New Frontiers in Druggability

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.

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

Identification of a druggable site on GRP78 at the GRP78-SARS-CoV-2 interface and virtual screening of compounds to disrupt that interface

SARS-CoV-2, the virus that causes COVID-19, led to a global health emergency that claimed the lives of millions. Despite the widespread availability of vaccines, the virus continues to exist in the population in an endemic state which allows for the continued emergence of new variants. Most of the current vaccines target the spike glycoprotein interface of SARS-CoV-2, creating a selection pressure favoring viral immune evasion. Antivirals targeting other molecular interactions of SARS-CoV-2 can help slow viral evolution by providing orthogonal selection pressures on the virus. GRP78 is a host auxiliary factor that mediates binding of the SARS-CoV-2 spike protein to human cellular ACE2, the primary pathway of cell infection. As GRP78 forms a ternary complex with SARS-CoV-2 spike protein and ACE2, disrupting the formation of this complex is expected to hinder viral entry into host cells. Here, we developed a model of the GRP78-Spike RBD-ACE2 complex. We then used that model together with hot spot mapping of the GRP78 structure to identify the putative binding site for spike protein on GRP78. Next, we performed structure-based virtual screening of known drug/candidate drug libraries to identify binders to GRP78 that are expected to disrupt spike protein binding to the GRP78, and thereby preventing viral entry to the host cell. A subset of these compounds has previously been shown to have some activity against SARS-CoV-2. The identified hits are starting points for the further development of novel SARS-CoV-2 therapeutics, potentially serving as proof-of-concept for GRP78 as a potential drug target for other viruses.

Mining gene expression data for rational identification of novel drug targets and vaccine candidates against the cattle tick, Rhipicephalus microplus

Control of complex parasites via vaccination remains challenging, with the current combination of vaccines and small drugs remaining the choice for an integrated control strategy. Studies conducted to date, are providing evidence that multicomponent vaccines will be needed for the development of protective vaccines against endo- and ectoparasites, though multicomponent vaccines require an in-depth understanding of parasite biology which remains insufficient for ticks. With the rapid development and spread of acaricide resistance in ticks, new targets for acaricide development also remains to be identified, along with novel targets that can be exploited for the design of lead compounds. In this study, we analysed the differential gene expression of Rhipicephalus microplus ticks that were fed on cattle vaccinated with a multi-component vaccine (Bm86 and 3 putative Bm86-binding proteins). The data was scrutinised for the identification of vaccine targets, small drug targets and novel pathways that can be evaluated in future studies. Limitations associated with targeting novel proteins for vaccine and/or drug design is also discussed and placed into the context of challenges arising when targeting large protein families and intracellular localised proteins. Lastly, this study provide insight into how Bm86-based vaccines may reduce successful uptake and digestion of the bloodmeal and overall tick fecundity.

Screening and Biological Evaluation of Soluble Epoxide Hydrolase Inhibitors: Assessing the Role of Hydrophobicity in the Pharmacophore-Guided Search of Novel Hits

The human soluble epoxide hydrolase (sEH) is a bifunctional enzyme that modulates the levels of regulatory epoxy lipids. The hydrolase activity is carried out by a catalytic triad located at the center of a wide L-shaped binding site, which contains two hydrophobic subpockets at both sides. On the basis of these structural features, it can be assumed that desolvation is a major factor in determining the maximal achievable affinity that can be attained for this pocket. Accordingly, hydrophobic descriptors may be better suited to the search of novel hits targeting this enzyme. This study examines the suitability of quantum mechanically derived hydrophobic descriptors in the discovery of novel sEH inhibitors. To this end, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophores were generated by combining electrostatic and steric or alternatively hydrophobic and hydrogen-bond parameters in conjunction with a tailored list of 76 known sEH inhibitors. The pharmacophore models were then validated by using two external sets chosen (i) to rank the potency of four distinct series of compounds and (ii) to discriminate actives from decoys, using in both cases datasets taken from the literature. Finally, a prospective study was performed including a virtual screening of two chemical libraries to identify new potential hits, which were subsequently experimentally tested for their inhibitory activity on human, rat, and mouse sEH. The use of hydrophobic-based descriptors led to the identification of six compounds as inhibitors of the human enzyme with IC50 < 20 nM, including two with IC50 values of 0.4 and 0.7 nM. The results support the use of hydrophobic descriptors as a valuable tool in the search of novel scaffolds that encode a proper hydrophilic/hydrophobic distribution complementary to the target's binding site.

Therapeutic potential of fucosyltransferases in cancer and recent development of targeted inhibitors

Fucosyltransferases (FUTs) have significant roles in various pathophysiological events. Their high expression is a signature of malignant cell transformation, contributing to many abnormal events during cancer development, such as uncontrolled cell proliferation, tumor cell invasion, angiogenesis, metastasis, immune evasion, and therapy resistance. Therefore, FUTs have evolved as an attractive therapeutic target for treating solid cancers, and many substrate analogs have been discovered with potential as FUT inhibitors for cancer therapy. Meanwhile, the development of FUT protein structures represents a significant advance in the design of FUT inhibitors with nonsubstrate structures. In this review, we summarize the role of FUTs in cancers, the resolved protein crystal structures and progress in the development of FUT inhibitors as cancer therapeutics.

Conservation of Allosteric Ligand Binding Sites in G-Protein Coupled Receptors

Despite the growing number of G protein-coupled receptor (GPCR) structures, only 39 structures have been cocrystallized with allosteric inhibitors. These structures have been studied by protein mapping using the FTMap server, which determines the clustering of small organic probe molecules distributed on the protein surface. The method has found druggable sites overlapping with the cocrystallized allosteric ligands in 21 GPCR structures. Mapping of Alphafold2 generated models of these proteins confirms that the same sites can be identified without the presence of bound ligands. We then mapped the 394 GPCR X-ray structures available at the time of the analysis (September 2020). Results show that for each of the 21 structures with bound ligands there exist many other GPCRs that have a strong binding hot spot at the same location, suggesting potential allosteric sites in a large variety of GPCRs. These sites cluster at nine distinct locations, and each can be found in many different proteins. However, ligands binding at the same location generally show little or no similarity, and the amino acid residues interacting with these ligands also differ. Results confirm the possibility of specifically targeting these sites across GPCRs for allosteric modulation and help to identify the most likely binding sites among the limited number of potential locations. The FTMap server is available free of charge for academic and governmental use at https://ftmap.bu.edu/.

The druggable genome: Twenty years later

The concept of the druggable genome has been with us for 20 years. During this time, researchers have developed several methods and resources to help assess a target’s druggability. In parallel, evidence for target-disease associations has been collated at scale by Open Targets. More recently, the Protein Data Bank in Europe (PDBe) have built a knowledge base matching per-residue annotations with available protein structure. While each resource is useful in isolation, we believe there is enormous potential in bringing all relevant data into a single knowledge graph, from gene-level to protein residue. Automation is vital for the processing and assessment of all available structures. We have developed scalable, automated workflows that provide hotspot-based druggability assessments for all available structures across large numbers of targets. Ultimately, we will run our method at a proteome scale, an ambition made more realistic by the arrival of AlphaFold 2. Bringing together annotations from the residue up to the gene level and building connections within the graph to represent pathways or protein-protein interactions will create complexity that mirrors the biological systems they represent. Such complexity is difficult for the human mind to utilise effectively, particularly at scale. We believe that graph-based AI methods will be able to expertly navigate such a knowledge graph, selecting the targets of the future.

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.

Mapping the binding sites of challenging drug targets

An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein-protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.

FTMove: A Web Server for Detection and Analysis of Cryptic and Allosteric Binding Sites by Mapping Multiple Protein Structures

Protein mapping distributes many copies of different molecular probes on the surface of a target protein in order to determine binding hot spots, regions that are highly preferable for ligand binding. While mapping of X-ray structures by the FTMap server is inherently static, this limitation can be overcome by the simultaneous analysis of multiple structures of the protein. FTMove is an automated web server that implements this approach. From the input of a target protein, by PDB code, the server identifies all structures of the protein available in the PDB, runs mapping on them, and combines the results to form binding hot spots and binding sites. The user may also upload their own protein structures, bypassing the PDB search for similar structures. Output of the server consists of the consensus binding sites and the individual mapping results for each structure - including the number of probes located in each binding site, for each structure. This level of detail allows the users to investigate how the strength of a binding site relates to the protein conformation, other binding sites, and the presence of ligands or mutations. In addition, the structures are clustered on the basis of their binding properties. The use of FTMove is demonstrated by application to 22 proteins with known allosteric binding sites; the orthosteric and allosteric binding sites were identified in all but one case, and the sites were typically ranked among the top five. The FTMove server is publicly available at https://ftmove.bu.edu.

In Silico Insights towards the Identification of SARS-CoV-2 NSP13 Helicase Druggable Pockets

The merging of distinct computational approaches has become a powerful strategy for discovering new biologically active compounds. By using molecular modeling, significant efforts have recently resulted in the development of new molecules, demonstrating high efficiency in reducing the replication of severe acute respiratory coronavirus 2 (SARS-CoV-2), the agent responsible for the COVID-19 pandemic. We have focused our interest on non-structural protein Nsp13 (NTPase/helicase), as a crucial protein, embedded in the replication–transcription complex (RTC), that controls the virus life cycle. To assist in the identification of the most druggable surfaces of Nsps13, we applied a combination of four computational tools: FTMap, SiteMap, Fpocket and LigandScout. These software packages explored the binding sites for different three-dimensional structures of RTC complexes (PDB codes: 6XEZ, 7CXM, 7CXN), thus, detecting several hot spots, that were clustered to obtain ensemble consensus sites, through a combination of four different approaches. The comparison of data provided new insights about putative druggable sites that might be employed for further docking simulations on druggable surfaces of Nsps13, in a scenario of repurposing drugs.

Modular Synthesis of Teraryl‐based alpha‐Helix Mimetics, Part 4: Core Fragments with two Halide Leaving Groups Featuring Side Chains of Proteinogenic Amino Acids

Teraryl‐based α‐helix mimetics have proven to be useful compounds for the inhibition of protein‐protein interactions (PPI). We have developed a modular and flexible approach for the synthesis of teraryl‐based α‐helix mimetics using a benzene core unit featuring two halide leaving groups of differentiated reactivity in the Pd‐catalyzed cross‐coupling used for teraryl assembly. The use of para‐bromo iodoarene core fragments resolved the issue of hydrolysis during cross‐coupling that was observed when using triflate as a leaving group. We report a complete set of para‐bromoiodoarene core fragments decorated with side chains of all proteinogenic amino acids relevant for PPI (Ala, Arg, Asn, Asp, Cys, Gln, Glu, His, Ile, Leu, Lys, Met, Phe, Ser, Thr, Trp, Tyr and Val). In order to be compatible with general cross‐coupling conditions, some of the nucleophilic side chains had to be provided in a protected form to serve as stable building blocks.

Unraveling the Cardiac Effects Induced by Carvacrol in Spontaneously Hypertensive Rats: Involvement of Transient Receptor Potential Melastatin Subfamily 4 and 7 Channels

ABSTRACT

Accumulating evidence indicates that transient receptor potential (TRP) channels are involved in the pathophysiological process in the heart, and monoterpenes, such as carvacrol, are able to modulate these channels activity. In this article, our purpose was to evaluate the direct cardiac effect of carvacrol on the contractility of cardiomyocytes and isolated right atria from spontaneously hypertensive and Wistar Kyoto rats. In this way, in vitro experiments were used to evaluate the ventricular cardiomyocytes contractility and the Ca2+ transient measuring, in addition to heart rhythm in the right atria. The role of TRPM channels in carvacrol-mediated cardiac activities was also investigated. The results demonstrated that carvacrol induced a significant reduction in ventricular cell contractility, without changes in transient Ca2+. In addition, carvacrol promoted a significant negative chronotropic response in spontaneously hypertensive and Wistar Kyoto rats' atria. Selective blockage of TRPM channels suggests the involvement of TRP melastatin subfamily 2 (TRPM2), TRPM4, and TRPM7 in the carvacrol-mediated cardiac effects. In silico studies were conducted to further investigate the putative role of TRPM4 in carvacrol-mediated cardiac action. FTMap underscores a conserved pocket in both TRPM4 and TRPM7, revealing a potential carvacrol binding site, and morphological similarity analysis demonstrated that carvacrol shares a more than 85% similarity to 9-phenanthrol. Taken together, these results suggest that carvacrol has direct cardiac actions, leading to reduced cellular contractility and inducing a negative chronotropic effect, which may be related to TRPM7 and TRPM4 modulation.

Hidden allosteric sites and De-Novo drug design

INTRODUCTION

Hidden allosteric sites are not visible in apo-crystal structures, but they may be visible in holo-structures when a certain ligand binds and maintains the ligand intended conformation. Several computational and experimental techniques have been used to investigate these hidden sites but identifying them remains a challenge.

AREAS COVERED

This review provides a summary of the many theoretical approaches for predicting hidden allosteric sites in disease-related proteins. Furthermore, promising cases have been thoroughly examined to reveal the hidden allosteric site and its modulator.

EXPERT OPINION

In the recent past, with the development in scientific techniques and bioinformatics tools, the number of drug targets for complex human diseases has significantly increased but unfortunately most of these targets are undruggable due to several reasons. Alternative strategies such as finding cryptic (hidden) allosteric sites are an attractive approach for exploitation of the discovery of new targets. These hidden sites are difficult to recognize compared to allosteric sites, mainly due to a lack of visibility in the crystal structure. In our opinion, after many years of development, MD simulations are finally becoming successful for obtaining a detailed molecular description of drug-target interaction.

Natural Products-Based Drug Design against SARS-CoV-2 Mpro 3CLpro

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has received global attention due to the serious threat it poses to public health. Since the outbreak in December 2019, millions of people have been affected and its rapid global spread has led to an upsurge in the search for treatment. To discover hit compounds that can be used alone or in combination with repositioned drugs, we first analyzed the pharmacokinetic and toxicological properties of natural products from Brazil's semiarid region. After, we analyzed the site prediction and druggability of the SARS-CoV-2 main protease (Mpro), followed by docking and molecular dynamics simulation. The best SARS-CoV-2 Mpro complexes revealed that other sites were accessed, confirming that our approach could be employed as a suitable starting protocol for ligand prioritization, reinforcing the importance of catalytic cysteine-histidine residues and providing new structural data that could increase the antiviral development mainly against SARS-CoV-2. Here, we selected 10 molecules that could be in vitro assayed in response to COVID-19. Two compounds (b01 and b02) suggest a better potential for interaction with SARS-CoV-2 Mpro and could be further studied.

Assessing the binding properties of CASP14 targets and models

An important question is how well the models submitted to CASP retain the properties of target structures. We investigate several properties related to binding. First we explore the binding of small molecules as probes, and count the number of interactions between each residue and such probes, resulting in a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model, is determined by calculating their correlation coefficient. The fingerprint similarity weakly correlates with global measures of accuracy, and GDT_TS higher than 80 is a necessary but not sufficient condition for the conservation of surface binding properties. The advantage of this approach is that it can be carried out without information on potential ligands and their binding sites. The latter information was available for a few targets, and we explored whether the CASP14 models can be used to predict binding sites and to dock small ligands. Finally, we tested the ability of models to reproduce protein-protein interactions by docking both the X-ray structures and the models to their interaction partners in complexes. The analysis showed that in CASP14 the quality of individual domain models is approaching that offered by X-ray crystallography, and hence such models can be successfully used for the identification of binding and regulatory sites, as well as for assembling obligatory protein-protein complexes. Success of ligand docking, however, often depends on fine details of the binding interface, and thus may require accounting for conformational changes by simulation methods.

Druggable hot spots in trypanothione reductase: novel insights and opportunities for drug discovery revealed by DRUGpy

Assessment of target druggability guided by search and characterization of hot spots is a pivotal step in early stages of drug-discovery. The raw output of FTMap provides the data to perform this task, but it relies on manual intervention to properly combine different sets of consensus sites, therefore allowing identification of hot spots and evaluation of strength, shape and distance among them. Thus, the user's previous experience on the target and the software has a direct impact on how data generated by FTMap server can be explored. DRUGpy plugin was developed to overcome this limitation. By automatically assembling and scoring all possible combinations of consensus sites, DRUGpy plugin provides FTMap users a straight-forward method to identify and characterize hot spots in protein targets. DRUGpy is available in all operating systems that support PyMOL software. DRUGpy promptly identifies and characterizes pockets that are predicted by FTMap to bind druglike molecules with high-affinity (druggable sites) or low-affinity (borderline sites) and reveals how protein conformational flexibility impacts on the target's druggability. The use of DRUGpy on the analysis of trypanothione reductases (TR), a validated drug target against trypanosomatids, showcases the usefulness of the plugin, and led to the identification of a druggable pocket in the conserved dimer interface present in this class of proteins, opening new perspectives to the design of selective inhibitors.

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.

Partitioning Pattern of Natural Products Based on Molecular Properties Descriptors Representing Drug-Likeness

A cheminformatics procedure for a partitioning model based on 135 natural compounds including Flavonoids, Saponins, Alkaloids, Terpenes and Triterpenes with drug-like features based on a descriptors pool was developed. The knowledge about the applicability of natural products as a unique source for the development of new candidates towards deadly infectious disease is a contemporary challenge for drug discovery. We propose a partitioning scheme for unveiling drug-likeness candidates with properties that are important for a prompt and efficient drug discovery process. In the present study, the vantage point is about the matching of descriptors to build the partitioning model applied to natural compounds with diversity in structures and complexity of action towards the severe diseases, as the actual SARS-CoV-2 virus. In the times of the de novo design techniques, such tools based on a chemometric and symmetrical effect by the implied descriptors represent another noticeable sign for the power and level of the descriptors applicability in drug discovery in establishing activity and target prediction pipeline for unknown drugs properties.

Defining and navigating macrocycle chemical space

Macrocyclic compounds (MCs) are of growing interest for inhibition of challenging drug targets. We consider afresh what structural and physicochemical features could be relevant to the bioactivity of this compound class. Using these features, we performed Principal Component Analysis to map oral and non-oral macrocycle drugs and clinical candidates, and also commercially available synthetic MCs, in structure–property space. We find that oral MC drugs occupy defined regions that are distinct from those of the non-oral MC drugs. None of the oral MC regions are effectively sampled by the synthetic MCs. We identify 13 properties that can be used to design synthetic MCs that sample regions overlapping with oral MC drugs. The results advance our understanding of what molecular features are associated with bioactive and orally bioavailable MCs, and illustrate an approach by which synthetic chemists can better evaluate MC designs. We also identify underexplored regions of macrocycle chemical space.

Macrocyclic compounds (MCs) are of high interest for inhibition of challenging drug targets, but existing oral MC drugs occupy regions of chemical space that are not well sampled by many available synthetic MC chemotypes.

Biochemical and Computational Studies of the Interaction between a Glucosamine Derivative, NAPA, and the IKKα Kinase

The glucosamine derivative 2-(N-Acetyl)-L-phenylalanylamido-2-deoxy-β-D-glucose (NAPA), was shown to inhibit the kinase activity of IKKα, one of the two catalytic subunits of IKK complex, decreasing the inflammatory status in osteoarthritis chondrocytes. In the present work we have investigated the inhibition mechanism of IKKα by NAPA by combining computational simulations, in vitro assays and Mass Spectrometry (MS) technique. The kinase in vitro assay was conducted using a recombinant IKKα and IKKtide, a 20 amino acid peptide substrate derived from IkBα kinase protein and containing the serine residues Ser32 and Ser36. Phosphorylated peptide production was measured by Ultra Performance Liquid Chromatography coupled with Mass Spectrometry (UPLC-MS), and the atomic interaction between IKKα and NAPA has been studied by molecular docking and Molecular Dynamics (MD) approaches. Here we report that NAPA was able to inhibit the IKKα kinase activity with an IC₅₀ of 0.5 mM, to decrease the K_m value from 0.337 mM to 0.402 mM and the V_max from 0.0257 mM·min−1 to 0.0076 mM·min−1. The computational analyses indicate the region between the KD, ULD and SDD domains of IKKα as the optimal binding site explored by NAPA. Biochemical data indicate that there is a non-significant difference between K_m and K_i whereas there is a statistically significant difference between the two V_max values. This evidence, combined with computational results, consistently indicates that the inhibition is non-competitive, and that the NAPA binding site is different than that of ATP or IKKtide.

Characterization of class II fumarase from Schistosoma mansoni provides the molecular basis for selective inhibition

Schistosomiasis is a neglected tropical disease that affects more than 250 million people worldwide. The only drug available for its treatment undergoes first-pass hepatic metabolism and is not capable of preventing reinfection, which makes the search of new therapies urgently needed. Due to the essential role of fumarases in metabolism, these enzymes represent potential targets for developing novel schistosomiasis treatments. Here, we evaluate the expression profiles for class I and class II fumarases from Schistosoma mansoni (SmFH_I and SmFH_II, respectively), and report the complete characterization of SmFH_II. The first SmFH_II structure in complex with L-malate was determined at 1.85 Å resolution. The significant thermoshift observed for SmFH_II in the presence of identified ligands makes the differential scanning fluorimetry an adequate technique for ligand screening. A complete kinetic characterization of SmFH_II was performed, and comparison with the human fumarase (HsFH) revealed differences regarding the turnover number (k_cat). Structural characterization allowed us to identify differences between SmFH_II and HsFH that could be explored to design new selective inhibitors. This work represents the very first step towards validate the fumarases as drug targets to treat schistosomiasis. Our results provide the structural basis to rational search for selective ligands.

Functional and druggability analysis of the SARS-CoV-2 proteome

The infectious coronavirus disease (COVID-19) pandemic, caused by the coronavirus SARS-CoV-2, appeared in December 2019 in Wuhan, China, and has spread worldwide. As of today, more than 46 million people have been infected and over 1.2 million fatalities. With the purpose of contributing to the development of effective therapeutics, we performed an in silico determination of binding hot-spots and an assessment of their druggability within the complete SARS-CoV-2 proteome. All structural, non-structural, and accessory proteins have been studied, and whenever experimental structural data of SARS-CoV-2 proteins were not available, homology models were built based on solved SARS-CoV structures. Several potential allosteric or protein-protein interaction druggable sites on different viral targets were identified, knowledge that could be used to expand current drug discovery endeavors beyond the currently explored cysteine proteases and the polymerase complex. It is our hope that this study will support the efforts of the scientific community both in understanding the molecular determinants of this disease and in widening the repertoire of viral targets in the quest for repurposed or novel drugs against COVID-19.

Identification of Novel Fragments Binding to the PDZ1-2 Domain of PSD-95

Inhibition of PSD-95 has emerged as a promising strategy for the treatment of ischemic stroke, as shown with peptide-based compounds that target the PDZ domains of PSD-95. In contrast, developing potent and drug-like small molecules against the PSD-95 PDZ domains has so far been unsuccessful. Here, we explore the druggability of the PSD-95 PDZ1-2 domain and use fragment screening to investigate if this protein is prone to binding small molecules. We screened 2500 fragments by fluorescence polarization (FP) and validated the hits by surface plasmon resonance (SPR), including an inhibition counter-test, and found four promising fragments. Three ligand efficient fragments were shown by ¹ H,¹⁵ N HSQC NMR to bind in the small hydrophobic P⁰ pockets of PDZ1-2, and one of them underwent structure-activity relationship (SAR) studies. Overall, we demonstrate that fragment screening can successfully be applied to PDZ1-2 of PSD-95 and disclose novel fragments that can serve as starting points for optimization towards small-molecule PDZ domain inhibitors.

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.

Mining Natural Products for Macrocycles to Drug Difficult Targets

Lead generation for difficult-to-drug targets that have large, featureless, and highly lipophilic or highly polar and/or flexible binding sites is highly challenging. Here, we describe how cores of macrocyclic natural products can serve as a high-quality in silico screening library that provides leads for difficult-to-drug targets. Two iterative rounds of docking of a carefully selected set of natural-product-derived cores led to the discovery of an uncharged macrocyclic inhibitor of the Keap1-Nrf2 protein–protein interaction, a particularly challenging target due to its highly polar binding site. The inhibitor displays cellular efficacy and is well-positioned for further optimization based on the structure of its complex with Keap1 and synthetic access. We believe that our work will spur interest in using macrocyclic cores for in silico-based lead generation and also inspire the design of future macrocycle screening collections.

Markov state models and NMR uncover an overlooked allosteric loop in p53

The tumor suppressor p53 is the most frequently mutated gene in human cancer, and thus reactivation of mutated p53 is a promising avenue for cancer therapy. Analysis of wildtype p53 and the Y220C cancer mutant long-timescale molecular dynamics simulations with Markov state models and validation by NMR relaxation studies has uncovered the involvement of loop L6 in the slowest motions of the protein. Due to its distant location from the DNA-binding surface, the conformational dynamics of this loop has so far remained largely unexplored. We observe mutation-induced stabilization of alternate L6 conformations, distinct from all experimentally-determined structures, in which the loop is both extended and located further away from the DNA-interacting surface. Additionally, the effect of the L6-adjacent Y220C mutation on the conformational landscape of the functionally-important loop L1 suggests an allosteric role to this dynamic loop and the inactivation mechanism of the mutation. Finally, the simulations reveal a novel Y220C cryptic pocket that can be targeted for p53 rescue efforts. Our approach exemplifies the power of the MSM methodology for uncovering intrinsic dynamic and kinetic differences among distinct protein ensembles, such as for the investigation of mutation effects on protein function.

Wildtype and Y220C L1 and L6 loops conformational landscape, with MSM-identified L6 states highlighted on the right.

Inhibition of intracellular Ca2+ mobilization and potassium channels activation are involved in the vasorelaxation induced by 7-hydroxycoumarin

Coumarins exhibit a wide variety of biological effects, including activities in the cardiovascular system and the aim of this study was to evaluate the vascular therapeutic potential of 7-Hydroxicoumarin (7-HC). The vascular effects induced by 7-HC (0.001 μM-300 μM), were investigated by in vitro approaches using isometric tension measurements in rat superior mesenteric arteries and by in silico assays using Ligand-based analysis. Our results suggest that the vasorelaxant effect of 7-HC seems to rely on potassium channels, notably through large conductance Ca²⁺-activated K⁺ (BK_Ca) channels activation. In fact, 7-HC (300 μM) significantly reduced CaCl₂-induced contraction as well as the reduction of intracellular calcium mobilization. However, the relaxation induced by 7-HC was independent of store-operated calcium entry (SOCE). Moreover, in silico analysis suggests that potassium channels have a common binding pocket, where 7-HC may bind and hint that its binding profile is more similar to quinine's than verapamil's. These results are compatible with the inhibition of Ca²⁺ release from intracellular stores, which is prompted by phenylephrine and caffeine. Taken together, these results demonstrate a therapeutic potential of 7-HC on the cardiovascular system, making it a promising lead compound for the development of drugs useful in the treatment of cardiovascular diseases.

In Silico Evaluation of the Binding Site of Fucosyltransferase 8 and First Attempts to Synthesize an Inhibitor with Drug-Like Properties

Core fucosylation of N-glycans is catalyzed by fucosyltransferase 8 and is associated with various types of cancer. Most reported fucosyltransferase inhibitors contain non-drug-like features, such as charged groups. New starting points for the development of inhibitors of fucosyltransferase 8 using a fragment-based strategy are presented. Firstly, we discuss the potential of a new putative binding site of fucosyltransferase 8 that, according to a molecular dynamics (MD) simulation, is made accessible by a significant motion of the SH3 domain. This might enable the design of completely new inhibitor types for fucosyltransferase 8. Secondly, we have performed a docking study targeting the donor binding site of fucosyltransferase 8, and this yielded two fragments that were linked and trimmed in silico. The resulting ligand was synthesized. Saturation transfer difference (STD) NMR confirmed binding of the ligand featuring a pyrazole core that mimics the guanine moiety. This ligand represents the first low-molecular-weight compound for the development of inhibitors of fucosyltransferase 8 with drug-like properties.

Future avenues for Alzheimer's disease detection and therapy: liquid biopsy, intracellular signaling modulation, systems pharmacology drug discovery

When Alzheimer's disease (AD) disease-modifying therapies will be available, global healthcare systems will be challenged by a large-scale demand for clinical and biological screening. Validation and qualification of globally accessible, minimally-invasive, and time-, cost-saving blood-based biomarkers need to be advanced. Novel pathophysiological mechanisms (and related candidate biomarkers) - including neuroinflammation pathways (TREM2 and YKL-40), axonal degeneration (neurofilament light chain protein), synaptic dysfunction (neurogranin, synaptotagmin, α-synuclein, and SNAP-25) - may be integrated into an expanding pathophysiological and biomarker matrix and, ultimately, integrated into a comprehensive blood-based liquid biopsy, aligned with the evolving ATN + classification system and the precision medicine paradigm. Liquid biopsy-based diagnostic and therapeutic algorithms are increasingly employed in Oncology disease-modifying therapies and medical practice, showing an enormous potential for AD and other brain diseases as well. For AD and other neurodegenerative diseases, newly identified aberrant molecular pathways have been identified as suitable therapeutic targets and are currently investigated by academia/industry-led R&D programs, including the nerve-growth factor pathway in basal forebrain cholinergic neurons, the sigma1 receptor, and the GTPases of the Rho family. Evidence for a clinical long-term effect on cognitive function and brain health span of cholinergic compounds, drug candidates for repositioning programs, and non-pharmacological multidomain interventions (nutrition, cognitive training, and physical activity) is developing as well. Ultimately, novel pharmacological paradigms, such as quantitative systems pharmacology-based integrative/explorative approaches, are gaining momentum to optimize drug discovery and accomplish effective pathway-based strategies for precision medicine. This article is part of the special issue on 'The Quest for Disease-Modifying Therapies for Neurodegenerative Disorders'.

Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?

The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.

The path to biomarker-based diagnostic criteria for the spectrum of neurodegenerative diseases

Introduction: The postmortem examination still represents the reference standard for detecting the pathological nature of chronic neurodegenerative diseases (NDD). This approach displays intrinsic conceptual limitations since NDD represent a dynamic spectrum of partially overlapping phenotypes, shared pathomechanistic alterations that often give rise to mixed pathologies.Areas covered: We scrutinized the international clinical diagnostic criteria of NDD and the literature to provide a roadmap toward a biomarker-based classification of the NDD spectrum. A few pathophysiological biomarkers have been established for NDD. These are time-consuming, invasive, and not suitable for preclinical detection. Candidate screening biomarkers are gaining momentum. Blood neurofilament light-chain represents a robust first-line tool to detect neurodegeneration tout court and serum progranulin helps detect genetic frontotemporal dementia. Ultrasensitive assays and retinal scans may identify Aβ pathology early, in blood and the eye, respectively. Ultrasound also represents a minimally invasive option to investigate the substantia nigra. Protein misfolding amplification assays may accurately detect α-synuclein in biofluids.Expert opinion: Data-driven strategies using quantitative rather than categorical variables may be more reliable for quantification of contributions from pathophysiological mechanisms and their spatial-temporal evolution. A systems biology approach is suitable to untangle the dynamics triggering loss of proteostasis, driving neurodegeneration and clinical evolution.

Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa

Antimicrobial Resistance (AMR) represents a serious threat to health and the global economy. However, interest in antibacterial drug development has decreased substantially in recent decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Although several macromolecular targets have been explored for this goal, their druggability is a vital piece of information that has been overlooked. This review explores this subject by showing how structure- based freely available in silico tools, such as PockDrug and FTMap, might be useful for designing novel inhibitors of the pyocyanin biosynthesis pathway and improving the potency/selectivity of compounds that target the Pseudomonas aeruginosa quorum sensing mechanism. The information provided by hotspot analysis, along with binding site features, reveals novel druggable targets (PhzA and PhzS) that remain largely unexplored. However, it also highlights that in silico druggability prediction tools have several limitations that might be overcome in the near future. Meanwhile, anti-virulence drug targets should be assessed by complementary methods, such as the combined use of FTMap/PockDrug, once the consensus druggability classification reduces the risk of wasting resources on undruggable proteins.

Toward Development of the Male Pill: A Decade of Potential Non-hormonal Contraceptive Targets

With the continued steep rise of the global human population, and the paucity of safe and practical contraceptive options available to men, the need for development of effective and reversible non-hormonal methods of male fertility control is widely recognized. Currently there are several contraceptive options available to men, however, none of the non-hormonal alternatives have been clinically approved. To advance progress in the development of a safe and reversible contraceptive for men, further identification of novel reproductive tract-specific druggable protein targets is required. Here we provide an overview of genes/proteins identified in the last decade as specific or highly expressed in the male reproductive tract, with deletion phenotypes leading to complete male infertility in mice. These phenotypes include arrest of spermatogenesis and/or spermiogenesis, abnormal spermiation, abnormal spermatid morphology, abnormal sperm motility, azoospermia, globozoospermia, asthenozoospermia, and/or teratozoospermia, which are all desirable outcomes for a novel male contraceptive. We also consider other associated deletion phenotypes that could impact the desirability of a potential contraceptive. We further discuss novel contraceptive targets underscoring promising leads with the objective of presenting data for potential druggability and whether collateral effects may exist from paralogs with close sequence similarity.

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.

Computational investigation reveals Picrasidine C as selective PPARα lead: binding pattern, selectivity mechanism and ADME/tox profile

Natural products and their derivatives have been recognized as an important source of therapeutic agents for many years. Previously we isolated a dimeric β-carboline-type alkaloid Picrasidine C from the root of Picrasma quassioides as subtype-selective peroxisome proliferator-activated receptor α (PPARα) agonist. In order to modify this natural product for better affinity and druggability, we investigated a series of properties exhibited by Picrasidine C, such as its binding mode with PPARα, the selectivity mechanism over PPARγ, as well as ADME/Tox profile through computational methods including sequence alignment, molecular docking, pharmacophore modeling and molecular dynamics simulations. The detailed information of binding pattern and affinity for Picrasidine C elucidated here will be valuable for chemical modification. Besides, the steric hindrance of residue Phe363 in PPARγ pocket was speculated as the main isoform selectivity mechanism for Picrasidine C, which would be helpful for the design of selective derivatives. ADME/Tox prediction was conducted to avoid potential undesirable pharmacokinetic properties for reducing the risk of failure. Finally, novel skeletons were derived from lead compound by core hopping method, validated through molecular dynamic simulations and MM-GBSA calculation. In short, the information obtained from computational strategy would be valuable for us to find more potent, safe and selective PPARα agonists during structural optimization.

Structure-Based Analysis of Cryptic-Site Opening

Many proteins in their unbound structures have cryptic sites that are not appropriately sized for drug binding. We consider here 32 proteins from the recently published CryptoSite set with validated cryptic sites, and study whether the sites remain cryptic in all available X-ray structures of the proteins solved without any ligand bound near the sites. It was shown that only few of these proteins have binding pockets that never form without ligand binding. Sites that are cryptic in some structures but spontaneously form in others are also rare. In most proteins the forming of pockets is affected by mutations or ligand binding at locations far from the cryptic site. To further explore these mechanisms, we applied adiabatic biased molecular dynamics simulations to guide the proteins from their ligand-free structures to ligand-bound conformations, and studied the distribution of druggability scores of the pockets located at the cryptic sites.

Computational and Experimental Druggability Assessment of Human DNA Glycosylases

Due to a polar or even charged binding interface, DNA-binding proteins are considered extraordinarily difficult targets for development of small-molecule ligands and only a handful of proteins have been targeted successfully to date. Recently, however, it has been shown that development of selective and efficient inhibitors of 8-oxoguanine DNA glycosylase is possible. Here, we describe the initial druggability assessment of DNA glycosylases in a computational setting and experimentally investigate several methods to target endonuclease VIII-like 1 (NEIL1) with small-molecule inhibitors. We find that DNA glycosylases exhibit good predicted druggability in both DNA-bound and -unbound states. Furthermore, we find catalytic sites to be highly flexible, allowing for a range of interactions and binding partners. One flexible catalytic site was rationalized for NEIL1 and further investigated experimentally using both a biochemical assay in the presence of DNA and a thermal shift assay in the absence of DNA.

Kinase Atlas: Druggability Analysis of Potential Allosteric Sites in Kinases

The inhibition of kinases has been pursued by the pharmaceutical industry for over 20 years. While the locations of the sites that bind type II and III inhibitors at or near the adenosine 5'-triphosphate binding sites are well defined, the literature describes 10 different regions that were reported as regulatory hot spots in some kinases and thus are potential target sites for type IV inhibitors. Kinase Atlas is a systematic collection of binding hot spots located at the above ten sites in 4910 structures of 376 distinct kinases available in the Protein Data Bank. The hot spots are identified by FTMap, a computational analogue of experimental fragment screening. Users of Kinase Atlas ( https://kinase-atlas.bu.edu ) may view summarized results for all structures of a particular kinase, such as which binding sites are present and how druggable they are, or they may view hot spot information for a particular kinase structure of interest.

FindTargetsWEB: A User-Friendly Tool for Identification of Potential Therapeutic Targets in Metabolic Networks of Bacteria

Background: Healthcare-associated infections (HAIs) are a serious public health problem. They can be associated with morbidity and mortality and are responsible for the increase in patient hospitalization. Antimicrobial resistance among pathogens causing HAI has increased at alarming levels. In this paper, a robust method for analyzing genome-scale metabolic networks of bacteria is proposed in order to identify potential therapeutic targets, along with its corresponding web implementation, dubbed FindTargetsWEB. The proposed method assumes that every metabolic network presents fragile genes whose blockade will impair one or more metabolic functions, such as biomass accumulation. FindTargetsWEB automates the process of identification of such fragile genes using flux balance analysis (FBA), flux variability analysis (FVA), extended Systems Biology Markup Language (SBML) file parsing, and queries to three public repositories, i.e., KEGG, UniProt, and DrugBank. The web application was developed in Python using COBRApy and Django.

Results: The proposed method was demonstrated to be robust enough to process even non-curated, incomplete, or imprecise metabolic networks, in addition to integrated host-pathogen models. A list of potential therapeutic targets and their putative inhibitors was generated as a result of the analysis of Pseudomonas aeruginosa metabolic networks available in the literature and a curated version of the metabolic network of a multidrug-resistant P. aeruginosa strain belonging to a clone endemic in Brazil (P. aeruginosa ST277). Genome-scale metabolic networks of other gram-positive and gram-negative bacteria, such as Staphylococcus aureus, Klebsiella pneumoniae, and Haemophilus influenzae, were also analyzed using FindTargetsWEB. Multiple potential targets have been found using the proposed method in all metabolic networks, including some overlapping between two or more pathogens. Among the potential targets, several have been previously reported in the literature as targets for antimicrobial development, and many targets have approved drugs. Despite similarities in the metabolic network structure for closely related bacteria, we show that the method is able to selectively identify targets in pathogenic versus non-pathogenic organisms.

Conclusions: This new computational system can give insights into the identification of new candidate therapeutic targets for pathogenic bacteria and discovery of new antimicrobial drugs through genome-scale metabolic network analysis and heterogeneous data integration, even for non-curated or incomplete networks.

Why Some Targets Benefit from beyond Rule of Five Drugs

Beyond rule-of-five (bRo5) compounds are increasingly used in drug discovery. Here we analyze 37 target proteins that have bRo5 drugs or clinical candidates. Targets can benefit from bRo5 drugs if they have "complex" hot spot structure with four or more hots spots, including some strong ones. Complex I targets show positive correlation between binding affinity and molecular weight. These targets are conventionally druggable, but reaching additional hot spots enables improved pharmaceutical properties. Complex II targets, mostly protein kinases, also have strong hot spots but show no correlation between affinity and ligand molecular weight, and the primary motivation for creating larger drugs is to increase selectivity. Each target considered as complex III has some specific reason for requiring bRo5 drugs. Finally, targets with "simple" hot spot structure, i.e., three or fewer weak hot spots, must use larger compounds that interact with surfaces beyond the hot spot region to achieve acceptable affinity.

Analysis of tractable allosteric sites in G protein-coupled receptors

Allosteric modulation of G protein-coupled receptors represent a promising mechanism of pharmacological intervention. Dramatic developments witnessed in the structural biology of membrane proteins continue to reveal that the binding sites of allosteric modulators are widely distributed, including along protein surfaces. Here we restrict consideration to intrahelical and intracellular sites together with allosteric conformational locks, and show that the protein mapping tools FTMap and FTSite identify 83% and 88% of such experimentally confirmed allosteric sites within the three strongest sites found. The methods were also able to find partially hidden allosteric sites that were not fully formed in X-ray structures crystallized in the absence of allosteric ligands. These results confirm that the intrahelical sites capable of binding druglike allosteric modulators are among the strongest ligand recognition sites in a large fraction of GPCRs and suggest that both FTMap and FTSite are useful tools for identifying allosteric sites and to aid in the design of such compounds in a range of GPCR targets.

Allostery in Its Many Disguises: From Theory to Applications

Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromolecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used here to provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts.

Biomarker-Drug and Liquid Biopsy Co-development for Disease Staging and Targeted Therapy: Cornerstones for Alzheimer's Precision Medicine and Pharmacology

Systems biology studies have demonstrated that different (epi)genetic and pathophysiological alterations may be mapped onto a single tumor’s clinical phenotype thereby revealing commonalities shared by cancers with divergent phenotypes. The success of this approach in cancer based on analyses of traditional and emerging body fluid-based biomarkers has given rise to the concept of liquid biopsy enabling a non-invasive and widely accessible precision medicine approach and a significant paradigm shift in the management of cancer. Serial liquid biopsies offer clues about the evolution of cancer in individual patients across disease stages enabling the application of individualized genetically and biologically guided therapies. Moreover, liquid biopsy is contributing to the transformation of drug research and development strategies as well as supporting clinical practice allowing identification of subsets of patients who may enter pathway-based targeted therapies not dictated by clinical phenotypes alone. A similar liquid biopsy concept is emerging for Alzheimer’s disease, in which blood-based biomarkers adaptable to each patient and stage of disease, may be used for positive and negative patient selection to facilitate establishment of high-value drug targets and counter-measures for drug resistance. Going beyond the “one marker, one drug” model, integrated applications of genomics, transcriptomics, proteomics, receptor expression and receptor cell biology and conformational status assessments during biomarker-drug co-development may lead to a new successful era for Alzheimer’s disease therapeutics. We argue that the time is now for implementing a liquid biopsy-guided strategy for the development of drugs that precisely target Alzheimer’s disease pathophysiology in individual patients.

Screening Approaches for Targeting Ribonucleoprotein Complexes: A New Dimension for Drug Discovery

RNA-binding proteins (RBPs) are pleiotropic factors that control the processing and functional compartmentalization of transcripts by binding primarily to mRNA untranslated regions (UTRs). The competitive and/or cooperative interplay between RBPs and an array of coding and noncoding RNAs (ncRNAs) determines the posttranscriptional control of gene expression, influencing protein production. Recently, a variety of well-recognized and noncanonical RBP domains have been revealed by modern system-wide analyses, underlying an evolving classification of ribonucleoproteins (RNPs) and their importance in governing physiological RNA metabolism. The possibility of targeting selected RNA-protein interactions with small molecules is now expanding the concept of protein "druggability," with new implications for medicinal chemistry and for a deeper characterization of the mechanism of action of bioactive compounds. Here, taking SF3B1, HuR, LIN28, and Musashi proteins as paradigmatic case studies, we review the strategies applied for targeting RBPs, with emphasis on the technological advancements to study protein-RNA interactions and on the requirements of appropriate validation strategies to parallel high-throughput screening (HTS) efforts.

Squaramides as novel class I and IIB histone deacetylase inhibitors for topical treatment of cutaneous t-cell lymphoma

A series of squaramide-based hydroxamic acids were designed, synthesized and evaluated against human HDAC enzyme. Squaramides were found to be potent in the Hut78 cell line, but initially suffered from low solubility. Leads with improved solubility and metabolic profiles were shown to be class I, IIB and IV selective.