Substrate Activity Screening with Kinases: Discovery of Small‐Molecule Substrate‐Competitive c‐Src Inhibitors

Substrate‐competitive kinase inhibitors represent a promising class of kinase inhibitors, however, there is no methodology to selectively identify this type of inhibitor. Substrate activity screening was applied to tyrosine kinases. By using this methodology, the first small‐molecule substrates for any protein kinase were discovered, as well as the first substrate‐competitive inhibitors of c‐Src with activity in both biochemical and cellular assays. Characterization of the lead inhibitor demonstrates that substrate‐competitive kinase inhibitors possess unique properties, including cellular efficacy that matches biochemical potency and synergy with ATP‐competitive inhibitors. SASsy inhibitors : Small‐molecule substrate‐competitive inhibitors of the tyrosine kinase c‐Src were discovered through the application of substrate activity screening (SAS). Characterization of the lead inhibitor demonstrates that substrate‐competitive kinase inhibitors possess unique properties, including cellular efficacy that matches biochemical potency and synergy with ATP‐competitive inhibitors.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107499/1/7010_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/107499/2/anie_201311096_sm_miscellaneous_information.pd

IL-4/IL-13 Stimulated Macrophages Enhance Breast Cancer Invasion Via Rho-GTPase Regulation of Synergistic VEGF/CCL-18 Signaling

Tumor associated macrophages (TAMs) are increasingly recognized as major contributors to the metastatic progression of breast cancer and enriched levels of TAMs often correlate with poor prognosis. Despite our current advances it remains unclear which subset of M2-like macrophages have the highest capacity to enhance the metastatic program and which mechanisms regulate this process. Effective targeting of macrophages that aid cancer progression requires knowledge of the specific mechanisms underlying their pro-metastatic actions, as to avoid the anticipated toxicities from generalized targeting of macrophages. To this end, we set out to understand the relationship between the regulation of tumor secretions by Rho-GTPases, which were previously demonstrated to affect them, macrophage differentiation, and the converse influence of macrophages on cancer cell phenotype. Our data show that IL-4/IL-13 in vitro differentiated M2a macrophages significantly increase migratory and invasive potential of breast cancer cells at a greater rate than M2b or M2c macrophages. Our previous work demonstrated that the Rho-GTPases are potent regulators of macrophage-induced migratory responses; therefore, we examined M2a-mediated responses in RhoA or RhoC knockout breast cancer cell models. We find that both RhoA and RhoC regulate migration and invasion in MDA-MB-231 and SUM-149 cells following stimulation with M2a conditioned media. Secretome analysis of M2a conditioned media reveals high levels of vascular endothelial growth factor (VEGF) and chemokine (C-C motif) ligand 18 (CCL-18). Results from our functional assays reveal that M2a TAMs synergistically utilize VEGF and CCL-18 to promote migratory and invasive responses. Lastly, we show that pretreatment with ROCK inhibitors Y-276332 or GSK42986A attenuated VEGF/CCL-18 and M2a-induced migration and invasion. These results support Rho-GTPase signaling regulates downstream responses induced by TAMs, offering a novel approach for the prevention of breast cancer metastasis by anti-RhoA/C therapies

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

Small Molecule Substrate Phosphorylation Site Inhibitors of Protein Kinases: Approaches and Challenges

Protein kinases are important mediators of cellular communication and attractive drug targets for many diseases. Although success has been achieved with developing ATP-competitive kinase inhibitors, the disadvantages of ATP-competitive inhibitors have led to increased interest in targeting sites outside of the ATP binding pocket. Kinase inhibitors with substrate-competitive, ATP-noncompetitive binding modes are promising due to the possibility of increased selectivity and better agreement between biochemical and in vitro potency. However, the difficulty of identifying these types of inhibitors has resulted in significantly fewer small molecule substrate phosphorylation site inhibitors being reported compared to ATP-competitive inhibitors. This review surveys reported substrate phosphorylation site inhibitors and methods that can be applied to the discovery of such inhibitors, including a discussion of the challenges inherent to these screening methods

Bivalent Inhibitors of c‑Src Tyrosine Kinase That Bind a Regulatory Domain

We have developed a general methodology to produce bivalent kinase inhibitors for c-Src that interact with the SH2 and ATP binding pockets. Our approach led to a highly selective bivalent inhibitor of c-Src. We demonstrate impressive selectivity for c-Src over homologous kinases. Exploration of the unexpected high level of selectivity yielded insight into the inherent flexibility of homologous kinases. Finally, we demonstrate that our methodology is modular and both the ATP-competitive fragment and conjugation chemistry can be swapped

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity.

Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ρ = 0.88-0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ρ = 0.11-0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman's ρ from a range of 0.43-0.62 to 0.30-0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference