Application of Fragment-Based Drug Discovery to Versatile Targets

Hijacking monopolar spindle 1 (MPS1) for various cancer types by small molecular inhibitors: Deep insights from a decade of research and patents

Monopolar spindle 1 (MPS1) has garnered significant attention due to its pivotal role in regulating the cell cycle. Anomalous expression and hyperactivation of MPS1 have been associated with the onset and advancement of diverse cancers, positioning it as a promising target for therapeutic interventions. This review focuses on MPS1 small molecule inhibitors from the past decade, exploring design strategies, structure-activity relationships (SAR), safety considerations, and clinical performance. Notably, we propose prospects for MPS1 degraders based on proteolysis targeting chimeras (PROTACs), as well as reversible covalent bonding as innovative MPS1 inhibitor design strategies. The objective is to provide valuable information for future development and novel perspectives on potential MPS1 inhibitors.

Computational Docking as a Tool in Guiding the Drug Design of Rutaecarpine Derivatives as Potential SARS-CoV-2 Inhibitors

COVID-19 continues to spread around the world. This is mainly because new variants of the SARS-CoV-2 virus emerge due to genomic mutations, evade the immune system and result in the effectiveness of current therapeutics being reduced. We previously established a series of detection platforms, comprising computational docking analysis, S-protein-based ELISA, pseudovirus entry, and 3CL protease activity assays, which allow us to screen a large library of phytochemicals from natural products and to determine their potential in blocking the entry of SARS-CoV-2. In this new screen, rutaecarpine (an alkaloid from Evodia rutaecarpa) was identified as exhibiting anti-SARS-CoV-2 activity. Therefore, we conducted multiple rounds of structure-activity-relationship (SAR) studies around this phytochemical and generated several rutaecarpine analogs that were subjected to in vitro evaluations. Among these derivatives, RU-75 and RU-184 displayed remarkable inhibitory activity when tested in the 3CL protease assay, S-protein-based ELISA, and pseudovirus entry assay (for both wild-type and omicron variants), and they attenuated the inflammatory response induced by SARS-CoV-2. Interestingly, RU-75 and RU-184 both appeared to be more potent than rutaecarpine itself, and this suggests that they might be considered as lead candidates for future pharmacological elaboration.

Fragment-based drug design of novel inhibitors targeting lipoprotein (a) kringle domain KIV-10-mediated cardiovascular disease

Cardiovascular diseases (CVDs) are the leading cause of death globally, attributed to a complex etiology involving metabolic, genetic, and protein-related factors. Lipoprotein(a) (Lp(a)), identified as a genetic risk factor, exhibits elevated levels linked to an increased risk of cardiovascular diseases. The lipoprotein(a) kringle domains have recently been identified as a potential target for the treatment of CVDs, in this study we utilized a fragment-based drug design approach to design a novel, potent, and safe inhibitor for lipoprotein(a) kringle domain. With the use of fragment library (61,600 fragments) screening, combined with analyses such as MM/GBSA, molecular dynamics simulation (MD), and principal component analysis, we successfully identified molecules effective against the kringle domains of Lipoprotein(a). The hybridization process (Breed) of the best fragments generated a novel 249 hybrid molecules, among them 77 exhibiting superior binding affinity (≤ -7 kcal/mol) compared to control AZ-02 (-6.9 kcal/mol), Importantly, the top ten molecules displayed high similarity to the control AZ-02. Among the top ten molecules, BR1 exhibited the best docking energy (-11.85 kcal/mol ), and higher stability within the protein LBS site, demonstrating the capability to counteract the pathophysiological effects of lipoprotein(a) [Lp(a)]. Additionally, principal component analysis (PCA) highlighted a similar trend of motion during the binding of BR1 and the control compound (AZ-02), limiting protein mobility and reducing conformational space. Moreover, ADMET analysis indicated favorable drug-like properties, with BR1 showing minimal violations of Lipinski's rules. Overall, the identified compounds hold promise as potential therapeutics, addressing a critical need in cardiovascular medicine. Further preclinical and clinical evaluations are needed to validate their efficacy and safety, potentially ushering in a new era of targeted therapies for CVDs.

Grouping strategies for assessing and managing persistent and mobile substances

Background

Persistent, mobile and toxic (PMT), or very persistent and very mobile (vPvM) substances are a wide class of chemicals that are recalcitrant to degradation, easily transported, and potentially harmful to humans and the environment. Due to their persistence and mobility, these substances are often widespread in the environment once emitted, particularly in water resources, causing increased challenges during water treatment processes. Some PMT/vPvM substances such as GenX and perfluorobutane sulfonic acid have been identified as substances of very high concern (SVHCs) under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) regulation. With hundreds to thousands of potential PMT/vPvM substances yet to be assessed and managed, effective and efficient approaches that avoid a case-by-case assessment and prevent regrettable substitution are necessary to achieve the European Union's zero-pollution goal for a non-toxic environment by 2050.

Main

Substance grouping has helped global regulation of some highly hazardous chemicals, e.g., through the Montreal Protocol and the Stockholm Convention. This article explores the potential of grouping strategies for identifying, assessing and managing PMT/vPvM substances. The aim is to facilitate early identification of lesser-known or new substances that potentially meet PMT/vPvM criteria, prompt additional testing, avoid regrettable use or substitution, and integrate into existing risk management strategies. Thus, this article provides an overview of PMT/vPvM substances and reviews the definition of PMT/vPvM criteria and various lists of PMT/vPvM substances available. It covers the current definition of groups, compares the use of substance grouping for hazard assessment and regulation, and discusses the advantages and disadvantages of grouping substances for regulation. The article then explores strategies for grouping PMT/vPvM substances, including read-across, structural similarity and commonly retained moieties, as well as the potential application of these strategies using cheminformatics to predict P, M and T properties for selected examples.

Conclusions

Effective substance grouping can accelerate the assessment and management of PMT/vPvM substances, especially for substances that lack information. Advances to read-across methods and cheminformatics tools are needed to support efficient and effective chemical management, preventing broad entry of hazardous chemicals into the global market and favouring safer and more sustainable alternatives.

High-resolution RNA tertiary structures in Zika virus stem-loop A for the development of inhibitory small molecules

Flaviviruses such as Zika (ZIKV) and dengue virus (DENV) are positive-sense RNA viruses belonging to Flaviviridae The flavivirus genome contains a 5' end stem-loop promoter sequence known as stem-loop A (SLA) that is recognized by the flavivirus polymerase NS5 during viral RNA synthesis and 5' guanosine cap methylation. The crystal structures of ZIKV and DENV SLAs show a well-defined fold, consisting of a bottom stem, side loop, and top stem-loop, providing unique interaction sites for small molecule inhibitors to disrupt the promoter function. To facilitate the identification of small molecule binding sites in flavivirus SLA, we determined high-resolution structures of the bottom and top stems of ZIKV SLA, which contain a single U- or G-bulge, respectively. Both bulge nucleotides exhibit multiple orientations, from folded back on the adjacent nucleotide to flipped out of the helix, and are stabilized by stacking or base triple interactions. These structures suggest that even a single unpaired nucleotide can provide flexibility to RNA structures, and its conformation is mainly determined by the stabilizing chemical environment. To facilitate discovery of small molecule inhibitors that interfere with the functions of ZIKV SLA, we screened and identified compounds that bind to the bottom and top stems of ZIKV SLA.

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.

Protein mimetic 2D FAST rescues alpha synuclein aggregation mediated early and post disease Parkinson's phenotypes

Abberent protein-protein interactions potentiate many diseases and one example is the toxic, self-assembly of α-Synuclein in the dopaminergic neurons of patients with Parkinson's disease; therefore, a potential therapeutic strategy is the small molecule modulation of α-Synuclein aggregation. In this work, we develop an Oligopyridylamide based 2-dimensional Fragment-Assisted Structure-based Technique to identify antagonists of α-Synuclein aggregation. The technique utilizes a fragment-based screening of an extensive array of non-proteinogenic side chains in Oligopyridylamides, leading to the identification of NS132 as an antagonist of the multiple facets of α-Synuclein aggregation. We further identify a more cell permeable analog (NS163) without sacrificing activity. Oligopyridylamides rescue α-Synuclein aggregation mediated Parkinson's disease phenotypes in dopaminergic neurons in early and post disease Caenorhabditis elegans models. We forsee tremendous potential in our technique to identify lead therapeutics for Parkinson's disease and other diseases as it is expandable to other oligoamide scaffolds and a larger array of side chains.

Fragment-based drug discovery for disorders of the central nervous system: designing better drugs piece by piece

Fragment-based drug discovery (FBDD) has emerged as a powerful strategy to confront the challenges faced by conventional drug development approaches, particularly in the context of central nervous system (CNS) disorders. FBDD involves the screening of libraries that comprise thousands of small molecular fragments, each no greater than 300 Da in size. Unlike the generally larger molecules from high-throughput screening that limit customisation, fragments offer a more strategic starting point. These fragments are inherently compact, providing a strong foundation with good binding affinity for the development of drug candidates. The minimal elaboration required to transition the hit into a drug-like molecule is not only accelerated, but also it allows for precise modifications to enhance both their activity and pharmacokinetic properties. This shift towards a fragment-centric approach has seen commercial success and holds considerable promise in the continued streamlining of the drug discovery and development process. In this review, we highlight how FBDD can be integrated into the CNS drug discovery process to enhance the exploration of a target. Furthermore, we provide recent examples where FBDD has been an integral component in CNS drug discovery programs, enabling the improvement of pharmacokinetic properties that have previously proven challenging. The FBDD optimisation process provides a systematic approach to explore this vast chemical space, facilitating the discovery and design of compounds piece by piece that are capable of modulating crucial CNS targets.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Cryo-electron microscopy-based drug design

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

Identification of small-molecule binding sites of a ubiquitin-conjugating enzyme-UBE2T through fragment-based screening

UBE2T is an attractive target for drug development due to its linkage with several types of cancers. However, the druggability of ubiquitin-conjugating E2 (UBE2T) is low because of the lack of a deep and hydrophobic pocket capable of forming strong binding interactions with drug-like small molecules. Here, we performed fragment screening using 19 F-nuclear magnetic resonance (NMR) and validated the hits with 1 H-15 N-heteronuclear single quantum coherence (HSQC) experiment and X-ray crystallographic studies. The cocrystal structures obtained revealed the binding modes of the hit fragments and allowed for the characterization of the fragment-binding sites. Further screening of structural analogues resulted in the identification of a compound series with inhibitory effect on UBE2T activity. Our current study has identified two new binding pockets in UBE2T, which will be useful for the development of small molecules to regulate the function of this protein. In addition, the compounds identified in this study can serve as chemical starting points for the development of UBE2T modulators.

Targeting the Main Protease (Mpro, nsp5) by Growth of Fragment Scaffolds Exploiting Structure-Based Methodologies

The main protease Mpro, nsp5, of SARS-CoV-2 (SCoV2) is one of its most attractive drug targets. Here, we report primary screening data using nuclear magnetic resonance spectroscopy (NMR) of four different libraries and detailed follow-up synthesis on the promising uracil-containing fragment Z604 derived from these libraries. Z604 shows time-dependent binding. Its inhibitory effect is sensitive to reducing conditions. Starting with Z604, we synthesized and characterized 13 compounds designed by fragment growth strategies. Each compound was characterized by NMR and/or activity assays to investigate their interaction with Mpro. These investigations resulted in the four-armed compound 35b that binds directly to Mpro. 35b could be cocrystallized with Mpro revealing its noncovalent binding mode, which fills all four active site subpockets. Herein, we describe the NMR-derived fragment-to-hit pipeline and its application for the development of promising starting points for inhibitors of the main protease of SCoV2.

An adamantyl-caffeoyl-anilide exhibits broad-spectrum antibacterial activity by inhibiting FtsZ assembly and Z-ring formation

Several harmful bacteria have evolved resistance to conventional antibiotics due to their extensive usage. FtsZ, a principal bacterial cell division protein, is considered as an important drug target to combat resistance. We identified a caffeoyl anilide derivative, (E)-N-(4-(3-(3,4-dihydroxyphenyl)acryloyl)phenyl)-1-adamantylamide (compound 11) as a new antimicrobial agent targeting FtsZ. Compound 11 caused cell elongation in Mycobacterium smegmatis, Bacillus subtilis, and Escherichia coli cells, indicating that it inhibits cell partitioning. Compound 11 inhibited the assembly of Mycobacterium smegmatis FtsZ (MsFtsZ), forming short and thin filaments in vitro. Interestingly, the compound increased the rate of GTP hydrolysis of MsFtsZ. Compound 11 also impeded the assembly of Mycobacterium tuberculosis FtsZ. Fluorescence and absorption spectroscopic analysis suggested that compound 11 binds to MsFtsZ and produces conformational changes in FtsZ. The docking analysis indicated that the compound binds at the interdomain cleft of MsFtsZ. Further, it caused delocalization of the Z-ring in Mycobacterium smegmatis and Bacillus subtilis without affecting DNA segregation. Notably, compound 11 did not inhibit tubulin polymerization, the eukaryotic homolog of FtsZ, suggesting its specificity on bacteria. The evidence indicated that compound 11 exerts its antibacterial effect by impeding FtsZ assembly and has the potential to be developed as a broad-spectrum antimicrobial agent.

Application scenario-oriented molecule generation platform developed for drug discovery

Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE, RNN, GAN, etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, active learning, etc.) and input representations (1D SMILES, 2D graph, 3D shape, binding site, pharmacophore, etc.), to enable customized solutions for a given molecular design scenario. Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization. We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations. In addition, remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics.

Aza-Heterocyclic Building Blocks with In-Ring CF2 -Fragment

Modern organic chemistry is a titan supporting and reinforcing pharmaceutical, agricultural, food and material science products. Over the past decades, the organic compounds market has been evolving to meet all the research demands. In this regard, medicinal chemistry is especially dependent on available chemical space as subtle tuning of the molecule structure is required to create a drug with relevant physicochemical properties and a remarkable activity profile. The recent rapid evolution of synthetic methodology to deploy fluorine has brought fluorinated compounds to the spotlight of MedChem community. And now unique properties of fluorine still keep fascinating more and more as its justified installation into a molecular framework has a beneficial impact on membrane permeability, lipophilicity, metabolic stability, pharmacokinetic properties, conformation, pKa , etc. The backward influence of medicinal chemistry on organic synthesis has also changed the landscape of the latter towards new fluorinated topologies as well. Such complex relationships create a flexible and ever-changing ecosystem. Given that MedChem investigations strongly lean on the ability to reach suitable building blocks and the existence of reliable synthetic methods in this review we collected advances in the chemistry of respectful, but still enigmatic gem-difluorinated aza-heterocyclic building blocks.

Target-based drug discovery: Applications of fluorescence techniques in high throughput and fragment-based screening

Target-based discovery of first-in-class therapeutics demands an in-depth understanding of the molecular mechanisms underlying human diseases. Precise measurements of cellular and biochemical activities are critical to gain mechanistic knowledge of biomolecules and their altered function in disease conditions. Such measurements enable the development of intervention strategies for preventing or treating diseases by modulation of desired molecular processes. Fluorescence-based techniques are routinely employed for accurate and robust measurements of in-vitro activity of molecular targets and for discovering novel chemical molecules that modulate the activity of molecular targets. In the current review, the authors focus on the applications of fluorescence-based high throughput screening (HTS) and fragment-based ligand discovery (FBLD) techniques such as fluorescence polarization (FP), Förster resonance energy transfer (FRET), fluorescence thermal shift assay (FTSA) and microscale thermophoresis (MST) for the discovery of chemical probe to exploring target's role in disease biology and ultimately, serve as a foundation for drug discovery. Some recent advancements in these techniques for compound library screening against important classes of drug targets, such as G-protein-coupled receptors (GPCRs) and GTPases, as well as phosphorylation- and acetylation-mediated protein-protein interactions, are discussed. Overall, this review presents a landscape of how these techniques paved the way for the discovery of small-molecule modulators and biologics against these targets for therapeutic benefits.

Targeting ion channels with ultra-large library screening for hit discovery

Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.

Measurement and utilization of the proteomic reactivity by mass spectrometry

Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.

Designing of fragment based inhibitors with improved activity against E. coli AmpC β-lactamase compared to the conventional antibiotics

One of the most common primary resistance mechanism of multi-drug resistant (MDR) Gram negative pathogenic bacteria to combat β-lactam antibiotics, such as penicillins, cephalosporins and carbapenems is the generation of β- lactamases. The uropathogenic E. coli is mostly getting multi-drug resistance due to the synthesis of AmpC β-lactamases and therefore new antibiotics and inhibitors are needed to treat the evolving infections. The current study was designed for targetting AmpC β-lactamase of E. coli using molecular docking based virtual screening, linking fragments for designing novel compounds and binding mode analysis using molecular dynamic simulation with target protein. The FCH group all-purpose fragment library consisting of 9388 fragments has been screened against AmpC β-lactamase protein of E. coli and the antibiotics and anti-infectives used in treatment of Urinary tract Infections (UTIs) were also screened with AmpC β-lactamase protein. Among the 9388 fragments, 339 fragment candidates were selected and linked with cefepime antibiotic having maximum binding affinity for AmpC target protein. Computational analysis of interactions as well as molecular dynamics (MD) simulations were also conducted for identifying the most promising ligand-pocket complexes from docking investigations to comprehend their thermodynamic properties and verify the docking outcomes as well. Overall, the linked complexes (LCs) showed good binding interactions with AmpC β-lactamase. Interestingly, our fragment-based LCs remained relatively stable in comparison with cefepime antibiotic. Moreover, S12 fragment linked complex remained the most stable during 50 ns with remarkable number of interactions indicating it as promising candidate in novel lead discovery against MDR E. coli infections.

What drives researcher preferences for chemical compounds? Evidence from conjoint analysis

We investigated the attributes and attribute levels that affect researcher preferences for chemical compounds. We conducted a conjoint analysis on survey data of Korean researchers using chemical compounds from the Korean Chemical Bank (KCB). The analysis estimated the part-worth utility for each attribute's level, calculated relative importance of attributes, and classified user segmentation with different patterns. The results show that the structure database offers the highest part-worth utility to researchers, followed by high new functionality, price, screening service, and drug action data provided only by the KCB. Notably, researchers view the offer of a structured database and high new functionality as more important than other attributes in decision-making about research and development of chemical compounds. Furthermore, the results of segmentation analysis demonstrated that researchers have distinct usage patterns of chemical compounds: researchers consider structure database and high new functionality in cluster 1; and high new functionality and price in cluster 2, to be the most appealing. We discussed some policy and strategic implications based on the findings of this study and proposed some limitations.

An Exploration of Small Molecules That Bind Human Single-Stranded DNA Binding Protein 1

Human single-stranded DNA binding protein 1 (hSSB1) is critical to preserving genome stability, interacting with single-stranded DNA (ssDNA) through an oligonucleotide/oligosaccharide binding-fold. The depletion of hSSB1 in cell-line models leads to aberrant DNA repair and increased sensitivity to irradiation. hSSB1 is over-expressed in several types of cancers, suggesting that hSSB1 could be a novel therapeutic target in malignant disease. hSSB1 binding studies have focused on DNA; however, despite the availability of 3D structures, small molecules targeting hSSB1 have not been explored. Quinoline derivatives targeting hSSB1 were designed through a virtual fragment-based screening process, synthesizing them using AlphaLISA and EMSA to determine their affinity for hSSB1. In parallel, we further screened a structurally diverse compound library against hSSB1 using the same biochemical assays. Three compounds with nanomolar affinity for hSSB1 were identified, exhibiting cytotoxicity in an osteosarcoma cell line. To our knowledge, this is the first study to identify small molecules that modulate hSSB1 activity. Molecular dynamics simulations indicated that three of the compounds that were tested bound to the ssDNA-binding site of hSSB1, providing a framework for the further elucidation of inhibition mechanisms. These data suggest that small molecules can disrupt the interaction between hSSB1 and ssDNA, and may also affect the ability of cells to repair DNA damage. This test study of small molecules holds the potential to provide insights into fundamental biochemical questions regarding the OB-fold.

Advancing small-molecule drug discovery by encoded dual-display technologies

DNA-encoded chemical library technology (DECL or DEL) has become an important pillar for small-molecule drug discovery. The technology rapidly identifies small-molecule hits for relevant target proteins at low cost and with a high success rate, including ligands for targeted protein degradation (TPD). More recently, the setup of DNA- or peptide nucleic acid (PNA)-encoded chemical libraries based on the simultaneous display of ligand pairs, termed dual-display, allows for more sophisticated applications which will be reviewed herein. Both stable and dynamic dual-display DEL technologies enable innovative affinity-based selection modalities, even on and in cells. Novel methods for a seamless conversion between single- and double-stranded library formats allow for even more versatility. We present the first candidates emerging from dual-display technologies and discuss the future potential of dual-display for drug discovery.

TWN-FS method: A novel fragment screening method for drug discovery

Fragment-based drug discovery (FBDD) is a well-established and effective method for generating diverse and novel hits in drug design. Kinases are suitable targets for FBDD due to their well-defined structure. Water molecules contribute to structure and function of proteins and also influence the environment within the binding pocket. Water molecules form a variety of hydrogen-bonded cyclic water-ring networks, collectively known as topological water networks (TWNs). Analyzing the TWNs in protein binding sites can provide valuable insights into potential locations and shapes for fragments within the binding site. Here, we introduce TWN-based fragment screening (TWN-FS) method, a novel screening method that suggests fragments through grouped TWN analysis within the protein binding site. We used this method to screen known CDK2, CHK1, IGF1R and ERBB4 inhibitors. Our findings suggest that TWN-FS method has the potential to effectively screen fragments. The TWN-FS method package is available on GitHub at https://github.com/pkj0421/TWN-FS.

Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists

Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the [Formula: see text]-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported [Formula: see text]-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale.

Discovery of novel potent drugs for influenza by inhibiting the vital function of neuraminidase via fragment-based drug design (FBDD) and molecular dynamics simulation strategies

The current work describes a fragment linking methodology to generate new neuraminidase inhibitors. A total number of 28,977 fragments from Zinc 20 have been obtained and screened for neuraminidase receptor affinity. Using Schrödinger software, the highest-scoring 270 fragment hits (with scores greater than -7.6) were subjected to fragment combining to create 100 new molecules. These 100 novel compounds were studied using XP docking to evaluate the molecular interaction modes and their binding affinity to neuraminidase receptor. The top ten molecules were selected, for ADMET, drug-likeness features. Based on these characteristics, the best four developed molecules and Zanamivir were submitted to a molecular dynamics simulation investigation to estimate their dynamics within the neuraminidase receptor using Gromacs software. All MD simulation findings show that the generated complexes are very stable when compared to the clinical inhibitor (Zanamivir). In addition, the four designed neuraminidase inhibitors formed very stable complexes with neuraminidase receptor (with total binding energies ranging from -83.50 to -107.85 Kj/mol) according to the total binding energy calculated by MM-PBSA. For the objective of developing new influenza medications, these novel molecules have the potential to be further evaluated in vitro and in vivo for influenza drug discovery.Communicated by Ramaswamy H. Sarma.

Recombinant protein production for structural and kinetic studies: A case study using M. tuberculosis α-methylacyl-CoA racemase (MCR)

Modern drug discovery is a target-driven approach in which a particular protein such as an enzyme is implicated in the disease process. Commonly, small-molecule drugs are identified using screening, rational design, and structural biology approaches. Drug screening, testing and optimization is typically conducted in vitro, and copious amounts of protein are required. The advent of recombinant DNA technologies has resulted in a rise in proteins purified by affinity techniques, typically by incorporating an "affinity tag" at the N- or C-terminus. Use of these tagged proteins and affinity techniques comes with a host of issues. This chapter describes the production of an untagged enzyme, α-methylacyl-CoA racemase (MCR) from Mycobacterium tuberculosis, using a recombinant E. coli system. Purification of the enzyme on a 100 mg scale using tandem anion-exchange chromatographies (DEAE-sepharose and RESOURCE-Q columns), and size-exclusion chromatographies is described. A modified protocol allowing the purification of cationic proteins is also described, based on tandem cation-exchange chromatographies (using CM-sepharose and RESOURCE-S columns) and size-exclusion chromatographies. The resulting MCR protein is suitable for biochemical and structural biology applications. The described protocols have wide applicability to the purification of other recombinant proteins and enzymes without using affinity chromatography.

Targeting RNA-binding proteins with small molecules: Perspectives, pitfalls and bifunctional molecules

RNA-binding proteins (RBPs) play vital roles in organisms through binding with RNAs to regulate their functions. Small molecules affecting the function of RBPs have been developed, providing new avenues for drug discovery. Herein, we describe the perspectives on developing small molecule regulators of RBPs. The following types of small molecule modulators are of great interest in drug discovery: small molecules binding to RBPs to affect interactions with RNA molecules, bifunctional molecules binding to RNA or RBP to influence their interactions, and other types of molecules that affect the stability of RNA or RBPs. Moreover, we emphasize that the bifunctional molecules may play important roles in small molecule development to overcome the challenges encountered in the process of drug discovery.

Fragment-Based Discovery of Novel VE-PTP Inhibitors Using Orthogonal Biophysical Techniques

Tyrosine phosphorylation is an essential post-translational modification that regulates various biological events and is implicated in many diseases including cancer and atherosclerosis. Vascular endothelial protein tyrosine phosphatase (VE-PTP), which plays an important role in vascular homeostasis and angiogenesis, is therefore an attractive drug target for these diseases. However, there are still no drugs targeting PTP including VE-PTP. In this paper, we report the discovery of a novel VE-PTP inhibitor, Cpd-2, by fragment-based screening combining various biophysical techniques. Cpd-2 is the first VE-PTP inhibitor with a weakly acidic structure and high selectivity, unlike known strongly acidic inhibitors. We believe that this compound represents a new possibility for the development of bioavailable VE-PTP inhibitors.

Synthetic Pathways to Non-Psychotropic Phytocannabinoids as Promising Molecules to Develop Novel Antibiotics: A Review

Due to the rapid emergence of multi drug resistant (MDR) pathogens against which current antibiotics are no longer functioning, severe infections are becoming practically untreatable. Consequently, the discovery of new classes of effective antimicrobial agents with novel mechanism of action is becoming increasingly urgent. The bioactivity of Cannabis sativa, an herbaceous plant used for millennia for medicinal and recreational purposes, is mainly due to its content in phytocannabinoids (PCs). Among the 180 PCs detected, cannabidiol (CBD), Δ⁸ and Δ⁹-tetrahydrocannabinols (Δ⁸-THC and Δ⁹-THC), cannabichromene (CBC), cannabigerol (CBG), cannabinol (CBN) and some of their acidic precursors have demonstrated from moderate to potent antibacterial effects against Gram-positive bacteria (MICs 0.5-8 µg/mL), including methicillin-resistant Staphylococcus aureus (MRSA), epidemic MRSA (EMRSA), as well as fluoroquinolone and tetracycline-resistant strains. Particularly, the non-psychotropic CBG was also capable to inhibit MRSA biofilm formation, to eradicate even mature biofilms, and to rapidly eliminate MRSA persiter cells. In this scenario, CBG, as well as other minor non-psychotropic PCs, such as CBD, and CBC could represent promising compounds for developing novel antibiotics with high therapeutic potential. Anyway, further studies are necessary, needing abundant quantities of such PCs, scarcely provided naturally by Cannabis plants. Here, after an extensive overture on cannabinoids including their reported antimicrobial effects, aiming at easing the synthetic production of the necessary amounts of CBG, CBC and CBD for further studies, we have, for the first time, systematically reviewed the synthetic pathways utilized for their synthesis, reporting both reaction schemes and experimental details.

Characterization of epitranscriptome reader proteins experimentally and in silico: Current knowledge and future perspectives beyond the YTH domain

To date, over 150 chemical modifications to the four canonical RNA bases have been discovered, known collectively as the epitranscriptome. Many of these modifications have been implicated in a variety of cellular processes and disease states. Additional work has been done to identify proteins known as "readers" that selectively interact with RNAs that contain specific chemical modifications. Protein interactomes with N6-methyladenosine (m⁶A), N1-methyladenosine (m¹A), N5-methylcytosine (m⁵C), and 8-oxo-7,8-dihydroguanosine (8-oxoG) have been determined, mainly through experimental advances in proteomics techniques. However, relatively few proteins have been confirmed to bind directly to RNA containing these modifications. Furthermore, for many of these protein readers, the exact binding mechanisms as well as the exclusivity for recognition of modified RNA species remain elusive, leading to questions regarding their roles within different cellular processes. In the case of the YT-521B homology (YTH) family of proteins, both experimental and in silico techniques have been leveraged to provide valuable biophysical insights into the mechanisms of m⁶A recognition at atomic resolution. To date, the YTH family is one of the best characterized classes of readers. Here, we review current knowledge about epitranscriptome recognition of the YTH domain proteins from previously published experimental and computational studies. We additionally outline knowledge gaps for proteins beyond the well-studied human YTH domains and the current in silico techniques and resources that can enable investigation of protein interactions with modified RNA outside of the YTH-m⁶A context.

Emerging Pharmacotherapeutic Strategies to Overcome Undruggable Proteins in Cancer

Targeted therapies in cancer treatment can improve in vivo efficacy and reduce adverse effects by altering the tissue exposure of specific biomolecules. However, there are still large number of target proteins in cancer are still undruggable, owing to the following factors including (1) lack of ligand-binding pockets, (2) function based on protein-protein interactions (PPIs), (3) the highly specific conserved active sites among protein family members, and (4) the variability of tertiary docking structures. The current status of undruggable targets proteins such as KRAS, TP53, C-MYC, PTP, are carefully introduced in this review. Some novel techniques and drug designing strategies have been applicated for overcoming these undruggable proteins, and the most classic and well-known technology is proteolysis targeting chimeras (PROTACs). In this review, the novel drug development strategies including targeting protein degradation, targeting PPI, targeting intrinsically disordered regions, as well as targeting protein-DNA binding are described, and we also discuss the potential of these strategies for overcoming the undruggable targets. Besides, intelligence-assisted technologies like Alpha-Fold help us a lot to predict the protein structure, which is beneficial for drug development. The discovery of new targets and the development of drugs targeting them, especially those undruggable targets, remain a huge challenge. New drug development strategies, better extraction processes that do not disrupt protein-protein interactions, and more precise artificial intelligence technologies may provide significant assistance in overcoming these undruggable targets.

Fragment-Based Approaches to Identify RNA Binders

Although fragment-based drug discovery (FBDD) has been successfully implemented and well-explored for protein targets, its feasibility for RNA targets is emerging. Despite the challenges associated with the selective targeting of RNA, efforts to integrate known methods of RNA binder discovery with fragment-based approaches have been fruitful, as a few bioactive ligands have been identified. Here, we review various fragment-based approaches implemented for RNA targets and provide insights into experimental design and outcomes to guide future work in the area. Indeed, investigations surrounding the molecular recognition of RNA by fragments address rather important questions such as the limits of molecular weight that confer selective binding and the physicochemical properties favorable for RNA binding and bioactivity.

Transient receptor potential ankyrin 1 (TRPA1) modulators: Recent update and future perspective

The transient receptor potential ankyrin 1 (TRPA1) channel is a non-selective cation channel that senses irritant chemicals. Its activation is closely associated with pain, inflammation, and pruritus. TRPA1 antagonists are promising treatments for these diseases, and there has been a recent upsurge in their application to new areas such as cancer, asthma, and Alzheimer's disease. However, due to the generally disappointing performance of TRPA1 antagonists in clinical studies, scientists must pursue the development of antagonists with higher selectivity, metabolic stability, and solubility. Moreover, TRPA1 agonists provide a deeper understanding of activation mechanisms and aid in antagonist screening. Therefore, we summarize the TRPA1 antagonists and agonists developed in recent years, with a particular focus on structure-activity relationships (SARs) and pharmacological activity. In this perspective, we endeavor to keep abreast of cutting-edge ideas and provide inspiration for the development of more effective TRPA1-modulating drugs.

Dissociation Rate Calculation via Constant-Force Steered Molecular Dynamics Simulation

Steered molecular dynamics (SMD) simulations are used to study molecular dissociation events by applying a harmonic force to the molecules and pulling them at a constant velocity. Instead of constant-velocity pulling, we use a constant force: the constant-force SMD (CF-SMD) simulation. The CF-SMD simulation employs a constant force to reduce the activation barrier of molecular dissociation, thereby enhancing the dissociation event. Here, we present the capability of the CF-SMD simulation to estimate the dissociation time at equilibrium. We performed all-atom CF-SMD simulations for NaCl and protein-ligand systems, producing dissociation time at various forces. We extrapolated these values to the dissociation rate without a constant force using Bell's model or the Dudko-Hummer-Szabo model. We demonstrate that the CF-SMD simulations with the models predicted the dissociation time in equilibrium. A CF-SMD simulation is a powerful tool for estimating the dissociation rate in a direct and computationally efficient manner.

Fragtory: Pharmacophore-Focused Design, Synthesis, and Evaluation of an sp3-Enriched Fragment Library

Fragment-based drug discovery has played an important role in medicinal chemistry and pharmaceutical research. Despite numerous demonstrated successes, the limited diversity and overrepresentation of planar, sp²-rich structures in commercial libraries often hamper the full potential of this approach. Hence, the thorough design of screening libraries inevitably determines the probability for meaningful hits and subsequent structural elaboration. Against this background, we present the generation of an exclusive fragment library based on iterative entry nomination by a specifically designed computational workflow: "Fragtory". Following a pharmacophore diversity-driven approach, we used Fragtory in an interdisciplinary academic setting to guide both tailored synthesis efforts and the implementation of in-house compounds to build a curated 288-member library of sp³-enriched fragments. Subsequent NMR screens against a model protein and hit validation by protein crystallography led to the identification of structurally novel ligands that were further characterized by isothermal titration calorimetry, demonstrating the applicability of our experimental approach.

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.

Chemical Proteomics with Novel Fully Functionalized Fragments and Stringent Target Prioritization Identifies the Glutathione-Dependent Isomerase GSTZ1 as a Lung Cancer Target

Photoreactive fragment-like probes have been applied to discover target proteins that constitute novel cellular vulnerabilities and to identify viable chemical hits for drug discovery. Through forming covalent bonds, functionalized probes can achieve stronger target engagement and require less effort for on-target mechanism validation. However, the design of probe libraries, which directly affects the biological target space that is interrogated, and effective target prioritization remain critical challenges of such a chemical proteomic platform. In this study, we designed and synthesized a diverse panel of 20 fragment-based probes containing natural product-based privileged structural motifs for small-molecule lead discovery. These probes were fully functionalized with orthogonal diazirine and alkyne moieties and used for protein crosslinking in live lung cancer cells, target enrichment via "click chemistry," and subsequent target identification through label-free quantitative liquid chromatography-tandem mass spectrometry analysis. Pair-wise comparison with a blunted negative control probe and stringent prioritization via individual cross-comparisons against the entire panel identified glutathione S-transferase zeta 1 (GSTZ1) as a specific and unique target candidate. DepMap database query, RNA interference-based gene silencing, and proteome-wide tyrosine reactivity profiling suggested that GSTZ1 cooperated with different oncogenic alterations by supporting survival signaling in refractory non-small cell lung cancer cells. This finding may form the basis for developing novel GSTZ1 inhibitors to improve the therapeutic efficacy of oncogene-directed targeted drugs. In summary, we designed a novel fragment-based probe panel and developed a target prioritization scheme with improved stringency, which allows for the identification of unique target candidates, such as GSTZ1 in refractory lung cancer.

Mining the Protein Data Bank to inspire fragment library design

The fragment approach has emerged as a method of choice for drug design, as it allows difficult therapeutic targets to be addressed. Success lies in the choice of the screened chemical library and the biophysical screening method, and also in the quality of the selected fragment and structural information used to develop a drug-like ligand. It has recently been proposed that promiscuous compounds, i.e., those that bind to several proteins, present an advantage for the fragment approach because they are likely to give frequent hits in screening. In this study, we searched the Protein Data Bank for fragments with multiple binding modes and targeting different sites. We identified 203 fragments represented by 90 scaffolds, some of which are not or hardly present in commercial fragment libraries. By contrast to other available fragment libraries, the studied set is enriched in fragments with a marked three-dimensional character (download at 10.5281/zenodo.7554649).

The Application of High-Throughput Approaches in Identifying Novel Therapeutic Targets and Agents to Treat Diabetes

During the past decades, unprecedented progress in technologies has revolutionized traditional research methodologies. Among these, advances in high-throughput drug screening approaches have permitted the rapid identification of potential therapeutic agents from drug libraries that contain thousands or millions of molecules. Moreover, high-throughput-based therapeutic target discovery strategies can comprehensively interrogate relationships between biomolecules (e.g., gene, RNA, and protein) and diseases and significantly increase the authors' knowledge of disease mechanisms. Diabetes is a chronic disease primarily characterized by the incapacity of the body to maintain normoglycemia. The prevalence of diabetes in modern society has become a severe public health issue that threatens the well-being of millions of patients. Although a number of pharmacological treatments are available, there is no permanent cure for diabetes, and discovering novel therapeutic targets and agents continues to be an urgent need. The present review discusses the technical details of high-throughput screening approaches in drug discovery, followed by introducing the applications of such approaches to diabetes research. This review aims to provide an example of the applicability of high-throughput technologies in facilitating different aspects of disease research.

Small molecules containing chalcogen elements (S, Se, Te) as new warhead to fight neglected tropical diseases

Neglected tropical diseases (NTDs) encompass a group of infectious diseases with a protozoan etiology, high incidence, and prevalence in developing countries. As a result, economic factors constitute one of the main obstacles to their management. Endemic countries have high levels of poverty, deprivation and marginalization which affect patients and limit their access to proper medical care. As a matter of fact, statistics remain uncollected in some affected areas due to non-reporting cases. World Health Organization and other organizations proposed a plan for the eradication and control of the vector, although many of these plans were halted by the COVID-19 pandemic. Despite of the available drugs to treat these pathologies, it exists a lack of effectiveness against several parasite strains. Treatment protocols for diseases such as American trypanosomiasis (Chagas disease), leishmaniasis, and human African trypanosomiasis (HAT) have not achieved the desired results. Unfortunately, these drugs present limitations such as side effects, toxicity, teratogenicity, renal, and hepatic impairment, as well as high costs that have hindered the control and eradication of these diseases. This review focuses on the analysis of a collection of scientific shreds of evidence with the aim of identifying novel chalcogen-derived molecules with biological activity against Chagas disease, leishmaniasis and HAT. Compounds illustrated in each figure share the distinction of containing at least one chalcogen element. Sulfur (S), selenium (Se), and tellurium (Te) have been grouped and analyzed in accordance with their design strategy, chemical synthesis process and biological activity. After an exhaustive revision of the related literature on S, Se, and Te compounds, 183 compounds presenting excellent biological performance were gathered against the different causative agents of CD, leishmaniasis and HAT.

Recent Advances of RNA m6A Modifications in Cancer Immunoediting and Immunotherapy

Cancer immunotherapy, which modulates immune responses against tumors using immune-checkpoint inhibitors or adoptive cell transfer, has emerged as a novel and promising therapy for tumors. However, only a minority of patients demonstrate durable responses, while the majority of patients are resistant to immunotherapy. The immune system can paradoxically constrain and promote tumor development and progression. This process is referred to as cancer immunoediting. The mechanisms of resistance to immunotherapy seem to be that cancer cells undergo immunoediting to evade recognition and elimination by the immune system. RNA modifications, specifically N6-methyladenosine (m6A) methylation, have emerged as a key regulator of various post-transcriptional gene regulatory processes, such as RNA export, splicing, stability, and degradation, which play unappreciated roles in various physiological and pathological processes, including immune system development and cancer pathogenesis. Therefore, a deeper understanding of the mechanisms by which RNA modifications impact the cancer immunoediting process can provide insight into the mechanisms of resistance to immunotherapies and the strategies that can be used to overcome such resistance. In this chapter, we briefly introduce the background of cancer immunoediting and immunotherapy. We also review and discuss the roles and mechanisms of RNA m6A modifications in fine-tuning the innate and adaptive immune responses, as well as in regulating tumor escape from immunosurveillance. Finally, we summarize the current strategies targeting m6A regulators for cancer immunotherapy.

An Integrated Analysis of Mechanistic Insights into Biomolecular Interactions and Molecular Dynamics of Bio-Inspired Cu(II) and Zn(II) Complexes towards DNA/BSA/SARS-CoV-2 3CLpro by Molecular Docking-Based Virtual Screening and FRET Detection

Novel constructed bioactive mixed-ligand complexes (1b) [Cu^II(L)₂(phen)] and (2b) [Zn^II(L)₂(phen)] {where, L = 2-(4-morpholinobenzylideneamino)phenol), phen = 1,10-phenanthroline} have been structurally analysed by various analytical and spectroscopic techniques, including, magnetic moments, thermogravimetric analysis, and X-ray crystallography. Various analytical and spectral measurements assigned showed that all complexes appear to have an octahedral geometry. Agar gel electrophoresis's output demonstrated that the Cu(II) complex (1b) had efficient deoxyribonucleic cleavage and complex (2b) demonstrated the partial cleavage accomplished with an oxidation agent, which generates spreadable OH^● through the Fenton type mechanism. The DNA binding constants observed from viscosity, UV-Vis spectral, fluorometric, and electrochemical titrations were in the following sequence: (1b) > (2b) > (HL), which suggests that the complexes (1b-2b) might intercalate DNA, a possibility that is supported by the biothermodynamic measurements. In addition, the observed binding constant results of BSA by electronic absorption and fluorometric titrations indicate that complex (1b) revealed the best binding efficacy as compared to complex (2b) and free ligand. Interestingly, all compounds are found to interact with BSA through a static approach, as further attested by FRET detection. The DFT and molecular docking calculations were also performed to realize the electronic structure, reactivity, and binding capability of all test samples with CT-DNA, BSA, and the SARS-CoV-2 3CL^Pro, which revealed the binding energies were in a range of -8.1 to -8.9, -7.5 to -10.5 and -6.7--8.8 kcal/mol, respectively. The higher reactivity of the complexes than the free ligand is supported by the FMO theory. Among all the observed data for antioxidant properties against DPPH^᛫, ^᛫OH, O₂^-• and NO^᛫ free radicals, complex (1a) had the best biological efficacy. The antimicrobial and cytotoxic characteristics of all test compounds have been studied by screening against certain selected microorganisms as well as against A549, HepG2, MCF-7, and NHDF cell lines, respectively. The observed findings revealed that the activity enhances coordination as compared to free ligand via Overtone's and Tweedy's chelation mechanisms. This is especially encouraging given that in every case, the experimental findings and theoretical detections were in perfect accord.

An overview of structural approaches to study therapeutic RNAs

RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity.

Large-Scale Crystallographic Fragment Screening Expedites Compound Optimization and Identifies Putative Protein-Protein Interaction Sites

The identification of starting points for compound development is one of the key steps in early-stage drug discovery. Information-rich techniques such as crystallographic fragment screening can potentially increase the efficiency of this step by providing the structural information of the binding mode of the ligands in addition to the mere binding information. Here, we present the crystallographic screening of our 1000-plus-compound F2X-Universal Library against the complex of the yeast spliceosomal Prp8 RNaseH-like domain and the snRNP assembly factor Aar2. The observed 269 hits are distributed over 10 distinct binding sites on the surface of the protein-protein complex. Our work shows that hit clusters from large-scale crystallographic fragment screening campaigns identify known interaction sites with other proteins and suggest putative additional interaction sites. Furthermore, the inherent binding pose validation within the hit clusters may accelerate downstream compound optimization.

Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking

We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmacophoric properties, describing fragment-bound microenvironments, is first aligned to the query target cavity by a computer vision method. The fragments of the most similar PDB subpockets are then directly positioned in the query cavity using the corresponding image transformation matrices. Lastly, suitable connectable atoms of oriented fragment pairs are linked by a deep generative model to yield fully connected molecules. POEM was applied to generate a library of 1.5 million potential cyclin-dependent kinase 8 inhibitors. By synthesizing and testing as few as 43 compounds, a few nanomolar inhibitors were quickly obtained with limited resources in just two iterative cycles.

The IQA Energy Partition in a Drug Design Setting: A Hepatitis C Virus RNA-Dependent RNA Polymerase (NS5B) Case Study

The interaction of the thumb site II of the NS5B protein of hepatitis C virus and a pair of drug candidates was studied using a topological energy decomposition method called interacting quantum atoms (IQA). The atomic energies were then processed by the relative energy gradient (REG) method, which extracts chemical insight by computation based on minimal assumptions. REG reveals the most important IQA energy contributions, by atom and energy type (electrostatics, sterics, and exchange–correlation), that are responsible for the behaviour of the whole system, systematically from a short-range ligand–pocket interaction until a distance of approximately 22 Å. The degree of covalency in various key interatomic interactions can be quantified. No exchange–correlation contribution is responsible for the changes in the energy profile of both pocket–ligand systems investigated in the ligand–pocket distances equal to or greater than that of the global minimum. Regarding the hydrogen bonds in the system, a “neighbour effect” was observed thanks to the REG method, which states that a carbon atom would rather not have its covalent neighbour oxygen form a hydrogen bond. The combination of IQA and REG enables the automatic identification of the pharmacophore in the ligands. The coarser Interacting Quantum Fragments (IQF) enables the determination of which amino acids of the pocket contribute most to the binding and the type of energy of said binding. This work is an example of the contribution topological energy decomposition methods can make to fragment-based drug design.

Fragment-Based Drug Discovery against Mycobacteria: The Success and Challenges

The emergence of drug-resistant mycobacteria, including Mycobacterium tuberculosis (Mtb) and non-tuberculous mycobacteria (NTM), poses an increasing global threat that urgently demands the development of new potent anti-mycobacterial drugs. One of the approaches toward the identification of new drugs is fragment-based drug discovery (FBDD), which is the most ingenious among other drug discovery models, such as structure-based drug design (SBDD) and high-throughput screening. Specialized techniques, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and many others, are part of the drug discovery approach to combat the Mtb and NTM global menaces. Moreover, the primary drawbacks of traditional methods, such as the limited measurement of biomolecular toxicity and uncertain bioavailability evaluation, are successfully overcome by the FBDD approach. The current review focuses on the recognition of fragment-based drug discovery as a popular approach using virtual, computational, and biophysical methods to identify potent fragment molecules. FBDD focuses on designing optimal inhibitors against potential therapeutic targets of NTM and Mtb (PurC, ArgB, MmpL3, and TrmD). Additionally, we have elaborated on the challenges associated with the FBDD approach in the identification and development of novel compounds. Insights into the applications and overcoming the challenges of FBDD approaches will aid in the identification of potential therapeutic compounds to treat drug-sensitive and drug-resistant NTMs and Mtb infections.