Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery.

The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs - a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

From hit to vial: Precision discovery and development of an imidazopyrimidine TLR7/8 agonist adjuvant formulation.

Vaccination can help prevent infection and can also be used to treat cancer, allergy, and potentially even drug overdose. Adjuvants enhance vaccine responses, but currently, the path to their advancement and development is incremental. We used a phenotypic small-molecule screen using THP-1 cells to identify nuclear factor-κB (NF-κB)-activating molecules followed by counterscreening lead target libraries with a quantitative tumor necrosis factor immunoassay using primary human peripheral blood mononuclear cells. Screening on primary cells identified an imidazopyrimidine, dubbed PVP-037. Moreover, while PVP-037 did not overtly activate THP-1 cells, it demonstrated broad innate immune activation, including NF-κB and cytokine induction from primary human leukocytes in vitro as well as enhancement of influenza and SARS-CoV-2 antigen-specific humoral responses in mice. Several de novo synthesis structural enhancements iteratively improved PVP-037's in vitro efficacy, potency, species-specific activity, and in vivo adjuvanticity. Overall, we identified imidazopyrimidine Toll-like receptor-7/8 adjuvants that act in synergy with oil-in-water emulsion to enhance immune responses.

Secondary metabolite profile of Streptomyces spp. changes when grown with the sub-lethal concentration of silver nanoparticles: possible implication in novel compound discovery.

The decline of new antibiotics and the emergence of multidrug resistance in pathogens necessitates a revisit of strategies used for lead compound discovery. This study proposes to induce the production of bioactive compounds with sub-lethal concentrations of silver nanoparticles (Ag-NPs). A total of Forty-two Actinobacteria isolates from four Saudi soil samples were grown with and without sub-lethal concentration of Ag-NPs (50 µg ml-1). The spent broth grown with Ag-NPs, or without Ag-NPs were screened for antimicrobial activity against four bacteria. Interestingly, out of 42 strains, broths of three strains grown with sub-lethal concentration of Ag-NPs exhibit antimicrobial activity against Staphylococcus aureus and Micrococcus luteus. Among these, two strains S4-4 and S4-21 identified as Streptomyces labedae and Streptomyces tirandamycinicus based on 16S rRNA gene sequence were selected for detailed study. The change in the secondary metabolites profile in the presence of Ag-NPs was evaluated using GC-MS and LC-MS analyses. Butanol extracts of spent broth grown with Ag-NPs exhibit strong antimicrobial activity against M. luteus and S. aureus. While the extracts of the controls with the same concentration of Ag-NPs do not show any activity. GC-analysis revealed a clear change in the secondary metabolite profile when grown with Ag-NPs. Similarly, the LC-MS patterns also differ significantly. Results of this study, strongly suggest that sub-lethal concentrations of Ag-NPs influence the production of secondary metabolites by Streptomyces. Besides, LC-MS results identified possible secondary metabolites, associated with oxidative stress and antimicrobial activities. This strategy can be used to possibly induce cryptic biosynthetic gene clusters for the discovery of new lead compounds.

Rapid Gene Silencing Followed by Antimicrobial Susceptibility Testing for Target Validation in Antibiotic Discovery.

Mycobacterium tuberculosis is the main causative agent of tuberculosis (TB)-an ancient yet widespread global infectious disease to which 1.6 million people lost their lives in 2021. Antimicrobial resistance (AMR) has been an ongoing crisis for decades; 4.95 million deaths were associated with antibiotic resistance in 2019. While AMR is a multi-faceted problem, drug discovery is an urgent part of the solution and is at the forefront of modern research.The landscape of drug discovery for TB has undoubtedly been transformed by the development of high-throughput gene-silencing techniques that enable interrogation of every gene in the genome, and their relative contribution to fitness, virulence, and AMR. A recent advance in this area is CRISPR interference (CRISPRi). The application of this technique to antimicrobial susceptibility testing (AST) is the subject of ongoing research in basic science.CRISPRi technology can be used in conjunction with the high-throughput SPOT-culture growth inhibition assay (HT-SPOTi) to rapidly evaluate and assess gene essentiality including non-essential, conditionally essential (by using appropriate culture conditions), and essential genes. In addition, the HT-SPOTi method can develop drug susceptibility and drug resistance profiles.This technology is further useful for drug discovery groups who have designed target-based inhibitors rationally and wish to validate the primary mechanisms of their novel compounds' antibiotic action against the proposed target.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Targeting Hdm2 and Hdm4 in Anticancer Drug Discovery: Implications for Checkpoint Inhibitor Immunotherapy.

Hdm2 and Hdm4 are structural homologs that regulate the tumor suppressor protein, p53. Since some tumors express wild-type p53, Hdm2 and Hdm4 are plausible targets for anticancer drugs, especially in tumors that express wild-type p53. Hdm4 can enhance and antagonize the activity of Tp53, thereby playing a critical role in the regulation of p53's activity and stability. Moreover, Hdm2 and Hdm4 are overexpressed in many cancers, some expressing wild-type Tp53. Due to experimental evidence suggesting that the activation of wild-type Tp53 can augment the antitumor activity by some checkpoint inhibitors, drugs targeting Hdm2 and Hdm4 may be strong candidates for combining with checkpoint inhibitor immunotherapy. However, other evidence suggests that the overexpression of Hdm2 and Hdm4 may indicate poor response to immune checkpoint inhibitors. These findings require careful examination and scrutiny. In this article, a comprehensive analysis of the Hdm2/Hdm4 partnership will be conducted. Furthermore, this article will address the current progress of drug development regarding molecules that target the Hdm2/Hdm4/Tp53 partnership.

Antiviral Drug Discovery.

A vast and painful price has been paid in the battle against viruses in global health [...].

Discovery of GPR84 Fluorogenic Probes Based on a Novel Antagonist for GPR84 Bioimaging.

GPR84 is a promising therapeutic target and biomarker for a range of diseases. In this study, we reported the discovery of BINOL phosphate (BINOP) derivatives as GPR84 antagonists. By investigating the structure-activity relationship, we identified 15S as a novel GPR84 antagonist. 15S exhibits low nanomolar potency and high selectivity for GPR84, while its enantiomer 15R is less active. Next, we rationally designed and synthesized a series of GPR84 fluorogenic probes by conjugating Nile red and compound 15S. The leading hybrid, probe F8, not only retained GPR84 activity but also exhibited low nonspecific binding and a turn-on fluorescent signal in an apolar environment. F8 enabled visualization and detection of GPR84 in GPR84-overexpressing HEK293 cells and lipopolysaccharide-stimulated neutrophils. Furthermore, we demonstrated that F8 can detect upregulated GPR84 protein levels in mice models of inflammatory bowel disease and acute lung injury. Thus, compound F8 represents a promising tool for studying GPR84 functions.

Discovery of BI-9508, a Brain-Penetrant GPR88-Receptor-Agonist Tool Compound for In Vivo Mouse Studies.

Decreased activity and expression of the G-protein coupled receptor GPR88 is linked to many behavior-linked neurological disorders. Published preclinical GPR88 allosteric agonists all have in vivo pharmacokinetic properties that preclude their progression to the clinic, including high lipophilicity and poor brain penetration. Here, we describe our attempts to improve GPR88 agonists' drug-like properties and our analysis of the trade-offs required to successfully target GPR88's allosteric pocket. We discovered two new GPR88 agonists: One that reduced morphine-induced locomotor activity in a murine proof-of-concept study, and the atropoisomeric BI-9508, which is a brain penetrant and has improved pharmacokinetic properties and dosing that recommend it for future in vivo studies in rodents. BI-9508 still suffers from high lipophilicity, and research on this series was halted. Because of its utility as a tool compound, we now offer researchers access to BI-9508 and a negative control free of charge via Boehringer Ingelheim's open innovation portal opnMe.com.

Discovery and Hit to Lead Optimization of Macrocyclic Peptides as Novel Tropomyosin Receptor Kinase A Antagonists.

Tropomyosin receptor kinases (Trks) are receptor tyrosine kinases activated by neurotrophic factors, called neurotrophins. Among them, TrkA interacts with the nerve growth factor (NGF), which leads to pain induction. mRNA-display screening was carried out to discover a hit compound 2, which inhibits protein-protein interactions between TrkA and NGF. Subsequent structure optimization improving phosphorylation inhibitory activity and serum stability was pursued using a unique process that took advantage of the peptide being synthesized by translation from mRNA. This gave peptide 19, which showed an analgesic effect in a rat incisional pain model. The peptides described here can serve as a new class of analgesics, and the structure optimization methods reported provide a strategy for discovering new peptide drugs.

In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot.

Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify since novel therapeutic targets are often hard-to-drug proteins. We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets. FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE). The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments. A neural network model is used to retain fragments with the highest likelihood of being native binders. The seeded fragments then inform ultra-large-scale virtual screening of commercially available compounds. We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising drug target implicated in triple negative breast cancer. FRASE-based virtual screening identifies a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depletion-insensitive cells.

GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph.

BACKGROUND: Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery. RESULTS: We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs. CONCLUSIONS: GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo's dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

[Advances in bioinformatics-based protein function prediction].

With the increasing of computer power and rapid expansion of biological data, the application of bioinformatics tools has become the mainstream approach to address biological problems. The accurate identification of protein function by bioinformatics tools is crucial for both biomedical research and drug discovery, making it a hot topic of research. In this paper, we categorize bioinformatics-based protein function prediction methods into three categories: protein sequence-based methods, protein structure-based methods, and protein interaction networks-based methods. We further analyze these specific algorithms, highlighting the latest research advancements and providing valuable references for the application of bioinformatics-based protein function prediction in biomedical research and drug discovery.

Chemical language modeling with structured state space sequence models.

Generative deep learning is reshaping drug design. Chemical language models (CLMs) - which generate molecules in the form of molecular strings - bear particular promise for this endeavor. Here, we introduce a recent deep learning architecture, termed Structured State Space Sequence (S4) model, into de novo drug design. In addition to its unprecedented performance in various fields, S4 has shown remarkable capabilities to learn the global properties of sequences. This aspect is intriguing in chemical language modeling, where complex molecular properties like bioactivity can 'emerge' from separated portions in the molecular string. This observation gives rise to the following question: Can S4 advance chemical language modeling for de novo design? To provide an answer, we systematically benchmark S4 with state-of-the-art CLMs on an array of drug discovery tasks, such as the identification of bioactive compounds, and the design of drug-like molecules and natural products. S4 shows a superior capacity to learn complex molecular properties, while at the same time exploring diverse scaffolds. Finally, when applied prospectively to kinase inhibition, S4 designs eight of out ten molecules that are predicted as highly active by molecular dynamics simulations. Taken together, these findings advocate for the introduction of S4 into chemical language modeling - uncovering its untapped potential in the molecular sciences.

MiRAGE: mining relationships for advanced generative evaluation in drug repositioning.

MOTIVATION: Drug repositioning, the identification of new therapeutic uses for existing drugs, is crucial for accelerating drug discovery and reducing development costs. Some methods rely on heterogeneous networks, which may not fully capture the complex relationships between drugs and diseases. However, integrating diverse biological data sources offers promise for discovering new drug-disease associations (DDAs). Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. However, the challenge lies in effectively integrating different biological data sources to identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms. RESULTS: In response to this challenge, we present MiRAGE, a novel computational method for drug repositioning. MiRAGE leverages a three-step framework, comprising negative sampling using hard negative mining, classification employing random forest models, and feature selection based on feature importance. We evaluate MiRAGE on multiple benchmark datasets, demonstrating its superiority over state-of-the-art algorithms across various metrics. Notably, MiRAGE consistently outperforms other methods in uncovering novel DDAs. Case studies focusing on Parkinson's disease and schizophrenia showcase MiRAGE's ability to identify top candidate drugs supported by previous studies. Overall, our study underscores MiRAGE's efficacy and versatility as a computational tool for drug repositioning, offering valuable insights for therapeutic discoveries and addressing unmet medical needs.

High-Throughput Covalent Modifier Screening with Acoustic Ejection Mass Spectrometry.

Interests in covalent drugs have grown in modern drug discovery as they could tackle challenging targets traditionally considered "undruggable". The identification of covalent binders to target proteins typically involves directly measuring protein covalent modifications using high-resolution mass spectrometry. With a continually expanding library of compounds, conventional mass spectrometry platforms such as LC-MS and SPE-MS have become limiting factors for high-throughput screening. Here, we introduce a prototype high-resolution acoustic ejection mass spectrometry (AEMS) system for the rapid screening of a covalent modifier library comprising ∼10,000 compounds against a 50 kDa-sized target protein─Werner syndrome helicase. The screening samples were arranged in a 1536-well format. The sample buffer containing high-concentration salts was directly analyzed without any cleanup steps, minimizing sample preparation efforts and ensuring protein stability. The entire AEMS analysis process could be completed within a mere 17 h. An automated data analysis tool facilitated batch processing of the sample data and quantitation of the formation of various covalent protein-ligand adducts. The screening results displayed a high degree of fidelity, with a Z' factor of 0.8 and a hit rate of 2.3%. The identified hits underwent orthogonal testing in a biochemical activity assay, revealing that 75% were functional antagonists of the target protein. Notably, a comparative analysis with LC-MS showcased the AEMS platform's low risk of false positives or false negatives. This innovative platform has enabled robust high-throughput covalent modifier screening, featuring a 10-fold increase in library size and a 10- to 100-fold increase in throughput when compared with similar reports in the existing literature.

DrugRepoBank: a comprehensive database and discovery platform for accelerating drug repositioning.

In recent years, drug repositioning has emerged as a promising alternative to the time-consuming, expensive and risky process of developing new drugs for diseases. However, the current database for drug repositioning faces several issues, including insufficient data volume, restricted data types, algorithm inaccuracies resulting from the neglect of multidimensional or heterogeneous data, a lack of systematic organization of literature data associated with drug repositioning, limited analytical capabilities and user-unfriendly webpage interfaces. Hence, we have established the first all-encompassing database called DrugRepoBank, consisting of two main modules: the 'Literature' module and the 'Prediction' module. The 'Literature' module serves as the largest repository of literature-supported drug repositioning data with experimental evidence, encompassing 169 repositioned drugs from 134 articles from 1 January 2000 to 1 July 2023. The 'Prediction' module employs 18 efficient algorithms, including similarity-based, artificial-intelligence-based, signature-based and network-based methods to predict repositioned drug candidates. The DrugRepoBank features an interactive and user-friendly web interface and offers comprehensive functionalities such as bioinformatics analysis of disease signatures. When users provide information about a drug, target or disease of interest, DrugRepoBank offers new indications and targets for the drug, proposes new drugs that bind to the target or suggests potential drugs for the queried disease. Additionally, it provides basic information about drugs, targets or diseases, along with supporting literature. We utilize three case studies to demonstrate the feasibility and effectiveness of predictively repositioned drugs within DrugRepoBank. The establishment of the DrugRepoBank database will significantly accelerate the pace of drug repositioning. Database URL:  https://awi.cuhk.edu.cn/DrugRepoBank.

Active Discovery of the Allosteric Inhibitor Targeting Botrytis cinerea Chitinase Based on Neural Relational Inference for Food Preservation.

Currently, allosteric inhibitors have emerged as an effective strategy in the development of preservatives against the drug-resistant Botrytis cinerea (B. cinerea). However, their passively driven development efficiency has proven challenging to meet the practical demands. Here, leveraging the deep learning Neural Relational Inference (NRI) framework, we actively identified an allosteric inhibitor targeting B. cinerea Chitinase, namely, 2-acetonaphthone. 2-Acetonaphthone binds to the crucial domain of Chitinase, forming the strong interaction with the allosteric sites. Throughout the interaction process, 2-acetonaphthone diminished the overall connectivity of the protein, inducing conformational changes. These findings align with the results obtained from Chitinase activity experiments, revealing an IC50 value of 67.6 µg/mL. Moreover, 2-acetonaphthone exhibited outstanding anti-B. cinerea activity by inhibiting Chitinase. In the gray mold infection model, 2-acetonaphthone significantly extended the preservation time of cherry tomatoes, positioning it as a promising preservative for fruit storage.