Experiences in fragment-based drug discovery

Optimizing NMR fragment-based drug screening for membrane protein targets

NMR spectroscopy has played a pivotal role in fragment-based drug discovery by coupling detection of weak ligand-target binding with structural mapping of the binding site. Fragment-based screening by NMR has been successfully applied to many soluble protein targets, but only to a limited number of membrane proteins, despite the fact that many drug targets are membrane proteins. This is partly because of difficulties preparing membrane proteins for NMR-especially human membrane proteins-and because of the inherent complexity associated with solution NMR spectroscopy on membrane protein samples, which require the inclusion of membrane-mimetic agents such as micelles, nanodiscs, or bicelles. Here, we developed a generalizable protocol for fragment-based screening of membrane proteins using NMR. We employed two human membrane protein targets, both in fully protonated detergent micelles: the single-pass C-terminal domain of the amyloid precursor protein, C99, and the tetraspan peripheral myelin protein 22 (PMP22). For both we determined the optimal NMR acquisition parameters, protein concentration, protein-to-micelle ratio, and upper limit to the concentration of D6-DMSO in screening samples. Furthermore, we conducted preliminary screens of a plate-format molecular fragment mixture library using our optimized conditions and were able to identify hit compounds that selectively bound to the respective target proteins. It is hoped that the approaches presented here will be useful in complementing existing methods for discovering lead compounds that target membrane proteins.

Fluoromethylketone-Fragment Conjugates Designed as Covalent Modifiers of EcDsbA are Atypical Substrates

Disulfide bond protein A (DsbA) is an oxidoreductase enzyme that catalyzes the formation of disulfide bonds in Gram-negative bacteria. In Escherichia coli, DsbA (EcDsbA) is essential for bacterial virulence, thus inhibitors have the potential to act as antivirulence agents. A fragment-based screen was conducted against EcDsbA and herein we describe the development of a series of compounds based on a phenylthiophene hit identified from the screen. A novel thiol reactive and "clickable" ethynylfluoromethylketone was designed for reaction with azide-functionalized fragments to enable rapid and versatile attachment to a range of fragments. The resulting fluoromethylketone conjugates showed selectivity for reaction with the active site thiol of EcDsbA, however unexpectedly, turnover of the covalent adduct was observed. A mechanism for this turnover was investigated and proposed which may have wider ramifications for covalent reactions with dithiol-disulfide oxidoreducatases.

NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer

Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO models were trained under different task scenarios by recognizing syntactic patterns and structure-property relationships. We further explored the performance of NIMO in structure-guided, activity-oriented, and pocket-based molecule generation tasks. Our results show that NIMO had excellent performance for molecule generation from scratch and structure optimization from a scaffold.

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.

Using Photoreactive Probes to Identify Viable Drug Targets in Non-small Cell Lung Cancer

Recent advancements in chemoproteomics have accelerated new chemical tools for exploring protein ligandability in native biological systems. However, a large fraction of ligandable proteome in cancer cells remains poorly studied. Here, we present a practical and efficient sample processing method for liquid chromatography high-resolution-tandem mass spectrometry (HPLC-MS/MS) analysis. This method uses fully functionalized photoreactive fragment-like probes for profiling protein-ligand interactions in live cancer cells. This method adopts "on-bead" digestion in conjunction with ZipTip desalting prior sample injection to MS. By using this protocol, fragment protein interactions can be visualized using fluorescent imaging, and fragment-associated proteins can be identified via HPLC-MS/MS analysis. Approximately 16 samples would generally expect to be processed within 3 days by following this protocol.

Fragment-based investigation of thiourea derivatives as VEGFR-2 inhibitors: a cross-validated approach of ligand-based and structure-based molecular modeling studies

Angiogenesis is mediated by the vascular endothelial growth factor (VEGF) that plays a key role in the modulation of progression, invasion and metastasis, related to solid tumors and hematological malignancies. Several small-molecule VEGFR-2 inhibitors are marketed, but their usage is restricted to specific cancers due to severe toxicities. Therefore, cost-effective novel small molecule VEGFR-2 inhibitors may be an alternative to overcome these adverse effects. Here, a set of thiourea-based VEGFR-2 inhibitors were considered for a combined fragment-based QSAR technique, structure-based molecular docking followed by molecular dynamics simulation studies to acquire insights into the key structural attributes and the binding pattern of enzyme-ligand interactions. Noticeably, amine-substituted quinazoline phenyl ring and a higher number of nitrogen atoms, and the hydrazide function in the molecular structure are crucial for VEGFR-2 inhibition whereas methoxy groups are detrimental to VEGFR-2 inhibition. The MD simulation study of sorafenib and thiourea derivatives explored the significance of urea and thiourea moiety binding at VEGFR-2 active site that can be utilized further in the future to design molecules for greater binding stability and better VEGFR-2 selectivity. Therefore, such findings can be beneficial for the development of newer VEGFR-2 inhibitors for further refinement to acquire better therapeutic efficacy.Communicated by Ramaswamy H. Sarma.

A Benchmark Study of Protein-Fragment Complex Structure Calculations with NMR2

Protein-fragment complex structures are particularly sought after in medicinal chemistry to rationally design lead molecules. These structures are usually derived using X-ray crystallography, but the failure rate is non-neglectable. NMR is a possible alternative for the calculation of weakly interacting complexes. Nevertheless, the time-consuming protein signal assignment step remains a barrier to its routine application. NMR Molecular Replacement (NMR2) is a versatile and rapid method that enables the elucidation of a protein-ligand complex structure. It has been successfully applied to peptides, drug-like molecules, and more recently to fragments. Due to the small size of the fragments, ca < 300 Da, solving the structures of the protein-fragment complexes is particularly challenging. Here, we present the expected performances of NMR2 when applied to protein-fragment complexes. The NMR2 approach has been benchmarked with the SERAPhic fragment library to identify the technical challenges in protein-fragment NMR structure calculation. A straightforward strategy is proposed to increase the method's success rate further. The presented work confirms that NMR2 is an alternative method to X-ray crystallography for solving protein-fragment complex structures.

E3 Ligases Meet Their Match: Fragment-Based Approaches to Discover New E3 Ligands and to Unravel E3 Biology

Ubiquitination is a key post-translational modification of proteins, affecting the regulation of multiple cellular processes. Cells are equipped with over 600 ubiquitin orchestrators, called E3 ubiquitin ligases, responsible for directing the covalent attachment of ubiquitin to substrate proteins. Due to their regulatory role in cells, significant efforts have been made to discover ligands for E3 ligases. The recent emergence of the proteolysis targeting chimera (PROTAC) and molecular glue degrader (MGD) modalities has further increased interest in E3 ligases as drug targets. This perspective focuses on how fragment based lead discovery (FBLD) methods have been used to discover new ligands for this important target class. In some cases these efforts have led to clinical candidates; in others, they have provided tools for deepening our understanding of E3 ligase biology. Recently, FBLD-derived ligands have inspired the design of PROTACs that are able to artificially modulate protein levels in cells.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

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.

Open access databases available for the pesticide lead discovery

Pesticide research is a multi-disciplinary collaborative study, and big data analysis based on integrating information from databases benefits decision-making in pesticide research. In the last 40 years, dozens of pesticide-related databases have been built up to describe their biological activities, toxicity, modes of action, and environmental risks, etc. However, these data are scattered and overlapping in different databases in multiple inconsistent formats, which is not convenient for information analysis and comparison. In this study, the content of 26 open access databases related to pesticide research was illustrated according to the information provided for the ligand-based drug design (LBDD) and receptor-based (or structure-based drug design, SBDD), and was summarized into three categories:1) the correspondence between the chemical structures and functional properties (biological activity, resistance, toxicity, environmental adaptation); 2) action mode study (target identification, target structures, and biological pathways); 3) computational servers for pesticide design. To our knowledge, this is the first review about the open access databases for pesticide research. The data classification could facilitate the information accessibility for pesticide research, and speed up the decision-making process in pesticide discovery.

Forces Driving a Magic Bullet to Its Target: Revisiting the Role of Thermodynamics in Drug Design, Development, and Optimization

Drug discovery strategies have advanced significantly towards prioritizing target selectivity to achieve the longstanding goal of identifying “magic bullets” amongst thousands of chemical molecules screened for therapeutic efficacy. A myriad of emerging and existing health threats, including the SARS-CoV-2 pandemic, alarming increase in bacterial resistance, and potentially fatal chronic ailments, such as cancer, cardiovascular disease, and neurodegeneration, have incentivized the discovery of novel therapeutics in treatment regimens. The design, development, and optimization of lead compounds represent an arduous and time-consuming process that necessitates the assessment of specific criteria and metrics derived via multidisciplinary approaches incorporating functional, structural, and energetic properties. The present review focuses on specific methodologies and technologies aimed at advancing drug development with particular emphasis on the role of thermodynamics in elucidating the underlying forces governing ligand–target interaction selectivity and specificity. In the pursuit of novel therapeutics, isothermal titration calorimetry (ITC) has been utilized extensively over the past two decades to bolster drug discovery efforts, yielding information-rich thermodynamic binding signatures. A wealth of studies recognizes the need for mining thermodynamic databases to critically examine and evaluate prospective drug candidates on the basis of available metrics. The ultimate power and utility of thermodynamics within drug discovery strategies reside in the characterization and comparison of intrinsic binding signatures that facilitate the elucidation of structural–energetic correlations which assist in lead compound identification and optimization to improve overall therapeutic efficacy.

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.

A commentary on the use of pharmacoenhancers in the pharmaceutical industry and the implication for DMPK drug discovery strategies

Paxlovid, a drug combining nirmatrelvir and ritonavir, was designed for the treatment of COVID-19 and its rapid development has led to emergency use approval by the FDA to reduce the impact of COVID-19 infection on patients.In order to overcome potentially suboptimal therapeutic exposures, nirmatrelvir is dosed in combination with ritonavir to boost the pharmacokinetics of the active product.Here we consider examples of drugs co-administered with pharmacoenhancers.Pharmacoenhancers have been adopted for multiple purposes such as ensuring therapeutic exposure of the active product, reducing formation of toxic metabolites, changing the route of administration, and increasing the cost-effectiveness of a therapy.We weigh the benefits and risks of this approach, examining the impact of technology developments on drug design and how enhanced integration between cross-discipline teams can improve the outcome of drug discovery.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

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.

Trends in coordination of rhenium organometallic complexes in the Protein Data Bank

Radiopharmaceutical development has similar overall characteristics to any biomedical drug development requiring a compound's stability, aqueous solubility and selectivity to a specific disease site. However, organometallic complexes containing ^188/186Re or ^99mTc involve a d-block transition-metal radioactive isotope and therefore bring additional factors such as metal oxidation states, isotope purity and half life into play. This topical review is focused on the development of radiopharmaceuticals containing the radioisotopes of rhenium and technetium and, therefore, on the occurrence of these organometallic complexes in protein structures in the Worldwide Protein Data Bank (wwPDB). The purpose of incorporating the group 7 transition metals of rhenium/technetium in the protein and the reasons for study by protein crystallography are described, as certain PDB studies were not aimed at drug development. Technetium is used as a medical diagnostic agent and involves the ^99mTc isotope which decays to release gamma radiation, thereby employed for its use in gamma imaging. Due to the periodic relationship among group 7 transition metals, the coordination chemistry of rhenium is similar (but not identical) to that of technetium. The types of reactions the potential model radiopharmaceutical would prefer to partake in, and by extension knowing which proteins and biomolecules the compound would react with in vivo, are needed. Crystallography studies, both small molecule and macromolecular, are a key aspect in understanding chemical coordination. Analyses of bonding modes, coordination to particular residues and crystallization conditions are presented. In our Forward look as a concluding summary of this topical review, the question we ask is: what is the best way for this field to progress?

Fragment based drug design

Rational approaches towards drug development have emerged as one of the most promising ways among the tedious conventional procedures with the aim of redefining the drug discovery process. The need of current medical system is demanding a much precise, faster and reliable approaches in parallel to faster growing technology for development of drugs with more intrinsic action and fewer side effects. Systematic and well-defined diagnostic studies have revealed the specific causes of disease and related targets for drug development. Designing a drug as per the specific target, studying it in-silico prior to its development has been proved as an added benefit to the studies. Many approaches like structure based drug design, fragment based drug design and ligand based drug design are been in practice for the drug discovery and development with the similar fundamental theory. Fragment based drug design utilizes a library of fragments designed specifically for the concerned target and these fragments are studied further before screening with virtual methods as well as with biophysical methods. The process follows a well-defined pathway which moulds a fragment into a perfect drug candidate. In this chapter we have tried to cover all the basic aspects of fragment based drug design and related technologies.

Preparation of Giant Escherichia coli spheroplasts for Electrophysiological Recordings

Prokaryotic ion channels have been instrumental in furthering our understanding of many fundamental aspects of ion channels' structure and function. However, characterizing the biophysical properties of a prokaryotic ion channel in a native membrane system using patch-clamp electrophysiology is technically challenging. Patch-clamp is regarded as a gold standard technique to study ion channel properties in both native and heterologous expression systems. The presence of a cell wall and the small size of bacterial cells makes it impossible to directly patch clamp using microelectrodes. Here, we describe a method for the preparation of giant E. coli spheroplasts in order to investigate the electrophysiological properties of bacterial cell membranes. Spheroplasts are formed by first inhibiting bacterial cell wall synthesis, followed by enzymatic digestion of the outer cell wall in the presence of a permeabilizing agent. This protocol can be used to characterize the function of any heterologous ion channels or ion transporters expressed in E. coli membranes.

Ligand-based quantitative structural assessments of SARS-CoV-2 3CLpro inhibitors: An analysis in light of structure-based multi-molecular modeling evidences

Due to COVID-19, the whole world is undergoing a devastating situation, but treatment with no such drug candidates still has been established exclusively. In that context, 69 diverse chemicals with potential SARS-CoV-2 3CL^pro inhibitory property were taken into consideration for building different internally and externally validated linear (SW-MLR and GA-MLR), non-linear (ANN and SVM) QSAR, and HQSAR models to identify important structural and physicochemical characters required for SARS-CoV-2 3CL^pro inhibition. Importantly, 2-oxopyrrolidinyl methyl and benzylester functions, and methylene (hydroxy) sulphonic acid warhead group, were crucial for retaining higher SARS-CoV-2 3CL^pro inhibition. These GA-MLR and HQSAR models were also applied to predict some already repurposed drugs. As per the GA-MLR model, curcumin, ribavirin, saquinavir, sepimostat, and remdesivir were found to be the potent ones, whereas according to the HQSAR model, lurasidone, saquinavir, lopinavir, elbasvir, and paritaprevir were the highly effective SARS-CoV-2 3CL^pro inhibitors. The binding modes of those repurposed drugs were also justified by the molecular docking, molecular dynamics (MD) simulation, and binding energy calculations conducted by several groups of researchers. This current work, therefore, may be able to find out important structural parameters to accelerate the COVID-19 drug discovery processes in the future.

Divergent Metal-Free [4 + 2] Cascade Reaction of 1-Indanylidenemalononitrile with 3-Benzylidenebenzofuran-2(3H)-one: Access to Spiro-dihydrofluorene-benzofuranone and Axially Chiral Fluorenylamine-phenol Derivatives

An efficient cascade reaction of 1-indanylidenemalononitrile with 3-benzylidenebenzofuran-2(3H)-one divergently promoted by DABCO or chiral organocatalyst was developed under mild reaction conditions, and various spiro-dihydrofluorene-benzofuranones were produced in gratifying results, respectively. It is worth noting that both the spiro and axially chiral products can be obtained by tuning the reaction conditions. The mechanism of the transformation was also studied by quantum chemical calculations.

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.

Fragment-based drug design facilitates selective kinase inhibitor discovery

Protein kinases (PKs) are important drug targets, but kinases selectivity poses a challenge to protein kinase inhibitors (PKIs) design. Fragment-based drug discovery (FBDD) has achieved great success in the discovery of highly specific PKIs. It makes full use of kinase-fragment interaction in target kinase subpockets to obtain promising selectivity. However, it's difficult to understand the complicated kinase-fragment interaction space, and systemic discussion of these interactions is still lacking. Herein, we introduce the advantages of the FBDD strategy in PKIs design. Key features of the selectivity of kinase-fragment interactions are summarized and analyzed. Some promising PKIs are introduced as case studies to help understand the fragment-to-lead (F2L) optimization process. Novel strategies and technologies for FBDD in PKIs discovery are also outlooked.

Protease targeted COVID-19 drug discovery: What we have learned from the past SARS-CoV inhibitors?

The fascinating similarity between the SARS-CoV and SARS-CoV-2, inspires scientific community to investigate deeper into the SARS-CoV proteases such as main protease (Mpro) and papain-like protease (PLpro) and their inhibitors for the discovery of SARS-CoV-2 protease inhibitors. Because of the similarity in the proteases of these two corona viruses, there is a greater chance for the previous SARS-CoV Mpro and PLpro inhibitors to provide effective results against SARS-CoV-2. In this context, the molecular fragments from the SARS-CoV protease inhibitors through the fragment-based drug design and discovery technique can be useful guidance for COVID-19 drug discovery. Here, we have focused on the structure-activity relationship studies of previous SARS-CoV protease inhibitors and discussed about crucial fragments generated from previous SARS-CoV protease inhibitors important for the lead optimization of SARS-CoV-2 protease inhibitors. This study surely offers different strategic options of lead optimization to the medicinal chemists to discover effective anti-viral agent against the devastating disease, COVID-19.

Benchmark Sets for Binding Hot Spot Identification in Fragment-Based Ligand Discovery

Binding hot spots are regions of proteins that, due to their potentially high contribution to the binding free energy, have high propensity to bind small molecules. We present benchmark sets for testing computational methods for the identification of binding hot spots with emphasis on fragment-based ligand discovery. Each protein structure in the set binds a fragment, which is extended into larger ligands in other structures without substantial change in its binding mode. Structures of the same proteins without any bound ligand are also collected to form an unbound benchmark. We also discuss a set developed by Astex Pharmaceuticals for the validation of hot and warm spots for fragment binding. The set is based on the assumption that a fragment that occurs in diverse ligands in the same subpocket identifies a binding hot spot. Since this set includes only ligand-bound proteins, we added a set with unbound structures. All four sets were tested using FTMap, a computational analogue of fragment screening experiments to form a baseline for testing other prediction methods, and differences among the sets are discussed.

Fragment-based drug discovery: opportunities for organic synthesis

This Review describes the increasing demand for organic synthesis to facilitate fragment-based drug discovery (FBDD), focusing on polar, unprotected fragments. In FBDD, X-ray crystal structures are used to design target molecules for synthesis with new groups added onto a fragment via specific growth vectors. This requires challenging synthesis which slows down drug discovery, and some fragments are not progressed into optimisation due to synthetic intractability. We have evaluated the output from Astex's fragment screenings for a number of programs, including urokinase-type plasminogen activator, hematopoietic prostaglandin D2 synthase, and hepatitis C virus NS3 protease-helicase, and identified fragments that were not elaborated due, in part, to a lack of commercially available analogues and/or suitable synthetic methodology. This represents an opportunity for the development of new synthetic research to enable rapid access to novel chemical space and fragment optimisation.

Computational Fragment-Based Design Facilitates Discovery of Potent and Selective Monoamine Oxidase-B (MAO-B) Inhibitor

Parkinson's disease (PD) is one of the most common age-related neurodegenerative diseases. Inhibition of monoamine oxidase-B (MAO-B), which is mainly found in the glial cells of the brain, may lead to an elevated level of dopamine (DA) in patients. MAO-B inhibitors have been used extensively for patients with PD. However, the discovery of the selective MAO-B inhibitor is still a challenge. In this study, a computational strategy was designed for the rapid discovery of selective MAO-B inhibitors. A series of (S)-2-(benzylamino)propanamide derivatives were designed. In vitro biological evaluations revealed that (S)-1-(4-((3-fluorobenzyl)oxy)benzyl)azetidine-2-carboxamide (C3) was more potent and selective than safinamide, a promising drug for regulating MAO-B. Further studies revealed that the selectivity mechanism of C3 was due to the steric clash caused by the residue difference of Phe208 (MAO-A) and Ile199 (MAO-B). Animal studies showed that compound C3 could inhibit cerebral MAO-B activity and alleviate 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopaminergic neuronal loss.

Novel naphthylamide derivatives as dual-target antifungal inhibitors: Design, synthesis and biological evaluation

Fungal infections have become a serious medical problem due to the high infection rate and the frequent emergence of drug resistance. Squalene epoxidase (SE) and 14α-demethylase (CYP51) are considered as the important antifungal targets, they can show the synergistic effect on antifungal therapy. In the study, a series of active fragments were screened through the method of De Novo Link, and these active fragments with the higher Ludi_Scores were selected, which can show the obvious binding ability with the dual targets (SE, CYP51). Subsequently, three series of target compounds with naphthyl amide scaffolds were constructed by connecting these core fragments, and their structures were synthesized. Most of compounds showed the antifungal activity in the treatment of pathogenic fungi. It was worth noting that compounds 10b-5 and 17a-2 with the excellent broad-spectrum antifungal properties also exhibited the obvious antifungal effects against drug-resistant fungi. Preliminary mechanism study has proved these target compounds can block the biosynthesis of ergosterol by inhibiting the activity of dual targets (SE, CYP51). Furthermore, target compounds 10-5 and 17a-2 with low toxicity side effects also demonstrated the excellent pharmacological effects in vivo. The molecular docking and ADMET prediction were performed, which can guide the optimization of subsequent lead compounds.

Development of a structure-analysis pipeline using multiple-solvent crystal structures of barrier-to-autointegration factor

The multiple-solvent crystal structure (MSCS) approach uses high concentrations of organic solvents to characterize the interactions and effects of solvents on proteins. Here, the method has been further developed and an MSCS data-handling pipeline is presented that uses the Detection of Related Solvent Positions (DRoP) program to improve data quality. DRoP is used to selectively model conserved water molecules, so that an advanced stage of structural refinement is reached quickly. This allows the placement of organic molecules more accurately and convergence on high-quality maps and structures. This pipeline was applied to the chromatin-associated protein barrier-to-autointegration factor (BAF), resulting in structural models with better than average statistics. DRoP and Phenix Structure Comparison were used to characterize the data sets and to identify a binding site that overlaps with the interaction site of BAF with emerin. The conserved water-mediated networks identified by DRoP suggested a mechanism by which water molecules are used to drive the binding of DNA. Normalized and differential B-factor analysis is shown to be a valuable tool to characterize the effects of specific solvents on defined regions of BAF. Specific solvents are identified that cause stabilization of functionally important regions of the protein. This work presents tools and a standardized approach for the analysis and comprehension of MSCS data sets.

Identification of structural fingerprints for ABCG2 inhibition by using Monte Carlo optimization, Bayesian classification, and structural and physicochemical interpretation (SPCI) analysis

The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Currently, it has been emerged as one of the best biological targets for the designing of small molecule drugs capable of eliminating multidrug resistance in breast cancer. In order to gain insights into the relationship between the molecular structure of compounds and the ABCG2 inhibition, a multi-QSAR approach using different methods was performed on a dataset of 294 ABCG2 inhibitors with diverse scaffolds. The best models obtained by different chemometric methods have the following statistical characteristics: Monte Carlo Optimization-based QSAR (sensitivity = 0.905, specificity = 0.6255, accuracy = 0.756, and MCC = 0.545), Bayesian classification model (sensitivity = 0.735, specificity = 0.775, and concordance = 0.757); structural and physicochemical interpretation analysis-random forest method (balance accuracy = 0.750, sensitivity = 0.810, and specificity = 0.700). Additionally, structural fingerprints modulating the ABCG2 inhibitory properties were identified from the best models of each method and also validated with each other. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints modulating ABCG2 inhibition.

Aromatic clusters in protein-protein and protein-drug complexes

Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein-drug and protein-protein interactions. They are relevant for both protein stability and molecular recognition processes due to their natural occurrence in aromatic aminoacids (Trp, Phe, Tyr and His) as well as in designed drugs since they are believed to contribute to optimizing both affinity and specificity of drug-like molecules. Despite the mentioned relevance, the impact of aromatic clusters on protein-protein and protein-drug complexes is still poorly characterized, especially in those that go beyond a dimer. In this work, we studied protein-drug and protein-protein complexes and systematically analyzed the presence and structure of their aromatic clusters. Our results show that aromatic clusters are highly prevalent in both protein-protein and protein-drug complexes, and suggest that protein-protein aromatic clusters have idealized interactions, probably because they were optimized by evolution, as compared to protein-drug clusters that were manually designed. Interestingly, the configuration, solvent accessibility and secondary structure of aromatic residues in protein-drug complexes shed light on the relation between these properties and compound affinity, allowing researchers to better design new molecules.

Applied Biophysical Methods in Fragment-Based Drug Discovery

Fragment-based drug discovery (FBDD) has come of age in the last decade with the FDA approval of four fragment-derived drugs. Biophysical methods are at the heart of hit discovery and validation in FBDD campaigns. The three most commonly used methods, thermal shift, surface plasmon resonance, and nuclear magnetic resonance, can be daunting for the novice user. We aim here to provide the nonexpert user of these methods with a summary of problems and challenges that might be faced, but also highlight the potential gains that each method can contribute to an FBDD project. While our view on FBDD is slightly biased toward enabling structure-guided drug discovery, most of the points we address in this review are also valid for non-structure-focused FBDD.

Synthesis and biological evaluation of novel (E)-N'-benzylidene hydrazides as novel c-Met inhibitors through fragment based virtual screening

C-Met plays a crucial role in the development and progression of neoplastic disease. Type II c-Met inhibitors recognise the inactive DFG-out conformation of the kinase, result in better anti-tumour effects due to synergistic effect against the other kinases. According to our previous works, an (E)-N'-benzylidene group was selected as the initial fragment. Two series of (E)-N'-benzylidene hydrazides were designed by fragment growth method. The inhibitory activities were in vitro investigated against c-Met and VEGFR-2. Compound 10b exhibited the most potent inhibitory activity against the c-Met inhibitor (IC₅₀ = 0.37 nM). Compound 11b exhibited multi-target c-Met kinase inhibitory activity as a potential type II c-Met inhibitor (IC₅₀ = 3.41 nM against c-Met; 25.34 nM against VEGFR-2). The two compounds also demonstrate the feasibility of fragment-based virtual screening method for drug discovery.

Fragments as Novel Starting Points for tRNA-Guanine Transglycosylase Inhibitors Found by Alternative Screening Strategies

Crystallography provides structural information crucial for fragment optimization, however several criteria must be met to screen directly on protein crystals as soakable, well-diffracting specimen must be available. We screened a 96-fragment library against the tRNA-modifying enzyme TGT using crystallography. Eight hits, some with surprising binding poses, were detected. However, the amount of data collection, reduction and refinement is assumed substantial. Therefore, having a reliable cascade of fast and cost-efficient methods available for pre-screening before embarking to elaborate crystallographic screening appears beneficial. This allows filtering of compounds to the most promising hits, available to rapidly progress from hit-to-lead. But how to ensure that this workflow is reliable? To answer this question, we also applied SPR and NMR to the same screening sample to study whether identical hits are retrieved. Upon hit-list comparisons, crystallography shows with NMR and SPR, only one overlapping hit and all three methods shared no common hits. This questions a cascade-type screening protocol at least in the current example. Compared to crystallography, SPR and NMR detected higher percentages of non-active-site binders suggesting the importance of running reporter ligand-based competitive screens in SPR and NMR, a requirement not needed in crystallography. Although not specific, NMR proved a more sensitive method relative to SPR and crystallography, as it picked up the highest numbers of binders.

Why is interoperability between the two fields of chemical crystallography and protein crystallography so difficult?

The interoperability of chemical and biological crystallographic data is a key challenge to research and its application to pharmaceutical design. Research attempting to combine data from the two disciplines, small-molecule or chemical crystallography (CX) and macromolecular crystallography (MX), will face unique challenges including variations in terminology, software development, file format and databases which differ significantly from CX to MX. This perspective overview spans the two disciplines and originated from the investigation of protein binding to model radiopharmaceuticals. The opportunities of interlinked research while utilizing the two databases of the CSD (Cambridge Structural Database) and the PDB (Protein Data Bank) will be highlighted. The advantages of software that can handle multiple file formats and the circuitous route to convert organometallic small-molecule structural data for use in protein refinement software will be discussed. In addition some pointers to avoid being shipwrecked will be shared, such as the care which must be taken when interpreting data precision involving small molecules versus proteins.

Formation of a highly dense tetra-rhenium cluster in a protein crystal and its implications in medical imaging

The fact that a protein crystal can serve as a chemical reaction vessel is intrinsically fascinating. That it can produce an electron-dense tetranuclear rhenium cluster compound from a rhenium tri-carbonyl tri-bromo starting compound adds to the fascination. Such a cluster has been synthesized previously in vitro, where it formed under basic conditions. Therefore, its synthesis in a protein crystal grown at pH 4.5 is even more unexpected. The X-ray crystal structures presented here are for the protein hen egg-white lysozyme incubated with a rhenium tri-carbonyl tri-bromo compound for periods of one and two years. These reveal a completed, very well resolved, tetra-rhenium cluster after two years and an intermediate state, where the carbonyl ligands to the rhenium cluster are not yet clearly resolved, after one year. A dense tetranuclear rhenium cluster, and its technetium form, offer enhanced contrast in medical imaging. Stimulated by these crystallography results, the unusual formation of such a species directly in an in vivo situation has been considered. It offers a new option for medical imaging compounds, particularly when considering the application of the pre-formed tetranuclear cluster, suggesting that it may be suitable for medical diagnosis because of its stability, preference of formation and biological compatibility.

Structure-guided fragment-based drug discovery at the synchrotron: screening binding sites and correlations with hotspot mapping

Structure-guided drug discovery emerged in the 1970s and 1980s, stimulated by the three-dimensional structures of protein targets that became available, mainly through X-ray crystal structure analysis, assisted by the development of synchrotron radiation sources. Structures of known drugs or inhibitors were used to guide the development of leads. The growth of high-throughput screening during the late 1980s and the early 1990s in the pharmaceutical industry of chemical libraries of hundreds of thousands of compounds of molecular weight of approximately 500 Da was impressive but still explored only a tiny fraction of the chemical space of the predicted 10⁴⁰ drug-like compounds. The use of fragments with molecular weights less than 300 Da in drug discovery not only decreased the chemical space needing exploration but also increased promiscuity in binding targets. Here we discuss advances in X-ray fragment screening and the challenge of identifying sites where fragments not only bind but can be chemically elaborated while retaining their positions and binding modes. We first describe the analysis of fragment binding using conventional X-ray difference Fourier techniques, with Mycobacterium abscessus SAICAR synthetase (PurC) as an example. We observe that all fragments occupy positions predicted by computational hotspot mapping. We compare this with fragment screening at Diamond Synchrotron Light Source XChem facility using PanDDA software, which identifies many more fragment hits, only some of which bind to the predicted hotspots. Many low occupancy sites identified may not support elaboration to give adequate ligand affinity, although they will likely be useful in drug discovery as 'warm spots' for guiding elaboration of fragments bound at hotspots. We discuss implications of these observations for fragment screening at the synchrotron sources. This article is part of the theme issue 'Fifty years of synchrotron science: achievements and opportunities'.

Fragment Hits: What do They Look Like and How do They Bind?

A “fragment hit”, a molecule of low molecular weight that has been validated to bind to a target protein, can be an effective chemical starting point for a drug discovery project. Our ability to find and progress fragment hits could potentially be improved by enhancing our understanding of their binding properties, which to date has largely been based on tacit knowledge and reports from individual projects. In the work reported here, we systematically analyzed the molecular and binding properties of fragment hits using 489 published protein–fragment complexes. We identified a number of notable features that these hits tend to have in common, including preferences in buried surface area upon binding, hydrogen bonding and other directional interactions with the protein targets, structural topology, functional-group occurrence, and degree of carbon saturation. In the future, taking account of these preferences in designing and selecting fragments to screen against protein targets may increase the chances of success in fragment screening campaigns.

Structure-based virtual screening for novel chymase inhibitors by in silico fragment mapping

The term chymase refers to a family of chymotrypsin-like serine proteases stored within the secretory granules of mast cells. Recently, a variety of small molecule inhibitors for chymase have been developed with a primary focus on the treatment of cardiovascular diseases. Despite the expected therapeutic benefit of these chymase inhibitors, they have not been used clinically. Here, we attempted to identify new chymase inhibitors using a multistep structure-based virtual screening protocol combined with our knowledge-based in silico fragment mapping technique. The mapping procedure identified fragments with novel modes of interaction at the oxyanion hole of chymase. Next, we constructed a three-dimensional (3D) pharmacophore model and retrieved eight candidate chymase inhibitors from a commercial database that included approximately five million compounds. This selection was achieved using a multistep virtual screening protocol, which combined a 3D pharmacophore-based search, docking calculations, and analyses of binding free energy. One of the eight compounds exhibited concentration-dependent chymase inhibitory activity, which could be further optimized to develop more potent chymase inhibitors.

Virtual Compound Libraries in Computer-Assisted Drug Discovery

The use of virtual compound libraries in computer-assisted drug discovery has gained in popularity and has already lead to numerous successes. Here, we examine key static and dynamic virtual library concepts that have been developed over the past decade. To facilitate the search for new drugs in the vastness of chemical space, there are still several hurdles to overcome, including the current difficulties in screening and parsing efficiency and the need for more reliable vendors and accurate synthesis prediction tools. These challenges should be tackled by both the developers of virtual libraries and by their users, in order for the exploration of chemical space to live up to its potential.

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

Fragment-based drug discovery typically requires an interplay between screening methods, structural methods, and medicinal chemistry. X-ray crystallography is generally the method of choice to obtain three-dimensional structures of the bound ligand/protein complex, but this can sometimes be difficult, particularly for early, low-affinity fragment hits. In this Perspective, we discuss strategies to advance and evolve fragments in the absence of crystal structures of protein-fragment complexes, although the structure of the unliganded protein may be available. The strategies can involve other structural techniques, such as NMR spectroscopy, molecular modeling, or a variety of chemical approaches. Often, these strategies are aimed at guiding evolution of initial fragment hits to a stage where crystal structures can be obtained for further structure-based optimization.

Computer-aided design, synthesis and biological characterization of novel inhibitors for PKMYT1

In the current work, we applied computational methods to analyze the membrane-associated inhibitory kinase PKMYT1 and small molecule inhibitors. PKMYT1 regulates the cell cycle at G2/M transition and phosphorylates Thr14 and Tyr15 in the Cdk1-cyclin B complex. A combination of in silico and in vitro screening was applied to identify novel PKMYT1 inhibitors. The computational approach combined structural analysis, molecular docking, binding free energy calculations, and quantitative structure-activity relationship (QSAR) models. In addition, a computational fragment growing approach was applied to a set of previously identified diaminopyrimidines. Based on the derived computational models, several derivatives were synthesized and tested in vitro on PKMYT1. Novel inhibitors active in the sub-micromolar range were identified which provide the basis for further characterization of PKMYT1 as putative target for cancer therapy.