We identified their target interactions computationally, and validated a subset in experiments. We also discovered their potential mechanisms of action including apoptosis and cell cycle arrest. Finally, we confirmed that synthetic lipopeptide versions of our inhibitors have similarly specific and dosage dependent effects on cancer cell growth. Our screen reveals new drug targets and peptide drug leads and it provides a rich dataset covering phenotypes for inhibition of thousands of interactions.

Rapid and accurate structure‐based therapeutic peptide design using GPU accelerated thermodynamic integration

“…The peptides were synthesized as previously described . Briefly, peptides were synthesized on a Liberty Blue Microwave peptide synthesizer (CEM Corporation) using Fmoc chemistry.…”

“…The peptides were synthesized as previously described. 5 (maximum concentration is 30 μM). The protein seems to be insoluble at higher concentrations, making it impossible to go higher than 30 μM.…”

“…Recently we reported a number of new drug targets and peptide drug leads discovered by screening a human peptide library of 50 000 peptides for effects on pancreatic cancer cell (RWP1) proliferation. 5 One of the peptides that significantly reduced cell viability mapped to a fragment of MUS81 at the interface with its interaction partner EME1. The MUS81-EME1 complex is an endonuclease that recognizes specific DNA structures at replication forks.…”

Myeloid leukemia factor 1 stabilizes tumor suppressor C/EBPα to prevent Trib1-driven acute myeloid leukemia

“…Alternatively, it is feasible to target particular functions of COP1 by inhibiting the specific interaction between COP1 and a limited set of adaptor proteins such as the Trib-COP1 complex. Most conventional anticancer molecular medicines target the catalytic activities of oncoproteins such as protein kinases and transcription factors; however, the recent development of small chemicals interfering with protein-protein interaction 37 may be applied to the regulation of COP1 activity; for example, inhibiting the interaction between COP1 and Trib1 by specifically blocking the COP1-binding site of Trib1. In this study, we found that MLF1 is a natural inhibitor of the COP1-Trib1 interaction, implying that the transcriptional enhancement of MLF1 expression and/or increased nuclear compartmentalization of MLF1 has potential as a novel strategy for cancer therapy.…”

Understanding Disease Biology and Informing the Management of Pancreas Cancer With Preclinical Model Systems

“…They are readily attainable, affordable, easy to work with and manipulate and can yield results quickly, making them suitable for genomic analyses and basic studies of signaling pathways and biochemical properties. For example, they have been used effectively to study KRAS signaling pathways, protein:protein interactions, identification of drug targets and other aspects of PDA biology 41–44 . Insights gleaned from such studies can provide a platform for hypothesis-driven experimentation in higher order models, such as patient-derived xenografts, organoids or genetically engineered mouse models (GEMM), but they should not be used alone or even as a primary justification for advancing strategies into the clinic.…”