Using DNA-encoded libraries of fragments for hit discovery of challenging therapeutic targets

INTRODUCTION

The effectiveness of Fragment-based drug design (FBDD) for targeting challenging therapeutic targets has been hindered by two factors: the small library size and the complexity of the fragment-to-hit optimization process. The DNA-encoded library (DEL) technology offers a compelling and robust high-throughput selection approach to potentially address these limitations.

AREA COVERED

In this review, the authors propose the viewpoint that the DEL technology matches perfectly with the concept of FBDD to facilitate hit discovery. They begin by analyzing the technical limitations of FBDD from a medicinal chemistry perspective and explain why DEL may offer potential solutions to these limitations. Subsequently, they elaborate in detail on how the integration of DEL with FBDD works. In addition, they present case studies involving both de novo hit discovery and full ligand discovery, especially for challenging therapeutic targets harboring broad drug-target interfaces.

EXPERT OPINION

The future of DEL-based fragment discovery may be promoted by both technical advances and application scopes. From the technical aspect, expanding the chemical diversity of DEL will be essential to achieve success in fragment-based drug discovery. From the application scope side, DEL-based fragment discovery holds promise for tackling a series of challenging targets.

Development of urease inhibitors by fragment-based dynamic combinatorial chemistry

In this study, fragment-based dynamic combinatorial chemistry (DCC) was explored for the development of novel urease inhibitors. Based on a rationally designed fragment, two iteratively evolved dynamic combinatorial libraries (DCLs) were generated and screened in the presence of urease template. The best ligand identified revealed not only strong urease inhibition but also low cytotoxicity. Additionally, possible inhibitory mechanism was elucidated in the binding kinetic study and docking simulation.

Development of potent inhibitors by fragment-linking strategies

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

FGDB: a comprehensive graph database of ligand fragments from the Protein Data Bank

This work presents Fragment Graph DataBase (FGDB), a graph database of ligand fragments extracted and generated from the protein entries available in the Protein Data Bank (PDB). FGDB is meant to support and elicit campaigns of fragment-based drug design, by enabling users to query it in order to construct ad hoc, target-specific libraries. In this regard, the database features more than 17 000 fragments, typically small, highly soluble and chemically stable molecules expressed via their canonical Simplified Molecular Input Line Entry System (SMILES) representation. For these fragments, the database provides information related to their contact frequencies with the amino acids, the ligands they are contained in and the proteins the latter bind to. The graph database can be queried via standard web forms and textual searches by a number of identifiers (SMILES, ligand and protein PDB ids) as well as via graphical queries that can be performed against the graph itself, providing users with an intuitive and effective view upon the underlying biological entities. Further search mechanisms via advanced conjunctive/disjunctive/negated textual queries are also possible, in order to allow scientists to look for specific relationships and export their results for further studies. This work also presents two sample use cases where maternal embryonic leucine zipper kinase and mesotrypsin are used as a target, being proteins of high biomedical relevance for the development of cancer therapies.

Database URL: http://biochimica3.bio.uniroma3.it/fragments-web/

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

Dynamic Combinatorial Chemistry to Identify Binders of ThiT, an S-Component of the Energy-Coupling Factor Transporter for Thiamine

We applied dynamic combinatorial chemistry (DCC) to identify ligands of ThiT, the S-component of the energy-coupling factor (ECF) transporter for thiamine in Lactococcus lactis. We used a pre-equilibrated dynamic combinatorial library (DCL) and saturation-transfer difference (STD) NMR spectroscopy to identify ligands of ThiT. This is the first report in which DCC is used for fragment growing to an ill-defined pocket, and one of the first reports for its application with an integral membrane protein as target.

Proteintemplat-gesteuerte Fragmentligationen - von der molekularen Erkennung zur Wirkstofffindung

Proteintemplat‐gesteuerte Fragmentligationen sind ein neuartiges Konzept zur Unterstützung der Wirkstofffindung und können dazu beitragen, die Wirksamkeit von Proteinliganden zu verbessern. Es handelt sich dabei um chemische Reaktionen zwischen niedermolekularen Verbindungen (“Fragmenten”), die die Oberfläche eines Proteins als Reaktionsgefäß verwenden, um die Bildung eines Proteinliganden mit erhöhter Bindungsaffinität zu katalysieren. Die Methode nutzt die molekulare Erkennung kleiner reaktiver Fragmente durch die Proteine sowohl zur Assemblierung der Liganden als auch zur Identifizierung bioaktiver Fragmentkombinationen. Chemische Synthese und Bioassay werden dabei in einem Schritt vereint. Dieser Aufsatz diskutiert die biophysikalischen Grundlagen der reversiblen und irreversiblen Fragmentligationen und gibt einen Überblick über die Methoden, mit denen die durch das Proteintemplat gebildeten Ligationsprodukte detektiert werden können. Der chemische Reaktionsraum und aktuelle Anwendungen wie auch die Bedeutung dieses Konzeptes für die Wirkstofffindung werden erörtert.

Protein-Templated Fragment Ligations-From Molecular Recognition to Drug Discovery

Protein-templated fragment ligation is a novel concept to support drug discovery and can help to improve the efficacy of protein ligands. Protein-templated fragment ligations are chemical reactions between small molecules ("fragments") utilizing a protein's surface as a reaction vessel to catalyze the formation of a protein ligand with increased binding affinity. The approach exploits the molecular recognition of reactive small-molecule fragments by proteins both for ligand assembly and for the identification of bioactive fragment combinations. In this way, chemical synthesis and bioassay are integrated in one single step. This Review discusses the biophysical basis of reversible and irreversible fragment ligations and gives an overview of the available methods to detect protein-templated ligation products. The chemical scope and recent applications as well as future potential of the concept in drug discovery are reviewed.

Identification of inhibitors targeting Mycobacterium tuberculosis cell wall biosynthesis via dynamic combinatorial chemistry

In this study, we report a dynamic combinatorial approach along with highly efficient in situ screening to identify inhibitors of UDP-galactopyranose mutase (UGM), an essential enzyme involved in mycobacterial cell wall biosynthesis.

Fragment-Based Drug Design Facilitated by Protein-Templated Click Chemistry: Fragment Linking and Optimization of Inhibitors of the Aspartic Protease Endothiapepsin

There is an urgent need for the development of efficient methodologies that accelerate drug discovery. We demonstrate that the strategic combination of fragment linking/optimization and protein-templated click chemistry is an efficient and powerful method that accelerates the hit-identification process for the aspartic protease endothiapepsin. The best binder, which inhibits endothiapepsin with an IC50 value of 43 μm, represents the first example of triazole-based inhibitors of endothiapepsin. Our strategy could find application on a whole range of drug targets.

Fragment Linking and Optimization of Inhibitors of the Aspartic Protease Endothiapepsin: Fragment-Based Drug Design Facilitated by Dynamic Combinatorial Chemistry

Fragment-based drug design (FBDD) affords active compounds for biological targets. While there are numerous reports on FBDD by fragment growing/optimization, fragment linking has rarely been reported. Dynamic combinatorial chemistry (DCC) has become a powerful hit-identification strategy for biological targets. We report the synergistic combination of fragment linking and DCC to identify inhibitors of the aspartic protease endothiapepsin. Based on X-ray crystal structures of endothiapepsin in complex with fragments, we designed a library of bis-acylhydrazones and used DCC to identify potent inhibitors. The most potent inhibitor exhibits an IC50 value of 54 nm, which represents a 240-fold improvement in potency compared to the parent hits. Subsequent X-ray crystallography validated the predicted binding mode, thus demonstrating the efficiency of the combination of fragment linking and DCC as a hit-identification strategy. This approach could be applied to a range of biological targets, and holds the potential to facilitate hit-to-lead optimization.

Six Biophysical Screening Methods Miss a Large Proportion of Crystallographically Discovered Fragment Hits: A Case Study

Fragment-based lead discovery (FBLD) has become a pillar in drug development. Typical applications of this method comprise at least two biophysical screens as prefilter and a follow-up crystallographic experiment on a subset of fragments. Clearly, structural information is pivotal in FBLD, but a key question is whether such a screening cascade strategy will retrieve the majority of fragment-bound structures. We therefore set out to screen 361 fragments for binding to endothiapepsin, a representative of the challenging group of aspartic proteases, employing six screening techniques and crystallography in parallel. Crystallography resulted in the very high number of 71 structures. Yet alarmingly, 44% of these hits were not detected by any biophysical screening approach. Moreover, any screening cascade, building on the results from two or more screening methods, would have failed to predict at least 73% of these hits. We thus conclude that, at least in the present case, the frequently applied biophysical prescreening filters deteriorate the number of possible X-ray hits while only the immediate use of crystallography enables exhaustive retrieval of a maximum of fragment structures, which represent a rich source guiding hit-to-lead-to-drug evolution.