Twenty years on: the impact of fragments on drug discovery

Non-Extensive Fragmentation of Natural Products and Pharmacophore-Based Virtual Screening as a Practical Approach to Identify Novel Promising Chemical Scaffolds

Fragment-based drug design (FBDD) and pharmacophore modeling have proven to be efficient tools to discover novel drugs. However, these approaches may become limited if the collection of fragments is highly repetitive, poorly diverse, or excessively simple. In this article, combining pharmacophore modeling and a non-classical type of fragmentation (herein called non-extensive) to screen a natural product (NP) library may provide fragments predicted as potent, diverse, and developable. Initially, we applied retrosynthetic combinatorial analysis procedure (RECAP) rules in two versions, extensive and non-extensive, in order to deconstruct a virtual library of NPs formed by the databases Traditional Chinese Medicine (TCM), AfroDb (African Medicinal Plants database), NuBBE (Nuclei of Bioassays, Biosynthesis, and Ecophysiology of Natural Products), and UEFS (Universidade Estadual de Feira de Santana). We then developed a virtual screening (VS) using two groups of natural-product-derived fragments (extensive and non-extensive NPDFs) and two overlapping pharmacophore models for each of 20 different proteins of therapeutic interest. Molecular weight, lipophilicity, and molecular complexity were estimated and compared for both types of NPDFs (and their original NPs) before and after the VS proceedings. As a result, we found that non-extensive NPDFs exhibited a much higher number of chemical entities compared to extensive NPDFs (45,355 vs. 11,525 compounds), accounting for the larger part of the hits recovered and being far less repetitive than extensive NPDFs. The structural diversity of both types of NPDFs and the NPs was shown to diminish slightly after VS procedures. Finally, and most interestingly, the pharmacophore fit score of the non-extensive NPDFs proved to be not only higher, on average, than extensive NPDFs (56% of cases) but also higher than their original NPs (69% of cases) when all of them were also recognized as hits after the VS. The findings obtained in this study indicated that the proposed cascade approach was useful to enhance the probability of identifying innovative chemical scaffolds, which deserve further development to become drug-sized candidate compounds. We consider that the knowledge about the deconstruction degree required to produce NPDFs of interest represents a good starting point for eventual synthesis, characterization, and biological activity studies.

Dual Bcl-XL /Bcl-2 inhibitors discovered from DNA-encoded libraries using a fragment pairing strategy

A dual Bcl-X_L / Bcl-2 inhibitor was discovered from DNA-encoded libraries using a two steps process. In the first step, DNA was used to pair PNA-encoded fragments exploring > 250 000 combinations. In the second step, a focused library combining the selected fragments with linkers of different lengths and geometries led to the identification of tight binding adducts that were further investigated for their selective target engagement in pull-down assays, for their affinity by SPR, and their selectivity in a cytotoxicity assay. The best compound showed comparable cellular activity to venetoclax, the first-in-class therapeutic targeting Bcl-2.

Structural and molecular bases to IRE1 activity modulation

The Unfolded Protein response is an adaptive pathway triggered upon alteration of endoplasmic reticulum (ER) homeostasis. It is transduced by three major ER stress sensors, among which the Inositol Requiring Enzyme 1 (IRE1) is the most evolutionarily conserved. IRE1 is an ER-resident type I transmembrane protein exhibiting an ER luminal domain that senses the protein folding status and a catalytic kinase and RNase cytosolic domain. In recent years, IRE1 has emerged as a relevant therapeutic target in various diseases including degenerative, inflammatory and metabolic pathologies and cancer. As such several drugs altering IRE1 activity were developed that target either catalytic activity and showed some efficacy in preclinical pathological mouse models. In this review, we describe the different drugs identified to target IRE1 activity as well as their mode of action from a structural perspective, thereby identifying common and different modes of action. Based on this information we discuss on how new IRE1-targeting drugs could be developed that outperform the currently available molecules.

DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges

DNA-encoded chemical libraries (DELs) represent a versatile and powerful technology platform for the discovery of small-molecule ligands to protein targets of biological and pharmaceutical interest. DELs are collections of molecules, individually coupled to distinctive DNA tags serving as amplifiable identification barcodes. Thanks to advances in DNA-compatible reactions, selection methodologies, next-generation sequencing, and data analysis, DEL technology allows the construction and screening of libraries of unprecedented size, which has led to the discovery of highly potent ligands, some of which have progressed to clinical trials. In this Review, we present an overview of diverse approaches for the generation and screening of DEL molecular repertoires. Recent success stories are described, detailing how novel ligands were isolated from DEL screening campaigns and were further optimized by medicinal chemistry. The goal of the Review is to capture some of the most recent developments in the field, while also elaborating on future challenges to further improve DEL technology as a therapeutic discovery platform.

Fragment-Based Drug Discovery for RNA Targets

Rapid development within the fields of both fragment-based drug discovery (FBDD) and medicinal targeting of RNA provides possibilities for combining technologies and methods in novel ways. This review provides an overview of fragment-based screening (FBS) against RNA targets, including a discussion of the most recently used screening and hit validation methods such as NMR spectroscopy, X-ray crystallography, and virtual screening methods. A discussion of fragment library design based on research from small-molecule RNA binders provides an overview on both the currently limited guidelines within RNA-targeting fragment library design, and future possibilities. Finally, future perspectives are provided on screening and hit validation methods not yet used in combination with both fragment screening and RNA targets.

Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery

Fragment based drug discovery (FBDD) by the aid of different modelling techniques have been emerged as a key drug discovery tool in the area of pharmaceutical science and technology. The merits of employing these methods, in place of other conventional molecular modelling techniques, endorsed clear detection of the possible structural fragments present in diverse set of investigated compounds and can create alternate possibilities of lead optimization in drug discovery. In this work, two fragment identification tools namely SARpy and Laplacian-corrected Bayesian analysis were used for previous SARS-CoV PLpro and 3CLpro inhibitors. A robust and predictive SARpy based fragments identification was performed which have been validated further by Laplacian-corrected Bayesian model. These comprehensive approaches have advantages since fragments are straight forward to interpret. Moreover, distinguishing the key molecular features (with respect to ECFP_6 fingerprint) revealed good or bad influences for the SARS-CoV protease inhibitory activities. Furthermore, the identified fragments could be implemented in the medicinal chemistry endeavors of COVID-19 drug discovery.

Structures of the Plasmodium falciparum heat-shock protein 70-x ATPase domain in complex with chemical fragments identify conserved and unique binding sites

Plasmodium falciparum invades erythrocytes and extensively modifies them in a manner that increases the virulence of this malaria parasite. A single heat-shock 70 kDa-type chaperone, PfHsp70-x, is among the parasite proteins exported to the host cell. PfHsp70-x assists in the formation of a key protein complex that underpins parasite virulence and supports parasite growth during febrile episodes. Previous work resolved the crystallographic structures of the PfHsp70-x ATPase and substrate-binding domains, and showed them to be highly similar to those of their human counterparts. Here, 233 chemical fragments were screened for binding to the PfHsp70-x ATPase domain, resulting in three crystallographic structures of this domain in complex with ligands. Two binding sites were identified, with most ligands binding proximal to the ATPase nucleotide-binding pocket. Although amino acids participating in direct ligand interactions are conserved between the parasite and human erythrocytic chaperones, one nonconserved residue is also present near the ligand. This work suggests that PfHsp70-x features binding sites that may be exploitable by small-molecule ligands towards the specific inhibition of the parasite chaperone.

A structural perspective on targeting the RTK/Ras/MAP kinase pathway in cancer

Precision oncology is premised on identifying and drugging proteins and pathways that drive tumorigenesis or are required for survival of tumor cells. Across diverse cancer types, the signaling pathway emanating from receptor tyrosine kinases on the cell surface to RAS and the MAP kinase pathway is the most frequent target of oncogenic mutations, and key proteins in this signaling axis including EGFR, SHP2, RAS, BRAF, and MEK have long been a focus in cancer drug discovery. In this review, we provide an overview of historical and recent efforts to develop inhibitors targeting these nodes with an emphasis on the role that an understanding of protein structure and regulation has played in inhibitor discovery and characterization. Beyond its well-established role in structure-based drug design, structural biology has revealed mechanisms of allosteric regulation, distinct effects of activating oncogenic mutations, and other vulnerabilities that have opened new avenues in precision cancer drug discovery.

The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling

Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.

Pseudo Natural Products-Chemical Evolution of Natural Product Structure

Pseudo-natural products (PNPs) combine natural product (NP) fragments in novel arrangements not accessible by current biosynthesis pathways. As such they can be regarded as non-biogenic fusions of NP-derived fragments. They inherit key biological characteristics of the guiding natural product, such as chemical and physiological properties, yet define small molecule chemotypes with unprecedented or unexpected bioactivity. We iterate the design principles underpinning PNP scaffolds and highlight their syntheses and biological investigations. We provide a cheminformatic analysis of PNP collections assessing their molecular properties and shape diversity. We propose and discuss how the iterative analysis of NP structure, design, synthesis, and biological evaluation of PNPs can be regarded as a human-driven branch of the evolution of natural products, that is, a chemical evolution of natural product structure.

Probing the Surface of a Parasite Drug Target Thioredoxin Glutathione Reductase Using Small Molecule Fragments

Fragment screening is a powerful drug discovery approach particularly useful for enzymes difficult to inhibit selectively, such as the thiol/selenol-dependent thioredoxin reductases (TrxRs), which are essential and druggable in several infectious diseases. Several known inhibitors are reactive electrophiles targeting the selenocysteine-containing C-terminus and thus often suffering from off-target reactivity in vivo. The lack of structural information on the interaction modalities of the C-terminus-targeting inhibitors, due to the high mobility of this domain and the lack of alternative druggable sites, prevents the development of selective inhibitors for TrxRs. In this work, fragments selected from actives identified in a large screen carried out against Thioredoxin Glutathione Reductase from Schistosoma mansoni (SmTGR) were probed by X-ray crystallography. SmTGR is one of the most promising drug targets for schistosomiasis, a devastating, neglected disease. Utilizing a multicrystal method to analyze electron density maps, structural analysis, and functional studies, three binding sites were characterized in SmTGR: two sites are close to or partially superposable with the NADPH binding site, while the third one is found between two symmetry related SmTGR subunits of the crystal lattice. Surprisingly, one compound bound to this latter site stabilizes, through allosteric effects mediated by the so-called guiding bar residues, the crucial redox active C-terminus of SmTGR, making it finally visible at high resolution. These results further promote fragments as small molecule probes for investigating functional aspects of the target protein, exemplified by the allosteric effect on the C-terminus, and providing fundamental chemical information exploitable in drug discovery.

Latency, thermal stability, and identification of an inhibitory compound of mirolysin, a secretory protease of the human periodontopathogen Tannerella forsythia

Mirolysin is a secretory protease of Tannerella forsythia, a member of the dysbiotic oral microbiota responsible for periodontitis. In this study, we show that mirolysin latency is achieved by a “cysteine-switch” mechanism exerted by Cys23 in the N-terminal profragment. Mutation of Cys23 shortened the time needed for activation of the zymogen from several days to 5 min. The mutation also decreased the thermal stability and autoproteolysis resistance of promirolysin. Mature mirolysin is a thermophilic enzyme and shows optimal activity at 65 °C. Through NMR-based fragment screening, we identified a small molecule (compound (cpd) 9) that blocks promirolysin maturation and functions as a competitive inhibitor (K_i = 3.2 µM), binding to the S1′ subsite of the substrate-binding pocket. Cpd 9 shows superior specificity and does not interact with other T. forsythia proteases or Lys/Arg-specific proteases.

Asymmetric organocatalysis: an enabling technology for medicinal chemistry

The efficacy and synthetic versatility of asymmetric organocatalysis have contributed enormously to the field of organic synthesis since the early 2000s. As asymmetric organocatalytic methods mature, they have extended beyond the academia and undergone scale-up for the production of chiral drugs, natural products, and enantiomerically enriched bioactive molecules. This review provides a comprehensive overview of the applications of asymmetric organocatalysis in medicinal chemistry. A general picture of asymmetric organocatalytic strategies in medicinal chemistry is firstly presented, and the specific applications of these strategies in pharmaceutical synthesis are systematically described, with a focus on the preparation of antiviral, anticancer, neuroprotective, cardiovascular, antibacterial, and antiparasitic agents, as well as several miscellaneous bioactive agents. The review concludes with a discussion of the challenges, limitations and future prospects for organocatalytic asymmetric synthesis of medicinally valuable compounds.

Discovery of a Cell-Active SuTEx Ligand of Prostaglandin Reductase 2

Sulfonyl-triazoles have emerged as a new reactive group for covalent modification of tyrosine sites on proteins through sulfur-triazole exchange (SuTEx) chemistry. The extent to which this sulfur electrophile can be tuned for developing ligands with cellular activity remains largely underexplored. Here, we performed fragment-based ligand discovery in live cells to identify SuTEx compounds capable of liganding tyrosine sites on diverse protein targets. We verified our quantitative chemical proteomic findings by demonstrating concentration-dependent activity of SuTEx ligands, but not inactive counterparts, against recombinant protein targets directly in live cells. Our structure-activity relationship studies identified the SuTEx ligand HHS-0701 as a cell-active inhibitor capable of blocking prostaglandin reductase 2 (PTGR2) biochemical activity.

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.

A fragment-based approach to assess the ligandability of ArgB, ArgC, ArgD and ArgF in the L-arginine biosynthetic pathway of Mycobacterium tuberculosis

The L-arginine biosynthesis pathway consists of eight enzymes that catalyse the conversion of L-glutamate to L-arginine. Arginine auxotrophs (argB/argF deletion mutants) of Mycobacterium tuberculosis are rapidly sterilised in mice, while inhibition of ArgJ with Pranlukast was found to clear chronic M. tuberculosis infection in a mouse model. Enzymes in the arginine biosynthetic pathway have therefore emerged as promising targets for anti-tuberculosis drug discovery. In this work, the ligandability of four enzymes of the pathway ArgB, ArgC, ArgD and ArgF is assessed using a fragment-based approach. We identify several hits against these enzymes validated with biochemical and biophysical assays, as well as X-ray crystallographic data, which in the case of ArgB were further confirmed to have on-target activity against M. tuberculosis. These results demonstrate the potential for more enzymes in this pathway to be targeted with dedicated drug discovery programmes.

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.

Identification of new building blocks by fragment screening for discovering GyrB inhibitors

DNA gyrase is an essential DNA topoisomerase that exists only in bacteria. Since novobiocin was withdrawn from the market, new scaffolds and new mechanistic GyrB inhibitors are urgently needed. In this study, we employed fragment screening and X-ray crystallography to identify new building blocks, as well as their binding mechanisms, to support the discovery of new GyrB inhibitors. In total, 84 of the 618 chemical fragments were shown to either thermally stabilize the ATPase domain of Escherichia coli GyrB or inhibit the ATPase activity of E. coli gyrase. Among them, the IC₅₀ values of fragments 10 and 23 were determined to be 605.3 μM and 446.2 μM, respectively. Cocrystal structures of the GyrB ATPase domain with twelve fragment hits were successfully determined at a high resolution. All twelve fragments were deeply inserted in the pocket and formed H-bonds with Asp73 and Thr165, and six fragments formed an additional H-bond with the backbone oxygen of Val71. Fragment screening further highlighted the capability of Asp73, Thr165 and Val71 to bind chemicals and provided diverse building blocks for the design of GyrB inhibitors.

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.

CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities

The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.

Active Learning and the Potential of Neural Networks Accelerate Molecular Screening for the Design of a New Molecule Effective against SARS-CoV-2

A global pandemic has emerged following the appearance of the new severe acute respiratory virus whose official name is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), strongly affecting the health sector as well as the world economy. Indeed, following the emergence of this new virus, despite the existence of a few approved and known effective vaccines at the time of writing this original study, a sense of urgency has emerged worldwide to discover new technical tools and new drugs as soon as possible. In this context, many studies and researches are currently underway to develop new tools and therapies against SARS CoV-2 and other viruses, using different approaches. The 3-chymotrypsin (3CL) protease, which is directly involved in the cotranslational and posttranslational modifications of viral polyproteins essential for the existence and replication of the virus in the host, is one of the coronavirus target proteins that has been the subject of these extensive studies. Currently, the majority of these studies are aimed at repurposing already known and clinically approved drugs against this new virus, but this approach is not really successful. Recently, different studies have successfully demonstrated the effectiveness of artificial intelligence-based techniques to understand existing chemical spaces and generate new small molecules that are both effective and efficient. In this framework and for our study, we combined a generative recurrent neural network model with transfer learning methods and active learning-based algorithms to design novel small molecules capable of effectively inhibiting the 3CL protease in human cells. We then analyze these small molecules to find the correct binding site that matches the structure of the 3CL protease of our target virus as well as other analyses performed in this study. Based on these screening results, some molecules have achieved a good binding score close to -18 kcal/mol, which we can consider as good potential candidates for further synthesis and testing against SARS-CoV-2.

The Glitazone Class of Drugs as Carbonic Anhydrase Inhibitors-A Spin-Off Discovery from Fragment Screening

The approved drugs that target carbonic anhydrases (CA, EC 4.2.1.1), a family of zinc metalloenzymes, comprise almost exclusively of primary sulfonamides (R-SO2NH2) as the zinc binding chemotype. New clinical applications for CA inhibitors, particularly for hard-to-treat cancers, has driven a growing interest in the development of novel CA inhibitors. We recently discovered that the thiazolidinedione heterocycle, where the ring nitrogen carries no substituent, is a new zinc binding group and an alternate CA inhibitor chemotype. This heterocycle is curiously also a substructure of the glitazone class of drugs used in the treatment options for type 2 diabetes. Herein, we investigate and characterise three glitazone drugs (troglitazone 11, rosiglitazone 12 and pioglitazone 13) for binding to CA using native mass spectrometry, protein X-ray crystallography and hydrogen-deuterium exchange (HDX) mass spectrometry, followed by CA enzyme inhibition studies. The glitazone drugs all displayed appreciable binding to and inhibition of CA isozymes. Given that thiazolidinediones are not credited as a zinc binding group nor known as CA inhibitors, our findings indicate that CA may be an off-target of these compounds when used clinically. Furthermore, thiazolidinediones may represent a new opportunity for the development of novel CA inhibitors as future drugs.

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.

Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 Mpro by Using Supervised Molecular Dynamics (SuMD) Simulations

Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low‐throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high‐throughput supervised molecular dynamics simulations (HT‐SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS‐CoV‐2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD‐based posing approaches, a high number of incorrect binding modes still complicate HT‐SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in‐silico pipeline, allowing us to prioritize only the more reliable predictions.

Expanding the arsenal of E3 ubiquitin ligases for proximity-induced protein degradation

Efficacy and selectivity of molecules inducing protein degradation depend on their affinity to the target protein but also on the type of E3 ubiquitin ligase that is recruited to trigger proteasomal degradation. While tremendous progress has been made on the former, the latter-the arsenal of E3 ligases that can be hijacked for targeted protein degradation-is still largely unexplored. Only about 2% of the more than 600 E3 ligases have been utilized to date. Exploiting additional E3 ligases that are, for example, selectively expressed in specific tissues or cells, or regulated under certain conditions, can considerably broaden the applicability of molecular degraders as a therapeutic modality. Here, we provide an overview of major classes of E3 ligases, review the enzymes that have been exploited for induced protein degradation and approaches used to identify or design E3 ligands, and highlight challenges and opportunities for targeting new E3 ligases.

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.

A COVID moonshot: assessment of ligand binding to the SARS-CoV-2 main protease by saturation transfer difference NMR spectroscopy

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological cause of the coronavirus disease 2019, for which no effective antiviral therapeutics are available. The SARS-CoV-2 main protease (M^pro) is essential for viral replication and constitutes a promising therapeutic target. Many efforts aimed at deriving effective M^pro inhibitors are currently underway, including an international open-science discovery project, codenamed COVID Moonshot. As part of COVID Moonshot, we used saturation transfer difference nuclear magnetic resonance (STD-NMR) spectroscopy to assess the binding of putative M^pro ligands to the viral protease, including molecules identified by crystallographic fragment screening and novel compounds designed as M^pro inhibitors. In this manner, we aimed to complement enzymatic activity assays of M^pro performed by other groups with information on ligand affinity. We have made the M^pro STD-NMR data publicly available. Here, we provide detailed information on the NMR protocols used and challenges faced, thereby placing these data into context. Our goal is to assist the interpretation of M^pro STD-NMR data, thereby accelerating ongoing drug design efforts.

Ubiquitin-proteasome system and the role of its inhibitors in cancer therapy

Despite all the other cells that have the potential to prevent cancer development and metastasis through tumour suppressor proteins, cancer cells can upregulate the ubiquitin–proteasome system (UPS) by which they can degrade tumour suppressor proteins and avoid apoptosis. This system plays an extensive role in cell regulation organized in two steps. Each step has an important role in controlling cancer. This demonstrates the importance of understanding UPS inhibitors and improving these inhibitors to foster a new hope in cancer therapy. UPS inhibitors, as less invasive chemotherapy drugs, are increasingly used to alleviate symptoms of various cancers in malignant states. Despite their success in reducing the development of cancer with the lowest side effects, thus far, an appropriate inhibitor that can effectively inactivate this system with the least drug resistance has not yet been fully investigated. A fundamental understanding of the system is necessary to fully elucidate its role in causing/controlling cancer. In this review, we first comprehensively investigate this system, and then each step containing ubiquitination and protein degradation as well as their inhibitors are discussed. Ultimately, its advantages and disadvantages and some perspectives for improving the efficiency of these inhibitors are discussed.

Fragment-Based Design of a Potent MAT2a Inhibitor and in Vivo Evaluation in an MTAP Null Xenograft Model

MAT2a is a methionine adenosyltransferase that synthesizes the essential metabolite S-adenosylmethionine (SAM) from methionine and ATP. Tumors bearing the co-deletion of p16 and MTAP genes have been shown to be sensitive to MAT2a inhibition, making it an attractive target for treatment of MTAP-deleted cancers. A fragment-based lead generation campaign identified weak but efficient hits binding in a known allosteric site. By use of structure-guided design and systematic SAR exploration, the hits were elaborated through a merging and growing strategy into an arylquinazolinone series of potent MAT2a inhibitors. The selected in vivo tool compound 28 reduced SAM-dependent methylation events in cells and inhibited proliferation of MTAP-null cells in vitro. In vivo studies showed that 28 was able to induce antitumor response in an MTAP knockout HCT116 xenograft model.

Conservation of binding properties in protein models

We study the models submitted to round 12 of the Critical Assessment of protein Structure Prediction (CASP) experiment to assess how well the binding properties are conserved when the X-ray structures of the target proteins are replaced by their models. To explore small molecule binding we generate distributions of molecular probes - which are fragment-sized organic molecules of varying size, shape, and polarity - around the protein, and count the number of interactions between each residue and the probes, resulting in a vector of interactions we call a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model of the protein, is determined by calculating the correlation coefficient between the two vectors. The resulting correlation coefficients are shown to correlate with global measures of accuracy established in CASP, and the relationship yields an accuracy threshold that has to be reached for meaningful binding surface conservation. The clusters formed by the probe molecules reliably predict binding hot spots and ligand binding sites in both X-ray structures and reasonably accurate models of the target, but ensembles of models may be needed for assessing the availability of proper binding pockets. We explored ligand docking to the few targets that had bound ligands in the X-ray structure. More targets were available to assess the ability of the models to reproduce protein-protein interactions by docking both the X-ray structures and models to their interaction partners in complexes. It was shown that this application is more difficult than finding small ligand binding sites, and the success rates heavily depend on the local structure in the potential interface. In particular, predicted conformations of flexible loops are frequently incorrect in otherwise highly accurate models, and may prevent predicting correct protein-protein interactions.

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.

Fragment dissolved molecular dynamics: a systematic and efficient method to locate binding sites

Diverse computational methods to support fragment-based drug discovery (FBDD) are available in the literature. Despite their demonstrated efficacy in supporting FBDD campaigns, they exhibit some drawbacks such as protein denaturation or ligand aggregation that have not yet been clearly overcome in the framework of biomolecular simulations. In the present work, we discuss a systematic semi-automatic novel computational procedure, designed to surpass these difficulties. The method, named fragment dissolved Molecular Dynamics (fdMD), utilizes simulation boxes of solvated small fragments, adding a repulsive Lennard-Jones potential term to avoid aggregation, which can be easily used to solvate the targets of interest. This method has the advantage of solvating the target with a low number of ligands, thus preventing the denaturation of the target, while simultaneously generating a database of ligand-solvated boxes that can be used in further studies. A number of scripts are made available to analyze the results and obtain the descriptors proposed as a means to trustfully discard spurious binding sites. To test our method, four test cases of different complexity have been solvated with ligand boxes and four molecular dynamics runs of 200 ns length have been run for each system, which have been extended up to 1 μs when needed. The reported results point out that the selected number of replicas are enough to identify the correct binding sites irrespective of the initial structure, even in the case of proteins having several close binding sites for the same ligand. We also propose a set of descriptors to analyze the results, among which the average MMGBSA and the average KDEEP energies have emerged as the most robust ones.

Targeting protein-protein interaction interfaces in COVID-19 drug discovery

To date, the COVID-19 pandemic has claimed over 1 million human lives, infected another 50 million individuals and wreaked havoc on the global economy. The crisis has spurred the ongoing development of drugs targeting its etiological agent, the SARS-CoV-2. Targeting relevant protein-protein interaction interfaces (PPIIs) is a viable paradigm for the design of antiviral drugs and enriches the targetable chemical space by providing alternative targets for drug discovery. In this review, we will provide a comprehensive overview of the theory, methods and applications of PPII-targeted drug development towards COVID-19 based on recent literature. We will also highlight novel developments, such as the successful use of non-native protein-protein interactions as targets for antiviral drug screening. We hope that this review may serve as an entry point for those interested in applying PPIIs towards COVID-19 drug discovery and speed up drug development against the pandemic.

MCSS-Based Predictions of Binding Mode and Selectivity of Nucleotide Ligands

Computational fragment-based approaches are widely used in drug design and discovery. One of their limitations is the lack of performance of docking methods, mainly the scoring functions. With the emergence of fragment-based approaches for single-stranded RNA ligands, we analyze the performance in docking and screening powers of an MCSS-based approach. The performance is evaluated on a benchmark of protein-nucleotide complexes where the four RNA residues are used as fragments. The screening power can be considered the major limiting factor for the fragment-based modeling or design of sequence-selective oligonucleotides. We show that the MCSS sampling is efficient even for such large and flexible fragments. Hybrid solvent models based on some partial explicit representations improve both the docking and screening powers. Clustering of the n best-ranked poses can also contribute to a lesser extent to better performance. A detailed analysis of molecular features suggests various ways to optimize the performance further.

Progress toward B-Cell Lymphoma 6 BTB Domain Inhibitors for the Treatment of Diffuse Large B-Cell Lymphoma and Beyond

B-cell lymphoma 6 (BCL6) is a master regulator of germinal center formation that produce antibody-secreting plasma cells and memory B-cells for sustained immune responses. The BTB domain of BCL6 (BCL6^BTB) forms a homodimer that mediates transcriptional repression by recruiting its corepressor proteins to form a biologically functional transcriptional complex. The protein-protein interaction (PPI) between the BCL6^BTB and its corepressors has emerged as a therapeutic target for the treatment of DLBCL and a number of other human cancers. This Perspective provides an overview of recent advances in the development of BCL6^BTB inhibitors from reversible inhibitors, irreversible inhibitors, to BCL6 degraders. Inhibitor design and medicinal chemistry strategies for the development of novel compounds will be provided. The binding mode of new inhibitors to BCL6^BTB are highlighted. Also, the in vitro and in vivo assays used for the evaluation of new compounds will be discussed.

Deconstructing Noncovalent Kelch-like ECH-Associated Protein 1 (Keap1) Inhibitors into Fragments to Reconstruct New Potent Compounds

Targeting the protein-protein interaction (PPI) between nuclear factor erythroid 2-related factor 2 (Nrf2) and Kelch-like ECH-associated protein 1 (Keap1) is a potential therapeutic strategy to control diseases involving oxidative stress. Here, six classes of known small-molecule Keap1-Nrf2 PPI inhibitors were dissected into 77 fragments in a fragment-based deconstruction reconstruction (FBDR) study and tested in four orthogonal assays. This gave 17 fragment hits of which six were shown by X-ray crystallography to bind in the Keap1 Kelch binding pocket. Two hits were merged into compound 8 with a 220-380-fold stronger affinity (K_i = 16 μM) relative to the parent fragments. Systematic optimization resulted in several novel analogues with K_i values of 0.04-0.5 μM, binding modes determined by X-ray crystallography, and enhanced microsomal stability. This demonstrates how FBDR can be used to find new fragment hits, elucidate important ligand-protein interactions, and identify new potent inhibitors of the Keap1-Nrf2 PPI.

Drugging the "Undruggable" MYCN Oncogenic Transcription Factor: Overcoming Previous Obstacles to Impact Childhood Cancers

Effective treatment of pediatric solid tumors has been hampered by the predominance of currently "undruggable" driver transcription factors. Improving outcomes while decreasing the toxicity of treatment necessitates the development of novel agents that can directly inhibit or degrade these elusive targets. MYCN in pediatric neural-derived tumors, including neuroblastoma and medulloblastoma, is a paradigmatic example of this problem. Attempts to directly and specifically target MYCN have failed due to its similarity to MYC, the unstructured nature of MYC family proteins in their monomeric form, the lack of an understanding of MYCN-interacting proteins and ability to test their relevance in vivo, the inability to obtain structural information on MYCN protein complexes, and the challenges of using traditional small molecules to inhibit protein-protein or protein-DNA interactions. However, there is now promise for directly targeting MYCN based on scientific and technological advances on all of these fronts. Here, we discuss prior challenges and the reasons for renewed optimism in directly targeting this "undruggable" transcription factor, which we hope will lead to improved outcomes for patients with pediatric cancer and create a framework for targeting driver oncoproteins regulating gene transcription.

Development and biological applications of sulfur-triazole exchange (SuTEx) chemistry

Sulfur electrophiles constitute an important class of covalent small molecules that have found widespread applications in synthetic chemistry and chemical biology. Various electrophilic scaffolds, including sulfonyl fluorides and arylfluorosulfates as recent examples, have been applied for protein bioconjugation to probe ligand sites amenable for chemical proteomics and drug discovery. In this review, we describe the development of sulfonyl-triazoles as a new class of electrophiles for sulfur-triazole exchange (SuTEx) chemistry. SuTEx achieves covalent reaction with protein sites through irreversible modification of a residue with an adduct group (AG) upon departure of a leaving group (LG). A principal differentiator of SuTEx from other chemotypes is the selection of a triazole heterocycle as the LG, which introduces additional capabilities for tuning the sulfur electrophile. We describe the opportunities afforded by modifications to the LG and AG alone or in tandem to facilitate nucleophilic substitution reactions at the SO2 center in cell lysates and live cells. As a result of these features, SuTEx serves as an efficient platform for developing chemical probes with tunable bioactivity to study novel nucleophilic sites on established and poorly annotated protein targets. Here, we highlight a suite of biological applications for the SuTEx electrophile and discuss future goals for this enabling covalent chemistry.

Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) macrodomain within the nonstructural protein 3 counteracts host-mediated antiviral adenosine diphosphate-ribosylation signaling. This enzyme is a promising antiviral target because catalytic mutations render viruses nonpathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of 2533 diverse fragments resulted in 214 unique macrodomain-binders. An additional 60 molecules were selected from docking more than 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several fragment hits were confirmed by solution binding using three biophysical techniques (differential scanning fluorimetry, homogeneous time-resolved fluorescence, and isothermal titration calorimetry). The 234 fragment structures explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Discovery of De Novo Macrocyclic Peptides by Messenger RNA Display

Macrocyclic peptides are a promising class of compounds that can often engage challenging therapeutic targets. Display technologies, such as mRNA display, allow for the efficient discovery of macrocyclic peptides. This article reviews the current approaches for generating macrocyclic peptide libraries using mRNA display and highlights some recent examples of ribosomal incorporation of nonproteinogenic amino acids into macrocyclic peptides.

Piecing the fragments together: Dynamical insights into the enhancement of BRD4-BD1 (BET protein) druggability in cancer chemotherapy using novel 8-methyl-pyrrolo[1,2-a]pyrazin-1(2H)-one derivatives

BACKGROUND

Fragment-based drug discovery in recent times has been explored in the design of highly potent therapeutics.

METHODS

In this study, we explored the inhibitory dynamics of Compound 38 (Cpd38), a newly synthesized Bromodomain-containing protein 4 bromodomain 1 (BRD4-BD1) protein inhibitor derived from the synthetic coupling of Fragment 47 (Fgt47) into ABBV-075 scaffold. Using dynamic simulation methods, we unraveled the augmentative effects of chemical fragmentation on improved BRD4-BD1 inhibition.

RESULTS

Findings from this study revealed that although Fgt47 exhibited a considerable ΔGbind, its incorporation into the difluoro-phenoxy pyridine scaffold (Cpd38) notably enhanced the binding affinity. Time-based analyses of interaction dynamics further revealed that the bulkiness of Cpd38 favored its interaction at the BRD4-BD1 active site relative to the fragment. Strikingly, when compared to Fgt47, Cpd38 demonstrated high mobility, which could have enabled it to bind optimally and complementarily with key residues of the active site such as Ile146, Asn140, Cys136, Tyr98, Leu94, Val87, Phe83 and Trp81.

DISCUSSION

On the contrary, majority of these interactions were gradually lost in Fgt47 which could further indicate the essence of coupling it with the difluoro-phenoxy pyridine scaffold. Furthermore, Cpd38 had a more altering effect on BRD4-BDI relative to Fgt47 which could also be as a result of its higher inhibitory activity.

CONCLUSION

Conclusively, the design of highly potent therapeutics could be facilitated by the incorporation of pharmacologically active small molecule fragments into the scaffold of existing drugs.

Emergent synthetic methods for the modular advancement of sp3-rich fragments

Fragment-based drug discovery is an important and increasingly reliable technology for the delivery of clinical candidates. Notably, however, sp3-rich fragments are a largely untapped resource in molecular discovery, in part due to the lack of general and suitably robust chemical methods available to aid their development into higher affinity lead and drug compounds. This Perspective describes the challenges associated with developing sp3-rich fragments, and succinctly highlights recent advances in C(sp3)-H functionalisations of high potential value towards advancing fragment hits by 'growing' functionalised rings and chains from unconventional, carbon-centred vectors.

Natural product-informed exploration of chemical space to enable bioactive molecular discovery

The search for new bioactive molecules remains an open challenge limiting our ability to discover new drugs to treat disease and chemical probes to comprehensively study biological processes. The vastness of chemical space renders its exploration unfeasible by synthesis alone. Historically, chemists have tended to explore chemical space unevenly without committing to systematic frameworks for navigation. This minireview covers a range of approaches that take inspiration from the structure or origin of natural products, and help focus molecular discovery on biologically-relevant regions of chemical space. All these approaches have enabled the discovery of distinctive and novel bioactive small molecules such as useful chemical probes of biological mechanisms. This minireview comments on how such approaches may be developed into more general frameworks for the systematic identification of currently unexplored regions of biologically-relevant chemical space, a challenge that is central to both chemical biology and medicinal chemistry.

Blueprint for cancer research: Critical gaps and opportunities

We are experiencing a revolution in cancer. Advances in screening, targeted and immune therapies, big data, computational methodologies, and significant new knowledge of cancer biology are transforming the ways in which we prevent, detect, diagnose, treat, and survive cancer. These advances are enabling durable progress in the goal to achieve personalized cancer care. Despite these gains, more work is needed to develop better tools and strategies to limit cancer as a major health concern. One persistent gap is the inconsistent coordination among researchers and caregivers to implement evidence-based programs that rely on a fuller understanding of the molecular, cellular, and systems biology mechanisms underpinning different types of cancer. Here, the authors integrate conversations with over 90 leading cancer experts to highlight current challenges, encourage a robust and diverse national research portfolio, and capture timely opportunities to advance evidence-based approaches for all patients with cancer and for all communities.

Genetic and structural validation of phosphomannomutase as a cell wall target in Aspergillus fumigatus

Aspergillus fumigatus is an opportunistic mold responsible for severe life-threatening fungal infections in immunocompromised patients. The cell wall, an essential structure composed of glucan, chitin, and galactomannan, is considered to be a target for the development of antifungal drugs. The nucleotide sugar donor GDP-mannose (GDP-Man) is required for the biosynthesis of galactomannan, glycosylphosphatidylinositol (GPI) anchors, glycolipid, and protein glycosylation. Starting from fructose-6-phosphate, GDP-Man is produced by the sequential action of the enzymes phosphomannose isomerase, phosphomannomutase (Pmm), and GDP-mannose pyrophosphorylase. Here, using heterokaryon rescue and gene knockdown approaches we demonstrate that the phosphomannomutase encoding gene in A. fumigatus (pmmA) is essential for survival. Reduced expression of pmmA is associated with significant morphological defects including retarded germination, growth, reduced conidiation, and abnormal polarity. Moreover, the knockdown strain exhibited an altered cell wall organization and sensitivity toward cell wall perturbing agents. By solving the first crystal structure of A. fumigatus phosphomannomutase (AfPmmA) we identified non-conservative substitutions near the active site when compared to the human orthologues. Taken together, this work provides a genetic and structural foundation for the exploitation of AfPmmA as a potential antifungal target.

Discovering High Potent Hsp90 Inhibitors as Antinasopharyngeal Carcinoma Agents through Fragment Assembling Approach

Hsp90 is a new promising target for cancer treatment. Many inhibitors have been discovered as therapeutic agents, and some have passed Phase I and II. However, no one is approved by FDA yet. Novel and druggable Hsp90 inhibitors are still demanding. Here, we report a new way to discover high potent Hsp90 inhibitors as antinasopharyngeal carcinoma agents through assembling fragments. With chemotyping analysis, we extract seven chemotypes from 3482 known Hsp90 inhibitors, screen 500 fragments that are compatible with the chemotypes, and confirm 15 anti-Hsp90 fragments. Click chemistry is employed to construct 172 molecules and synthesize 21 compounds among them. The best inhibitor 3d was further optimized and resulted in more potent 4f (IC50 = 0.16 μM). In vitro and in vivo experiments confirmed that 4f is a promising agent against nasopharyngeal carcinoma. This study may provide a strategy in discovering new ligands against targets without well-understood structures.

The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding

The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.