Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets

In Silico Discovery of JMJD6 Inhibitors for Cancer Treatment

The 2-oxoglutarate (2OG)-dependent oxygenase JMJD6 is emerging as a potential anticancer target, but its inhibitors have not been reported so far. In this study, we reported an in silico protocol to discover JMJD6 inhibitors targeting the druggable 2OG-binding site. Following this protocol, one compound, which we named as WL12, was found to be able to inhibit JMJD6 enzymatic activity and JMJD6-dependent cell proliferation. To our best knowledge, this is the first case in drug discovery targeting JMJD6

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets

Mass Flow and Metabolic Pathway of Nonaeration Greywater Treatment in an Oxygenic Microalgal–Bacterial Biofilm

A symbiotic microalgal–bacterial biofilm can enable efficient carbon (C) and nitrogen (N) removal during aeration-free wastewater treatment. However, the contributions of microalgae and bacteria to C and N removal remain unexplored. Here, we developed a baffled oxygenic microalgal–bacterial biofilm reactor (MBBfR) for the nonaerated treatment of greywater. A hydraulic retention time (HRT) of 6 h gave the highest biomass concentration and biofilm thickness as well as the maximum removal of chemical oxygen demand (94.8%), linear alkylbenzenesulfonates (LAS, 99.7%), and total nitrogen (97.4%). An HRT of 4 h caused a decline in all of the performance metrics due to LAS biotoxicity. Most of C (92.6%) and N (95.7%) removals were ultimately associated with newly synthesized biomass, with only minor fractions transformed into CO2 (2.2%) and N2 (1.7%) on the function of multifarious-related enzymes in the symbiotic biofilm. Specifically, microalgae photosynthesis contributed to the removal of C and N at 75.3 and 79.0%, respectively, which accounted for 17.3% (C) and 16.7% (N) by bacteria assimilation. Oxygen produced by microalgae favored the efficient organics mineralization and CO2 supply by bacteria. The symbiotic biofilm system achieved stable and efficient removal of C and N during greywater treatment, thus providing a novel technology to achieve low-energy-input wastewater treatment, reuse, and resource recovery

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets

Kinase Inhibitor Scaffold Hopping with Deep Learning Approaches

The protein kinase family contains many promising drug targets. Many kinase inhibitors target the ATP-binding pocket, leading to approved drugs in past decades. Scaffold hopping is an effective approach for drug design. The kinase ATP-binding pocket is highly conserved, crossing the whole kinase family. This provides an opportunity to develop a scaffold hopping approach to explore diversified scaffolds among various kinase inhibitors. In this work, we report the SyntaLinker-Hybrid scheme for kinase inhibitor scaffold hopping. With this scheme, we replace molecular fragments bound at the conserved kinase hinge region with deep generative models. Thus, we are able to generate new kinase-inhibitor-like structures hybridizing the privileged fragments against the hinge region. We demonstrate that this scheme allows generation of kinase-inhibitor-like molecules with novel scaffolds, while retaining the binding features of existing kinase inhibitors. This work can be employed in lead identification against kinase targets

Virtual Screening with a Structure-Based Pharmacophore Model to Identify Small-Molecule Inhibitors of CARM1

CARM1 (coactivator-associated arginine methyltransferase 1), also known as PRMT4 (protein arginine N-methyltransferase 4), belongs to the protein arginine methyltransferase (PRMT) family, which has emerged as a potential anticancer drug target. To discover new CARM1 inhibitors, we performed virtual screening against the substrate-binding site in CARM1. Structure-based pharmacophore models, which were generated according to three druggable subpockets embedding critical residues for ligand binding, were applied for virtual screening. The importance of the solvent-exposed substrate-binding cavity was highlighted due to significant hydrophobicity. Aided by molecular docking, 15 compounds structurally distinct from known CARM1 inhibitors were selected to evaluate their inhibitory effects on CARM1 methyltransferase activity, which resulted in seven compounds exhibiting micromolar inhibition, with selectivity over other members in the PRMT protein family. Moreover, three of them exhibited potent antiproliferation activities in breast cancer cells. Particularly, compound NO.2 exhibited potent activity both in vitro and in cultured cells, which will serve as a leading hit for developing CARM1 inhibitors with improved efficacy. The virtual screening strategy in this study will be applicable for the discovery of substrate-competitive inhibitors targeting other members in the PRMT protein family

Peptide Inhibitor Targeting the Extraterminal Domain in BRD4 Potently Suppresses Breast Cancer Both <i>In Vitro</i> and <i>In Vivo</i>

BRD4 is associated with a variety of human diseases, including breast cancer. The crucial roles of amino-terminal bromodomains (BDs) of BRD4 in binding with acetylated histones to regulate oncogene expression make them promising drug targets. However, adverse events impede the development of the BD inhibitors. BRD4 adopts an extraterminal (ET) domain, which recruits proteins to drive oncogene expression. We discovered a peptide inhibitor PiET targeting the ET domain to disrupt BRD4/JMJD6 interaction, a protein complex critical in oncogene expression and breast cancer. The cell-permeable form of PiET, TAT-PiET, and PROTAC-modified TAT-PiET, TAT-PiET-PROTAC, potently inhibits the expression of BRD4/JMJD6 target genes and breast cancer cell growth. Combination therapy with TAT-PiET/TAT-PiET-PROTAC and JQ1, iJMJD6, or Fulvestrant exhibits synergistic effects. TAT-PiET or TAT-PiET-PROTAC treatment overcomes endocrine therapy resistance in ERα-positive breast cancer cells. Taken together, we demonstrated that targeting the ET domain is effective in suppressing breast cancer, providing a therapeutic avenue in the clinic