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Dilemmas in Elderly Diabetes and Clinical Practice Involving Traditional Chinese Medicine.

Diabetes is a widespread chronic disease that occurs mainly in the elderly population. Due to the difference in pathophysiology between elderly and young patients, the current clinical practice to treat elderly patients with anti-diabetes medications still faces some challenges and dilemmas, such as the urgent need for early diagnosis and prevention, and an imbalance between restricted dietary intake and the risk of undernutrition. Traditional Chinese medicine (TCM) offers various treatment regimens that are actively utilized in the field of diabetes management. Through multiple targets and multiple pathways, TCM formulas, medicinal herbs, and active natural products enhance the efficacy of diabetes prevention and diabetes control measures, simplify complex medication management, and improve common symptoms and common diabetic complications in elderly people. Historically, natural products have played a key role in material composition analysis of TCM and mechanism interpretation to enable drug discovery. However, there have been few conclusions on this topic. This review summarizes the development of TCM for the prevention and management of diabetes in elderly people, existing evidence-based clinical practices, and prospects for future development.

Laminin-associated integrins mediate Diffuse Intrinsic Pontine Glioma infiltration and therapy response within a neural assembloid model

Diffuse Intrinsic Pontine Glioma (DIPG) is a highly aggressive and fatal pediatric brain cancer. One pre-requisite for tumor cells to infiltrate is adhesion to extracellular matrix (ECM) components. However, it remains largely unknown which ECM proteins are critical in enabling DIPG adhesion and migration and which integrin receptors mediate these processes. Here, we identify laminin as a key ECM protein that supports robust DIPG cell adhesion and migration. To study DIPG infiltration, we developed a DIPG-neural assembloid model, which is composed of a DIPG spheroid fused to a human induced pluripotent stem cell-derived neural organoid. Using this assembloid model, we demonstrate that knockdown of laminin-associated integrins significantly impedes DIPG infiltration. Moreover, laminin-associated integrin knockdown improves DIPG response to radiation and HDAC inhibitor treatment within the DIPG-neural assembloids. These findings reveal the critical role of laminin-associated integrins in mediating DIPG progression and drug response. The results also provide evidence that disrupting integrin receptors may offer a novel therapeutic strategy to enhance DIPG treatment outcomes. Finally, these results establish DIPG-neural assembloid models as a powerful tool to study DIPG disease progression and enable drug discovery.

Physio-omics? Robotic development, software engineering, and machine learning for high throughput, whole animal physiology assays as a path to disease mechanism and drug discovery

Measurement and analysis of cardio-respiratory function in animal models is essential for our understanding of functional and disease mechanisms that pave the way for human therapeutics and treatments. However, physiological phenotyping in conscious animals is often an onerous process requiring significant observer time, attention, and manual effort while being susceptible to bias and variability that ultimately limits the kinds of questions that can be asked and answered. Once data is collected, comprehensive and accurate analysis is challenging as it often requires either the use of expensive commercial software(s) with limited flexibility to meet the investigator’s needs or lab developed scripts that are often arduous and require specialized skills to utilize, resulting in incremental insights. Here we present an overview of our high throughput physiology assessment and analysis pipeline for interrogating disease mechanisms and enabling drug discovery in congenital cardio-respiratory disorders such as the lethal sudden infant death syndrome (SIDS). We first developed a closed loop robotic platform for automated neonate cardio-respiratory experiments (Looper) that enables automated interventional studies (i.e. hypoxic induction or drug application) based on real-time physiological feedback during the experiment. We show that deployment of multiple Looper platforms in parallel enables an order of magnitude increase in data collection for a single observer over a single workday and is highly scalable to achieve yet greater outputs. When combined with well designed genetically engineered or other disease mouse models, large scale genetic, molecular, and cellular mechanistic insights and drug discovery become feasible through physiological screening paradigms. Next, we developed an open-sourced cardio-respiratory assessment software tool (Breathe Easy) that enables facile deep interrogation of terabytes of data in a high throughput fashion while offering extensive customization capabilities to allow for unique case by case analysis by more skilled users. Breathe Easy output offers near publication ready graphical outputs with significance markings and statistical tables for rapid presentation of results through manuscripts and web-publishing. Lastly, we will present our early work leveraging our robotic and software pipeline to build an analytical machine learning framework that can predict cardio-respiratory failure (death) with high temporal specificity (more than 10 min out) in our SIDS like assay and our efforts to utilize the trained algorithms for 1) real-time prediction, feedback, and intervention application in our SIDS like assays on the Looper platform to screen small molecules for novel therapeutics; 2) elucidating unique physiological mechanisms through comparative training in distinct SIDS models; and 3) determining the potential for these machine learning approaches to be used in human polysomnographic recordings to identify at risk infants. R01HL161142, R01HL130249. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Image-based quantification of mitochondrial iron uptake via Mitoferrin-2

Iron is a trace element that is critical for most living organisms and plays a key role in a wide variety of metabolic processes. In the mitochondrion, iron is involved in producing iron-sulfur clusters and synthesis of heme and kept within physiological ranges by concerted activity of multiple molecules. Mitochondrial iron uptake is mediated by the solute carrier transporters Mitoferrin-1 (SLC25A37) and Mitoferrin-2 (SLC25A28). While Mitoferrin-1 is mainly involved in erythropoiesis, the cellular function of the ubiquitously expressed Mitoferrin-2 remains less well defined. Furthermore, Mitoferrin-2 is associated with several human diseases, including cancer, cardiovascular and metabolic diseases, hence representing a potential therapeutic target. Here, we developed a robust approach to quantify mitochondrial iron uptake mediated by Mitoferrin-2 in living cells. We utilize HEK293 cells with inducible expression of Mitoferrin-2 and measure iron-induced quenching of rhodamine B[(1,10-phenanthroline-5-yl)-aminocarbonyl]benzyl ester (RPA) fluorescence and validate this assay for medium-throughput screening. This assay may allow identification and characterization of Mitoferrin-2 modulators and could enable drug discovery for this target.

Comprehensive phenotypic characterization of an allelic series of zebrafish models of NEB-related nemaline myopathy.

Nemaline myopathy (NM) is a rare congenital neuromuscular disorder characterized by muscle weakness and hypotonia, slow gross motor development, and decreased respiratory function. Mutations in at least twelve genes, all of each encode proteins that are either components of the muscle thin filament or regulate its length and stability, have been associated with NM. Mutations in Nebulin (NEB), a giant filamentous protein localized in the sarcomere, account for more than 50% of NM cases. At present, there remains a lack of understanding of whether NEB genotype influences nebulin function and NM-patient phenotypes. In addition, there is a lack of therapeutically tractable models that can enable drug discovery and address the current unmet treatment needs of patients. To begin to address these gaps, here we have characterized five new zebrafish models of NEB-related NM. These mutants recapitulate most aspects of NEB-based NM, showing drastically reduced survival, defective muscle structure, reduced contraction force, shorter thin filaments, presence of electron-dense structures in myofibers, and thickening of the Z-disks. This study represents the first extensive investigation of an allelic series of nebulin mutants, and thus provides an initial examination in pre-clinical models of potential genotype-phenotype correlations in human NEB patients. It also represents the first utilization of a set of comprehensive outcome measures in zebrafish, including correlation between molecular analyses, structural and biophysical investigations, and phenotypic outcomes. Therefore, it provides a rich source of data for future studies exploring the NM pathomechanisms, and an ideal springboard for therapy identification and development for NEB-related NM.

Decrypting orphan GPCR drug discovery via multitask learning

The drug discovery of G protein-coupled receptors (GPCRs) superfamily using computational models is often limited by the availability of protein three-dimensional (3D) structures and chemicals with experimentally measured bioactivities. Orphan GPCRs without known ligands further complicate the process. To enable drug discovery for human orphan GPCRs, multitask models were proposed for predicting half maximal effective concentrations (EC50) of the pairs of chemicals and GPCRs. Protein multiple sequence alignment features, and physicochemical properties and fingerprints of chemicals were utilized to encode the protein and chemical information, respectively. The protein features enabled the transfer of data-rich GPCRs to orphan receptors and the transferability based on the similarity of protein features. The final model was trained using both agonist and antagonist data from 200 GPCRs and showed an excellent mean squared error (MSE) of 0.24 in the validation dataset. An independent test using the orphan dataset consisting of 16 receptors associated with less than 8 bioactivities showed a reasonably good MSE of 1.51 that can be further improved to 0.53 by considering the transferability based on protein features. The informative features were identified and mapped to corresponding 3D structures to gain insights into the mechanism of GPCR-ligand interactions across the GPCR family. The proposed method provides a novel perspective on learning ligand bioactivity within the diverse human GPCR superfamily and can potentially accelerate the discovery of therapeutic agents for orphan GPCRs.

Synthetic ramoplanin analogues are accessible by effective incorporation of arylglycines in solid-phase peptide synthesis.

The threat of antimicrobial resistance to antibiotics requires a continual effort to develop alternative treatments. Arylglycines (or phenylglycines) are one of the signature amino acids found in many natural peptide antibiotics, but their propensity for epimerization in solid-phase peptide synthesis (SPPS) has prevented their use in long peptide sequences. We have now identified an optimized protocol that allows the synthesis of challenging non-ribosomal peptides including precursors of the glycopeptide antibiotics and an analogue of feglymycin (1 analogue, 20%). We have exploited this protocol to synthesize analogues of the peptide antibiotic ramoplanin using native chemical ligation/desulfurization (1 analogue, 6.5%) and head-to-tail macrocyclization in excellent yield (6 analogues, 3-9%), with these compounds extensively characterized by NMR (U-shaped structure) and antimicrobial activity assays (two clinical isolates). This method significantly reduces synthesis time (6-9 days) when compared with total syntheses (2-3 months) and enables drug discovery programs to include arylglycines in structure-activity relationship studies and drug development.

OmicSynth: an open multi‐omic community resource for identifying druggable targets across neurodegenerative diseases

AbstractBackgroundWhile there have been recent successes for Alzheimer’s disease (AD) therapeutics, successful disease‐modifying treatments for AD and neurodegenerative disorders (NDD) remain rare, highlighting the importance of a mechanistic approach in creating therapies. There is an urgent need for disease‐modifying therapeutics, as the World Health Organization estimates dementia diagnosis to reach 78 million by 2030. Ultimately, in the absence of disease‐modifying therapies, the devastating health, social, and economic impacts of dementia and related NDDs will be catastrophic. Here, we generate new understandings on the basis of six NDDs, including AD, and to identify rational therapeutic targets that are genetically backed.MethodWe utilized six NDD specific genome wide association studies (GWAS) ‐ AD, amyotrophic lateral sclerosis (ALS), Lewy body dementia (LBD), Parkinson’s disease (PD), and Progressive supranuclear palsy (PSP) ‐ and multi‐omic data sets to perform Summary‐data‐based Mendelian Randomization (SMR) to identify genetic targets. We created a therapeutic scheme to classify identified target genes into strata based on known druggability and approved therapeutics for enabling drug discovery and repurposing. We prioritized identified genes as therapeutic targets of interest into three categories: “novel”, “known”, and “difficult”. In parallel, we provide mechanistic insights into disease processes and potential network‐level consequences of gene‐based therapeutics.ResultWe identified 116 AD, 3 ALS, 5 LBD, 46 PD, and 9 PSP target genes passing multiple test corrections (pSMR_multi < 2.95E‐06 and pHEIDI > 0.01) of which 15 were significant in more than two NDDs. Identified target genes were then classified into 41 novel targets, 3 known targets, and 115 difficult targets ‐ corresponding to our therapeutic scheme. Overall, we found four significant target genes in AD and PD, APOC4, LRRC37A2, MMRN1, and ZNF646, and one gene in companion gene networks in ALS, OXT, to present potential liver toxicity issues.ConclusionOur findings and a therapeutic scheme nominate 41 novel targets as promising for drug discovery and repurposing. In particular, KAT8 and KCNN4 present as promising gene targets for further drug development and repurposing. Existing therapeutic compounds, MG149 and Riluzole, target them both genes and have been successfully used in reducing NDD‐related symptomatology.

Automated high-throughput, high-content 3D imaging of intact pancreatic islets.

Diabetes poses a global health crisis affecting individuals across age groups and backgrounds, with a prevalence estimate of 700 million people worldwide by 2045. Current therapeutic strategies primarily rely on insulin therapy or hypoglycemic agents, which fail to address the root cause of the disease - the loss of pancreatic insulin-producing beta-cells. Therefore, bioassays that recapitulate intact islets are needed to enable drug discovery for beta-cell replenishment, protection from beta-cell loss, and islet-cell interactions. Standard cancer insulinoma beta-cell lines MIN6 and INS-1 have been used to interrogate beta-cell metabolic pathways and function but are not suitable for studying proliferative effects. Screening using primary human/rodent intact islets offers a higher level of physiological relevance to enhance diabetes drug discovery and development. However, the 3-dimensionality of intact islets have presented challenges in developing robust, high-throughput assays to detect beta-cell proliferative effects. Established methods rely on either dissociated islet cells plated in 2D monolayer cultures for imaging or reconstituted pseudo-islets formed in round bottom plates to achieve homogeneity. These approaches have significant limitations due to the islet cell dispersion process. To address these limitations, we have developed a robust, intact ex vivo pancreatic islet bioassay in 384-well format that is capable of detecting diabetes-relevant endpoints including beta-cell proliferation, chemoprotection, and islet spatial morphometrics.

Development of assays to support identification and characterization of modulators of DExH-box helicase DHX9

DHX9 is a DExH-box RNA helicase that utilizes hydrolysis of all four nucleotide triphosphates (NTPs) to power cycles of 3′ to 5′ directional movement to resolve and/or unwind double stranded RNA, DNA, and RNA/DNA hybrids, R-loops, triplex-DNA and G-quadraplexes. DHX9 activity is important for both viral amplification and maintaining genomic stability in cancer cells; therefore, it is a therapeutic target of interest for drug discovery efforts. Biochemical assays measuring ATP hydrolysis and oligonucleotide unwinding for DHX9 have been developed and characterized, and these assays can support high-throughput compound screening efforts under balanced conditions. Assay development efforts revealed DHX9 can use double stranded RNA with 18-mer poly(U) 3′ overhangs and as well as significantly shorter overhangs at the 5′ or 3′ end as substrates. The enzymatic assays are augmented by a robust SPR assay for compound validation. A mechanism-derived inhibitor, GTPγS, was characterized as part of the validation of these assays and a crystal structure of GDP bound to cat DHX9 has been solved. In addition to enabling drug discovery efforts for DHX9, these assays may be extrapolated to other RNA helicases providing a valuable toolkit for this important target class.

Unlocking Neural Function with 3D In Vitro Models: A Technical Review of Self-Assembled, Guided, and Bioprinted Brain Organoids and Their Applications in the Study of Neurodevelopmental and Neurodegenerative Disorders

Understanding the complexities of the human brain and its associated disorders poses a significant challenge in neuroscience. Traditional research methods have limitations in replicating its intricacies, necessitating the development of in vitro models that can simulate its structure and function. Three-dimensional in vitro models, including organoids, cerebral organoids, bioprinted brain models, and functionalized brain organoids, offer promising platforms for studying human brain development, physiology, and disease. These models accurately replicate key aspects of human brain anatomy, gene expression, and cellular behavior, enabling drug discovery and toxicology studies while providing insights into human-specific phenomena not easily studied in animal models. The use of human-induced pluripotent stem cells has revolutionized the generation of 3D brain structures, with various techniques developed to generate specific brain regions. These advancements facilitate the study of brain structure development and function, overcoming previous limitations due to the scarcity of human brain samples. This technical review provides an overview of current 3D in vitro models of the human cortex, their development, characterization, and limitations, and explores the state of the art and future directions in the field, with a specific focus on their applications in studying neurodevelopmental and neurodegenerative disorders.

Abstract B037: Hit-finding by Cysteine-Scanning (HCS): A method for finding druggable pockets

Abstract Large-scale cancer sequencing efforts coupled with deep functional genomic interrogation of cancer models provides us an increasingly detailed view of the critical cancer drivers required for persistent growth and survival of cancer cells. Targeting such drivers has provided significant therapeutic benefit to patients across a wide range of cancers, yet progress has been, in large part, confined to the targeting of catalytic domains in kinases. To expand the reach of therapeutics into new classes of therapeutic targets, we need to identify and validate specific mechanisms and pockets to enable drug discovery. To enable this, we are developing a method called Hit-finding by Cysteine-Scanning (HCS) for identifying druggable pockets in proteins of interest. Here, we seek to couple the ability to create systematic variant libraries across proteins of interest with increasingly diverse covalent small molecule/fragment libraries. Thus, HCS involves screening pooled cysteine (cys)-variants against libraries of covalent compounds. To demonstrate the utility of this approach, we applied HCS to systematically screen 189 KRAS-G12D variants. We successfully purified KRAS cys-variant recombinant proteins in pools of seven or eight achieving coverage of 184/189 variants. KRAS variant pools were then screened against a pilot library of 47 covalent compounds with diverse reactivity detecting reactive variant-ligand pairs by intact LC-MS. This allowed us to quantify the accessibility and reactivity across the entire KRAS-G12D protein. The data confirmed the known SII-pocket in KRAS and revealed potentially novel ligand-bound conformations of oncogenic KRAS mutant proteins. In parallel, the same KRAS cys-variant library was generated as a barcoded library in a mammalian expression system. Upon screening this library in intact cells we observed that Sotorasib functionally inhibit the relevant KRAS SII-pocket cys-variants, enabling enrichment of these variants in a negative selection screen. Importantly, binding of a panel of SII-pocket compounds to variant H95C was found to be independent of the KRAS mutant context. Our results indicate that HCS could be a useful tool for the development of inhibitors for currently undruggable oncogenic KRAS mutants as well as potentially other drug targets. Citation Format: Laurens Moore van Tienen, Shadwa Bayoumi, Khaja Muneeruddin, Nancy Leymarie, David Kornfilt, Yuemin Bian, Devishi Kesar, Ruitong Li, Thomas Atack, Alexandra-Mariela Popa, Zuzana Jandova, Philipp Trollmann, Andreas Bergner, Klaus Rumpel, Dirk Kessler, William R. Sellers. Hit-finding by Cysteine-Scanning (HCS): A method for finding druggable pockets [abstract]. In: Proceedings of the AACR Special Conference: Targeting RAS; 2023 Mar 5-8; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Res 2023;21(5_Suppl):Abstract nr B037.

Abstract 2822: Maximizing radiotherapy impact: a novel pre-clinical platform for screening of radiosensitizing agents in patient-derived tumor organoids

Abstract Introduction: Radiotherapy (RT) is widely used for various cancer types, however, radiotoxicity in healthy neighbouring tissues remains challenging. To optimize effectiveness of current therapeutic regimens, there is a clinical need to enhance radiotherapy-mediated killing by radiosensitizing agents. Radiosensitizers show great potential by accelerating DNA damage, production of free radicals and disturbing cell cycle processes by induction of cytostatic and -toxic effects, but pre-clinical drug efficacy testing methods are limited. Patient-derived organoids (PDOs) have emerged as clinically relevant in vitro screening tools to enable drug discovery for specific mutational and molecular subtypes of many cancer indications. Although some radiosensitizing agents have been identified, potential radiosensitizing effects of standard of care as well as novel anti-cancer drugs remain largely unknown. Here, we present a unique screening platform that enables radiosensitizer discovery by showing in-depth insight in cytostatic-and toxic changes and RT-drug synergy. Methods: Twelve PDO models from colorectal, pancreas, breast, lung, melanoma, cervical and ovarian tumors were screened for responsiveness to potential radiosensitizing agents using our organoid screening platforms. PDOs were seeded in 384-wells format in suspension or 3D gel (for cell-titer-glo (CTG) or imaging purposes) and compound exposure (three concentrations) was performed prior to irradiation (0, 2, 4, 6, 10 Gy). Cell viability data was obtained using both CTG-based assays and high content imaging (HCI)-based assay. Assay robustness and reproducibility were ensured (Z-scores <0.6, reproducible IC50 values). To assess radiosensitizing effects, data was normalized to non-irradiated control conditions. Morphological changes were assessed using HCI. Results: Clear differences in radiosensitivity were observed, with six out of twelve PDOs showing sensitivity to 10 Gy RT (<30% viability), one breast, two melanoma and two lung PDOs showing limited sensitivity (47-86% viability) and one ovarian PDO showing no response to RT (98% viability). A wide range of responses to the chemo-irradiation treatments were observed, with oxaliplatin exhibiting radiosensitizing effects in cervical and colorectal PDOs, gemcitabine in cervical PDOs and docetaxel showing radioprotective properties in melanoma PDO. Conclusion and Discussion: Our novel platform for testing potential radiosensitizing anti-cancer agents holds great promise for advanced drug discovery in a clinically relevant and readily available PDO panel, harbouring a wide range of RT-sensitive to insensitive models. CTG and HCI read-outs offer both fast and in-depth assessment of cytotoxic and cytostatic effects and organoid morphology, creating omnipotent potential for tailored compound screening of radiosensitizers. Citation Format: Liza Wijler, Jara Garcia, Muntaser Abdulrahman, Linda van Seters, My Nguyen, Annelot Staes, Bram Herpers, Leo Price, Marrit Putker. Maximizing radiotherapy impact: a novel pre-clinical platform for screening of radiosensitizing agents in patient-derived tumor organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2822.

An approach combining deep learning and molecule docking for drug discovery of cathepsin L

ABSTRACT Objectives Cathepsin L (CTSL) is a promising therapeutic target for metabolic disorders and COVID-19. However, there are still no clinically available CTSL inhibitors. Our objective is to develop an approach for the discovery of potential reversible covalent CTSL inhibitors. Methods The authors combined Chemprop, a deep learning-based strategy, and the Schrödinger CovDock algorithm to identify potential CTSL inhibitors. First, they used Chemprop to train a deep learning model capable of predicting whether a molecule would inhibit the activity of CTSL and performed predictions on ZINC20 in-stock librarie (~9.2 million molecules). Then, they selected the top-200 predicted molecules and performed the Schrödinger covalent docking algorithm to explore the binding patterns to CTSL (PDB: 5MQY). The authors then calculated the binding energies using Prime MM/GBSA and examined the stability between the best two molecules and CTSL using 100ns molecular dynamics simulations. Results The authors found five molecules that showed better docking results than the well-known cathepsin inhibitor odanacatib. Notably, two of these molecules, ZINC-35287427 and ZINC-1857528743, showed better docking results with CTSL compared to other cathepsins. Conclusion Our approach enables drug discovery from large-scale databases with little computational consumption, which will save the cost and time required for drug discovery.

Functional Expression of Recombinant Candida auris Proteins in Saccharomyces cerevisiae Enables Azole Susceptibility Evaluation and Drug Discovery

Candida auris infections are difficult to treat due to acquired drug resistance against one or multiple antifungal drug classes. The most prominent resistance mechanisms in C. auris are overexpression and point mutations in Erg11, and the overexpression of efflux pump genes CDR1 and MDR1. We report the establishment of a novel platform for molecular analysis and drug screening based on acquired azole-resistance mechanisms found in C. auris. Constitutive functional overexpression of wild-type C. auris Erg11, Erg11 with amino acid substitutions Y132F or K143R and the recombinant efflux pumps Cdr1 and Mdr1 has been achieved in Saccharomyces cerevisiae. Phenotypes were evaluated for standard azoles and the tetrazole VT-1161. Overexpression of CauErg11 Y132F, CauErg11 K143R, and CauMdr1 conferred resistance exclusively to the short-tailed azoles Fluconazole and Voriconazole. Strains overexpressing the Cdr1 protein were pan-azole resistant. While CauErg11 Y132F increased VT-1161 resistance, K143R had no impact. Type II binding spectra showed tight azole binding to the affinity-purified recombinant CauErg11 protein. The Nile Red assay confirmed the efflux functions of CauMdr1 and CauCdr1, which were specifically inhibited by MCC1189 and Beauvericin, respectively. CauCdr1 exhibited ATPase activity that was inhibited by Oligomycin. The S. cerevisiae overexpression platform enables evaluation of the interaction of existing and novel azole drugs with their primary target CauErg11 and their susceptibility to drug efflux.

High-affinity chromodomains engineered for improved detection of histone methylation and enhanced CRISPR-based gene repression

Histone methylation is an important post-translational modification that plays a crucial role in regulating cellular functions, and its dysregulation is implicated in cancer and developmental defects. Therefore, systematic characterization of histone methylation is necessary to elucidate complex biological processes, identify biomarkers, and ultimately, enable drug discovery. Studying histone methylation relies on the use of antibodies, but these suffer from lot-to-lot variation, are costly, and cannot be used in live cells. Chromatin-modification reader domains are potential affinity reagents for methylated histones, but their application is limited by their modest affinities. We used phage display to identify key residues that greatly enhance the affinities of Cbx chromodomains for methylated histone marks and develop a general strategy for enhancing the affinity of chromodomains of the human Cbx protein family. Our strategy allows us to develop powerful probes for genome-wide binding analysis and live-cell imaging. Furthermore, we use optimized chromodomains to develop extremely potent CRISPR-based repressors for tailored gene silencing. Our results highlight the power of engineered chromodomains for analyzing protein interaction networks involving chromatin and represent a modular platform for efficient gene silencing.

Toward drug-miRNA resistance association prediction by positional encoding graph neural network and multi-channel neural network.

Drug discovery is a costly and time-consuming process, and most drugs exert therapeutic efficacy by targeting specific proteins. However, there are a large number of proteins that are not targeted by any drug. Recently, miRNA-based therapeutics are becoming increasingly important, since miRNA can regulate the expressions of specific genes and affect a variety of human diseases. Therefore, it is of great significance to study the associations between miRNAs and drugs to enable drug discovery and disease treatment. In this work, we propose a novel method named DMR-PEG, which facilitates drug-miRNA resistance association (DMRA) prediction by leveraging positional encoding graph neural network with layer attention (LAPEG) and multi-channel neural network (MNN). LAPEG considers both the potential information in the miRNA-drug resistance heterogeneous network and the specific characteristics of entities (i.e., drugs and miRNAs) to learn favorable representations of drugs and miRNAs. And MNN models various sophisticated relations and synthesizes the predictions from different perspectives effectively. In the comprehensive experiments, DMR-PEG achieves the area under the precision-recall curve (AUPR) score of 0.2793 and the area under the receiver-operating characteristic curve (AUC) score of 0.9475, which outperforms the most state-of-the-art methods. Further experimental results show that our proposed method has good robustness and stability. The ablation study demonstrates each component in DMR-PEG is essential for drug-miRNA drug resistance association prediction. And real-world case study presents that DMR-PEG is promising for DMRA inference.

2]-Ladderanes as isosteres for meta-substituted aromatic rings and rigidified cyclohexanes

Aromatic ring isosteres and rigidified saturated hydrocarbons are important motifs to enable drug discovery. Herein we disclose [2]-ladderanes as a class of meta-substituted aromatic ring isosteres and rigidified cyclohexanes. A straightforward synthesis of the building blocks is presented along with representative derivatization. Preliminary studies reveal that the [2]-ladderanes offer similar metabolic and physicochemical properties thus establishing this class of molecules as interesting motifs.

Structure of human spermine oxidase in complex with a highly selective allosteric inhibitor

Human spermine oxidase (hSMOX) plays a central role in polyamine catabolism. Due to its association with several pathological processes, including inflammation and cancer, hSMOX has garnered interest as a possible therapeutic target. Therefore, determination of the structure of hSMOX is an important step to enable drug discovery and validate hSMOX as a drug target. Using insights from hydrogen/deuterium exchange mass spectrometry (HDX-MS), we engineered a hSMOX construct to obtain the first crystal structure of hSMOX bound to the known polyamine oxidase inhibitor MDL72527 at 2.4 Å resolution. While the overall fold of hSMOX is similar to its homolog, murine N1-acetylpolyamine oxidase (mPAOX), the two structures contain significant differences, notably in their substrate-binding domains and active site pockets. Subsequently, we employed a sensitive biochemical assay to conduct a high-throughput screen that identified a potent and selective hSMOX inhibitor, JNJ-1289. The co-crystal structure of hSMOX with JNJ-1289 was determined at 2.1 Å resolution, revealing that JNJ-1289 binds to an allosteric site, providing JNJ-1289 with a high degree of selectivity towards hSMOX. These results provide crucial insights into understanding the substrate specificity and enzymatic mechanism of hSMOX, and for the design of highly selective inhibitors.

Surface Plasmon Resonance Screening to Identify Active and Selective Adenosine Receptor Binding Fragments.

Surface plasmon resonance (SPR) is a widely used method to study ligand-protein interactions. The throughput and sensitivity of SPR has made it an important technology for measuring low-affinity, ultralow weight fragments (<200 Da) in the early stages of drug discovery. However, the biochemistry of membrane proteins, such as G-protein-coupled receptors (GPCRs), makes their SPR fragment screening particularly challenging, especially for native/wild-type, nonthermostabilized mutant receptors. In this study, we demonstrate the use of SPR-based biosensors to study the entire human family of adenosine receptors and present biologically active novel binders with a range of selectivity to human adenosine 2a receptor (hA2AR) from an ultralow weight fragment library and the public GlaxoSmithKline (GSK) kinase library. Thus, we demonstrate the ability of SPR to screen ultra-low-affinity fragments and identify biologically meaningful chemical equity and that SPR campaigns are highly effective "chemical filters" for screening small building block fragments that can be used to enable drug discovery programs.