Current perspectives in fragment-based lead discovery (FBLD)

Perspectives on Applications of 19F-NMR in Fragment-Based Drug Discovery

Fragment-based drug discovery is a powerful approach in drug discovery, applicable to a wide range of targets. This method enables the discovery of potent compounds that can modulate target functions, starting from fragment compounds that bind weakly to the targets. While biochemical, biophysical, and cell-based assays are commonly used to identify fragments, 19F-NMR spectroscopy has emerged as a powerful tool for exploring interactions between biomolecules and ligands. Because fluorine atoms are not naturally present in biological systems, 19F-NMR serves as a sensitive method for fragment screening against diverse targets. Herein, we reviewed the applications of 19F-NMR in fragment screening, highlighting its effectiveness in identifying fragments that bind weakly to various targets such as proteins and RNA. The accumulated evidence suggests that 19F-NMR will continue to be a crucial tool in drug discovery.

Development of a high-throughput screening system targeting the protein-protein interactions between PRL and CNNM

Phosphatase of regenerating liver (PRL) is an oncogenic protein that promotes tumor progression by directly binding to cyclin M (CNNM) membrane proteins and inhibiting their Mg2+ efflux activity. In this study, we have developed a high-throughput screening system to detect the interactions between PRL and CNNM proteins based on homogenous time-resolved fluorescence resonance energy transfer (HTR-FRET, HTRF). We optimized the tag sequences attached to the recombinant proteins of the CNNM4 CBS domains and PRL3 lacking the carboxyl terminal CAAX motif, and successfully detected the interaction by observing the FRET signal in the mixture of the tagged proteins and fluorophore-conjugated antibodies. Moreover, we performed compound library screening using this system and discovered several compounds that could efficiently inhibit the PRL-CNNM interaction. Characterization of one candidate compound revealed that it was relatively stable compared with thienopyridone, a known inhibitor of the PRL-CNNM interaction. The candidate compound can also inhibit PRL function in cells: suppression of CNNM-dependent Mg2+ efflux, and has sufficient in vitro drug metabolism and pharmacokinetic properties. Overall, these results demonstrate the effectiveness of this screening system for identifying novel inhibitors of the PRL-CNNM interaction, which could contribute to the development of novel anti-cancer drugs.

Application of CoLD-CoP to Detecting Competitively and Cooperatively Binding Ligands

NMR utilization in fragment-based drug discovery requires techniques to detect weakly binding fragments and to subsequently identify cooperatively binding fragments. Such cooperatively binding fragments can then be optimized or linked in order to develop viable drug candidates. Similarly, ligands or substrates that bind macromolecules (including enzymes) in competition with the endogenous ligand or substrate are valuable probes of macromolecular chemistry and function. The lengthy and costly process of identifying competitive or cooperative binding can be streamlined by coupling computational biochemistry and spectroscopy tools. The Clustering of Ligand Diffusion Coefficient Pairs (CoLD-CoP) method, previously developed by Snyder and co-workers, detects weakly binding ligands by analyzing pairs of diffusion spectra, obtained in the absence and the presence of a protein. We extended the CoLD-CoP method to analyze spectra pairs (each in the presence of a protein) with or without a critical ligand, to detect both competitive and cooperative binding.

Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells

Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advanced fragment-based ligand discovery toward cellular systems, but throughput limitations have stymied the scalable identification of fragment-protein interactions. We report proteome-wide maps of protein-binding propensity for 407 structurally diverse small-molecule fragments. We verified that identified interactions can be advanced to active chemical probes of E3 ubiquitin ligases, transporters, and kinases. Integrating machine learning binary classifiers further enabled interpretable predictions of fragment behavior in cells. The resulting resource of fragment-protein interactions and predictive models will help to elucidate principles of molecular recognition and expedite ligand discovery efforts for hitherto undrugged proteins.

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated.

LpxC inhibition: Potential and opportunities with carbohydrate scaffolds

Uridine diphosphate-3-O-(hydroxymyristoyl)-N-acetylglucosamine deacetylase (LpxC) is a key enzyme involved in the biosynthesis of lipid A, an essential building block, for the construction and assembly of the outer membrane (OM) of Gram-negative bacteria. The enzyme is highly conserved in almost all Gram-negative bacteria and hence has emerged as a promising target for drug discovery in the fight against multi-drug resistant Gram-negative infections. Since the first nanomolar LpxC inhibitor, L-161,240, an oxazoline-based hydroxamate, the two-decade-long ongoing search has provided valuable information regarding essential features necessary for inhibition. Although the design and structure optimization for arriving at the most efficacious inhibitor of this enzyme has made good use of different heterocyclic moieties, the use of carbohydrate scaffold is scant. This review briefly covers the advancement and progress made in LpxC inhibition. The field awaits the use of potential associated with carbohydrate-based scaffolds for LpxC inhibition and the discovery of anti-bacterial agents against Gram-negative infections.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

Coumarins as factor XIIa inhibitors: Potency and selectivity improvements using a fragment-based strategy

Previously, we described weak coumarin inhibitors of factor XIIa, a promising target for artificial surface-induced thrombosis and various inflammatory diseases. In this work, we used fragment-based drug discovery approach to improve our coumarin series. First, we screened about 200 fragments for the S1 pocket. The S1 pocket of trypsin-like serine proteases, such as factor XIIa, is highly conserved and is known to drive a major part of the association energy. From the screening, we selected fragments displaying a micromolar activity and studied their selectivity on other serine proteases. Then, these fragments were merged to our coumarin templates, leading to the generation of nanomolar inhibitors. The mechanism of inhibition was further studied by mass spectrometry demonstrating the covalent binding through the formation of an acyl enzyme complex. The most potent compound was tested in plasma to evaluate its stability and efficacy on coagulation assays. It exhibited a plasmatic half-life of 1.9 h and a good selectivity for the intrinsic coagulation pathway over the extrinsic one.

Discovery of rigid biphenyl Plasmodium falciparum DHFR inhibitors using a fragment linking strategy

Plasmodium falciparum dihydrofolate reductase (PfDHFR), a historical target for antimalarials, has been considered compromised due to resistance inducing mutations caused by pyrimethamine (PYR) overexposure. The clinical candidate P218 has demonstrated that inhibitors could efficiently target both PYR-sensitive and PYR-resistant parasites through careful drug design. Yet, P218 clinical development has been limited by its pharmacokinetic profile, incompatible with single dose regimen. Herein, we report the design of new PfDHFR inhibitors using fragment-based design, aiming at improved lipophilicity and overall drug-like properties. Fragment-based screening identified hits binding in the pABA site of the enzyme. Using structure-guided design, hits were elaborated into leads by fragment linking with 2,4-diaminopyrimidine. Resulting compounds display nM range inhibition of both drug-sensitive and resistant PfDHFR, high selectivity against the human isoform, drug-like lipophilicity and metabolic stability. Compound 4 and its ester derivative 3 kill blood stage TM4/8.2 parasite at nM concentrations while showing no toxicity against Vero cells.

From Tethered to Freestanding Stabilizers of 14-3-3 Protein-Protein Interactions through Fragment Linking

Small-molecule stabilization of protein-protein interactions (PPIs) is a promising strategy in chemical biology and drug discovery. However, the systematic discovery of PPI stabilizers remains a largely unmet challenge. Herein we report a fragment-linking approach targeting the interface of 14-3-3 and a peptide derived from the estrogen receptor alpha (ERα) protein. Two classes of fragments-a covalent and a noncovalent fragment-were co-crystallized and subsequently linked, resulting in a noncovalent hybrid molecule in which the original fragment interactions were largely conserved. Supported by 20 crystal structures, this initial hybrid molecule was further optimized, resulting in selective, 25-fold stabilization of the 14-3-3/ERα interaction. The high-resolution structures of both the single fragments, their co-crystal structures and those of the linked fragments document a feasible strategy to develop orthosteric PPI stabilizers by linking to an initial tethered fragment.

Construction of Histone-Protein Complex Structures by Peptide Growing

The structures of histone complexes are master keys to epigenetics. Linear histone peptide tails often bind to shallow pockets of reader proteins via weak interactions, rendering their structure determination challenging. In the present study, a new protocol, PepGrow, is introduced. PepGrow uses docked histone fragments as seeds and grows the full peptide tails in the reader-binding pocket, producing atomic-resolution structures of histone-reader complexes. PepGrow is able to handle the flexibility of histone peptides, and it is demonstrated to be more efficient than linking pre-docked peptide fragments. The new protocol combines the advantages of popular program packages and allows fast generation of solution structures. AutoDock, a force-field-based program, is used to supply the docked peptide fragments used as structural seeds, and the building algorithm of Modeller is adopted and tested as a peptide growing engine. The performance of PepGrow is compared to ten other docking methods, and it is concluded that in situ growing of a ligand from a seed is a viable strategy for the production of complex structures of histone peptides at atomic resolution.

Deconstructing allostery by computational assessment of the binding determinants of allosteric PTP1B modulators

Fragment-based drug discovery is an established methodology for finding hit molecules that can be elaborated into lead compounds. However it is currently challenging to predict whether fragment hits that do not bind to an orthosteric site could be elaborated into allosteric modulators, as in these cases binding does not necessarily translate into a functional effect. We propose a workflow using Markov State Models (MSMs) with steered molecular dynamics (sMD) to assess the allosteric potential of known binders. sMD simulations are employed to sample protein conformational space inaccessible to routine equilibrium MD timescales. Protein conformations sampled by sMD provide starting points for seeded MD simulations, which are combined into MSMs. The methodology is demonstrated on a dataset of protein tyrosine phosphatase 1B ligands. Experimentally confirmed allosteric inhibitors are correctly classified as inhibitors, whereas the deconstructed analogues show reduced inhibitory activity. Analysis of the MSMs provide insights into preferred protein-ligand arrangements that correlate with functional outcomes. The present methodology may find applications for progressing fragments towards lead molecules in FBDD campaigns.

Fragment Merging Using a Graph Database Samples Different Catalogue Space than Similarity Search

Fragment merging is a promising approach to progressing fragments directly to on-scale potency: each designed compound incorporates the structural motifs of overlapping fragments in a way that ensures compounds recapitulate multiple high-quality interactions. Searching commercial catalogues provides one useful way to quickly and cheaply identify such merges and circumvents the challenge of synthetic accessibility, provided they can be readily identified. Here, we demonstrate that the Fragment Network, a graph database that provides a novel way to explore the chemical space surrounding fragment hits, is well-suited to this challenge. We use an iteration of the database containing >120 million catalogue compounds to find fragment merges for four crystallographic screening campaigns and contrast the results with a traditional fingerprint-based similarity search. The two approaches identify complementary sets of merges that recapitulate the observed fragment-protein interactions but lie in different regions of chemical space. We further show our methodology is an effective route to achieving on-scale potency by retrospective analyses for two different targets; in analyses of public COVID Moonshot and Mycobacterium tuberculosis EthR inhibitors, potential inhibitors with micromolar IC₅₀ values were identified. This work demonstrates the use of the Fragment Network to increase the yield of fragment merges beyond that of a classical catalogue search.

Fragment-Based Approaches to Identify RNA Binders

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

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.

Green Drug Discovery: Novel Fragment Space from the Biomass-Derived Molecule Dihydrolevoglucosenone (CyreneTM)

Biomass-derived molecules can provide a basis for sustainable drug discovery. However, their full exploration is hampered by the dominance of millions of old-fashioned screening compounds in classical high-throughput screening (HTS) libraries frequently utilized. We propose a fragment-based drug discovery (FBDD) approach as an efficient method to navigate biomass-derived drug space. Here, we perform a proof-of-concept study with dihydrolevoglucosenone (Cyrene^TM), a pyrolysis product of cellulose. Diverse synthetic routes afforded a 100-membered fragment library with a diversity in functional groups appended. The library overall performs well in terms of novelty, physicochemical properties, aqueous solubility, stability, and three-dimensionality. Our study suggests that Cyrene-based fragments are a valuable green addition to the drug discovery toolbox. Our findings can help in paving the way for new hit drug candidates that are based on renewable resources.

A Multifaceted Hit-Finding Approach Reveals Novel LC3 Family Ligands

Autophagy-related proteins (Atgs) drive the lysosome-mediated degradation pathway, autophagy, to enable the clearance of dysfunctional cellular components and maintain homeostasis. In humans, this process is driven by the mammalian Atg8 (mAtg8) family of proteins comprising the LC3 and GABARAP subfamilies. The mAtg8 proteins play essential roles in the formation and maturation of autophagosomes and the capture of specific cargo through binding to the conserved LC3-interacting region (LIR) sequence within target proteins. Modulation of interactions of mAtg8 with its target proteins via small-molecule ligands would enable further interrogation of their function. Here we describe unbiased fragment and DNA-encoded library (DEL) screening approaches for discovering LC3 small-molecule ligands. Both strategies resulted in compounds that bind to LC3, with the fragment hits favoring a conserved hydrophobic pocket in mATG8 proteins, as detailed by LC3A-fragment complex crystal structures. Our findings demonstrate that the malleable LIR-binding surface can be readily targeted by fragments; however, rational design of additional interactions to drive increased affinity proved challenging. DEL libraries, which combine small, fragment-like building blocks into larger scaffolds, yielded higher-affinity binders and revealed an unexpected potential for reversible, covalent ligands. Moreover, DEL hits identified possible vectors for synthesizing fluorescent probes or bivalent molecules for engineering autophagic degradation of specific targets.

Fragment-Based Lead Discovery Strategies in Antimicrobial Drug Discovery

Fragment-based lead discovery (FBLD) is a powerful application for developing ligands as modulators of disease targets. This approach strategy involves identification of interactions between low-molecular weight compounds (100-300 Da) and their putative targets, often with low affinity (K_D ~0.1-1 mM) interactions. The focus of this screening methodology is to optimize and streamline identification of fragments with higher ligand efficiency (LE) than typical high-throughput screening. The focus of this review is on the last half decade of fragment-based drug discovery strategies that have been used for antimicrobial drug discovery.

Increased slow dynamics defines ligandability of BTB domains

Efficient determination of protein ligandability, or the propensity to bind small-molecules, would greatly facilitate drug development for novel targets. Ligandability is currently assessed using computational methods that typically consider the static structural properties of putative binding sites or by experimental fragment screening. Here, we evaluate ligandability of conserved BTB domains from the cancer-relevant proteins LRF, KAISO, and MIZ1. Using fragment screening, we discover that MIZ1 binds multiple ligands. However, no ligands are uncovered for the structurally related KAISO or LRF. To understand the principles governing ligand-binding by BTB domains, we perform comprehensive NMR-based dynamics studies and find that only the MIZ1 BTB domain exhibits backbone µs-ms time scale motions. Interestingly, residues with elevated dynamics correspond to the binding site of fragment hits and recently defined HUWE1 interaction site. Our data argue that examining protein dynamics using NMR can contribute to identification of cryptic binding sites, and may support prediction of the ligandability of novel challenging targets.

Fragment-Based and Structural Investigation for Discovery of JNK3 Inhibitors

The c-Jun N-terminal kinases (JNKs) are members of the mitogen-activated protein kinase (MAPK) family and are related to cell proliferation, gene expression, and cell death. JNK isoform 3 (JNK3) is an important therapeutic target in varieties of pathological conditions including cancers and neuronal death. There is no approved drug targeting JNKs. To discover chemical inhibitors of JNK3, virtual fragment screening, the saturation transfer difference (STD) NMR, in vitro kinase assay, and X-ray crystallography were employed. A total of 27 fragments from the virtually selected 494 compounds were identified as initial hits via STD NMR and some compounds showed the inhibition of the activity of JNK3 in vitro. The structures of JNK3 with a fragment and a potent inhibitor were determined by X-ray crystallography. The fragment and inhibitor shared a common JNK3-binding feature. The result shows that fragment screening by NMR spectroscopy is a very efficient method to screen JNK3 binders and the structure of JNK3-inhibitor complex can be used to design and develop more potent inhibitors.

Evaluating the use of absolute binding free energy in the fragment optimisation process

Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.

Binding Energy Partition of Promising IRAK-4 Inhibitor (Zimlovisertib) for the Treatment of COVID-19 Pneumonia

The technique of Fragment-Based Drug Design (FBDD) considers the interactions of different moieties of molecules with biological targets for the rational construction of potential drugs. One basic assumption of FBDD is that the different functional groups of a ligand interact with a biological target in an approximately additive, that is, independent manner. We investigated the interactions of different fragments of ligands and Interleukin-1 Receptor-Associated Kinase 4 (IRAK-4) throughout the FBDD design of Zimlovisertib, a promising anti-inflammatory, currently in trials to be used for the treatment of COVID-19 pneumonia. We utilised state-of-the-art methods of wave function analyses mainly the Interacting Quantum Atoms (IQA) energy partition for this purpose. By means of IQA, we assessed the suitability of every change to the ligand in the five stages of FBDD which led to Zimlovisertib on a quantitative basis. We determined the energetics of the interaction of different functional groups in the ligands with the IRAK-4 protein target and thereby demonstrated the adequacy (or lack thereof) of the changes made across the design of this drug. This analysis permits to verify whether a given alteration of a prospective drug leads to the intended tuning of non-covalent interactions with its protein objective. Overall, we expect that the methods exploited in this paper will prove valuable in the understanding and control of chemical modifications across FBDD processes.

Design of a Potent, Selective, and Brain-Penetrant Inhibitor of Wnt-Deactivating Enzyme Notum by Optimization of a Crystallographic Fragment Hit

Notum is a carboxylesterase that suppresses Wnt signaling through deacylation of an essential palmitoleate group on Wnt proteins. There is a growing understanding of the role Notum plays in human diseases such as colorectal cancer and Alzheimer's disease, supporting the need to discover improved inhibitors, especially for use in models of neurodegeneration. Here, we have described the discovery and profile of 8l (ARUK3001185) as a potent, selective, and brain-penetrant inhibitor of Notum activity suitable for oral dosing in rodent models of disease. Crystallographic fragment screening of the Diamond-SGC Poised Library for binding to Notum, supported by a biochemical enzyme assay to rank inhibition activity, identified 6a and 6b as a pair of outstanding hits. Fragment development of 6 delivered 8l that restored Wnt signaling in the presence of Notum in a cell-based reporter assay. Assessment in pharmacology screens showed 8l to be selective against serine hydrolases, kinases, and drug targets.

Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration

Despite recent interest in deep generative models for scaffold elaboration, their applicability to fragment-to-lead campaigns has so far been limited. This is primarily due to their inability to account for local protein structure or a user's design hypothesis. We propose a novel method for fragment elaboration, STRIFE, that overcomes these issues. STRIFE takes as input fragment hotspot maps (FHMs) extracted from a protein target and processes them to provide meaningful and interpretable structural information to its generative model, which in turn is able to rapidly generate elaborations with complementary pharmacophores to the protein. In a large-scale evaluation, STRIFE outperforms existing, structure-unaware, fragment elaboration methods in proposing highly ligand-efficient elaborations. In addition to automatically extracting pharmacophoric information from a protein target's FHM, STRIFE optionally allows the user to specify their own design hypotheses.

The search for new herbicide mechanisms of action: Is there a 'holy grail'?

New herbicide modes of action (MOAs) are in great demand because of the burgeoning evolution of resistance of weeds to existing commercial herbicides. This need has been exacerbated by the almost complete lack of introduction of herbicides with new MOAs for almost 40 years. There are many highly phytotoxic compounds with MOAs not represented by commercial herbicides, but neither these compounds nor structural analogues have been developed as herbicides for a variety of reasons. Natural products provide knowledge of many MOAs that are not being utilized by commercial herbicides. Other means of identifying new herbicide targets are discussed, including pharmaceutical target sites and metabolomic and proteomic information, as well as the use of artificial intelligence and machine learning to predict herbicidal compounds with new MOAs. Information about several newly discovered herbicidal compounds with new MOAs is summarized. The currently increased efforts of both established companies and start-up companies are likely to result in herbicides with new MOAs that can be used in herbicide resistance management within the next decade. © 2021 Society of Chemical Industry.

Identification of a small compound that specifically inhibits Zika virus in vitro and in vivo by targeting the NS2B-NS3 protease

Zika virus (ZIKV) has rapid become a global threat, but no ZIKV-specific vaccines or drugs are currently available. In this study, inhibitors of ZIKV NS2B-NS3 protease were screened from a library containing 4,452 compound fragments. One of the compounds, 6-bromo-1,2-naphthalenedione, exhibited high specific inhibition against ZIKV NS2B-NS3 protease, but had no inhibitory effects against other viral proteases. A microscale thermophoresis (MST) assay confirmed that the compound bound to ZIKV NS2B-NS3 protein with a binding constant (Kd) of 12.26 μM. Indirect immunofluorescence assays, Western blots, and plaque assays indicated that the compound inhibited virus replication in cells. Virus titer was reduced by more than 75% when the compound was present at 1 μM. A time-of-addition assay showed that inhibition occurred at the virus replication stage, but not at the adsorption or invasion stages. The half cytotoxicity concentration (CC50) of the compound on HeLa, Vero, and BHK-21 cells were 445.44 μM, 123.87 μM, and 123.64 μM, respectively. In vivo tests using infected AG129 mice demonstrated that treatment with the compound reduced mortality by up to 60%. Mice treated with the compound showed a reduction in histopathological lesions in brain, testis, and ovary. Viral RNA, IL-1β, and IL-6 mRNA levels decreased significantly in these tissues. In summary, this study has identified a small compound with high and specific inhibitory effects on ZIKV. The compound can be used as a therapeutic agent and is also an ideal starting point for drug optimization.

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.

Efficient Hit-to-Lead Searching of Kinase Inhibitor Chemical Space via Computational Fragment Merging

In early-stage drug discovery, the hit-to-lead optimization (or "hit expansion") stage entails starting from a newly identified active compound and improving its potency or other properties. Traditionally, this process relies on synthesizing and evaluating a series of analogues to build up structure-activity relationships. Here, we describe a computational strategy focused on kinase inhibitors, intended to expedite the process of identifying analogues with improved potency. Our protocol begins from an inhibitor of the target kinase and generalizes the synthetic route used to access it. By searching for commercially available replacements for the individual building blocks used to make the parent inhibitor, we compile an enumerated library of compounds that can be accessed using the same chemical transformations; these huge libraries can exceed many millions─or billions─of compounds. Because the resulting libraries are much too large for explicit virtual screening, we instead consider alternate approaches to identify the top-scoring compounds. We find that contributions from individual substituents are well described by a pairwise additivity approximation, provided that the corresponding fragments position their shared core in precisely the same way relative to the binding site. This key insight allows us to determine which fragments are suitable for merging into single new compounds and which are not. Further, the use of pairwise approximation allows interaction energies to be assigned to each compound in the library without the need for any further structure-based modeling: interaction energies instead can be reliably estimated from the energies of the component fragments, and the reduced computational requirements allow for flexible energy minimizations that allow the kinase to respond to each substitution. We demonstrate this protocol using libraries built from six representative kinase inhibitors drawn from the literature, which target five different kinases: CDK9, CHK1, CDK2, EGFR^T790M, and ACK1. In each example, the enumerated library includes additional analogues reported by the original study to have activity, and these analogues are successfully prioritized within the library. We envision that the insights from this work can facilitate the rapid assembly and screening of increasingly large libraries for focused hit-to-lead optimization. To enable adoption of these methods and to encourage further analyses, we disseminate the computational tools needed to deploy this protocol.

Discovery of Novel BRD4 Ligand Scaffolds by Automated Navigation of the Fragment Chemical Space

Fragment-based drug discovery (FBDD) is a very effective hit identification method. However, the evolution of fragment hits into suitable leads remains challenging and largely artisanal. Fragment evolution is often scaffold-centric, meaning that its outcome depends crucially on the chemical structure of the starting fragment. Considering that fragment screening libraries cover only a small proportion of the corresponding chemical space, hits should be seen as probes highlighting privileged areas of the chemical space rather than actual starting points. We have developed an automated computational pipeline to mine the chemical space around any specific fragment hit, rapidly finding analogues that share a common interaction motif but are structurally novel and diverse. On a prospective application on the bromodomain-containing protein 4 (BRD4), starting from a known fragment, the platform yields active molecules with nonobvious scaffold changes. The procedure is fast and inexpensive and has the potential to uncover many hidden opportunities in FBDD.

Unique Diacidic Fragments Inhibit the OXA-48 Carbapenemase and Enhance the Killing of Escherichia coli Producing OXA-48

Despite the advances in β-lactamase inhibitor development, limited options exist for the class D carbapenemase known as OXA-48. OXA-48 is one of the most prevalent carbapenemases in carbapenem-resistant Enterobacteriaceae infections and is not susceptible to most available β-lactamase inhibitors. Here, we screened various low-molecular-weight compounds (fragments) against OXA-48 to identify functional scaffolds for inhibitor development. Several biphenyl-, naphthalene-, fluorene-, anthraquinone-, and azobenzene-based compounds were found to inhibit OXA-48 with low micromolar potency despite their small size. Co-crystal structures of OXA-48 with several of these compounds revealed key interactions with the carboxylate-binding pocket, Arg214, and various hydrophobic residues of β-lactamase that can be exploited in future inhibitor development. A number of the low-micromolar-potency inhibitors, across different scaffolds, synergize with ampicillin to kill Escherichia coli expressing OXA-48, albeit at high concentrations of the respective inhibitors. Additionally, several compounds demonstrated micromolar potency toward the OXA-24 and OXA-58 class D carbapenemases that are prevalent in Acinetobacter baumannii. This work provides foundational information on a variety of chemical scaffolds that can guide the design of effective OXA-48 inhibitors that maintain efficacy as well as potency toward other major class D carbapenemases.

Deep generative design with 3D pharmacophoric constraints

Generative models have increasingly been proposed as a solution to the molecular design problem. However, it has proved challenging to control the design process or incorporate prior knowledge, limiting their practical use in drug discovery. In particular, generative methods have made limited use of three-dimensional (3D) structural information even though this is critical to binding. This work describes a method to incorporate such information and demonstrates the benefit of doing so. We combine an existing graph-based deep generative model, DeLinker, with a convolutional neural network to utilise physically-meaningful 3D representations of molecules and target pharmacophores. We apply our model, DEVELOP, to both linker and R-group design, demonstrating its suitability for both hit-to-lead and lead optimisation. The 3D pharmacophoric information results in improved generation and allows greater control of the design process. In multiple large-scale evaluations, we show that including 3D pharmacophoric constraints results in substantial improvements in the quality of generated molecules. On a challenging test set derived from PDBbind, our model improves the proportion of generated molecules with high 3D similarity to the original molecule by over 300%. In addition, DEVELOP recovers 10× more of the original molecules compared to the baseline DeLinker method. Our approach is general-purpose, readily modifiable to alternate 3D representations, and can be incorporated into other generative frameworks. Code is available at https://github.com/oxpig/DEVELOP.

Fragment Hit Discovery and Binding Site Characterization by Indirect Affinity Capillary Electrophoresis: Application to Factor XIIa

Fragment-based lead discovery is a usual strategy in drug discovery to identify innovative lead compounds. The success of this approach strongly relies on the capacity to detect weak binders and characterize their binding site. NMR and X-ray crystallography are the conventional technologies used to tackle this challenge. However, their large protein consumption and the cost of equipment reduce their accessibility. Here, an affinity capillary electrophoresis methodology was developed that enables the detection of mM binders, the determination of dissociation constants, and the characterization of the fragment binding site. On the basis of multiple equilibrium theory, dissociation constants in the μM-mM range were determined, and a new methodology is proposed to establish graphically if two fragments bind the same protein pocket. The applicability of this methodology was demonstrated experimentally on coagulation factor XIIa by evaluating pairs of fragments with expected behavior. This study reinforces the significance of using affinity capillary electrophoresis to gather valuable information for medicinal chemistry projects.

Photocatalysis in the Life Science Industry

In the pursuit of new pharmaceuticals and agrochemicals, chemists in the life science industry require access to mild and robust synthetic methodologies to systematically modify chemical structures, explore novel chemical space, and enable efficient synthesis. In this context, photocatalysis has emerged as a powerful technology for the synthesis of complex and often highly functionalized molecules. This Review aims to summarize the published contributions to the field from the life science industry, including research from industrial-academic partnerships. An overview of the synthetic methodologies developed and strategic applications in chemical synthesis, including peptide functionalization, isotope labeling, and both DNA-encoded and traditional library synthesis, is provided, along with a summary of the state-of-the-art in photoreactor technology and the effective upscaling of photocatalytic reactions.

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

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

A fragment-based approach identifies an allosteric pocket that impacts malate dehydrogenase activity

Malate dehydrogenases (MDHs) sustain tumor growth and carbon metabolism by pathogens including Plasmodium falciparum. However, clinical success of MDH inhibitors is absent, as current small molecule approaches targeting the active site are unselective. The presence of an allosteric binding site at oligomeric interface allows the development of more specific inhibitors. To this end we performed a differential NMR-based screening of 1500 fragments to identify fragments that bind at the oligomeric interface. Subsequent biophysical and biochemical experiments of an identified fragment indicate an allosteric mechanism of 4-(3,4-difluorophenyl) thiazol-2-amine (4DT) inhibition by impacting the formation of the active site loop, located >30 Å from the 4DT binding site. Further characterization of the more tractable homolog 4-phenylthiazol-2-amine (4PA) and 16 other derivatives are also reported. These data pave the way for downstream development of more selective molecules by utilizing the oligomeric interfaces showing higher species sequence divergence than the MDH active site.

Romero et al. perform NMR-based screening of 1500 fragments to identify fragments that bind at the oligomeric interface of malate dehydrogenase (MDH). Their study indicates an allosteric mechanism impacting enzymatic activity, paving the way for development of more selective molecules and a starting point for the future development of specific MDH inhibitors.

Elaboration of a benzofuran scaffold and evaluation of binding affinity and inhibition of Escherichia coli DsbA: A fragment-based drug design approach to novel antivirulence compounds

Bacterial thiol-disulfide oxidoreductase DsbA is essential for bacterial virulence factor assembly and has been identified as a viable antivirulence target. Herein, we report a structure-based elaboration of a benzofuran hit that bound to the active site groove of Escherichia coli DsbA. Substituted phenyl groups were installed at the 5- and 6-position of the benzofuran using Suzuki-Miyaura coupling. HSQC NMR titration experiments showed dissociation constants of this series in the high µM to low mM range and X-ray crystallography produced three co-structures, showing binding in the hydrophobic groove, comparable with that of the previously reported benzofurans. The 6-(m-methoxy)phenyl analogue (2b), which showed a promising binding pose, was chosen for elaboration from the C-2 position. The 2,6-disubstituted analogues bound to the hydrophobic region of the binding groove and the C-2 groups extended into the more polar, previously un-probed, region of the binding groove. Biochemical analysis of the 2,6-disubsituted analogues showed they inhibited DsbA oxidation activity in vitro. The results indicate the potential to develop the elaborated benzofuran series into a novel class of antivirulence compounds.

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.

Discovery of new non-pyrimidine scaffolds as Plasmodium falciparum DHFR inhibitors by fragment-based screening

In various malaria-endemic regions, the appearance of resistance has precluded the use of pyrimidine-based antifolate drugs. Here, a three-step fragment screening was used to identify new non-pyrimidine Plasmodium falciparum dihydrofolate reductase (PfDHFR) inhibitors. Starting from a 1163-fragment commercial library, a two-step differential scanning fluorimetry screen identified 75 primary fragment hits. Subsequent enzyme inhibition assay identified 11 fragments displaying IC₅₀ in the 28-695 μM range and selectivity for PfDHFR. In addition to the known pyrimidine, three new anti-PfDHFR chemotypes were identified. Fragments from each chemotype were successfully co-crystallized with PfDHFR, revealing a binding in the active site, in the vicinity of catalytic residues, which was confirmed by molecular docking on all fragment hits. Finally, comparison with similar non-hit fragments provides preliminary input on available growth vectors for future drug development.

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.

Capillary electrophoresis as a fragment screening tool to cross-validate hits from chromogenic assay: Application to FXIIa

In this study, a partial-filling affinity capillary electrophoresis (pf-ACE) method was developed for the cross-validation of fragment hits revealed by chromogenic factor XIIa (FXIIa) assay. Chromogenic assay produces false positives, mainly due to spectrophotometric interferences and sample purity issues. pf-ACE was selected as counter-screening technology because of its separative character and the fact that the target does not have to be attached or tagged. The effects of protein plug length, applied voltage and composition of the running buffer were examined and optimized. Detection limit in terms of dissociation constant was estimated at 400 μM. The affinity evaluation was performed close to physiological conditions (pH 7.4, ionic strength 0.13 mol L^-1) in a poly (ethylene oxide)-coated capillary of 75 μm internal diameter x 33 cm length with an applied voltage of 3 kV. This method uncovered chromogenic assay's false positives due to zinc contamination. Moreover, pf-ACE supported the evaluation of compounds absorbing at 405 nm.

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 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.

Fragment-Based Discovery of Novel Allosteric MEK1 Binders

The MEK1 kinase plays a critical role in key cellular processes, and as such, its dysfunction is strongly linked to several human diseases, particularly cancer. MEK1 has consequently received considerable attention as a drug target, and a significant number of small-molecule inhibitors of this kinase have been reported. The majority of these inhibitors target an allosteric pocket proximal to the ATP binding site which has proven to be highly druggable, with four allosteric MEK1 inhibitors approved to date. Despite the significant attention that the MEK1 allosteric site has received, chemotypes which have been shown structurally to bind to this site are limited. With the aim of discovering novel allosteric MEK1 inhibitors using a fragment-based approach, we report here a screening method which resulted in the discovery of multiple allosteric MEK1 binders, one series of which was optimized to sub-μM affinity for MEK1 with promising physicochemical and ADMET properties.

Applications of Solution NMR in Drug Discovery

During the past decades, solution nuclear magnetic resonance (NMR) spectroscopy has demonstrated itself as a promising tool in drug discovery. Especially, fragment-based drug discovery (FBDD) has benefited a lot from the NMR development. Multiple candidate compounds and FDA-approved drugs derived from FBDD have been developed with the assistance of NMR techniques. NMR has broad applications in different stages of the FBDD process, which includes fragment library construction, hit generation and validation, hit-to-lead optimization and working mechanism elucidation, etc. In this manuscript, we reviewed the current progresses of NMR applications in fragment-based drug discovery, which were illustrated by multiple reported cases. Moreover, the NMR applications in protein-protein interaction (PPI) modulators development and the progress of in-cell NMR for drug discovery were also briefly summarized.

Discovery of small molecules targeting the tandem tudor domain of the epigenetic factor UHRF1 using fragment-based ligand discovery

Despite the established roles of the epigenetic factor UHRF1 in oncogenesis, no UHRF1-targeting therapeutics have been reported to date. In this study, we use fragment-based ligand discovery to identify novel scaffolds for targeting the isolated UHRF1 tandem Tudor domain (TTD), which recognizes the heterochromatin-associated histone mark H3K9me3 and supports intramolecular contacts with other regions of UHRF1. Using both binding-based and function-based screens of a ~ 2300-fragment library in parallel, we identified 2,4-lutidine as a hit for follow-up NMR and X-ray crystallography studies. Unlike previous reported ligands, 2,4-lutidine binds to two binding pockets that are in close proximity on TTD and so has the potential to be evolved into more potent inhibitors using a fragment-linking strategy. Our study provides a useful starting point for developing potent chemical probes against UHRF1.

Novel Immune Modulators Enhance Caenorhabditis elegans Resistance to Multiple Pathogens

Trends moving in opposite directions (increasing antimicrobial resistance and declining novel antimicrobial development) have precipitated a looming crisis: a nearly complete inability to safely and effectively treat bacterial infections. To avert this, new approaches are needed.

Traditionally, treatments for bacterial infection have focused on killing the microbe or preventing its growth. As antimicrobial resistance becomes more ubiquitous, the feasibility of this approach is beginning to wane and attention has begun to shift toward disrupting the host-pathogen interaction by improving the host defense. Using a high-throughput, fragment-based screen to identify compounds that alleviate Pseudomonas aeruginosa-mediated killing of Caenorhabditis elegans, we identified over 20 compounds that stimulated host defense gene expression. Five of these molecules were selected for further characterization. Four of five compounds showed little toxicity against mammalian cells or worms, consistent with their identification in a phenotypic, high-content screen. Each of the compounds activated several host defense pathways, but the pathways were generally dispensable for compound-mediated rescue in liquid killing, suggesting redundancy or that the activation of unknown pathway(s) may be driving compound effects. A genetic mechanism was identified for LK56, which required the Mediator subunit MDT-15/MED15 and NHR-49/HNF4 for its function. Interestingly, LK32, LK34, LK38, and LK56 also rescued C. elegans from P. aeruginosa in an agar-based assay, which uses different virulence factors and defense mechanisms. Rescue in an agar-based assay for LK38 entirely depended upon the PMK-1/p38 MAPK pathway. Three compounds—LK32, LK34, and LK56—also conferred resistance to Enterococcus faecalis, and the two lattermost, LK34 and LK56, also reduced pathogenesis from Staphylococcus aureus. This study supports a growing role for MDT-15 and NHR-49 in immune response and identifies five molecules that have significant potential for use as tools in the investigation of innate immunity.

IMPORTANCE Trends moving in opposite directions (increasing antimicrobial resistance and declining novel antimicrobial development) have precipitated a looming crisis: the nearly complete inability to safely and effectively treat bacterial infections. To avert this, new approaches are needed. One idea is to stimulate host defense pathways to improve the clearance of bacterial infection. Here, we describe five small molecules that promote resistance to infectious bacteria by activating C. elegans’ innate immune pathways. Several are effective against both Gram-positive and Gram-negative pathogens. One of the compounds was mapped to the action of MDT-15/MED15 and NHR-49/HNF4, a pair of transcriptional regulators more generally associated with fatty acid metabolism, potentially highlighting a new link between these biological functions. These studies pave the way for future characterization of the anti-infective activity of the molecules in higher organisms and highlight the compounds’ potential utility for further investigation of immune modulation as a novel therapeutic approach.

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.