Druggable Sites Research Articles

Exploring the orphan immune receptor TREM2 and its non-protein ligands: In silico characterization

The triggering receptor expressed on myeloid cells 2 (TREM2) is an immunoreceptor that interacts with a wide range of non-protein ligands, and it has been implicated in infectious and non-infectious diseases. However, there is a limited understanding on how this receptor interacts with non-protein ligands and the potential of such information to develop new therapeutic drugs. Therefore, our study aimed to elucidate the interactions between TREM2 and its non-protein ligands. First, we searched PubChem and Protein Data Bank (PDB) for TREM2 structures and their corresponding non-protein ligands. Subsequently, these structures were employed in molecular docking and MM/GBSA simulations with the Maestro software and molecular dynamics in GROMACS software. TREM2 was subsequently subjected to druggable site prediction using CavityPlus and receptor-based drug repositioning via the DrugRep server. TREM2 interacts with high affinity with its 12 non-protein ligands, with affinity values ranging from −33.01 kcal/mol for phosphatidylserine to −80.87 kcal/mol for cardiolipin (CLP). In molecular dynamics simulations, homodimeric TREM2 bound more stably to its lipid ligands, such as CLP and PSF, whereas it was unstable when unbound. The interactions between the receptor and its non-protein ligands were driven by the complementarity determining regions (CDR) 1 and 2, that are present in the hydrophobic and positively charged regions, highlighting that the Y38–R98 region is fundamental for drugs targeting TREM2. Our data underscore the significance of TREM2's CDRs in recognizing its ligands, suggesting they as promising targets for prospective drug design studies.

Mapping Nanoscale-To-Single-Cell Phosphoproteomic Landscape by Chip-DIA.

Protein phosphorylation plays a crucial role in regulating disease phenotypes and serves as a key target for drug development. Mapping nanoscale-to-single-cell samples can unravel the heterogeneity of cellular signaling events. However, it remains a formidable analytical challenge due to the low detectability, abundance, and stoichiometry of phosphorylation sites. Here, we present a Chip-DIA strategy,integratinga microfluidic-based phosphoproteomic chip (iPhosChip) with data-independent acquisition mass spectrometry (DIA-MS) for ultrasensitive nanoscale-to-single-cell phosphoproteomic profiling. The iPhosChip operates as an all-in-one station that accommodates both quantifiable cell capture/imaging and the entire phosphoproteomic workflow in a highly streamlined and multiplexed manner. Coupled with a sample size-comparable library-based DIA-MS strategy, Chip-DIA achieved ultra-high sensitivity, detecting 1076±158 to 15869±1898 phosphopeptides from 10±0 to 1013±4 cells, and revealed the first single-cell phosphoproteomic landscape comprising druggable sites and basal phosphorylation-mediated networks in lung cancer. Notably, the sensitivity and coverage enabled the illumination of heterogeneous cytoskeleton remodeling and cytokeratin signatures in patient-derived cells resistant to third-generation EGFR therapy, stratifying mixed-lineage adenocarcinoma-squamous cell carcinoma subtypes, and identifying alternative targeted therapy for late-stage patients. With flexibility in module design and functionalization, Chip-DIA can be adapted to other PTM-omics to explore dysregulated PTM landscapes, thereby guiding therapeutic strategies toward precision oncology.

Exploring Druggable Binding Sites on the Class A GPCRs Using the Residue Interaction Network and Site Identification by Ligand Competitive Saturation.

G protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites. Here, we validate the combination of two computational techniques, the residue interaction network (RIN) model and the site identification by ligand competitive saturation (SILCS) method, to predict putative allosteric binding sites of class A GPCRs. RIN analysis identifies hub residues that mediate allosteric signaling within a receptor and have a high capacity to alter receptor dynamics upon ligand binding. The known orthosteric (and allosteric) binding sites of 18 distinct class A GPCRs were successfully predicted by RIN through a dataset of 105 crystal structures (91 ligand-bound, 14 unbound) with up to 77.8% (76.9%) sensitivity, 92.5% (95.3%) specificity, 51.9% (50%) precision, and 86.2% (92.4%) accuracy based on the experimental and theoretical binding site data. Moreover, graph spectral analysis of the residue networks revealed that the proposed sites were located at the interfaces of highly interconnected residue clusters with a high ability to coordinate the functional dynamics. Then, we employed the SILCS-Hotspots method to assess the druggability of the novel sites predicted for 7 distinct class A GPCRs that are critical for a variety of diseases. While the known orthosteric and allosteric binding sites are successfully explored by our approach, numerous putative allosteric sites with the potential to bind drug-like molecules are proposed. The computational approach presented here promises to be a highly effective tool to predict putative allosteric sites of GPCRs to facilitate the design of effective modulators.

Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Competitive Saturation (SILCS) method accounts for the flexibility of the target protein using all-atom molecular simulations that include various small molecule solutes in aqueous solution. During the simulations, the combination of protein flexibility and comprehensive sampling of the water and solute spatial distributions can identify buried binding pockets absent in experimentally determined structures. Previously, we reported a method for leveraging the information in the SILCS sampling to identify binding sites (termed Hotspots) of small mono- or bicyclic compounds, a subset of which coincide with known binding sites of drug-like molecules. Here, we build on that physics-based approach and present a ML model for ranking the Hotspots according to the likelihood they can accommodate drug-like molecules (e.g., molecular weight >200 Da). In the independent validation set, which includes various enzymes and receptors, our model recalls 67% and 89% of experimentally validated ligand binding sites in the top 10 and 20 ranked Hotspots, respectively. Furthermore, we show that the model's output Decision Function is a useful metric to predict binding sites and their potential druggability in new targets. Given the utility the SILCS method for ligand discovery and optimization, the tools presented represent an important advancement in the identification of orthosteric and allosteric binding sites and the discovery of drug-like molecules targeting those sites.

CC chemokine receptor 2 is allosterically modulated by sodium ions and amiloride derivatives through a distinct sodium ion binding site

CC chemokine receptor 2 and CCL2 are highly involved in cancer growth and metastasis, and immune escape. Raised sodium ion concentrations in solid tumours have also been correlated to metastasis and immune modulation. Sodium ions can modulate class A G protein-coupled receptors through the sodium ion binding site characterized by a highly conserved aspartic acid residue (D2.50), also present in CCR2. Hence, we further explored this binding site in CCR2 by radioligand binding studies and mutagenesis. Modulation of three distinctly binding radioligands by sodium ions and amiloride derivates was investigated. Sodium ions were observed to be relatively weak modulators of antagonist binding, but substantially increased 125I-CCL2 dissociation from CCR2. 6-Substituted Hexamethylene Amiloride (HMA) modulated all tested radioligands. Induced-fit docking of HMA in the presumed sodium ion binding site of CCR2 confirmed its binding site. Finally, investigation of (cancer-associated) mutations in the sodium ion binding site showed a markedly decreased expression compared to wild type. Only two mutants, G123A3.35 and G127K3.39, were able to be bound by [3H]INCB3344 and [3H]CCR2-RA-[R]. Thus, mutagenesis showed that the sodium ion binding site residues, which are distinct from other class A GPCRs and related to chemokine receptor evolution, are crucial for receptor integrity. Moreover, the tested mutations appeared to have no effect on modulation observed by HMA or a minor effect on sodium chloride modulation on the tested radioligands. All in all, these results invite further exploration of the CCR2 sodium ion binding site in (cancer) biology, and potentially as a third druggable binding site.

Unified Aedes aegypti Protein Resource Database (UAAPRD): An Integrated High-Throughput In Silico Platform for Comprehensive Protein Structure Modeling and Functional Target Analysis to Enhance Vector Control Strategies.

A comprehensive examination of Aedes aegypti's proteome to detect key proteins that can be targeted with small molecules can disrupt blood feeding and disease transmission. However, research currently only focuses on finding repellent-like compounds, limiting studies on identifying unexplored proteins in its proteome. High-throughput analysis generates vast amounts of data, raising concerns about accessibility and usability. Establishing a dedicated database is a solution, centralizing information on identified proteins, functions, and modeled structures for easy access and research. This study focuses on scrutinizing key proteins in A. aegypti, modeling their structures using RaptorX standalone tool, identification of druggable binding sites using BiteNet, validating the models via Ramachandran plot studies and refining them via 50-ns molecular dynamic simulations using Schrodinger Maestro. By analyzing ~ 18k proteins in the proteome of A. aegypti in our previous studies, all proteins involved in the light and dark circadian rhythm of the mosquito, inclusive of proteins in blood feeding, metabolism, etc. were chosen for the current study. The outcome is UAAPRD, a unique repository housing information on hundreds of previously unmodeled and un-simulated mosquito proteins. This robust MYSQL database ( https://uaaprd.onrender.com/user ) houses data on 309 modeled & simulated proteins of A. aegypti. It allows users to obtain protein data, view evolutionary analysis data of the protein categories, visualize proteins of interest, and send request to screen against the pharmacophore models present in UAAPRD against ligand of interest. This study offers crucial insights for developing targeted studies, which will ultimately contribute to more effective vector control strategies.

Deep attention network for identifying ligand-protein binding sites

One of the critical aspects of structure-based drug design is to choose important druggable binding sites in the protein's crystallography structures. As experimental processes are costly and time-consuming, computational drug design using machine learning algorithms is recommended. Over recent years, deep learning methods have been utilized in a wide variety of research applications such as binding site prediction. In this study, a new combination of attention blocks in the 3D U-Net model based on semantic segmentation methods is used to improve localization of pocket prediction. The attention blocks are tuned to find which point and channel of features should be emphasized along spatial and channel axes. Our model's performance is evaluated through extensive experiments on several datasets from different sources, and the results are compared to the most recent deep learning-based models. The results indicate the proposed attention model (Att-UNet) can predict binding sites accurately, i.e. the overlap of the predicted pocket using the proposed method with the true binding site shows statistically significant improvement when compared to other state-of-the-art models. The attention blocks may help the model focus on the target structure by suppressing features in irrelevant regions.

Allosteric activation of VCP, an AAA unfoldase, by small molecule mimicry

The loss of function of AAA (ATPases associated with diverse cellular activities) mechanoenzymes has been linked to diseases, and small molecules that activate these proteins can be powerful tools to probe mechanisms and test therapeutic hypotheses. Unlike chemical inhibitors that can bind a single conformational state to block enzyme function, activator binding must be permissive to different conformational states needed for mechanochemistry. However, we do not know how AAA proteins can be activated by small molecules. Here, we focus on valosin-containing protein (VCP)/p97, an AAA unfoldase whose loss of function has been linked to protein aggregation-based disorders, to identify druggable sites for chemical activators. We identified VCP ATPase Activator 1 (VAA1), a compound that dose-dependently stimulates VCP ATPase activity up to ~threefold. Our cryo-EM studies resulted in structures (ranging from ~2.9 to 3.7 Å-resolution) of VCP in apo and ADP-bound states and revealed that VAA1 binds an allosteric pocket near the C-terminus in both states. Engineered mutations in the VAA1-binding site confer resistance to VAA1, and furthermore, modulate VCP activity. Mutation of a phenylalanine residue in the VCP C-terminal tail that can occupy the VAA1 binding site also stimulates ATPase activity, suggesting that VAA1 acts by mimicking this interaction. Together, our findings uncover a druggable allosteric site and a mechanism of enzyme regulation that can be tuned through small molecule mimicry.

Repurposing DrugBank compounds as potential Plasmodium falciparum class 1a aminoacyl tRNA synthetase multi-stage pan-inhibitors with a specific focus on mitomycin

Plasmodium falciparum aminoacyl tRNA synthetases (PfaaRSs) are potent antimalarial targets essential for proteome fidelity and overall parasite survival in every stage of the parasite's life cycle. So far, some of these proteins have been singly targeted yielding inhibitor compounds that have been limited by incidences of resistance which can be overcome via pan-inhibition strategies. Hence, herein, for the first time, we report the identification and in vitro antiplasmodial validation of Mitomycin (MMC) as a probable pan-inhibitor of class 1a (arginyl(A)-, cysteinyl(C), isoleucyl(I)-, leucyl(L), methionyl(M), and valyl(V)-) PfaaRSs which hypothetically may underlie its previously reported activity on the ribosomal RNA to inhibit protein translation and biosynthesis. We combined multiple in silico structure-based discovery strategies that first helped identify functional and druggable sites that were preferentially targeted by the compound in each of the plasmodial proteins: Ins1-Ins2 domain in Pf-ARS; anticodon binding domain in Pf-CRS; CP1-editing domain in Pf-IRS and Pf-MRS; C-terminal domain in Pf-LRS; and CP-core region in Pf-VRS. Molecular dynamics studies further revealed that MMC allosterically induced changes in the global structures of each protein. Likewise, prominent structural perturbations were caused by the compound across the functional domains of the proteins. More so, MMC induced systematic alterations in the binding of the catalytic nucleotide and amino acid substrates which culminated in the loss of key interactions with key active site residues and ultimate reduction in the nucleotide-binding affinities across all proteins, as deduced from the binding energy calculations. These altogether confirmed that MMC uniformly disrupted the structure of the target proteins and essential substrates. Further, MMC demonstrated IC50 < 5 μM against the Dd2 and 3D7 strains of parasite making it a good starting point for malarial drug development. We believe that findings from our study will be important in the current search for highly effective multi-stage antimalarial drugs.

Investigating the Taenia solium Fatty Acid Binding Protein Superfamily for Their Immunological Outlook and Prospect for Therapeutic Targets.

Taenia solium, like other helminthic parasites, lacks key components of cellular machinery required for endogenous lipid biosynthesis. This deficiency compels the parasite to obtain all of its lipid requirements from its host. The passage of lipids across the cell membrane is tightly regulated. To facilitate effective lipid transport, the cestode parasite utilizes certain lipid binding proteins called FABPs. These FABPs bind with the lipid ligands and allow the transport of lipids across the membranes and into the cytosol. Here, by integrating a computational with homology protein prediction tools, we had identified five FABPs in the T. solium proteome. We confirmed their presence by RNA expression analysis of respective genes from the parasite's cysticerci transcript. During the molecular modeling and MD simulation studies, two of them, TsM_000544100 and TsM_001185100, were most stable. Furthermore, they had a robust interaction with the IgG1 molecule, as evidenced by MD simulation. In addition, by employing in silico screening, we had identified potential ligand interacting residues that are present on the probable druggable site. In combination with in vitro cysticidal assays, enalaprilat dihydrate showed efficacy against cysticerci, which suggests that FABPs play a significant role in the cysticercus life cycle. Together, we provided a detailed distribution of all FABPs expressed by T. solium cysticerci and the critical role of TsM_001185100 in cysticercus viability.

Cryo-EM structure of the Rev1-Polζ holocomplex reveals the mechanism of their cooperativity in translesion DNA synthesis.

Rev1-Polζ-dependent translesion synthesis (TLS) of DNA is crucial for maintaining genome integrity. To elucidate the mechanism by which the two polymerases cooperate in TLS, we determined the cryogenic electron microscopic structure of the Saccharomyces cerevisiae Rev1-Polζ holocomplex in the act of DNA synthesis (3.53 Å). We discovered that a composite N-helix-BRCT module in Rev1 is the keystone of Rev1-Polζ cooperativity, interacting directly with the DNA template-primer and with the Rev3 catalytic subunit of Polζ. The module is positioned akin to the polymerase-associated domain in Y-family TLS polymerases and is set ideally to interact with PCNA. We delineate the full extent of interactions that the carboxy-terminal domain of Rev1 makes with Polζ and identify potential new druggable sites to suppress chemoresistance from first-line chemotherapeutics. Collectively, our results provide fundamental new insights into the mechanism of cooperativity between Rev1 and Polζ in TLS.

Identification of Druggable Binding Sites and Small Molecules as Modulators of TMC1.

Our ability to hear and maintain balance relies on the proper functioning of inner ear sensory hair cells, which translate mechanical stimuli into electrical signals via mechano-electrical transducer (MET) channels, composed of TMC1/2 proteins. However, the therapeutic use of ototoxic drugs, such as aminoglycosides and cisplatin, which can enter hair cells through MET channels, often leads to profound auditory and vestibular dysfunction. Despite extensive research on otoprotective compounds targeting MET channels, our understanding of how small molecule modulators interact with these channels remains limited, hampering the discovery of novel compounds. Here, we propose a structure-based screening approach, integrating 3D-pharmacophore modeling, molecular simulations, and experimental validation. Our pipeline successfully identified several novel compounds and FDA-approved drugs that reduced dye uptake in cultured cochlear explants, indicating MET modulation activity. Molecular docking and free-energy estimations for binding allowed us to identify three potential drug binding sites within the channel pore, phospholipids, and key amino acids involved in modulator interactions. We also identified shared ligand-binding features between TMC and structurally related TMEM16 protein families, providing novel insights into their distinct inhibition, while potentially guiding the rational design of MET-channel-specific modulators. Our pipeline offers a broad application to discover small molecule modulators for a wide spectrum of mechanosensitive ion channels.

Structure-Based Discovery of Allosteric Inhibitors Targeting a New Druggable Site in the Respiratory Syncytial Virus Polymerase.

Respiratory syncytial virus (RSV) is a major cause of severe lower respiratory infections for which effective treatment options remain limited. Herein, we employed a computational structure-based design strategy aimed at identifying potential targets for a new class of allosteric inhibitors. Our investigation led to the discovery of a previously undisclosed allosteric binding site within the RSV polymerase, the large (L) protein. This discovery was achieved through a combination of virtual screening and molecular dynamics simulations. Subsequently, we identified two inhibitors, 6a and 10b, which both exhibited promising antiviral activity in the low micromolar range. Resistance profiling revealed a distinctive pattern in how RSV evaded treatment with this class of inhibitors. This pattern strongly suggested that this class of small molecules was targeting a new binding site in the RSV L protein, aligning with the computational predictions made in our study. This study paves the way for the development of more potent inhibitors for combating RSV infections by targeting a new druggable pocket within the RdRp which does not overlap with previously known resistance sites.

Identification of novel small-molecule inhibitors of SARS-CoV-2 by chemical genetics

There are only eight approved small molecule antiviral drugs for treating COVID-19. Among them, four are nucleotide analogues (remdesivir, JT001, molnupiravir, and azvudine), while the other four are protease inhibitors (nirmatrelvir, ensitrelvir, leritrelvir, and simnotrelvir-ritonavir). Antiviral resistance, unfavourable drug‒drug interaction, and toxicity have been reported in previous studies. Thus there is a dearth of new treatment options for SARS-CoV-2. In this work, a three-tier cell-based screening was employed to identify novel compounds with anti-SARS-CoV-2 activity. One compound, designated 172, demonstrated broad-spectrum antiviral activity against multiple human pathogenic coronaviruses and different SARS-CoV-2 variants of concern. Mechanistic studies validated by reverse genetics showed that compound 172 inhibits the 3-chymotrypsin-like protease (3CLpro) by binding to an allosteric site and reduces 3CLpro dimerization. A drug synergistic checkerboard assay demonstrated that compound 172 can achieve drug synergy with nirmatrelvir in vitro. In vivo studies confirmed the antiviral activity of compound 172 in both Golden Syrian Hamsters and K18 humanized ACE2 mice. Overall, this study identified an alternative druggable site on the SARS-CoV-2 3CLpro, proposed a potential combination therapy with nirmatrelvir to reduce the risk of antiviral resistance and shed light on the development of allosteric protease inhibitors for treating a range of coronavirus diseases.

Identification of Potential Inhibitors of the SARS-CoV-2 NSP13 Helicase via Structure-Based Ligand Design, Molecular Docking and Nonequilibrium Alchemical Simulations.

We have assembled a computational pipeline based on virtual screening, docking techniques, and nonequilibrium molecular dynamics simulations, with the goal of identifying possible inhibitors of the SARS-CoV-2 NSP13 helicase, catalyzing by ATP hydrolysis the unwinding of double or single-stranded RNA in the viral replication process inside the host cell. The druggable sites for broad-spectrum inhibitors are represented by the RNA binding sites at the 5' entrance and 3' exit of the central channel, a structural motif that is highly conserved across coronaviruses. Potential binders were first generated using structure-based ligand techniques. Their potency was estimated by using four popular docking scoring functions. Common docking hits for NSP13 were finally tested using advanced nonequilibrium alchemical techniques for binding free energy calculations on a high-performing parallel cluster. Four potential NSP13 inhibitors with potency from submicrimolar to nanomolar were finally identified.

Abstract 7073: Integrating fragment-based crystallographic screening with dTAG-PROTAC degradation and genetic rescue to explore the function of eIF4E

Abstract The translation initiation factor eIF4E is a rate-limiting factor for protein synthesis that binds the mRNA m7G-cap to initiate the recruitment and binding of eIF4F components such as eIF4G. Targeting eIF4E has long been considered a promising anticancer strategy but it has remained undruggable using conventional screening approaches. Fragment-based crystallographic screening is a powerful technique for probing the surface of proteins to identify potentially druggable binding sites. Here, we describe fragment-based screening using a combination of Xray crystallography and NMR to identify low affinity fragment hits binding to the mRNA cap-binding site or at an additional site of unknown functional relevance (site 2). Subsequent rounds of iterative structure-based design yielded a lead molecule, with 100nM binding potency for site 2, that could disrupt the eIF4E:eIF4G interaction and inhibit cap-dependent translation in cell lysates. To explore the functional relevance of site 2 we established a cell model expressing an eIF4E-FKBP12F36V fusion protein that was sensitive to degradation following treatment with the heterobifunctional dTAGV-1 molecule. Degradation of eIF4E resulted in inhibition of cell growth and reduced expression of MCL1 protein, that were rescued by re-expression of a non-degradable wild-type eIF4E. A mutation reported to block canonical eIF4G binding (W73F) disrupted binding to eIF4G but to our surprise retained the ability to rescue the eIF4E degradation phenotype. Mutation of site 2 (L85R and L134R) also disrupted the eIF4E:eIF4G interaction. The L85R mutant remained able to partially rescue the cellular responses, in contrast to the L134R mutant that could not rescue eIF4E loss. The W73F/L85R double mutant resulted in a combinatorial loss of eIF4E function. Despite disrupting eIF4E function in lysates, there was a significant drop-off of compound activity in cells. However, treatment of the W73F mutant with compound recapitulated the combination of the W73F canonical eIF4G binding mutation with the L85R site 2 mutation. This approach demonstrates the power of coupling fragment screening with target-degradation and genetic rescue approaches to find and explore novel functional pockets. Our data suggest it may be necessary to disrupt the extended protein-protein interaction made by both the canonical and non-canonical regions of eIF4E to drive a strong functional effect in cells. However, the discovery of a lead compound, the associated structural understanding, and the knowledge that one site 2 mutation inactivates eIF4E provides hope that a significantly more potent small molecule inhibitor or degrader could drive a more profound cellular effect which may lead to improved cellular activity and a deeper understanding of eIF4E biology. Citation Format: Paul A. Clarke, Swee Y. Sharp, Marianna Martella, Christopher I. Milton, Charlotte East, Mladen Vinkovic, Nicola Wallis, George Ward, Caroline Richardson, Andrew Woodhead. Integrating fragment-based crystallographic screening with dTAG-PROTAC degradation and genetic rescue to explore the function of eIF4E [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7073.

Colabind: A Cloud-Based Approach for Prediction of Binding Sites Using Coarse-Grained Simulations with Molecular Probes.

Binding site prediction is a crucial step in understanding protein-ligand and protein-protein interactions (PPIs) with broad implications in drug discovery and bioinformatics. This study introduces Colabind, a robust, versatile, and user-friendly cloud-based approach that employs coarse-grained molecular dynamics simulations in the presence of molecular probes, mimicking fragments of drug-like compounds. Our method has demonstrated high effectiveness when validated across a diverse range of biological targets spanning various protein classes, successfully identifying orthosteric binding sites, as well as known druggable allosteric or PPI sites, in both experimentally determined and AI-predicted protein structures, consistently placing them among the top-ranked sites. Furthermore, we suggest that careful inspection of the identified regions with a high affinity for specific probes can provide valuable insights for the development of pharmacophore hypotheses. The approach is available at https://github.com/porekhov/CG_probeMD.

Graph Attention Site Prediction (GrASP): Identifying Druggable Binding Sites Using Graph Neural Networks with Attention.

Identifying and discovering druggable protein binding sites is an important early step in computer-aided drug discovery, but it remains a difficult task where most campaigns rely on a priori knowledge of binding sites from experiments. Here, we present a binding site prediction method called Graph Attention Site Prediction (GrASP) and re-evaluate assumptions in nearly every step in the site prediction workflow from data set preparation to model evaluation. GrASP is able to achieve state-of-the-art performance at recovering binding sites in PDB structures while maintaining a high degree of precision which will minimize wasted computation in downstream tasks such as docking and free energy perturbation.

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel.

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.

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.