Inhibition of M. tuberculosis and human ATP synthase by BDQ and TBAJ-587.

Bedaquiline (BDQ), a first-in-class diarylquinoline anti-tuberculosis drug, and its analogue, TBAJ-587, prevent the growth and proliferation of Mycobacterium tuberculosis by inhibiting ATP synthase1,2. However, BDQ also inhibits human ATP synthase3. At present, how these compounds interact with either M. tuberculosis ATP synthase or human ATP synthase is unclear. Here we present cryogenic electron microscopy structures of M. tuberculosis ATP synthase with and without BDQ and TBAJ-587 bound, and human ATP synthase bound to BDQ. The two inhibitors interact with subunit a and the c-ring at the leading site, c-only sites and lagging site in M. tuberculosis ATP synthase, showing that BDQ and TBAJ-587 have similar modes of action. The quinolinyl and dimethylamino units of the compounds make extensive contacts with the protein. The structure of human ATP synthase in complex with BDQ reveals that the BDQ-binding site is similar to that observed for the leading site in M. tuberculosis ATP synthase, and that the quinolinyl unit also interacts extensively with the human enzyme. This study will improve researchers' understanding of the similarities and differences between human ATP synthase and M. tuberculosis ATP synthase in terms of the mode of BDQ binding, and will allow the rational design of novel diarylquinolines as anti-tuberculosis drugs.

Structure of the human respiratory complex II.

Human complex II is a key protein complex that links two essential energy-producing processes: the tricarboxylic acid cycle and oxidative phosphorylation. Deficiencies due to mutagenesis have been shown to cause mitochondrial disease and some types of cancers. However, the structure of this complex is yet to be resolved, hindering a comprehensive understanding of the functional aspects of this molecular machine. Here, we have determined the structure of human complex II in the presence of ubiquinone at 2.86 Å resolution by cryoelectron microscopy, showing it comprises two water-soluble subunits, SDHA and SDHB, and two membrane-spanning subunits, SDHC and SDHD. This structure allows us to propose a route for electron transfer. In addition, clinically relevant mutations are mapped onto the structure. This mapping provides a molecular understanding to explain why these variants have the potential to produce disease.

3D based generative PROTAC linker design with reinforcement learning.

Proteolysis targeting chimera (PROTAC), has emerged as an effective modality to selectively degrade disease-related proteins by harnessing the ubiquitin-proteasome system. Due to PROTACs' hetero-bifunctional characteristics, in which a linker joins a warhead binding to a protein of interest (POI), conferring specificity and a E3-ligand binding to an E3 ubiquitin ligase, this could trigger the ubiquitination and transportation of POI to the proteasome, followed by degradation. The rational PROTAC linker design is challenging due to its relatively large molecular weight and the complexity of maintaining the binding mode of warhead and E3-ligand in the binding pockets of counterpart. Conventional linker generation method can only generate linkers in either 1D SMILES or 2D graph, without taking into account the information of ternary structures. Here we propose a novel 3D linker generative model PROTAC-INVENT which can not only generate SMILES of PROTAC but also its 3D putative binding conformation coupled with the target protein and the E3 ligase. The model is trained jointly with the RL approach to bias the generation of PROTAC structures toward pre-defined 2D and 3D based properties. Examples were provided to demonstrate the utility of the model for generating reasonable 3D conformation of PROTACs. On the other hand, our results show that the associated workflow for 3D PROTAC conformation generation can also be used as an efficient docking protocol for PROTACs.

A specific JMJD6 inhibitor potently suppresses multiple types of cancers both in vitro and in vivo.

Jumonji C-domain-containing protein 6 (JMJD6), an iron (Fe2+) and α-ketoglutarate (α-KG)-dependent oxygenase, is expressed at high levels, correlated with poor prognosis, and considered as a therapeutic target in multiple cancer types. However, specific JMJD6 inhibitors that are potent in suppressing tumorigenesis have not been reported so far. We herein report that iJMJD6, a specific small-molecule inhibitor of JMJD6 with favorable physiochemical properties, inhibits the enzymatic activity of JMJD6 protein both in vitro and in cultured cells. iJMJD6 is effective in suppressing cell proliferation, migration, and invasion in multiple types of cancer cells in a JMJD6-dependent manner, while it exhibits minimal toxicity in normal cells. Mechanistically, iJMJD6 represses the expression of oncogenes, including Myc and CCND1, in accordance with JMJD6 function in promoting the transcription of these genes. iJMJD6 exhibits suitable pharmacokinetic properties and suppresses tumor growth in multiple cancer cell line- and patient-derived xenograft models safely. Furthermore, combination therapy with iJMJD6 and BET protein inhibitor (BETi) JQ1 or estrogen receptor antagonist fulvestrant exhibits synergistic effects in suppressing tumor growth. Taken together, we demonstrate that inhibition of JMJD6 enzymatic activity by using iJMJD6 is effective in suppressing oncogene expression and cancer development, providing a therapeutic avenue for treating cancers that are dependent on JMJD6 in the clinic.

An extensive benchmark study on biomedical text generation and mining with ChatGPT.

MOTIVATION: In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models (LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5 and GPT-4, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP-related tasks and benchmarks and got excellent results. With exciting performance on daily chat, researchers began to explore the capacity of ChatGPT on expertise that requires professional education for human and we are interested in the biomedical domain. RESULTS: To evaluate the performance of ChatGPT on biomedical-related tasks, this article presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions, and so on. Typical NLP tasks like named entity recognization, relation extraction, sentence similarity, question and answering, and document classification are included. Overall, ChatGPT got a BLURB score of 58.50 while the state-of-the-art model had a score of 84.30. Through a series of experiments, we demonstrated the effectiveness and versatility of ChatGPT in biomedical text understanding, reasoning and generation, and the limitation of ChatGPT build on GPT-3.5. AVAILABILITY AND IMPLEMENTATION: All the datasets are available from BLURB benchmark https://microsoft.github.io/BLURB/index.html. The prompts are described in the article.

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.

Development of 3-acetylindole derivatives that selectively target BRPF1 as new inhibitors of receptor activator of NF-κB ligand (RANKL)-Induced osteoclastogenesis.

Bromodomain and PHD finger-containing (BRPF) proteins function as epigenetic readers that specifically recognize acetylated lysine residues on histone tails. The acetyl-lysine binding pocket of BRPF has emerged as an attractive target for the development of protein interaction inhibitors owing to its potential druggability. In this study, we identified 3-acetylindoles as bone antiresorptive agents with a novel scaffold by performing structure-based virtual screening and hit optimization. Among those derivatives, compound 18 exhibited potent and selective inhibitory activities against BRPF1B (IC50 = 102 nM) as well as outstanding inhibitory activity against osteoclastogenesis (73.8% @ 1 µM) and differentiation (IC50 = 0.19 µM) without cytotoxicity. Besides, cellular mechanism assays demonstrated that compound 18 exhibited a strong bone antiresorptive effect by modulating the RANKL/RANK/NFATc1 pathway. Structural and functional studies on BRPF1 inhibitors aid in making advances to understand the epigenetic mechanisms of bone cell development and create innovative therapeutics for treating bone metastases from solid tumors and other bone erosive diseases.

De Novo Molecule Design Through the Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites.

De novo molecule design through the molecular generative model has gained increasing attention in recent years. Here, a novel generative model was proposed by integrating the three-dimensional (3D) structural information of the protein binding pocket into the conditional RNN (cRNN) model to control the generation of drug-like molecules. In this model, the composition of the protein binding pocket is effectively characterized through a coarse-grain strategy and the 3D information of the pocket can be represented by the sorted eigenvalues of the Coulomb matrix (EGCM) of the coarse-grained atoms composing the binding pocket. In current work, we used our EGCM method and a previously reported binding pocket descriptor, DeeplyTough, to train cRNN models and evaluated their performance. It has been shown that the model trained with the constraint of protein environment information has a clear tendency on generating compounds with higher similarity to the original X-ray-bound ligand than the normal RNN model and also better docking scores. Our results demonstrate the potential application of the controlled generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.

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.

CHARMM36m: an improved force field for folded and intrinsically disordered proteins.

The all-atom additive CHARMM36 protein force field is widely used in molecular modeling and simulations. We present its refinement, CHARMM36m (http://mackerell.umaryland.edu/charmm_ff.shtml), with improved accuracy in generating polypeptide backbone conformational ensembles for intrinsically disordered peptides and proteins.

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.

QSAR modeling and in silico design of small-molecule inhibitors targeting the interaction between E3 ligase VHL and HIF-1α.

Protein-protein interactions (PPIs) have attracted much attention recently because of their preponderant role in most biological processes. The prevention of the interaction between E3 ligase VHL and HIF-1[Formula: see text] may improve tolerance to hypoxia and ameliorate the prognosis of many diseases. To obtain novel potent inhibitors of VHL/HIF-1[Formula: see text] interaction, a series of hydroxyproline-based inhibitors were investigated for structural optimization using a combination of QSAR modeling and molecular docking. Here, 2D- and 3D-QSAR models were developed by genetic function approximation (GFA) and comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods, respectively. The top-ranked models with strict validation revealed satisfactory statistical parameters (CoMFA with [Formula: see text], 0.637; [Formula: see text], 0.955; [Formula: see text], 0.944; CoMSIA with [Formula: see text], 0.649; [Formula: see text], 0.954; [Formula: see text], 0.911; GFA with [Formula: see text], 0.721; [Formula: see text], 0.801; [Formula: see text], 0.861). The selected five 2D-QSAR descriptors were in good accordance with the 3D-QSAR results, and contour maps gave the visualization of feature requirements for inhibitory activity. A new diverse molecular database was created by molecular fragment replacement and BREED techniques for subsequent virtual screening. Eventually, 31 novel hydroxyproline derivatives stood out as potential VHL/HIF-1[Formula: see text] inhibitors with favorable predictions by the CoMFA, CoMSIA and GFA models. The reliability of this protocol suggests that it could also be applied to the exploration of lead optimization of other PPI targets.

Identification of Covalent Binding Sites Targeting Cysteines Based on Computational Approaches.

Covalent drugs have attracted increasing attention in recent years due to good inhibitory activity and selectivity. Targeting noncatalytic cysteines with irreversible inhibitors is a powerful approach for enhancing pharmacological potency and selectivity because cysteines can form covalent bonds with inhibitors through their nucleophilic thiol groups. However, most human kinases have multiple noncatalytic cysteines within the active site; to accurately predict which cysteine is most likely to form covalent bonds is of great importance but remains a challenge when designing irreversible inhibitors. In this work, FTMap was first applied to check its ability in predicting covalent binding site defined as the region where covalent bonds are formed between cysteines and irreversible inhibitors. Results show that it has excellent performance in detecting the hot spots within the binding pocket, and its hydrogen bond interaction frequency analysis could give us some interesting instructions for identification of covalent binding cysteines. Furthermore, we proposed a simple but useful covalent fragment probing approach and showed that it successfully predicted the covalent binding site of seven targets. By adopting a distance-based method, we observed that the closer the nucleophiles of covalent warheads are to the thiol group of a cysteine, the higher the possibility that a cysteine is prone to form a covalent bond. We believe that the combination of FTMap and our distance-based covalent fragment probing method can become a useful tool in detecting the covalent binding site of these targets.

Targeting epigenetic reader and eraser: Rational design, synthesis and in vitro evaluation of dimethylisoxazoles derivatives as BRD4/HDAC dual inhibitors.

The bromodomain protein module and histone deacetylase (HDAC), which recognize and remove acetylated lysine, respectively, have emerged as important epigenetic therapeutic targets in cancer treatments. Herein we presented a novel design approach for cancer drug development by combination of bromodomain and HDAC inhibitory activity in one molecule. The designed compounds were synthesized which showed inhibitory activity against bromodomain 4 and HDAC1. The representative dual bromodomain/HDAC inhibitors, compound 11 and 12, showed potent antiproliferative activities against human leukaemia cell line K562 and MV4-11 in cellular assays. This work may lay the foundation for developing dual bromodomain/HDAC inhibitors as potential anticancer therapeutics.

Synthesis and Biological Evaluation of 1-(2-Aminophenyl)-3-arylurea Derivatives as Potential EphA2 and HDAC Dual Inhibitors.

A series of 1-(2-aminophenyl)-3-arylurea novel derivatives were synthesized and evaluated against Ephrin type-A receptor 2 (EphA2) and histone deacetylases (HDACs) kinase. Most of the compounds exhibited inhibitory activity against EphA2 and HDAC. The antiproliferative activities were evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) (thiazolyl blue, tetrazolium blue) against the human cancer cell lines HCT116, K562 and MCF7. Compounds 5a and b showed the most potent inhibitory activity against EphA2 and HDAC. However, compound 5b exhibited higher potency against HCT116 (IC50=5.29 µM) and MCF7 (IC50=7.42 µM). 1-(2-Aminophenyl)-3-arylurea analogues may serve as new EphA2-HDAC dual inhibitors.

A selectivity study of sodium-dependent glucose cotransporter 2/sodium-dependent glucose cotransporter 1 inhibitors by molecular modeling.

Sodium-dependent glucose cotransporters (SGLTs) play an important role in glucose reabsorption in the kidney and have been identified as promising targets to treat diabetes. Because of the side effects like glucose and galactose malabsorption by targeting SGLT1, highly selective SGLT2 inhibitors are more promising in the treatment of diabetes. To understand the mechanism of selectivity, we conducted selectivity-based three-dimensional quantitative structure-activity relationship studies to highlight the structure requirements for highly selective SGLT2 inhibitors. The best comparative molecular field analysis and comparative molecular similarity indices analysis models showed the noncross-validated coefficient (r(2) ) of 0.967 and 0.943, respectively. The predicted correlation coefficients (r(2) pred ) of 0.974 and 0.938 validated the reliability and predictability of these models. Besides, homology models of SGLT2 and SGLT1 were also constructed to investigate the selective mechanism from structure-based perspective. Molecular dynamics simulation and binding free energy calculation were performed on the systems of a potent and selective compound interacting with SGLT2 and SGLT1 to compare the different binding modes. The simulation results showed that the stretch of the methylthio group on Met241 had an essential effect on the different binding modes between SGLT1 and SGLT2, which was consistent with the three-dimensional quantitative structure-activity relationship analysis. Hydrogen bond analysis and binding free energy calculation revealed that SGLT2 binding complex was more stable and favorable than SGLT1 complex, which was highly correlated with the experimental results. Our obtained results give useful information for the investigation of the inhibitors' selectivity between SGLT2 and SGLT1 and will help for further development of highly selective SGLT2 inhibitors.

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds.

The discovery of novel scaffolds against a specific target has long been one of the most significant but challengeable goals in discovering lead compounds. A scaffold that binds in important regions of the active pocket is more favorable as a starting point because scaffolds generally possess greater optimization possibilities. However, due to the lack of sufficient chemical space diversity of the databases and the ineffectiveness of the screening methods, it still remains a great challenge to discover novel active scaffolds. Since the strengths and weaknesses of both fragment-based drug design and traditional virtual screening (VS), we proposed a fragment VS concept based on Bayesian categorization for the discovery of novel scaffolds. This work investigated the proposal through an application on VEGFR-2 target. Firstly, scaffold and structural diversity of chemical space for 10 compound databases were explicitly evaluated. Simultaneously, a robust Bayesian classification model was constructed for screening not only compound databases but also their corresponding fragment databases. Although analysis of the scaffold diversity demonstrated a very unevenly distribution of scaffolds over molecules, results showed that our Bayesian model behaved better in screening fragments than molecules. Through a literature retrospective research, several generated fragments with relatively high Bayesian scores indeed exhibit VEGFR-2 biological activity, which strongly proved the effectiveness of fragment VS based on Bayesian categorization models. This investigation of Bayesian-based fragment VS can further emphasize the necessity for enrichment of compound databases employed in lead discovery by amplifying the diversity of databases with novel structures.

Studies on [5,6]-Fused Bicyclic Scaffolds Derivatives as Potent Dual B-RafV600E/KDR Inhibitors Using Docking and 3D-QSAR Approaches.

Research and development of multi-target inhibitors has attracted increasing attention as anticancer therapeutics. B-RafV600E synergistically works with vascular endothelial growth factor receptor 2 (KDR) to promote the occurrence and progression of cancers, and the development of dual-target drugs simultaneously against these two kinds of kinase may offer a better treatment advantage. In this paper, docking and three-dimensional quantitative structure activity relationship (3D-QSAR) studies were performed on a series of dual B-Raf/KDR inhibitors with a novel hinge-binding group, [5,6]-fused bicyclic scaffold. Docking studies revealed optimal binding conformations of these compounds interacting with both B-Raf and KDR. Based on these conformations, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models were constructed, and the best CoMFA (q²=0.542, r²=0.989 for B-Raf; q²=0.768, r²=0.991 for KDR) and CoMSIA models (q²=0.519, r²=0.992 for B-Raf; q²=0.849, r²=0.993 for KDR) were generated. Further external validations confirmed their predictability, yielding satisfactory correlation coefficients (r²pred=0.764 (CoMFA), r²pred=0.841 (CoMSIA) for B-Raf, r²pred=0.912 (CoMFA), r²pred=0.846 (CoMSIA) for KDR, respectively). Through graphical analysis and comparison on docking results and 3D-QSAR contour maps, key amino acids that affect the ligand-receptor interactions were identified and structural features influencing the activities were discussed. New potent derivatives were designed, and subjected to preliminary pharmacological evaluation. The study may offer useful references for the modification and development of novel dual B-Raf/KDR inhibitors.

An efficient multistep ligand-based virtual screening approach for GPR40 agonists.

G protein-coupled receptor 40/free fatty acid receptor 1 (GPR40/FFAR1) is a member of the GPCR superfamily, and GPR40 agonists have therapeutic potential for type 2 diabetes. With the crystal structure of GPR40 currently unavailable, various ligand-based virtual screening approaches can be applied to identify novel agonists of GPR40. It is known that each ligand-based method has its own advantages and limitations. To improve the efficiency of individual ligand-based methods, an efficient multistep ligand-based virtual screening approach is presented in this study, including the pharmacophore-based screening, physicochemical property filtering, protein-ligand interaction fingerprint similarity analysis, and 2D-fingerprint structural similarity search. A focused decoy library was generated and used to evaluate the efficiency of this virtual screening protocol. This multistep workflow not only significantly improved the hit rate compared with each individual ligand-based method, but also identified diverse known actives from decoys. This protocol may serve as an efficient virtual screening tool for the targets without crystal structures available to discover novel active compounds.

Novel 1H-pyrazole-3-carboxamide derivatives: synthesis, anticancer evaluation and identification of their DNA-binding interaction.

Four novel 1H-pyrazole-3-carboxamide derivatives were synthesized, and their antiproliferative effect on cancer cells, kinase inhibition, and in particular, the DNA-binding interaction were investigated to interpret the antitumor mechanisms. A DNA minor groove binding model was developed, and the binding energy was predicted for the compounds. In consistence with the prediction, the binding ability was determined by the electronic absorption spectroscopy under physiological conditions for the compounds, and further verified by viscosity measurement. One compound 5-(3-cyclopropylureido)-N-[4-[(4-methylpiperazin-1-yl)methyl]phenyl]-1-H-pyrazole-3-carboxamide (pym-5) exerted the highest DNA-binding affinity (K(pym-5)=1.06×10(5) M(-1)). And it demonstrated more than 50% decrease of the emission intensity of the ethidium bromide-calf thymus DNA (EB-CT-DNA) complex in fluorescence spectra, suggesting that pym-5 could strongly affect the DNA conformation. Furthermore, pym-5 showed the cleavage activity upon the supercoiled plasmid pBR322 DNA in the pBR322 DNA cleavage assay. Our study suggests that DNA may serve as a potential target to these pyrazole derivatives.