Histone methyltransferases are involved in various biological functions, and these methylation regulating enzymes' abnormal expression or activity has been noted in several human cancers. Within this context, SET domain-containing (lysine methyltransferase) 7 (SET7, also called KMT7, SETD7, SET9) is of increasing significance due to its diverse roles in biological functions and diseases, such as diabetes, cancers, alopecia areata, atherosclerotic vascular disease, HIV, and HCV. In this study, DC-S100, which was discovered by pharmacophore- and docking-based virtual screening, was identified as the hit compound of SET7 inhibitor. Structure-activity relationship (SAR) analysis was performed on analogs of DC-S100 and according to the putative binding mode of DC-S100, structure modifications were made to improve its activity. Of note, compounds DC-S238 and DC-S239, with IC50 values of 4.88 and 4.59 μM, respectively, displayed selectivity for DNMT1, DOT1L, EZH2, NSD1, SETD8, and G9a. Taken together, DC-S238 and DC-S239 can serve as leads for further investigation as SET7 inhibitors and the chemical toolkits for functional biology studies of SET7.

Elucidating the conformational heterogeneity of proteins is essential for understanding protein function and developing exogenous ligands. With the rapid development of experimental and computational methods, it is of great interest to integrate these approaches to illuminate the conformational landscapes of target proteins. SETD8 is a protein lysine methyltransferase (PKMT), which functions in vivo via the methylation of histone and nonhistone targets. Utilizing covalent inhibitors and depleting native ligands to trap hidden conformational states, we obtained diverse X-ray structures of SETD8. These structures were used to seed distributed atomistic molecular dynamics simulations that generated a total of six milliseconds of trajectory data. Markov state models, built via an automated machine learning approach and corroborated experimentally, reveal how slow conformational motions and conformational states are relevant to catalysis. These findings provide molecular insight on enzymatic catalysis and allosteric mechanisms of a PKMT via its detailed conformational landscape.

Fibroblast growth factor receptor (FGFR) represents an attractive oncology target for cancer therapy in view of its critical role in promoting cancer formation and progression, as well as causing resistance to approved therapies. In this article, we describe the identification of the potent pan-FGFR inhibitor (R)-21c (FGFR1-4 IC50 values of 0.9, 2.0, 2.0, and 6.1 nM, respectively). Compound (R)-21c exhibited excellent in vitro inhibitory activity against a panel of FGFR-amplified cell lines. Western blot analysis demonstrated that (R)-21c suppressed FGF/FGFR and downstream signaling pathways at nanomolar concentrations. Moreover, (R)-21c provided nearly complete inhibition of tumor growth (96.9% TGI) in NCI-H1581 (FGFR1-amplified) xenograft mice model at the dose of 10 mg/kg/qd via oral administration.

The highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limited chemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical log BB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB−) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB and 10.6084/m9.figshare.15634230.v3 (version 3). We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB− compounds.

Histone methyltransferases are involved in many important biological processes, and abnormalities in these enzymes are associated with tumorigenesis and progression. Disruptor of telomeric silencing 1-like (DOT1L), a key hub in histone lysine methyltransferases, has been reported to play an important role in the processes of mixed-lineage leukemia (MLL)-rearranged leukemias and validated to be a potential therapeutic target. In this study, we identified a novel DOT1L inhibitor, DC_L115 (CAS no. 1163729-79-0), by combining structure-based virtual screening with biochemical analyses. This potent inhibitor DC_L115 shows high inhibitory activity toward DOT1L (IC50 = 1.5 μM). Through a process of surface plasmon resonance-based binding assays, DC_L115 was founded to bind to DOT1L with a binding affinity of 0.6 μM in vitro. Moreover, this compound selectively inhibits MLL-rearranged cell proliferation with an IC50 value of 37.1 μM. We further predicted the binding modes of DC_L115 through molecular docking analysis and found that the inhibitor competitively occupies the binding site of S-adenosylmethionine. Overall, this study demonstrates the development of potent DOT1L inhibitors with novel scaffolds.

Modern drug design aims to discover novel lead compounds with attractable chemical profiles to enable further exploration of the intersection of chemical space and biological space. Identification of small molecules with good ligand efficiency, high activity, and selectivity is crucial toward developing effective and safe drugs. However, the intersection is one of the most challenging tasks in the pharmaceutical industry, as chemical space is almost infinity and continuous, whereas the biological space is very limited and discrete. This bottleneck potentially limits the discovery of molecules with desirable properties for lead optimization. Herein, we present a new direction leveraging posttranslational modification (PTM) protein isoforms target space to inspire drug design termed as “Post‐translational Modification Inspired Drug Design (PTMI‐DD).” PTMI‐DD aims to extend the intersections of chemical space and biological space. We further rationalized and highlighted the importance of PTM protein isoforms and their roles in various diseases and biological functions. We then laid out a few directions to elaborate the PTMI‐DD in drug design including discovering covalent binding inhibitors mimicking PTMs, targeting PTM protein isoforms with distinctive binding sites from that of wild‐type counterpart, targeting protein‐protein interactions involving PTMs, and hijacking protein degeneration by ubiquitination for PTM protein isoforms. These directions will lead to a significant expansion of the biological space and/or increase the tractability of compounds, primarily due to precisely targeting PTM protein isoforms or complexes which are highly relevant to biological functions. Importantly, this new avenue will further enrich the personalized treatment opportunity through precision medicine targeting PTM isoforms.

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Post-translational modification (PTM) on protein plays important roles in the regulation of cellular function and disease pathogenesis. The systematic analysis of PTM dynamics presents great opportunities to enlarge the target space by PTM allosteric regulation. Here, we presented a framework by integrating the sequence, structural topology, and particular dynamics features to characterize the functional context and druggabilities of PTMs in the well-known kinase family. The machine learning models with these biophysical features could successfully predict PTMs. On the other hand, PTMs were identified to be significantly enriched in the reported allosteric pockets and the allosteric potential of PTM pockets were thus proposed through these biophysical features. In the end, the covalent inhibitor DC-Srci-6668 targeting the PTM pocket in c-Src kinase was identified, which inhibited the phosphorylation and locked c-Src in the inactive state. Our findings represent a crucial step toward PTM-inspired drug design in the kinase family.