Ligand deconstruction: Why some fragment binding positions are conserved and others are not

Subpocket-Based Analysis Approach for the Protein Pocket Dynamics

Structural and dynamic characteristics of protein pockets remarkably influence their biological functions and are also important for enzyme engineering and new drug research and development. To date, several softwares have been developed to analyze the dynamic properties of protein pockets. However, due to the complexity and diversity of the pocket information during the kinetic relaxation, further improvement and capacity expansion of current tools are required. Here, we developed a platform software AlphaTraj in which a computational strategy that divides the whole protein pocket into subpockets and examines various properties of the subpockets such as survival time, stability, and correlation was proposed and implemented. We also proposed a scoring function for the subpockets as well as the whole pocket to visualize the quality of the pocket. Furthermore, we implemented automated conformational search functions for ligand docking and ligand optimization. These functions may help us to gain a deep understanding of the dynamic properties of protein pockets and accelerate the protein engineering and the design of inhibitors and small-molecule drugs. The software is freely available at https://github.com/dooo12332/AlphaTraj.git under the GNU GPL license.

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.

RBD spatial orientation of the spike protein and its binding to ACE2: insight into the high infectivity of the SARS-CoV-2 Delta variant from MD simulations

The spike glycoprotein on the surface of the SARS-CoV-2 envelope plays an important role in its invasion into host cells. The binding of the spike glycoprotein RBD to the angiotensin-converting enzyme 2 (ACE2) receptor as a critical step in the spread of the virus has been explored intensively since the outbreak of COVID-19, but the high transmissibility of the virus such as the Delta variant is still not fully understood. Here, molecular simulations on the binding interactions of the wild-type spike protein and its four variants (Beta, Kappa, Delta, and Mu) with ACE2 and the antibody were performed, and the present results reveal that the residue mutations will not strengthen the binding affinity of the variant for ACE2, but remarkably influences the spatial orientation of the spike protein. Only the up-right conformational receptor binding domain (RBD) can bind ACE2, which is stabilized by the nearby RBDs in the down state, revealing that the RBD bears dual functional characteristics. The present results provide new insights into plausible mechanisms for high infectivity of the virus variants and their immune escape.

Exploring the Surface of the Ectodomain of the PD-L1 Immune Checkpoint with Small-Molecule Fragments

Development of small molecules targeting the PD-L1/PD-1 interface is advancing both in industry and academia, but only a few have reached early-stage clinical trials. Here, we take a closer look at the general druggability of PD-L1 using in silico hot spot mapping and nuclear magnetic resonance (NMR)-based characterization. We found that the conformational elasticity of the PD-L1 surface strongly influences the formation of hot spots. We deconstructed several generations of known inhibitors into fragments and examined their binding properties using differential scanning fluorimetry (DSF) and protein-based nuclear magnetic resonance (NMR). These biophysical analyses showed that not all fragments bind to the PD-L1 ectodomain despite having the biphenyl scaffold. Although most of the binding fragments induced PD-L1 oligomerization, two compounds, TAH35 and TAH36, retain the monomeric state of proteins upon binding. Additionally, the presence of the entire ectodomain did not affect the binding of the hit compounds and dimerization of PD-L1. The data demonstrated here provide important information on the PD-L1 druggability and the structure-activity relationship of the biphenyl core moiety and therefore may aid in the design of novel inhibitors and focused fragment libraries for PD-L1.

Phenotypic screening of low molecular weight compounds is rich ground for repurposed, on-target drugs

A target-based drug discovery strategy has led to a bias away from low molecular weight (MWT) drug discovery. Analysis of the ACS chemistry registration system shows that most low MWT drugs were first made in the time era before target-based drug discovery. Therapeutic activity among most low MWT drugs was identified in the era of phenotypic drug discovery when drugs were selected based on their phenotypic effects and before in vitro screening, mechanism of action considerations and experiences with fragment screening became known. The common perception that drugs cannot be found among low MWT compounds is incorrect based on both drug discovery history and our own experience with MLR-1023. The greater proportion of low MWT compounds that are commercially available compared to higher MWT compounds is a factor that should facilitate biology study. We posit that low MWT compounds are more suited to identification of new therapeutic activity using phenotypic screens provided that the phenotypic screening method has enough screening capacity. On-target and off-target therapeutic activities are discussed from both a chemistry and biology perspective because of a concern that either phenotypic or low MWT drug discovery might bias towards promiscuous compounds that combine on-target and off-target effects. Among ideal drug repositioning candidates (late-stage pre-clinical or clinically-experience compounds), pleiotropic activity (multiple therapeutic actions) is far more likely due to on-target effects arising where a single target mediates multiple therapeutic benefits, a desirable outcome for drug development purposes compared to the off-target alternative. Our exemplar of a low MWT compound, MLR-1023, discovered by phenotypic screening and subsequently found to have a single mechanism of action would have been overlooked based on current era medicinal chemistry precedent. The diverse therapeutic activities described for this compound by us, and others arise from the same pleiotropic lyn kinase activation molecular target. MLR-1023 serves as a proof-of-principle that potent, on target, low MWT drugs can be discovered by phenotypic screening.

Decomposing compounds enables reconstruction of interaction fingerprints for structure-based drug screening

Background

Structure-based drug repositioning has emerged as a promising alternative to conventional drug development. Regardless of the many success stories reported over the past years and the novel breakthroughs on the AI-based system AlphaFold for structure prediction, the availability of structural data for protein–drug complexes remains very limited. Whereas the chemical libraries contain millions of drug compounds, the vast majority of them do not have structures to crystallized targets,and it is, therefore, impossible to characterize their binding to targets from a structural view. However, the concept of building blocks offers a novel perspective on the structural problem. A drug compound is considered a complex of small chemical blocks or fragments, which confer the relevant properties to the drug and have a high proportion of functional groups involved in protein binding. Based on this, we propose a novel approach to expand the scope of structure-based repositioning approaches by transferring the structural knowledge from a fragment to a compound level.

Results

We fragmented over 100,000 compounds in the Protein Data Bank (PDB) and characterized the structural binding mode of 153,000 fragments to their crystallized targets. Using the fragment’s data, we were able to artificially reconstruct the binding mode of over 7,800 complexes between ChEMBL compounds and their known targets, for which no structural data is available. We proved that the conserved binding tendency of fragments, when binding to the same targets, highly influences the drug’s binding specificity and carries the key information to reconstruct full drugs binding mode. Furthermore, our approach was able to reconstruct multiple compound-target pairs at optimal thresholds and high similarity to the actual binding mode.

Conclusions

Such reconstructions are of great value and benefit structure-based drug repositioning since they automatically enlarge the technique’s scope and allow exploring the so far ‘unexplored compounds’ from a structural perspective. In general, the transfer of structural information is a promising technique that could be applied to any chemical library, to any compound that has no crystal structure available in PDB, and even to transfer any other feature that may be relevant for the drug discovery process and that due to data limitations is not yet fully available. In that sense, the results of this work document the full potential of structure-based screening even beyond PDB.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13321-022-00592-w.

Progress in Free Energy Perturbation: Options for Evolving Fragments

One of the remaining bottlenecks in fragment-based drug design (FBDD) is the initial exploration and optimization of the identified hit fragments. There is a growing interest in computational approaches that can guide these efforts by predicting the binding affinity of newly designed analogues. Among others, alchemical free energy (AFE) calculations promise high accuracy at a computational cost that allows their application during lead optimization campaigns. In this review, we discuss how AFE could have a strong impact in fragment evolution, and we raise awareness on the challenges that could be encountered applying this methodology in FBDD studies.

Exploring protein hotspots by optimized fragment pharmacophores

Fragment-based drug design has introduced a bottom-up process for drug development, with improved sampling of chemical space and increased effectiveness in early drug discovery. Here, we combine the use of pharmacophores, the most general concept of representing drug-target interactions with the theory of protein hotspots, to develop a design protocol for fragment libraries. The SpotXplorer approach compiles small fragment libraries that maximize the coverage of experimentally confirmed binding pharmacophores at the most preferred hotspots. The efficiency of this approach is demonstrated with a pilot library of 96 fragment-sized compounds (SpotXplorer0) that is validated on popular target classes and emerging drug targets. Biochemical screening against a set of GPCRs and proteases retrieves compounds containing an average of 70% of known pharmacophores for these targets. More importantly, SpotXplorer0 screening identifies confirmed hits against recently established challenging targets such as the histone methyltransferase SETD2, the main protease (3CLPro) and the NSP3 macrodomain of SARS-CoV-2.

Inhibition of the HEG1-KRIT1 interaction increases KLF4 and KLF2 expression in endothelial cells

The transmembrane protein heart of glass1 (HEG1) directly binds to and recruits Krev interaction trapped protein 1 (KRIT1) to endothelial junctions to form the HEG1-KRIT1 protein complex that establishes and maintains junctional integrity. Genetic inactivation or knockdown of endothelial HEG1 or KRIT1 leads to the upregulation of transcription factors Krüppel-like factors 4 and 2 (KLF4 and KLF2), which are implicated in endothelial vascular homeostasis; however, the effect of acute inhibition of the HEG1-KRIT1 interaction remains incompletely understood. Here, we report a high-throughput screening assay and molecular design of a small-molecule HEG1-KRIT1 inhibitor to uncover acute changes in signaling pathways downstream of the HEG1-KRIT1 protein complex disruption. The small-molecule HEG1-KRIT1 inhibitor 2 (HKi2) was demonstrated to be a bona fide inhibitor of the interaction between HEG1 and KRIT1 proteins, by competing orthosterically with HEG1 through covalent reversible interactions with the FERM (4.1, ezrin, radixin, and moesin) domain of KRIT1. The crystal structure of HKi2 bound to KRIT1 FERM revealed that it occupies the same binding pocket on KRIT1 as the HEG1 cytoplasmic tail. In human endothelial cells (ECs), acute inhibition of the HEG1-KRIT1 interaction by HKi2 increased KLF4 and KLF2 mRNA and protein levels, whereas a structurally similar inactive compound failed to do so. In zebrafish, HKi2 induced expression of klf2a in arterial and venous endothelium. Furthermore, genome-wide RNA transcriptome analysis of HKi2-treated ECs under static conditions revealed that, in addition to elevating KLF4 and KLF2 expression, inhibition of the HEG1-KRIT1 interaction mimics many of the transcriptional effects of laminar blood flow. Furthermore, HKi2-treated ECs also triggered Akt signaling in a phosphoinositide 3-kinase (PI3K)-dependent manner, as blocking PI3K activity blunted the Akt phosphorylation induced by HKi2. Finally, using an in vitro colocalization assay, we show that HKi6, an improved derivative of HKi2 with higher affinity for KRIT1, significantly impedes recruitment of KRIT1 to mitochondria-localized HEG1 in CHO cells, indicating a direct inhibition of the HEG1-KRIT1 interaction. Thus, our results demonstrate that early events of the acute inhibition of HEG1-KRIT1 interaction with HKi small-molecule inhibitors lead to: (i) elevated KLF4 and KLF2 gene expression; and (ii) increased Akt phosphorylation. Thus, HKi's provide new pharmacologic tools to study acute inhibition of the HEG1-KRIT1 protein complex and may provide insights to dissect early signaling events that regulate vascular homeostasis.

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.

Direct Keap1-kelch inhibitors as potential drug candidates for oxidative stress-orchestrated diseases: A review on In silico perspective

The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.

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.

Fragment-Based Nuclear Magnetic Resonance Screen against a Regulator of G Protein Signaling Identifies a Binding "Hot Spot"

Regulator of G protein signaling (RGS) proteins have attracted attention as a result of their primary role in directing the specificity as well as the temporal and spatial aspects of G protein-coupled receptor signaling. In addition, alterations in RGS protein expression have been observed in a number of disease states, including certain cancers. In this area, RGS17 is of particular interest. It has been demonstrated that, while RGS17 is expressed primarily in the central nervous system, it has been found to be inappropriately expressed in lung, prostate, breast, cervical, and hepatocellular carcinomas. Overexpression of RGS17 leads to dysfunction in inhibitory G protein signaling and an overproduction of the intracellular second messenger cAMP, which in turn alters the transcription patterns of proteins known to promote various cancer types. Suppressing RGS17 expression with RNA interference (RNAi) has been found to decrease tumorigenesis and sufficiently prevents cancer cell migration, leading to the hypothesis that pharmacological blocking of RGS17 function could be useful in anticancer therapies. We have identified small-molecule fragments capable of binding the RGS homology (RH) domain of RGS17 by using a nuclear magnetic resonance fragment-based screening approach. By chemical shift mapping of the two-dimensional ¹⁵ N,¹ H heteronuclear single quantum coherence (HSQC) spectra of the backbone-assigned ¹⁵ N-labeled RGS17-RH, we determined the fragment binding sites to be distant from the Gα interface. Thus, our study identifies a putative fragment binding site on RGS17 that was previously unknown.

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.

Interaction Energetics and Druggability of the Protein-Protein Interaction between Kelch-like ECH-Associated Protein 1 (KEAP1) and Nuclear Factor Erythroid 2 Like 2 (Nrf2)

Development of small molecule inhibitors of protein-protein interactions (PPIs) is hampered by our poor understanding of the druggability of PPI target sites. Here, we describe the combined application of alanine-scanning mutagenesis, fragment screening, and FTMap computational hot spot mapping to evaluate the energetics and druggability of the highly charged PPI interface between Kelch-like ECH-associated protein 1 (KEAP1) and nuclear factor erythroid 2 like 2 (Nrf2), an important drug target. FTMap identifies four binding energy hot spots at the active site. Only two of these are exploited by Nrf2, which alanine scanning of both proteins shows to bind primarily through E79 and E82 interacting with KEAP1 residues S363, R380, R415, R483, and S508. We identify fragment hits and obtain X-ray complex structures for three fragments via crystal soaking using a new crystal form of KEAP1. Combining these results provides a comprehensive and quantitative picture of the origins of binding energy at the interface. Our findings additionally reveal non-native interactions that might be exploited in the design of uncharged synthetic ligands to occupy the same site on KEAP1 that has evolved to bind the highly charged DEETGE binding loop of Nrf2. These include π-stacking with KEAP1 Y525 and interactions at an FTMap-identified hot spot deep in the binding site. Finally, we discuss how the complementary information provided by alanine-scanning mutagenesis, fragment screening, and computational hot spot mapping can be integrated to more comprehensively evaluate PPI druggability.

Selectivity, ligand deconstruction, and cellular activity analysis of a BPTF bromodomain inhibitor

Bromodomain and PHD finger containing protein transcription factor (BPTF) is an epigenetic protein involved in chromatin remodelling and is a potential anticancer target. The BPTF bromodomain has one reported small molecule inhibitor (AU1, rac-1). Here, advances made on the structure-activity relationship of a BPTF bromodomain ligand are reported using a combination of experimental and molecular dynamics simulations leading to the active enatiomer (S)-1. Additionally, a ligand deconstruction analysis was conducted to characterize important pharmacophores for engaging the BPTF bromodomain. These studies have been enabled by a protein-based fluorine NMR approach, highlighting the versatility of the method for selectivity, ligand deconstruction, and ligand binding. To enable future analysis of biological activity, cell growth analyses in a panel of cancer cell lines were carried out using CRISPR-Cas9 and (S)-1 to identify cell-based model systems that are sensitive to BPTF inhibition.

qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X-Ray Electron Density Maps

Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.

Surface Probing by Fragment-Based Screening and Computational Methods Identifies Ligandable Pockets on the von Hippel-Lindau (VHL) E3 Ubiquitin Ligase

Beyond the targeting of E3 ubiquitin ligases to inhibit protein homeostasis, E3 ligase binders can be repurposed as targeted protein degraders (PROTACs or molecular glues). We sought to identify new binders of the VHL E3 ligase by biophysical fragment-based screening followed by X-ray crystallographic soaking. We identified fragments binding at the ElonginC:Cullin2 interface and a new cryptic pocket in VHL, along with other potential ligandable sites predicted computationally and found to bind solvent molecules in crystal structures. The elucidated interactions provide starting points for future ligand development.

In Silico Discovery of Plant-Origin Natural Product Inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL)

An in silico drug discovery pipeline for the virtual screening of plant-origin natural products (NPs) was developed to explore new direct inhibitors of TNF and its close relative receptor activator of nuclear factor kappa-B ligand (RANKL), both representing attractive therapeutic targets for many chronic inflammatory conditions. Direct TNF inhibition through identification of potent small molecules is a highly desired goal; however, it is often hampered by severe limitations. Our approach yielded a priority list of 15 NPs as potential direct TNF inhibitors that were subsequently tested in vitro against TNF and RANKL. We thus identified two potent direct inhibitors of TNF function with low micromolar IC₅₀ values and minimal toxicity even at high concentrations. Most importantly, one of them (A11) was proved to be a dual inhibitor of both TNF and RANKL. Extended molecular dynamics simulations with the fully automated EnalosMD suite rationalized the mode of action of the compounds at the molecular level. To our knowledge, these compounds constitute the first NP TNF inhibitors, one of which being the first NP small-molecule dual inhibitor of TNF and RANKL, and could serve as lead compounds for the development of novel treatments for inflammatory and autoimmune diseases.

Fluorogenic structure activity library pinpoints molecular variations in substrate specificity of structurally homologous esterases

Cellular esterases catalyze many essential biological functions by performing hydrolysis reactions on diverse substrates. The promiscuity of esterases complicates assignment of their substrate preferences and biological functions. To identify universal factors controlling esterase substrate recognition, we designed a 32-member structure-activity relationship (SAR) library of fluorogenic ester substrates and used this library to systematically interrogate esterase preference for chain length, branching patterns, and polarity to differentiate common classes of esterase substrates. Two structurally homologous bacterial esterases were screened against this library, refining their previously broad overlapping substrate specificity. Vibrio cholerae esterase ybfF displayed a preference for γ-position thioethers and ethers, whereas Rv0045c from Mycobacterium tuberculosis displayed a preference for branched substrates with and without thioethers. We determined that this substrate differentiation was partially controlled by individual substrate selectivity residues Tyr-119 in ybfF and His-187 in Rv0045c; reciprocal substitution of these residues shifted each esterase's substrate preference. This work demonstrates that the selectivity of esterases is tuned based on transition state stabilization, identifies thioethers as an underutilized functional group for esterase substrates, and provides a rapid method for differentiating structural isozymes. This SAR library could have multifaceted future applications, including in vivo imaging, biocatalyst screening, molecular fingerprinting, and inhibitor design.

Structural Insights on Fragment Binding Mode Conservation

Aiming at a deep understanding of fragment binding to ligandable targets, we performed a large scale analysis of the Protein Data Bank. Binding modes of 1832 drug-like ligands and 1079 fragments to 235 proteins were compared. We observed that the binding modes of fragments and their drug-like superstructures binding to the same protein are mostly conserved, thereby providing experimental evidence for the preservation of fragment binding modes during molecular growing. Furthermore, small chemical changes in the fragment are tolerated without alteration of the fragment binding mode. The exceptions to this observation generally involve conformational variability of the molecules. Our data analysis also suggests that, provided enough fragments have been crystallized within a protein, good interaction coverage of the binding pocket is achieved. Last, we extended our study to 126 crystallization additives and discuss in which cases they provide information relevant to structure-based drug design.

Integrated Strategy for Lead Optimization Based on Fragment Growing: The Diversity-Oriented-Target-Focused-Synthesis Approach

Over the past few decades, hit identification has been greatly facilitated by advances in high-throughput and fragment-based screenings. One major hurdle remaining in drug discovery is process automation of hit-to-lead (H2L) optimization. Here, we report a time- and cost-efficient integrated strategy for H2L optimization as well as a partially automated design of potent chemical probes consisting of a focused-chemical-library design and virtual screening coupled with robotic diversity-oriented de novo synthesis and automated in vitro evaluation. The virtual library is generated by combining an activated fragment, corresponding to the substructure binding to the target, with a collection of functionalized building blocks using in silico encoded chemical reactions carefully chosen from a list of one-step organic transformations relevant in medicinal chemistry. The proof of concept was demonstrated using the optimization of bromodomain inhibitors as a test case, leading to the validation of several compounds with improved affinity by several orders of magnitude.

Computational Analysis for the Rational Design of Anti-Amyloid Beta (Aβ) Antibodies

Alzheimer's disease (AD) is a neurodegenerative disorder that lacks effective treatment options. Anti-amyloid beta (Aβ) antibodies are the leading drug candidates to treat AD, but the results of clinical trials have been disappointing. Introducing rational mutations into anti-Aβ antibodies to increase their effectiveness is a way forward, but the path to take is unclear. In this study, we demonstrate the use of computational fragment-based docking and MMPBSA binding free energy calculations in the analysis of anti-Aβ antibodies for rational drug design efforts. Our fragment-based docking method successfully predicts the emergence of the common EFRH epitope. MD simulations coupled with MMPBSA binding free energy calculations are used to analyze scenarios described in prior studies, and we computationally introduce rational mutations into PFA1 to predict mutations that can improve its binding affinity toward the pE3-Aβ_3-8 form of Aβ. Two out of our four proposed mutations are predicted to stabilize binding. Our study demonstrates that a computational approach may lead to an improved drug candidate for AD in the future.

Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors

Fragment-based drug discovery (FBDD) has become a mainstream approach in drug design because it allows the reduction of the chemical space and screening libraries while identifying fragments with high protein-ligand efficiency interactions that can later be grown into drug-like leads. In this work, we leverage high-throughput molecular dynamics (MD) simulations to screen a library of 129 fragments for a total of 5.85 ms against the CXCL12 monomer, a chemokine involved in inflammation and diseases such as cancer. Our in silico binding assay was able to recover binding poses, affinities, and kinetics for the selected library and was able to predict 8 mM-affinity fragments with ligand efficiencies higher than 0.3. All of the fragment hits present a similar chemical structure, with a hydrophobic core and a positively charged group, and bind to either sY7 or H1S68 pockets, where they share pharmacophoric properties with experimentally resolved natural binders. This work presents a large-scale screening assay using an exclusive combination of thousands of short MD adaptive simulations analyzed with a Markov state model (MSM) framework.

Exhaustive sampling of the fragment space associated to a molecule leading to the generation of conserved fragments

The first step in hit optimization is the identification of the pharmacophore, which is normally achieved by deconstruction of the hit molecule to generate "deletion analogues." In silico fragmentation approaches often focus on the generation of small fragments that do not describe properly the fragment space associated to the deletion analogues. We present significant modifications to the molecular fragmentation programme molBLOCKS, which allows the exhaustive sampling of the fragment space associated with a molecule to generate all possible molecular fragments. This generates larger fragments, by combining the smallest fragments. Additionally, it has been modified to deal with the problem of changing pharmacophoric properties through fragmentation, by highlighting bond cuts. The modified molBLOCKS programme was used on a set of drug compounds, where it generated more unique fragments than standard fragmentation approaches by increasing the number of fragments derived per compound. This fragment set was found to be more diverse than those generated by standard fragmentation programmes and was relevant to drug discovery as it contains the key fragments representing the pharmacophoric elements associated with ligand recognition. The use of dummy atoms to highlight bond cuts further increases the information content of fragments by visualizing their previous bonding pattern.

Binding-Site Compatible Fragment Growing Applied to the Design of β2-Adrenergic Receptor Ligands

Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the β₂-adrenergic receptor (β₂AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.

Label-free NMR-based dissociation kinetics determination

Understanding the dissociation of molecules is the basis to modulate interactions of biomedical interest. Optimizing drugs for dissociation rates is found to be important for their efficacy, selectivity, and safety. Here, we show an application of the high-power relaxation dispersion (RD) method to the determination of the dissociation rates of weak binding ligands from receptors. The experiment probes proton RD on the ligand and, therefore, avoids the need for any isotopic labeling. The large ligand excess eases the detection significantly. Importantly, the use of large spin-lock fields allows the detection of faster dissociation rates than other relaxation approaches. Moreover, this experimental approach allows to access directly the off-rate of the binding process without the need for analyzing a series of samples with increasing ligand saturation. The validity of the method is shown with small molecule interactions using two macromolecules, bovine serum albumin and tubulin heterodimers.

The Ligand Binding Landscape of Diacylglycerol Kinases

Diacylglycerol kinases (DGKs) are integral components of signal transduction cascades that regulate cell biology through ATP-dependent phosphorylation of the lipid messenger diacylglycerol. Methods for direct evaluation of DGK activity in native biological systems are lacking and needed to study isoform-specific functions of these multidomain lipid kinases. Here, we utilize ATP acyl phosphate activity-based probes and quantitative mass spectrometry to define, for the first time, ATP and small-molecule binding motifs of representative members from all five DGK subtypes. We use chemical proteomics to discover an unusual binding mode for the DGKα inhibitor, ritanserin, including interactions at the atypical C1 domain distinct from the ATP binding region. Unexpectedly, deconstruction of ritanserin yielded a fragment compound that blocks DGKα activity through a conserved binding mode and enhanced selectivity against the kinome. Collectively, our studies illustrate the power of chemical proteomics to profile protein-small molecule interactions of lipid kinases for fragment-based lead discovery.

Do Fragments and Crystallization Additives Bind Similarly to Drug-like Ligands?

The success of fragment-based drug design (FBDD) hinges upon the optimization of low-molecular-weight compounds (MW < 300 Da) with weak binding affinities to lead compounds with high affinity and selectivity. Usually, structural information from fragment-protein complexes is used to develop ideas about the binding mode of similar but drug-like molecules. In this regard, crystallization additives such as cryoprotectants or buffer components, which are highly abundant in crystal structures, are frequently ignored. Thus, the aim of this study was to investigate the information present in protein complexes with fragments as well as those with additives and how they relate to the binding modes of their drug-like counterparts. We present a thorough analysis of the binding modes of crystallographic additives, fragments, and drug-like ligands bound to four diverse targets of wide interest in drug discovery and highly represented in the Protein Data Bank: cyclin-dependent kinase 2, β-secretase 1, carbonic anhydrase 2, and trypsin. We identified a total of 630 unique molecules bound to the catalytic binding sites, among them 31 additives, 222 fragments, and 377 drug-like ligands. In general, we observed that, independent of the target, protein-fragment interaction patterns are highly similar to those of drug-like ligands and mostly cover the residues crucial for binding. Crystallographic additives are also able to show conserved binding modes and recover the residues important for binding in some of the cases. Moreover, we show evidence that the information from fragments and drug-like ligands can be applied to rescore docking poses in order to improve the prediction of binding modes.

Binding Pose Flip Explained via Enthalpic and Entropic Contributions

The anomalous binding modes of five highly similar fragments of TIE2 inhibitors, showing three distinct binding poses, are investigated. We report a quantitative rationalization for the changes in binding pose based on molecular dynamics simulations. We investigated five fragments in complex with the transforming growth factor β receptor type 1 kinase domain. Analyses of these simulations using Grid Inhomogeneous Solvation Theory (GIST), pK_A calculations, and a tool to investigate enthalpic differences upon binding unraveled the various thermodynamic contributions to the different binding modes. While one binding mode flip can be rationalized by steric repulsion, the second binding pose flip revealed a different protonation state for one of the ligands, leading to different enthalpic and entropic contributions to the binding free energy. One binding pose is stabilized by the displacement of entropically unfavored water molecules (binding pose determined by solvation entropy), ligands in the other binding pose are stabilized by strong enthalpic interactions, overcompensating the unfavorable water entropy in this pose (binding pose determined by enthalpic interactions). This analysis elucidates unprecedented details determining the flipping of the binding modes, which can elegantly explain the experimental findings for this system.

Ligand and Target Discovery by Fragment-Based Screening in Human Cells

Advances in the synthesis and screening of small-molecule libraries have accelerated the discovery of chemical probes for studying biological processes. Still, only a small fraction of the human proteome has chemical ligands. Here, we describe a platform that marries fragment-based ligand discovery with quantitative chemical proteomics to map thousands of reversible small molecule-protein interactions directly in human cells, many of which can be site-specifically determined. We show that fragment hits can be advanced to furnish selective ligands that affect the activity of proteins heretofore lacking chemical probes. We further combine fragment-based chemical proteomics with phenotypic screening to identify small molecules that promote adipocyte differentiation by engaging the poorly characterized membrane protein PGRMC2. Fragment-based screening in human cells thus provides an extensive proteome-wide map of protein ligandability and facilitates the coordinated discovery of bioactive small molecules and their molecular targets.

When Does Chemical Elaboration Induce a Ligand To Change Its Binding Mode?

Traditional hit-to-lead optimization assumes that upon elaboration of chemical structure, the ligand retains its binding mode relative to the receptor. Here, we build a large-scale collection of related ligand pairs solved in complex with the same protein partner: we find that for 41 of 297 pairs (14%), the binding mode changes upon elaboration of the smaller ligand. While certain ligand physiochemical properties predispose changes in binding mode, particularly those properties that define fragments, simple structure-based modeling proves far more effective for identifying substitutions that alter the binding mode. Some ligand pairs change binding mode because the added substituent would irreconcilably conflict with the receptor in the original pose, whereas others change because the added substituent enables new, stronger interactions that are available only in a different pose. Scaffolds that can engage their target using alternate poses may enable productive structure-based optimization along multiple divergent pathways.

Binding thermodynamics discriminates fragments from druglike compounds: a thermodynamic description of fragment-based drug discovery

Small is beautiful - reducing the size and complexity of chemical starting points for drug design allows better sampling of chemical space, reveals the most energetically important interactions within protein-binding sites and can lead to improvements in the physicochemical properties of the final drug. The impact of fragment-based drug discovery (FBDD) on recent drug discovery projects and our improved knowledge of the structural and thermodynamic details of ligand binding has prompted us to explore the relationships between ligand-binding thermodynamics and FBDD. Information on binding thermodynamics can give insights into the contributions to protein-ligand interactions and could therefore be used to prioritise compounds with a high degree of specificity in forming key interactions.

Mycobacterium tuberculosis Malate Synthase Structures with Fragments Reveal a Portal for Substrate/Product Exchange

Fragment screening and high throughput screening are complementary approaches that combine with structural biology to explore the binding capabilities of an active site. We have used a fragment-based approach on malate synthase (GlcB) from Mycobacterium tuberculosis and discovered several novel binding chemotypes. In addition, the crystal structures of GlcB in complex with these fragments indicated conformational changes in the active site that represent the enzyme conformations during catalysis. Additional structures of the complex with malate and of the apo form of GlcB supported that hypothesis. Comparative analysis of GlcB structures in complex with 18 fragments allowed us to characterize the preferred chemotypes and their binding modes. The fragment structures showed a hydrogen bond to the backbone carbonyl of Met-631. We successfully incorporated an indole group from a fragment into an existing phenyl-diketo acid series. The resulting indole-containing inhibitor was 100-fold more potent than the parent phenyl-diketo acid with an IC₅₀ value of 20 nm.

Substrate Fragmentation for the Design of M. tuberculosis CYP121 Inhibitors

The cyclo-dipeptide substrates of the essential M. tuberculosis (Mtb) enzyme CYP121 were deconstructed into their component fragments and screened against the enzyme. A number of hits were identified, one of which exhibited an unexpected inhibitor-like binding mode. The inhibitory pharmacophore was elucidated, and fragment binding affinity was rapidly improved by synthetic elaboration guided by the structures of CYP121 substrates. The resulting inhibitors have low micromolar affinity, good predicted physicochemical properties and selectivity for CYP121 over other Mtb P450s. Spectroscopic characterisation of the inhibitors' binding mode provides insight into the effect of weak nitrogen-donor ligands on the P450 heme, an improved understanding of factors governing CYP121-ligand recognition and speculation into the biological role of the enzyme for Mtb.

Twenty years on: the impact of fragments on drug discovery

After 20 years of sometimes quiet growth, fragment-based drug discovery (FBDD) has become mainstream. More than 30 drug candidates derived from fragments have entered the clinic, with two approved and several more in advanced trials. FBDD has been widely applied in both academia and industry, as evidenced by the large number of papers from universities, non-profit research institutions, biotechnology companies and pharmaceutical companies. Moreover, FBDD draws on a diverse range of disciplines, from biochemistry and biophysics to computational and medicinal chemistry. As the promise of FBDD strategies becomes increasingly realized, now is an opportune time to draw lessons and point the way to the future. This Review briefly discusses how to design fragment libraries, how to select screening techniques and how to make the most of information gleaned from them. It also shows how concepts from FBDD have permeated and enhanced drug discovery efforts.

Computational functional group mapping for drug discovery

Computational functional group mapping (cFGM) is emerging as a high-impact complement to existing widely used experimental and computational structure-based drug discovery methods. cFGM provides comprehensive atomic-resolution 3D maps of the affinity of functional groups that can constitute drug-like molecules for a given target, typically a protein. These 3D maps can be intuitively and interactively visualized by medicinal chemists to rapidly design synthetically accessible ligands. Given that the maps can inform selection of functional groups for affinity, specificity, and pharmacokinetic properties, they are of utility for both the optimization of existing drug candidates and creating novel ones. Here, I review recent advances in cFGM with emphasis on the unique information content in the approach that offers the potential of broadly facilitating structure-based ligand design.

Focused grid-based resampling for protein docking and mapping

The fast Fourier transform (FFT) sampling algorithm has been used with success in application to protein-protein docking and for protein mapping, the latter docking a variety of small organic molecules for the identification of binding hot spots on the target protein. Here we explore the local rather than global usage of the FFT sampling approach in docking applications. If the global FFT based search yields a near-native cluster of docked structures for a protein complex, then focused resampling of the cluster generally leads to a substantial increase in the number of conformations close to the native structure. In protein mapping, focused resampling of the selected hot spot regions generally reveals further hot spots that, while not as strong as the primary hot spots, also contribute to ligand binding. The detection of additional ligand binding regions is shown by the improved overlap between hot spots and bound ligands.

Pharmacological modulation of oncogenic Ras by natural products and their derivatives: Renewed hope in the discovery of novel anti-Ras drugs

Oncogenic rat sarcoma (Ras) is linked to the most fatal cancers such as those of the pancreas, colon, and lung. Decades of research to discover an efficacious drug that can block oncogenic Ras signaling have yielded disappointing results; thus, Ras was considered "undruggable" until recently. Inhibitors that directly target Ras by binding to previously undiscovered pockets have been recently identified. Some of these molecules are either isolated from natural products or derived from natural compounds. In this review, we described the potential of these compounds and other inhibitors of Ras signaling in drugging Ras. We highlighted the modes of action of these compounds in suppressing signaling pathways activated by oncogenic Ras, such as mitogen-activated protein kinase (MAPK) signaling and the phosphoinositide-3-kinase (PI3K) pathways. The anti-Ras strategy of these compounds can be categorized into four main types: inhibition of Ras-effector interaction, interference of Ras membrane association, prevention of Ras-guanosine triphosphate (GTP) formation, and downregulation of Ras proteins. Another promising strategy that must be validated experimentally is enhancement of the intrinsic Ras-guanosine triphosphatase (GTPase) activity by small chemical entities. Among the inhibitors of Ras signaling that were reported thus far, salirasib and TLN-4601 have been tested for their clinical efficacy. Although both compounds passed phase I trials, they failed in their respective phase II trials. Therefore, new compounds of natural origin with relevant clinical activity against Ras-driven malignancies are urgently needed. Apart from salirasib and TLN-4601, some other compounds with a proven inhibitory effect on Ras signaling include derivatives of salirasib, sulindac, polyamine, andrographolide, lipstatin, levoglucosenone, rasfonin, and quercetin.

On the enthalpic preference of fragment binding

Detailed thermodynamic analysis of fragment binding revealed that unlike drug-like compounds, fragments bind with significant enthalpic preference.

Lessons from Hot Spot Analysis for Fragment-Based Drug Discovery

Analysis of binding energy hot spots at protein surfaces can provide crucial insights into the prospects for successful application of fragment-based drug discovery (FBDD), and whether a fragment hit can be advanced into a high-affinity, drug-like ligand. The key factor is the strength of the top ranking hot spot, and how well a given fragment complements it. We show that published data are sufficient to provide a sophisticated and quantitative understanding of how hot spots derive from a protein 3D structure, and how their strength, number, and spatial arrangement govern the potential for a surface site to bind to fragment-sized and larger ligands. This improved understanding provides important guidance for the effective application of FBDD in drug discovery.