Fragment-Based Drug Discovery: Advancing Fragments in the Absence of Crystal Structures

Multi-tiered chemical proteomic maps of tryptoline acrylamide-protein interactions in cancer cells

Covalent chemistry is a versatile approach for expanding the ligandability of the human proteome. Activity-based protein profiling (ABPP) can infer the specific residues modified by electrophilic compounds through competition with broadly reactive probes. However, the extent to which such residue-directed platforms fully assess the protein targets of electrophilic compounds in cells remains unclear. Here we evaluate a complementary protein-directed ABPP method that identifies proteins showing stereoselective reactivity with alkynylated, chiral electrophilic compounds-termed stereoprobes. Integration of protein- and cysteine-directed data from cancer cells treated with tryptoline acrylamide stereoprobes revealed generally well-correlated ligandability maps and highlighted features, such as protein size and the proteotypicity of cysteine-containing peptides, that explain gaps in each ABPP platform. In total, we identified stereoprobe binding events for >300 structurally and functionally diverse proteins, including compounds that stereoselectively and site-specifically disrupt MAD2L1BP interactions with the spindle assembly checkpoint complex leading to delayed mitotic exit in cancer cells.

How to Find a Fragment: Methods for Screening and Validation in Fragment-Based Drug Discovery

Fragment-based drug discovery (FBDD) is a crucial strategy for developing new drugs that have been applied to diverse targets, from neglected infectious diseases to cancer. With at least seven drugs already launched to the market, this approach has gained interest in both academics and industry in the last 20 years. FBDD relies on screening small libraries with about 1000-2000 compounds of low molecular weight (about 300 Da) using several biophysical methods. Because of the reduced size of the compounds, the chemical space and diversity can be better explored than large libraries used in high throughput screenings. This review summarises the most common biophysical techniques used in fragment screening and orthogonal validation. We also explore the advantages and drawbacks of the different biophysical techniques and examples of applications and strategies.

Fragment-based drug discovery campaigns guided by native mass spectrometry

Native mass spectrometry (nMS) is well established as a biophysical technique for characterising biomolecules and their interactions with endogenous or investigational small molecule ligands. The high sensitivity mass measurements make nMS particularly well suited for applications in fragment-based drug discovery (FBDD) screening campaigns where the detection of weakly binding ligands to a target biomolecule is crucial. We first reviewed the contributions of nMS to guiding FBDD hit identification in 2013, providing a comprehensive perspective on the early adoption of nMS for fragment screening. Here we update this initial progress with a focus on contributions of nMS that have guided FBDD for the period 2014 until end of 2023. We highlight the development of nMS adoption in FBDD in the context of other biophysical fragment screening techniques. We also discuss the roadmap for increased adoption of nMS for fragment screening beyond soluble proteins, including for guiding the discovery of fragments supporting advances in PROTAC discovery, RNA-binding small molecules and covalent therapeutic drug discovery.

Fast Quantitative Validation of 3D Models of Low-Affinity Protein-Ligand Complexes by STD NMR Spectroscopy

Low-affinity protein-ligand interactions are important for many biological processes, including cell communication, signal transduction, and immune responses. Structural characterization of these complexes is also critical for the development of new drugs through fragment-based drug discovery (FBDD), but it is challenging due to the low affinity of fragments for the binding site. Saturation transfer difference (STD) NMR spectroscopy has revolutionized the study of low-affinity receptor-ligand interactions enabling binding detection and structural characterization. Comparison of relaxation and exchange matrix calculations with 1H STD NMR experimental data is essential for the validation of 3D structures of protein-ligand complexes. In this work, we present a new approach based on the calculation of a reduced relaxation matrix, in combination with funnel metadynamics MD simulations, that allows a very fast generation of experimentally STD-NMR-validated 3D structures of low-affinity protein-ligand complexes.

Biocatalytic strategy for the construction of sp3-rich polycyclic compounds from directed evolution and computational modelling

Catalysis with engineered enzymes has provided more efficient routes for the production of active pharmaceutical agents. However, the potential of biocatalysis to assist in early-stage drug discovery campaigns remains largely untapped. In this study, we have developed a biocatalytic strategy for the construction of sp3-rich polycyclic compounds via the intramolecular cyclopropanation of benzothiophenes and related heterocycles. Two carbene transferases with complementary regioisomer selectivity were evolved to catalyse the stereoselective cyclization of benzothiophene substrates bearing diazo ester groups at the C2 or C3 position of the heterocycle. The detailed mechanisms of these reactions were elucidated by a combination of crystallographic and computational analyses. Leveraging these insights, the substrate scope of one of the biocatalysts could be expanded to include previously unreactive substrates, highlighting the value of integrating evolutionary and rational strategies to develop enzymes for new-to-nature transformations. The molecular scaffolds accessed here feature a combination of three-dimensional and stereochemical complexity with 'rule-of-three' properties, which should make them highly valuable for fragment-based drug discovery campaigns.

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.

The role of NMR in advancing small molecule drug discovery

Navigating the ever-evolving landscape of nuclear magnetic resonance (NMR) poses challenges for the industry. This work explores promising approaches that illuminate protein-ligand interactions in the context of structural dynamics, facilitating targeted drug discovery. I acknowledge existing limitations and highlight future opportunities, which may pave the way for broader NMR integration and faster therapeutic development.

THOUSAND-GRAIN WEIGHT 6, which is an IAA-glucose hydrolase, preferentially recognizes the structure of the indole ring

An indole-3-acetic acid (IAA)-glucose hydrolase, THOUSAND-GRAIN WEIGHT 6 (TGW6), negatively regulates the grain weight in rice. TGW6 has been used as a target for breeding increased rice yield. Moreover, the activity of TGW6 has been thought to involve auxin homeostasis, yet the details of this putative TGW6 activity remain unclear. Here, we show the three-dimensional structure and substrate preference of TGW6 using X-ray crystallography, thermal shift assays and fluorine nuclear magnetic resonance (19F NMR). The crystal structure of TGW6 was determined at 2.6 Å resolution and exhibited a six-bladed β-propeller structure. Thermal shift assays revealed that TGW6 preferably interacted with indole compounds among the tested substrates, enzyme products and their analogs. Further analysis using 19F NMR with 1,134 fluorinated fragments emphasized the importance of indole fragments in recognition by TGW6. Finally, docking simulation analyses of the substrate and related fragments in the presence of TGW6 supported the interaction specificity for indole compounds. Herein, we describe the structure and substrate preference of TGW6 for interacting with indole fragments during substrate recognition. Uncovering the molecular details of TGW6 activity will stimulate the use of this enzyme for increasing crop yields and contributes to functional studies of IAA glycoconjugate hydrolases in auxin homeostasis.

A Novel NMR-Based Protocol to Screen Ultralow Molecular Weight Fragments

Fragment-based lead discovery has emerged as one of the most efficient screening strategies for finding hit molecules in drug discovery. Recently, a novel strategy based on a class of fragments characterized by an ultralow molecular weight (ULMW) has been proposed. These fragments bind to the target with a very low affinity, requiring reliable biophysical methods for detection. The most notable application of ULMW used a set of 81 fragments, named MiniFrags, and screened them by X-ray crystallography. We extended the utilization of this novel class of fragments to another gold standard technique for fragment-based screening: nuclear magnetic resonance (NMR). Here, we present a novel NMR protocol to detect and analyze such weak interactions in a challenging real-world scenario: a flexible target with a flat, water-exposed binding site. We identified a subset of 69 highly water-soluble MiniFrags that were screened against the antiapoptotic protein human Bfl-1.

Molecular fragmentation as a crucial step in the AI-based drug development pathway

The AI-based small molecule drug discovery has become a significant trend at the intersection of computer science and life sciences. In the pursuit of novel compounds, fragment-based drug discovery has emerged as a novel approach. The Generative Pre-trained Transformers (GPT) model has showcased remarkable prowess across various domains, rooted in its pre-training and representation learning of fundamental linguistic units. Analogous to natural language, molecular encoding, as a form of chemical language, necessitates fragmentation aligned with specific chemical logic for accurate molecular encoding. This review provides a comprehensive overview of the current state of the art in molecular fragmentation. We systematically summarize the approaches and applications of various molecular fragmentation techniques, with special emphasis on the characteristics and scope of applicability of each technique, and discuss their applications. We also provide an outlook on the current development trends of molecular fragmentation techniques, including some potential research directions and challenges.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

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.

Fragment-Based Screening Identifies New Quinazolinone-Based Inositol Hexakisphosphate Kinase (IP6K) Inhibitors

A high-throughput fragment-based screen has been employed to discover a series of quinazolinone inositol hexakisphosphate kinase (IP6K) inhibitors. IP6Ks have been studied for their role in glucose homeostasis, metabolic disease, fatty liver disease, chronic kidney disease, blood coagulation, neurological development, and psychiatric disease. IP6Ks phosphorylate inositol hexakisphosphate (IP6) to form pyrophosphate 5-diphospho-1,2,3,4,6-pentakisphosphate (IP7). Molecular docking studies and investigation of structure-activity relationships around the quinazolinone core resulted in compounds with submicromolar potency and interesting selectivity for IP6K1 versus the closely related IP6K2 and IP6K3 isoforms.

On-DNA hydroalkylation of N-vinyl heterocycles via photoinduced EDA-complex activation

The emergence of DNA-encoded library (DEL) technology has provided a considerable advantage to the pharmaceutical industry in the pursuit of discovering novel therapeutic candidates for their drug development initiatives. This combinatorial technique not only offers a more economical, spatially efficient, and time-saving alternative to the existing ligand discovery methods, but also enables the exploration of additional chemical space by utilizing novel DNA-compatible synthetic transformations to leverage multifunctional building blocks from readily available substructures. In this report, a decarboxylative-based hydroalkylation of DNA-conjugated N-vinyl heterocycles enabled by single-electron transfer (SET) and subsequent hydrogen atom transfer through electron-donor/electron-acceptor (EDA) complex activation is detailed. The simplicity and robustness of this method permits inclusion of a broad array of alkyl radical precursors and DNA-tethered nitrogenous heterocyles to generate medicinally relevant substituted heterocycles with pendant functional groups. Moreover, a successful telescoped route provides the opportunity to access a broad range of intricate structural scaffolds by employing basic carboxylic acid feedstocks.

Design, Synthesis, and Activity Evaluation of Novel Dual-Target Inhibitors with Antifungal and Immunoregulatory Properties

Dual-target (CYP51/PD-L1) plays an important role in the process of fungal proliferation and immune suppression. A series of novel quinazoline compounds with dual-target inhibition function was constructed using the skeleton growth method, and their structures were synthesized, characterized, and evaluated. Among them, the perfected compounds (L11, L20, L21) were selected for further study, which exhibited remarkable biological activity against different fungal strains (MIC50, 0.25-2.0 μg/mL) in vitro. On the one hand, these compounds inhibited CYP51 activity, induced ROS aggregation, and mitochondrial damage; this ultimately caused fungal lysis and death. On the other hand, they also effectively activated the body's immune ability by blocking the interaction between PD-L1 and PD-1, slowed down the inflammatory reaction, and accelerated the recovery process of fungal infections.

The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives

Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.

Design of Tracers in Fluorescence Polarization Assay for Extensive Application in Small Molecule Drug Discovery

Development of fluorescence polarization (FP) assays, especially in a competitive manner, is a potent and mature tool for measuring the binding affinities of small molecules. This approach is suitable for high-throughput screening (HTS) for initial ligands and is also applicable for further study of the structure-activity relationships (SARs) of candidate compounds for drug discovery. Buffer and tracer, especially rational design of the tracer, play a vital role in an FP assay system. In this perspective, we provided different kinds of approaches for tracer design based on successful cases in recent years. We classified these tracers by different types of ligands in tracers, including peptide, nucleic acid, natural product, and small molecule. To make this technology accessible for more targets, we briefly described the basic theory and workflow, followed by highlighting the design and application of typical FP tracers from a perspective of medicinal chemistry.

Protein-protein interactions: developing small-molecule inhibitors/stabilizers through covalent strategies

The development of small-molecule inhibitors or stabilizers of selected protein-protein interactions (PPIs) of interest holds considerable promise for the development of research tools as well as candidate therapeutics. In this context, the covalent modification of selected residues within the target protein has emerged as a promising mechanism of action to obtain small-molecule modulators of PPIs with appropriate selectivity and duration of action. Different covalent labeling strategies are now available that can potentially allow for a rational, ground-up discovery and optimization of ligands as PPI inhibitors or stabilizers. This review article provides a synopsis of recent developments and applications of such tactics, with a particular focus on site-directed fragment tethering and proximity-enabled approaches.

Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-α, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

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.

Discovery of chemical probes that perturb protein complexes using size exclusion chromatography and chemical proteomics

Most human proteins lack small-molecule ligands, rendering these proteins "undruggable." In this issue of Molecular Cell, Lazear et al.¹ develop a novel chemical proteomic screening platform and discover new chemical probes targeting previously undruggable protein complexes.

Wild-type and mutant p53 in cancer-related ferroptosis. A matter of stress management?

Cancer cells within tumor masses are chronically exposed to stress caused by nutrient deprivation, oxygen limitation, and high metabolic demand. They also accumulate hundreds of mutations, potentially generating aberrant proteins that can induce proteotoxic stress. Finally, cancer cells are exposed to various damages during chemotherapy. In a growing tumor, transformed cells eventually adapt to these conditions, eluding the death-inducing outcomes of signaling cascades triggered by chronic stress. One such extreme outcome is ferroptosis, a form of iron-dependent non-apoptotic cell death mediated by lipid peroxidation. Not surprisingly, the tumor suppressor p53 is involved in this process, with evidence suggesting that it acts as a pro-ferroptotic factor and that its ferroptosis-inducing activity may be relevant for tumor suppression. Missense alterations of the TP53 gene are extremely frequent in human cancers and give rise to mutant p53 proteins (mutp53) that lose tumor suppressive function and can acquire powerful oncogenic activities. This suggests that p53 mutation provides a selective advantage during tumor progression, raising interesting questions on the impact of p53 mutant proteins in modulating the ferroptotic process. Here, we explore the role of p53 and its cancer-related mutants in ferroptosis, using a perspective centered on the resistance/sensitivity of cancer cells to exogenous and endogenous stress conditions that can trigger ferroptotic cell death. We speculate that an accurate molecular understanding of this particular axis may improve cancer treatment options.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Drugging the Undruggable Trypanosoma brucei Monothiol Glutaredoxin 1

Trypanosoma brucei is a species of kinetoplastid causing sleeping sickness in humans and nagana in cows and horses. One of the peculiarities of this species of parasites is represented by their redox metabolism. One of the proteins involved in this redox machinery is the monothiol glutaredoxin 1 (1CGrx1) which is characterized by a unique disordered N-terminal extension exclusively conserved in trypanosomatids and other organisms. This region modulates the binding profile of the glutathione/trypanothione binding site, one of the functional regions of 1CGrx1. No endogenous ligands are known to bind this protein which does not present well-shaped binding sites, making it target particularly challenging to target. With the aim of targeting this peculiar system, we carried out two different screenings: (i) a fragment-based lead discovery campaign directed to the N-terminal as well as to the canonical binding site of 1CGrx1; (ii) a structure-based virtual screening directed to the 1CGrx1 canonical binding site. Here we report a small molecule that binds at the glutathione binding site in which the binding mode of the molecule was deeply investigated by Nuclear Magnetic Resonance (NMR). This compound represents an important step in the attempt to develop a novel strategy to interfere with the peculiar Trypanosoma Brucei redox system, making it possible to shed light on the perturbation of this biochemical machinery and eventually to novel therapeutic possibilities.

Fragment-to-Lead Medicinal Chemistry Publications in 2021

This Perspective is the seventh in an annual series that summarizes successful Fragment-to-Lead (F2L) case studies published in a given year. A tabulated summary of relevant articles published in 2021 is provided, and features such as target class, screening methods, and ligand efficiency are discussed, both for the 2021 examples and for the combined examples over the years 2015-2021. In addition, trends and new developments in the field are summarized. In particular, the use of structural information in fragment-based drug discovery is discussed.

MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities

SUMMARY

Construction of high-quality fragment libraries by segmenting organic compounds is an important part of the drug discovery paradigm. This article presents a new method, MacFrag, for efficient molecule fragmentation. MacFrag utilized a modified version of BRICS rules to break chemical bonds and introduced an efficient subgraphs extraction algorithm for rapid enumeration of the fragment space. The evaluation results with ChEMBL dataset exhibited that MacFrag was overall faster than BRICS implemented in RDKit and modified molBLOCKS. Meanwhile, the fragments acquired through MacFrag were more compliant with the 'Rule of Three'.

AVAILABILITY AND IMPLEMENTATION

https://github.com/yydiao1025/MacFrag.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Fragment-based drug discovery-the importance of high-quality molecule libraries

Fragment-based drug discovery (FBDD) is now established as a complementary approach to high-throughput screening (HTS). Contrary to HTS, where large libraries of drug-like molecules are screened, FBDD screens involve smaller and less complex molecules which, despite a low affinity to protein targets, display more 'atom-efficient' binding interactions than larger molecules. Fragment hits can, therefore, serve as a more efficient start point for subsequent optimisation, particularly for hard-to-drug targets. Since the number of possible molecules increases exponentially with molecular size, small fragment libraries allow for a proportionately greater coverage of their respective 'chemical space' compared with larger HTS libraries comprising larger molecules. However, good library design is essential to ensure optimal chemical and pharmacophore diversity, molecular complexity, and physicochemical characteristics. In this review, we describe our views on fragment library design, and on what constitutes a good fragment from a medicinal and computational chemistry perspective. We highlight emerging chemical and computational technologies in FBDD and discuss strategies for optimising fragment hits. The impact of novel FBDD approaches is already being felt, with the recent approval of the covalent KRASG12C inhibitor sotorasib highlighting the utility of FBDD against targets that were long considered undruggable.

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.

Development of potent inhibitors by fragment-linking strategies

Fragment-based drug discovery (FBDD) is a method of identifying small molecule hits that can be elaborated rationally through fragment growing, merging, and linking, to afford high affinity ligands for biological targets. Despite the promised theoretical potential of fragment linking, examples are still surprisingly sparse and remain overshadowed by the successes of fragment growing. The aim of this review is to outline a number of key examples of fragment linking strategies and discuss their strengths and limitations. Structure-based approaches including X-ray crystallography and in silico methods fragment optimisation are discussed, as well as fragment linking guided by NMR experiments. Target-guided approaches, exploiting the biological target to assemble its own inhibitors through dynamic combinatorial chemistry (DCC) and kinetic target-guided synthesis (KTGS), are identified as alternative efficient methods for fragment linking.

Boosting the Discovery of Small Molecule Inhibitors of Glucose-6-Phosphate Dehydrogenase for the Treatment of Cancer, Infectious Diseases, and Inflammation

We present an overview of small molecule glucose-6-phosphate dehydrogenase (G6PD) inhibitors that have potential for use in the treatment of cancer, infectious diseases, and inflammation. Both steroidal and nonsteroidal inhibitors have been identified with steroidal inhibitors lacking target selectivity. The main scaffolds encountered in nonsteroidal inhibitors are quinazolinones and benzothiazinones/benzothiazepinones. Three molecules show promise for development as antiparasitic (25 and 29) and anti-inflammatory (32) agents. Regarding modality of inhibition (MOI), steroidal inhibitors have been shown to be uncompetitive and reversible. Nonsteroidal small molecules have exhibited all types of MOI. Strategies to boost the discovery of small molecule G6PD inhibitors include exploration of structure-activity relationships (SARs) for established inhibitors, employment of high-throughput screening (HTS), and fragment-based drug discovery (FBDD) for the identification of new hits. We discuss the challenges and gaps associated with drug discovery efforts of G6PD inhibitors from in silico, in vitro, and in cellulo to in vivo studies.

Jumping From Fragment To Drug Via Smart Scaffolds

A focused drug repurposing approach is described where an FDA-approved drug is rationally selected for biological testing based on structural similarities to a fragment compound found to bind a target protein by an NMR screen. The approach is demonstrated by first screening a curated fragment library using 19F NMR to discover a quality binder to ACE2, the human receptor required for entry and infection by the SARS-CoV-2 virus. Based on this binder, a highly related scaffold was derived and used as a "smart scaffold" or template in a computer-aided finger-print search of a library of FDA-approved or marketed drugs. The most interesting structural match involved the drug vortioxetine which was then experimentally shown by NMR spectroscopy to bind directly to human ACE2. Also, an ELISA assay showed that the drug inhibits the interaction of human ACE2 to the SARS-CoV-2 receptor-binding-domain (RBD). Moreover, our cell-culture infectivity assay confirmed that vortioxetine is active against SARS-CoV-2 and inhibits viral replication. Thus, the use of "smart scaffolds" based on binders from fragment screens may have general utility for identifying candidates of FDA-approved or marketed drugs as a rapid repurposing strategy. Similar approaches can be envisioned for other fields involving small-molecule chemical applications.

The current toolbox for APOBEC drug discovery

Mutational processes driving genome evolution and heterogeneity contribute to immune evasion and therapy resistance in viral infections and cancer. APOBEC3 (A3) enzymes promote such mutations by catalyzing the deamination of cytosines to uracils in single-stranded DNA. Chemical inhibition of A3 enzymes may yield an antimutation therapeutic strategy to improve the durability of current drug therapies that are prone to resistance mutations. A3 small-molecule drug discovery efforts to date have been restricted to a single high-throughput biochemical activity assay; however, the arsenal of discovery assays has significantly expanded in recent years. The assays used to study A3 enzymes are reviewed here with an eye towards their potential for small-molecule discovery efforts.

HT-SuMD: making molecular dynamics simulations suitable for fragment-based screening. A comparative study with NMR

Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-XL, an oncological protein target involved in the regulation of apoptosis through protein-protein interactions. Initially, HT-SuMD performances were validated against a robust NMR-based screening, using the same set of 100 fragments. These independent results showed a remarkable agreement between the two methods. Then, a virtual screening on a larger library of additional 300 fragments was carried out and the best hits were validated by NMR. Remarkably, all the in silico selected fragments were confirmed as Bcl-XL binders. This represents, to date, the largest computational fragments screening entirely based on MD.

Construction and activity evaluation of novel dual-target (SE/CYP51) anti-fungal agents containing amide naphthyl structure

With the increase of fungal infection and drug resistance, it is becoming an urgent task to discover the highly effective antifungal drugs. In the study, we selected the key ergosterol bio-synthetic enzymes (Squalene epoxidase, SE; 14 α-demethylase, CYP51) as dual-target receptors to guide the construction of novel antifungal compounds, which could achieve the purpose of improving drug efficacy and reducing drug-resistance. Three different series of amide naphthyl compounds were generated through the method of skeleton growth, and their corresponding target products were synthesized. Most of compounds displayed the obvious biological activity against different Candida spp. and Aspergillus fumigatus. Among of them, target compounds 14a-2 and 20b-2 not only possessed the excellent broad-spectrum anti-fungal activity (MIC₅₀, 0.125-2 μg/mL), but also maintained the anti-drug-resistant fungal activity (MIC₅₀, 1-4 μg/mL). Preliminary mechanism study revealed the compounds (14a-2, 20b-2) could block the bio-synthetic pathway of ergosterol by inhibiting the dual-target (SE/CYP51) activity, and this finally caused the cleavage and death of fungal cells. In addition, we also discovered that compounds 14a-2 and 20b-2 with low toxic and side effects could exert the excellent therapeutic effect in mice model of fungal infection, which was worthy for further in-depth study.

Comprehensive Approach of 19F Nuclear Magnetic Resonance, Enzymatic, and In Silico Methods for Site-Specific Hit Selection and Validation of Fragment Molecules that Inhibit Methionine γ-Lyase Activity

Fragment-based screening using ¹⁹F NMR (¹⁹F-FS) is an efficient method for exploring seed and lead compounds for drug discovery. Here, we demonstrate the utility and merits of using ¹⁹F-FS for methionine γ-lyase-binding fragments, together with a ¹⁹F NMR-based competition and mutation assay, as well as enzymatic and in silico methods. ¹⁹F NMR-based assays provided useful information on binding between ¹⁹F-FS hit fragments and target proteins. Although the ¹⁹F-FS and enzymatic assay were weakly correlated, they show that the ¹⁹F-FS hit fragments contained compounds with inhibitory activity. Furthermore, we found that in silico calculations partially account for the differences in activity levels between the ¹⁹F-FS hits as per NMR analysis. A comprehensive approach combining the ¹⁹F-FS and other methods not only identified fragment hits but also distinguished structural differences in chemical groups with diverse activity levels.

Fragment-based in silico design of SARS-CoV-2 main protease inhibitors

3CLpro is essential for SARS-CoV-2 replication and infection; its inhibition using small molecules is a potential therapeutic strategy. In this study, a comprehensive crystallography-guided fragment-based drug discovery approach was employed to design new inhibitors for SARS-CoV-2 3CLpro. All small molecules co-crystallized with SARS-CoV-2 3CLpro with structures deposited in the Protein Data Bank were used as inputs. Fragments sitting in the binding pocket (87) were grouped into eight geographical types. They were interactively coupled using various synthetically reasonable linkers to generate larger molecules with divalent binding modes taking advantage of two different fragments' interactions. In total, 1,251 compounds were proposed, and 7,158 stereoisomers were screened using Glide (standard precision and extra precision), AutoDock Vina, and Prime MMGBSA. The top 22 hits having conformations approaching the linear combination of their constituent fragments were selected for MD simulation on Desmond. MD simulation suggested 15 of these did adopt conformations very close to their constituent pieces with far higher binding affinity than either constituent domain alone. These structures could provide a starting point for the further design of SARS-CoV-2 3CLpro inhibitors with improved binding, and structures are provided.

Bioisosteric Modification of To042: Synthesis and Evaluation of Promising Use-Dependent Inhibitors of Voltage-Gated Sodium Channels

Three analogues of To042, a tocainide-related lead compound recently reported for the treatment of myotonia, were synthesized and evaluated in vitro as skeletal muscle sodium channel blockers possibly endowed with enhanced use-dependent behavior. Patch-clamp experiments on hNav1.4 expressed in HEK293 cells showed that N-[(naphthalen-1-yl)methyl]-4-[(2,6-dimethyl)phenoxy]butan-2-amine, the aryloxyalkyl bioisostere of To042, exerted a higher use-dependent block than To042 thus being able to preferentially block the channels in over-excited membranes while preserving healthy tissue function. It also showed the lowest active transport across BBB according to the results of P-glycoprotein (P-gp) interacting activity evaluation and the highest cytoprotective effect on HeLa cells. Quantum mechanical calculations and dockings gave insights on the most probable conformation of the aryloxyalkyl bioisostere of To042 in solution and the target residues involved in the binding, respectively. Both approaches indicated the conformations that might be adopted in both the unbound and bound state of the ligand. Overall, N-[(naphthalen-1-yl)methyl]-4-[(2,6-dimethyl)phenoxy]butan-2-amine exhibits an interesting toxico-pharmacological profile and deserves further investigation.

Frag4Lead: growing crystallographic fragment hits by catalog using fragment-guided template docking

In recent years, crystallographic fragment screening has matured into an almost routine experiment at several modern synchrotron sites. The hits of the screening experiment, i.e. small molecules or fragments binding to the target protein, are revealed along with their 3D structural information. Therefore, they can serve as useful starting points for further structure-based hit-to-lead development. However, the progression of fragment hits to tool compounds or even leads is often hampered by a lack of chemical feasibility. As an attractive alternative, compound analogs that embed the fragment hit structurally may be obtained from commercial catalogs. Here, a workflow is reported based on filtering and assessing such potential follow-up compounds by template docking. This means that the crystallographic binding pose was integrated into the docking calculations as a central starting parameter. Subsequently, the candidates are scored on their interactions within the binding pocket. In an initial proof-of-concept study using five starting fragments known to bind to the aspartic protease endothiapepsin, 28 follow-up compounds were selected using the designed workflow and their binding was assessed by crystallography. Ten of these compounds bound to the active site and five of them showed significantly increased affinity in isothermal titration calorimetry of up to single-digit micromolar affinity. Taken together, this strategy is capable of efficiently evolving the initial fragment hits without major synthesis efforts and with full control by X-ray crystallography.

Newly designed compounds from scaffolds of known actives as inhibitors of survivin: computational analysis from the perspective of fragment-based drug design

Survivin is an apoptosis suppressing protein linked to different forms of cancer. As it stands, there are no approved drugs for the inhibition of survivin in cancer cells despite a number of promising compounds in clinical trials. This study designed a new set of compounds from fragments of active survivin inhibitors to potentiate their binding with survivin at BIR domain. Three hundred and five (305) fragments made from eight potent inhibitors of survivin were reconstructed to form a new set of compounds. The compounds were optimized using R group enumeration and bioisostere replacement after extensive docking analysis. The optimised compounds were filtered by a validated pharmacophore model to reveal how well they are aligned to the pharmacophore sites. Molecular docking of the well aligned compounds revealed the top-scoring compounds; and these compounds were compared with the eight inhibitors used as template for fragment-based design on the basis of binding affinity (rigid and flexible docking), predicted pIC50 and intermolecular interactions. The electronic behaviours (global descriptors, HOMO/LUMO, molecular electrostatic potential and Fukui functions) of newly designed compounds were calculated to investigate their reactivity and atomic sites prone to neutrophilic/electrophilic attack. The nine newly designed compounds had better rigid and flexible docking scores, free energy of binding and intermolecular interactions with survivin at BIR domain than the eight active inhibitors. Based on frontier molecular orbitals, OPE-3 was found to be the most reactive and less stable compound (0.13194 eV), followed by OPE-4 and OPE-9. The global descriptive parameters showed that OPE-3 had highest softness value (7.5245 eV) while OPE-8 recorded the maximum hardness value (0.08486 eV). The well-validated QSAR model also showed that OPE-3, OPE-7 and OPE-8 had the most significant bioactivity of all the inhibitors. This study thus provides new insight into the design of compounds capable of modulating the activity of survivin.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-021-00108-8.

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Reversible Covalent Imine-Tethering for Selective Stabilization of 14-3-3 Hub Protein Interactions

The stabilization of protein complexes has emerged as a promising modality, expanding the number of entry points for novel therapeutic intervention. Targeting proteins that mediate protein–protein interactions (PPIs), such as hub proteins, is equally challenging and rewarding as they offer an intervention platform for a variety of diseases, due to their large interactome. 14-3-3 hub proteins bind phosphorylated motifs of their interaction partners in a conserved binding channel. The 14-3-3 PPI interface is consequently only diversified by its different interaction partners. Therefore, it is essential to consider, additionally to the potency, also the selectivity of stabilizer molecules. Targeting a lysine residue at the interface of the composite 14-3-3 complex, which can be targeted explicitly via aldimine-forming fragments, we studied the de novo design of PPI stabilizers under consideration of potential selectivity. By applying cooperativity analysis of ternary complex formation, we developed a reversible covalent molecular glue for the 14-3-3/Pin1 interaction. This small fragment led to a more than 250-fold stabilization of the 14-3-3/Pin1 interaction by selective interfacing with a unique tryptophan in Pin1. This study illustrates how cooperative complex formation drives selective PPI stabilization. Further, it highlights how specific interactions within a hub proteins interactome can be stabilized over other interactions with a common binding motif.

An Exploration of Chemical Properties Required for Cooperative Stabilization of the 14-3-3 Interaction with NF-κB-Utilizing a Reversible Covalent Tethering Approach

Protein–protein modulation has emerged as a proven approach to drug discovery. While significant progress has been gained in developing protein–protein interaction (PPI) inhibitors, the orthogonal approach of PPI stabilization lacks established methodologies for drug design. Here, we report the systematic ″bottom-up″ development of a reversible covalent PPI stabilizer. An imine bond was employed to anchor the stabilizer at the interface of the 14-3-3/p65 complex, leading to a molecular glue that elicited an 81-fold increase in complex stabilization. Utilizing protein crystallography and biophysical assays, we deconvoluted how chemical properties of a stabilizer translate to structural changes in the ternary 14-3-3/p65/molecular glue complex. Furthermore, we explore how this leads to high cooperativity and increased stability of the complex.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

PPI Modulators of E6 as Potential Targeted Therapeutics for Cervical Cancer: Progress and Challenges in Targeting E6

Advanced cervical cancer is primarily managed using cytotoxic therapies, despite evidence of limited efficacy and known toxicity. There is a current lack of alternative therapeutics to treat the disease more effectively. As such, there have been more research endeavors to develop targeted therapies directed at oncogenic host cellular targets over the past 4 decades, but thus far, only marginal gains in survival have been realized. The E6 oncoprotein, a protein of human papillomavirus origin that functionally inactivates various cellular antitumor proteins through protein–protein interactions (PPIs), represents an alternative target and intriguing opportunity to identify novel and potentially effective therapies to treat cervical cancer. Published research has reported a number of peptide and small-molecule modulators targeting the PPIs of E6 in various cell-based models. However, the reported compounds have rarely been well characterized in animal or human subjects. This indicates that while notable progress has been made in targeting E6, more extensive research is needed to accelerate the optimization of leads. In this review, we summarize the current knowledge and understanding of specific E6 PPI inhibition, the progress and challenges being faced, and potential approaches that can be utilized to identify novel and potent PPI inhibitors for cervical cancer treatment.

Endogenous modulators of neurotrophin signaling: Landscape of the transient ATP-NGF interactions

•

High-resolution solution NMR structure of rhNGF has been determined.

•

Quinary interactions characterize ATP binding to rhNGF.

•

SPR, ITC and STD-NMR reveal ATP binding to rhNGF with mM affinity.

•

NMR and MD analysis pinpoint to the presence of two binding sites of ATP on rhNGF.

•

Stoichiometry of ATP-Mg²⁺ or Zn²⁺-rhNGF mixtures affects K_D affinity to TrkA/p75^NTR.

The Nerve Growth Factor (NGF) neurotrophin acts in the maintenance and growth of neuronal populations. Despite the detailed knowledge of NGF’s role in neuron physiology, the structural and mechanistic determinants of NGF bioactivity modulated by essential endogenous ligands are still lacking. We present the results of an integrated structural and advanced computational approach to characterize the extracellular ATP-NGF interaction. We mapped by NMR the interacting surface and ATP orientation on NGF and revealed the functional role of this interaction in the binding to TrkA and p75^NTR receptors by SPR. The role of divalent ions was explored in conjunction with ATP. Our results pinpoint ATP as a likely transient molecular modulator of NGF signaling, in health and disease states.

Getting a Grip on the Undrugged: Targeting β-Catenin with Fragment-Based Methods

Aberrant WNT pathway activation, leading to nuclear accumulation of β-catenin, is a key oncogenic driver event. Mutations in the tumor suppressor gene APC lead to impaired proteasomal degradation of β-catenin and subsequent nuclear translocation. Restoring cellular degradation of β-catenin represents a potential therapeutic strategy. Here, we report the fragment-based discovery of a small molecule binder to β-catenin, including the structural elucidation of the binding mode by X-ray crystallography. The difficulty in drugging β-catenin was confirmed as the primary screening campaigns identified only few and very weak hits. Iterative virtual and NMR screening techniques were required to discover a compound with sufficient potency to be able to obtain an X-ray co-crystal structure. The binding site is located between armadillo repeats two and three, adjacent to the BCL9 and TCF4 binding sites. Genetic studies show that it is unlikely to be useful for the development of protein-protein interaction inhibitors but structural information and established assays provide a solid basis for a prospective optimization towards β-catenin proteolysis targeting chimeras (PROTACs) as alternative modality.

Computational Approaches for the Design of (Mutant-)Selective Tyrosine Kinase Inhibitors: State-of-the-Art and Future Prospects

Kinases remain one of the major attractive therapeutic targets for a large number of indications such as cancer, rheumatoid arthritis, cardiac failure and many others. Design and development of kinase inhibitors (ATP-competitive, allosteric or covalent) is a clinically validated and successful strategy in the pharmaceutical industry. The perks come with limitations, particularly the development of resistance to highly potent and selective inhibitors. When this happens, the cycle needs to be repeated, i.e., the design and development of kinase inhibitors active against the mutated forms. The complexity of tumor milieu makes it awfully difficult for these molecularly-targeted therapies to work. Every year newer and better versions of these agents are introduced in the clinic. Several computational approaches such as structure-, ligand-based or hybrid ones continue to live up to their potential in discovering novel kinase inhibitors. New schools of thought in this area continue to emerge, e.g., development of dual-target kinase inhibitors. But there are fundamental issues with this approach. It is indeed difficult to selectively optimize binding at two entirely different or related kinases. In addition to the conventional strategies, modern technologies (machine learning, deep learning, artificial intelligence, etc.) started yielding the results and building success stories. Computational tools invariably played a critical role in catalysing the phenomenal progress in kinase drug discovery field. The present review summarized the progress in utilizing computational methods and tools for discovering (mutant-)selective tyrosine kinase inhibitor drugs in the last three years (2017-2019). Representative investigations have been discussed, while others are merely listed. The author believes that the enthusiastic reader will be inspired to dig out the cited literature extensively to appreciate the progress made so far and the future prospects of the field.

Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries

Current methods used for measuring amino acid side-chain reactivity lack the throughput needed to screen large chemical libraries for interactions across the proteome. Here we redesigned the workflow for activity-based protein profiling of reactive cysteine residues by using a smaller desthiobiotin-based probe, sample multiplexing, reduced protein starting amounts and software to boost data acquisition in real time on the mass spectrometer. Our method, streamlined cysteine activity-based protein profiling (SLC-ABPP), achieved a 42-fold improvement in sample throughput, corresponding to profiling library members at a depth of >8,000 reactive cysteine sites at 18 min per compound. We applied it to identify proteome-wide targets of covalent inhibitors to mutant Kirsten rat sarcoma (KRAS)^G12C and Bruton's tyrosine kinase (BTK). In addition, we created a resource of cysteine reactivity to 285 electrophiles in three human cell lines, which includes >20,000 cysteines from >6,000 proteins per line. The goal of proteome-wide profiling of cysteine reactivity across thousand-member libraries under several cellular contexts is now within reach.

Fragment Screening by NMR

This chapter describes the use of NMR to screen a fragment library as part of a fragment-based lead discovery (FBLD) campaign. The emphasis is on the practicalities involved in fragment screening by NMR, with particular attention to the use of 1D ligand-observed ¹H NMR experiments. An overview of the theoretical considerations underlying the choice of method and experimental configuration is given, along with a discussion of steps that can be taken in order to minimize the risk of experimental artifacts often associated with the identification of low-affinity interactions.