Photoreactive Stapled BH3 Peptides to Dissect the BCL-2 Family Interactome

SummaryDefining protein interactions forms the basis for discovery of biological pathways, disease mechanisms, and opportunities for therapeutic intervention. To harness the robust binding affinity and selectivity of structured peptides for interactome discovery, we engineered photoreactive stapled BH3 peptide helices that covalently capture their physiologic BCL-2 family targets. The crosslinking α helices covalently trap both static and dynamic protein interactors, and enable rapid identification of interaction sites, providing a critical link between interactome discovery and targeted drug design

Targeted Disruption of the EZH2/EED Complex Inhibits EZH2-dependent Cancer

Enhancer of zeste homolog2 (EZH2) is the histone lysine N-methyltransferase component of the Polycomb repressive complex 2 (PRC2), which in conjunction with embryonic ectoderm development (EED) and suppressor of zeste 12 homolog (SUZ12), regulates cell lineage determination and homeostasis. Enzymatic hyperactivity has been linked to aberrant repression of tumor suppressor genes in diverse cancers. Here, we report the development of stabilized alpha-helix of EZH2 (SAH-EZH2) peptides that selectively inhibit H3 Lys27 trimethylation by dose-responsively disrupting the EZH2/EED complex and reducing EZH2 protein levels, a mechanism distinct from that reported for small molecule EZH2 inhibitors targeting the enzyme catalytic domain. MLL-AF9 leukemia cells, which are dependent on PRC2, undergo growth arrest and monocyte/macrophage differentiation upon treatment with SAH-EZH2, consistent with observed changes in expression of PRC2-regulated, lineage-specific marker genes. Thus, by dissociating the EZH2/EED complex, we pharmacologically modulate an epigenetic “writer” and suppress PRC2-dependent cancer cell growth

Stapled HIV-1 Peptides Recapitulate Antigenic Structures and Engage Broadly Neutralizing Antibodies

Hydrocarbon stapling can restore bioactive, α-helical structure to natural peptides, yielding research tools and prototype therapeutics to dissect and target protein interactions. Here, we explore the capacity of peptide stapling to generate high fidelity, protease-resistant mimics of antigenic structures for vaccine development. HIV-1 has been refractory to vaccine technologies thus far, although select human antibodies can broadly neutralize HIV-1 by targeting sequences of the gp41 juxtamembrane fusion apparatus. To develop candidate HIV-1 immunogens, we generated and characterized stabilized α-helices of the membrane proximal external region (SAH-MPER) of gp41. SAH-MPER peptides were remarkably protease-resistant and bound to the broadly neutralizing 4E10 and 10E8 antibodies with high affinity, recapitulating the structure of the MPER epitope when differentially engaged by the two anti-HIV Fabs. Thus, stapled peptides may provide a new opportunity to develop chemically-stabilized antigens for vaccination

The MCL-1 BH3 helix is an exclusive MCL-1 inhibitor and apoptosis sensitizer

available in PMC 2011 February 3.MCL-1 has emerged as a major oncogenic and chemoresistance factor. A screen of stapled peptide helices identified the MCL-1 BH3 domain as selectively inhibiting MCL-1 among the related anti-apoptotic Bcl-2 family members, providing insights into the molecular determinants of binding specificity and a new approach for sensitizing cancer cells to apoptosis.National Institutes of Health (U.S.) (NIH award 5RO1GM084181)National Institutes of Health (U.S.) (NIH grant 5P01CA92625)National Institutes of Health (U.S.) (Ruth L. Kirschstein National Research Service Award 1F31CA144566)Burroughs Wellcome Fund (Career Award

Preclinical models for prediction of immunotherapy outcomes and immune evasion mechanisms in genetically heterogeneous multiple myeloma

The historical lack of preclinical models reflecting the genetic heterogeneity of multiple myeloma (MM) hampers the advance of therapeutic discoveries. To circumvent this limitation, we screened mice engineered to carry eight MM lesions (NF-κB, KRAS, MYC, TP53, BCL2, cyclin D1, MMSET/NSD2 and c-MAF) combinatorially activated in B lymphocytes following T cell-driven immunization. Fifteen genetically diverse models developed bone marrow (BM) tumors fulfilling MM pathogenesis. Integrative analyses of ∼500 mice and ∼1,000 patients revealed a common MAPK-MYC genetic pathway that accelerated time to progression from precursor states across genetically heterogeneous MM. MYC-dependent time to progression conditioned immune evasion mechanisms that remodeled the BM microenvironment differently. Rapid MYC-driven progressors exhibited a high number of activated/exhausted CD8+ T cells with reduced immunosuppressive regulatory T (Treg) cells, while late MYC acquisition in slow progressors was associated with lower CD8+ T cell infiltration and more abundant Treg cells. Single-cell transcriptomics and functional assays defined a high ratio of CD8+ T cells versus Treg cells as a predictor of response to immune checkpoint blockade (ICB). In clinical series, high CD8+ T/Treg cell ratios underlie early progression in untreated smoldering MM, and correlated with early relapse in newly diagnosed patients with MM under Len/Dex therapy. In ICB-refractory MM models, increasing CD8+ T cell cytotoxicity or depleting Treg cells reversed immunotherapy resistance and yielded prolonged MM control. Our experimental models enable the correlation of MM genetic and immunological traits with preclinical therapy responses, which may inform the next-generation immunotherapy trials

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery