Application of Mixed-Solvent Molecular Dynamics Simulations for Prediction of Allosteric Sites on G Protein-Coupled Receptors.

The development of small molecule allosteric modulators acting at G protein-coupled receptors (GPCRs) is becoming increasingly attractive. Such compounds have advantages over traditional drugs acting at orthosteric sites on these receptors, in particular target specificity. However, the number and locations of druggable allosteric sites within most clinically relevant GPCRs are unknown. In the present study, we describe the development and application of a mixed-solvent molecular dynamics (MixMD)-based method for the identification of allosteric sites on GPCRs. The method employs small organic probes with druglike qualities to identify druggable hotspots in multiple replicate short-timescale simulations. As proof of principle, we first applied the method retrospectively to a test set of five GPCRs (cannabinoid receptor type 1, C-C chemokine receptor type 2, M2 muscarinic receptor, P2Y purinoceptor 1, and protease-activated receptor 2) with known allosteric sites in diverse locations. This resulted in the identification of the known allosteric sites on these receptors. We then applied the method to the µ-opioid receptor. Several allosteric modulators for this receptor are known, although the binding sites for these modulators are not known. The MixMD-based method revealed several potential allosteric sites on the mu-opioid receptor. Implementation of the MixMD-based method should aid future efforts in the structure-based drug design of drugs targeting allosteric sites on GPCRs. SIGNIFICANCE STATEMENT: Allosteric modulation of G protein-coupled receptors (GPCRs) has the potential to provide more selective drugs. However, there are limited structures of GPCRs bound to allosteric modulators, and obtaining such structures is problematic. Current computational methods utilize static structures and therefore may not identify hidden or cryptic sites. Here we describe the use of small organic probes and molecular dynamics to identify druggable allosteric hotspots on GPCRs. The results reinforce the importance of protein dynamics in allosteric site identification.

Computational Identification of Possible Allosteric Sites and Modulators of the SARS-CoV-2 Main Protease.

In this study, we target the main protease (Mpro) of the SARS-CoV-2 virus as it is a crucial enzyme for viral replication. Herein, we report three plausible allosteric sites on Mpro that can expand structure-based drug discovery efforts for new Mpro inhibitors. To find these sites, we used mixed-solvent molecular dynamics (MixMD) simulations, an efficient computational protocol that finds binding hotspots through mapping the surface of unbound proteins with 5% cosolvents in water. We have used normal mode analysis to support our claim of allosteric control for these sites. Further, we have performed virtual screening against the sites with 361 hits from Mpro screenings available through the National Center for Advancing Translational Sciences (NCATS). We have identified the NCATS inhibitors that bind to the remote sites better than the active site of Mpro, and we propose these molecules may be allosteric regulators of the system. After identifying our sites, new X-ray crystal structures were released that show fragment molecules in the sites we found, supporting the notion that these sites are accurate and druggable.

Mixed-solvent molecular dynamics simulation-based discovery of a putative allosteric site on regulator of G protein signaling 4.

Regulator of G protein signaling 4 (RGS4) is an intracellular protein that binds to the Gα subunit ofheterotrimeric G proteins and aids in terminating G protein coupled receptor signaling. RGS4 has been implicated in pain, schizophrenia, and the control of cardiac contractility. Inhibitors of RGS4 have been developed but bind covalently to cysteine residues on the protein. Therefore, we sought to identify alternative druggable sites on RGS4 using mixed-solvent molecular dynamics simulations, which employ low concentrations of organic probes to identify druggable hotspots on the protein. Pseudo-ligands were placed in consensus hotspots, and perturbation with normal mode analysis led to the identification and characterization of a putative allosteric site, which would be invaluable for structure-based drug design of non-covalent, small molecule inhibitors. Future studies on the mechanism of this allostery will aid in the development of novel therapeutics targeting RGS4.

Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors.

MOTIVATION: Accurate prediction and interpretation of ligand bioactivities are essential for virtual screening and drug discovery. Unfortunately, many important drug targets lack experimental data about the ligand bioactivities; this is particularly true for G protein-coupled receptors (GPCRs), which account for the targets of about a third of drugs currently on the market. Computational approaches with the potential of precise assessment of ligand bioactivities and determination of key substructural features which determine ligand bioactivities are needed to address this issue. RESULTS: A new method, SED, was proposed to predict ligand bioactivities and to recognize key substructures associated with GPCRs through the coupling of screening for Lasso of long extended-connectivity fingerprints (ECFPs) with deep neural network training. The SED pipeline contains three successive steps: (i) representation of long ECFPs for ligand molecules, (ii) feature selection by screening for Lasso of ECFPs and (iii) bioactivity prediction through a deep neural network regression model. The method was examined on a set of 16 representative GPCRs that cover most subfamilies of human GPCRs, where each has 300-5000 ligand associations. The results show that SED achieves excellent performance in modelling ligand bioactivities, especially for those in the GPCR datasets without sufficient ligand associations, where SED improved the baseline predictors by 12% in correlation coefficient (r2) and 19% in root mean square error. Detail data analyses suggest that the major advantage of SED lies on its ability to detect substructures from long ECFPs which significantly improves the predictive performance. AVAILABILITY AND IMPLEMENTATION: The source code and datasets of SED are freely available at https://zhanglab.ccmb.med.umich.edu/SED/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Homologous G Protein-Coupled Receptors Boost the Modeling and Interpretation of Bioactivities of Ligand Molecules.

G protein-coupled receptors (GPCRs) are one of the most important drug targets, accounting for ∼34% of drugs on the market. For drug discovery, accurate modeling and explanation of bioactivities of ligands is critical for the screening and optimization of hit compounds. Homologous GPCRs are more likely to interact with chemically similar ligands, and they tend to share common binding modes with ligand molecules. The inclusion of homologous GPCRs in learning bioactivities of ligands potentially enhances the accuracy and interpretability of models due to utilizing increased training sample size and the existence of common ligand substructures that control bioactivities. Accurate modeling and interpretation of bioactivities of ligands by combining homologous GPCRs can be formulated as multitask learning with joint feature learning problem and naturally matched with the group lasso learning algorithm. Thus, we proposed a multitask regression learning with group lasso (MTR-GL) implemented by l2,1-norm regularization to model bioactivities of ligand molecules and then tested the algorithm on a series of thirty-five representative GPCRs datasets that cover nine subfamilies of human GPCRs. The results show that MTR-GL is overall superior to single-task learning methods and classic multitask learning with joint feature learning methods. Moreover, MTR-GL achieves better performance than state-of-the-art deep multitask learning based methods of predicting ligand bioactivities on most datasets (31/35), where MTR-GL obtained an average improvement of 38% on correlation coefficient (r2) and 29% on root-mean-square error over the DeepNeuralNet-QSAR predictors.

WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest.

Motivation: Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally determined activities. In particular, many G protein-coupled receptors (GPCRs), which are the largest integral membrane protein family and represent targets of nearly 40% drugs on the market, lack published experimental data about ligand interactions. Computational methods with the ability to accurately predict the bioactivity of ligands can help efficiently address this problem. Results: We proposed a new method, WDL-RF, using weighted deep learning and random forest, to model the bioactivity of GPCR-associated ligand molecules. The pipeline of our algorithm consists of two consecutive stages: (i) molecular fingerprint generation through a new weighted deep learning method, and (ii) bioactivity calculations with a random forest model; where one uniqueness of the approach is that the model allows end-to-end learning of prediction pipelines with input ligands being of arbitrary size. The method was tested on a set of twenty-six non-redundant GPCRs that have a high number of active ligands, each with 200-4000 ligand associations. The results from our benchmark show that WDL-RF can generate bioactivity predictions with an average root-mean square error 1.33 and correlation coefficient (r2) 0.80 compared to the experimental measurements, which are significantly more accurate than the control predictors with different molecular fingerprints and descriptors. In particular, data-driven molecular fingerprint features, as extracted from the weighted deep learning models, can help solve deficiencies stemming from the use of traditional hand-crafted features and significantly increase the efficiency of short molecular fingerprints in virtual screening. Availability and implementation: The WDL-RF web server, as well as source codes and datasets of WDL-RF, is freely available at https://zhanglab.ccmb.med.umich.edu/WDL-RF/ for academic purposes. Supplementary information: Supplementary data are available at Bioinformatics online.

GLASS: a comprehensive database for experimentally validated GPCR-ligand associations.

MOTIVATION: G protein-coupled receptors (GPCRs) are probably the most attractive drug target membrane proteins, which constitute nearly half of drug targets in the contemporary drug discovery industry. While the majority of drug discovery studies employ existing GPCR and ligand interactions to identify new compounds, there remains a shortage of specific databases with precisely annotated GPCR-ligand associations. RESULTS: We have developed a new database, GLASS, which aims to provide a comprehensive, manually curated resource for experimentally validated GPCR-ligand associations. A new text-mining algorithm was proposed to collect GPCR-ligand interactions from the biomedical literature, which is then crosschecked with five primary pharmacological datasets, to enhance the coverage and accuracy of GPCR-ligand association data identifications. A special architecture has been designed to allow users for making homologous ligand search with flexible bioactivity parameters. The current database contains ∼500 000 unique entries, of which the vast majority stems from ligand associations with rhodopsin- and secretin-like receptors. The GLASS database should find its most useful application in various in silico GPCR screening and functional annotation studies. AVAILABILITY AND IMPLEMENTATION: The website of GLASS database is freely available at http://zhanglab.ccmb.med.umich.edu/GLASS/. CONTACT: zhng@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The assembly of proline-rich membrane anchor (PRiMA)-linked acetylcholinesterase enzyme: glycosylation is required for enzymatic activity but not for oligomerization.

Acetylcholinesterase (AChE) anchors onto cell membranes by a transmembrane protein PRiMA (proline-rich membrane anchor) as a tetrameric form in vertebrate brain. The assembly of AChE tetramer with PRiMA requires the C-terminal "t-peptide" in AChE catalytic subunit (AChE(T)). Although mature AChE is well known N-glycosylated, the role of glycosylation in forming the physiologically active PRiMA-linked AChE tetramer has not been studied. Here, several lines of evidence indicate that the N-linked glycosylation of AChE(T) plays a major role for acquisition of AChE full enzymatic activity but does not affect its oligomerization. The expression of the AChE(T) mutant, in which all N-glycosylation sites were deleted, together with PRiMA in HEK293T cells produced a glycan-depleted PRiMA-linked AChE tetramer but with a much higher K(m) value as compared with the wild type. This glycan-depleted enzyme was assembled in endoplasmic reticulum but was not transported to Golgi apparatus or plasma membrane.

In silico analysis of SARS-CoV-2 proteins as targets for clinically available drugs.

The ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires treatments with rapid clinical translatability. Here we develop a multi-target and multi-ligand virtual screening method to identify FDA-approved drugs with potential activity against SARS-CoV-2 at traditional and understudied viral targets. 1,268 FDA-approved small molecule drugs were docked to 47 putative binding sites across 23 SARS-CoV-2 proteins. We compared drugs between binding sites and filtered out compounds that had no reported activity in an in vitro screen against SARS-CoV-2 infection of human liver (Huh-7) cells. This identified 17 "high-confidence", and 97 "medium-confidence" drug-site pairs. The "high-confidence" group was subjected to molecular dynamics simulations to yield six compounds with stable binding poses at their optimal target proteins. Three drugs-amprenavir, levomefolic acid, and calcipotriol-were predicted to bind to 3 different sites on the spike protein, domperidone to the Mac1 domain of the non-structural protein (Nsp) 3, avanafil to Nsp15, and nintedanib to the nucleocapsid protein involved in packaging the viral RNA. Our "two-way" virtual docking screen also provides a framework to prioritize drugs for testing in future emergencies requiring rapidly available clinical drugs and/or treating diseases where a moderate number of targets are known.

The genetic heterogeneity and drug resistance mechanisms of relapsed refractory multiple myeloma.

Multiple myeloma is the second most common hematological malignancy. Despite significant advances in treatment, relapse is common and carries a poor prognosis. Thus, it is critical to elucidate the genetic factors contributing to disease progression and drug resistance. Here, we carry out integrative clinical sequencing of 511 relapsed, refractory multiple myeloma (RRMM) patients to define the disease's molecular alterations landscape. The NF-κB and RAS/MAPK pathways are more commonly altered than previously reported, with a prevalence of 45-65% each. In the RAS/MAPK pathway, there is a long tail of variants associated with the RASopathies. By comparing our RRMM cases with untreated patients, we identify a diverse set of alterations conferring resistance to three main classes of targeted therapy in 22% of our cohort. Activating mutations in IL6ST are also enriched in RRMM. Taken together, our study serves as a resource for future investigations of RRMM biology and potentially informs clinical management.

The PRiMA-linked cholinesterase tetramers are assembled from homodimers: hybrid molecules composed of acetylcholinesterase and butyrylcholinesterase dimers are up-regulated during development of chicken brain.

Acetylcholinesterase (AChE) is anchored onto cell membranes by the transmembrane protein PRiMA (proline-rich membrane anchor) as a tetrameric globular form that is prominently expressed in vertebrate brain. In parallel, the PRiMA-linked tetrameric butyrylcholinesterase (BChE) is also found in the brain. A single type of AChE-BChE hybrid tetramer was formed in cell cultures by co-transfection of cDNAs encoding AChE(T) and BChE(T) with proline-rich attachment domain-containing proteins, PRiMA I, PRiMA II, or a fragment of ColQ having a C-terminal GPI addition signal (Q(N-GPI)). Using AChE and BChE mutants, we showed that AChE-BChE hybrids linked with PRiMA or Q(N-GPI) always consist of AChE(T) and BChE(T) homodimers. The dimer formation of AChE(T) and BChE(T) depends on the catalytic domains, and the assembly of tetramers with a proline-rich attachment domain-containing protein requires the presence of C-terminal "t-peptides" in cholinesterase subunits. Our results indicate that PRiMA- or ColQ-linked cholinesterase tetramers are assembled from AChE(T) or BChE(T) homodimers. Moreover, the PRiMA-linked AChE-BChE hybrids occur naturally in chicken brain, and their expression increases during development, suggesting that they might play a role in cholinergic neurotransmission.

Targeting acetylcholinesterase to membrane rafts: a function mediated by the proline-rich membrane anchor (PRiMA) in neurons.

In the mammalian brain, acetylcholinesterase (AChE) is anchored in cell membranes by a transmembrane protein PRiMA (proline-rich membrane anchor). We present evidence that at least part of the PRiMA-linked AChE is integrated in membrane microdomains called rafts. A significant proportion of PRiMA-linked AChE tetramers from rat brain was recovered in raft fractions; this proportion was markedly higher at low rather than at high concentrations of cold Triton X-100. The detergent-resistant fraction increased during brain development. In NG108-15 neuroblastoma cells transfected with cDNAs encoding AChE(T) and PRiMA, PRiMA-linked G(4) AChE was found in membrane rafts and showed the same sensitivity to cold Triton X-100 extraction as in the brain. The association of PRiMA-linked AChE with rafts was weaker than that of glycosylphosphatidylinositol-anchored G(2) AChE or G(4) Q(N)-H(C)-linked AChE. It was found to depend on the presence of a cholesterol-binding motif, called CRAC (cholesterol recognition/interaction amino acid consensus), located at the junction of transmembrane and cytoplasmic domains of both PRiMA I and II isoforms. The cytoplasmic domain of PRiMA, which differs between PRiMA I and PRiMA II, appeared to play some role in stabilizing the raft localization of G(4) AChE, because the Triton X-100-resistant fraction was smaller with the shorter PRiMA II isoform than that with the longer PRiMA I isoform.

Ontological modeling and analysis of experimentally or clinically verified drugs against coronavirus infection.

Our systematic literature collection and annotation identified 106 chemical drugs and 31 antibodies effective against the infection of at least one human coronavirus (including SARS-CoV, SAR-CoV-2, and MERS-CoV) in vitro or in vivo in an experimental or clinical setting. A total of 163 drug protein targets were identified, and 125 biological processes involving the drug targets were significantly enriched based on a Gene Ontology (GO) enrichment analysis. The Coronavirus Infectious Disease Ontology (CIDO) was used as an ontological platform to represent the anti-coronaviral drugs, chemical compounds, drug targets, biological processes, viruses, and the relations among these entities. In addition to new term generation, CIDO also adopted various terms from existing ontologies and developed new relations and axioms to semantically represent our annotated knowledge. The CIDO knowledgebase was systematically analyzed for scientific insights. To support rational drug design, a "Host-coronavirus interaction (HCI) checkpoint cocktail" strategy was proposed to interrupt the important checkpoints in the dynamic HCI network, and ontologies would greatly support the design process with interoperable knowledge representation and reasoning.

Virtual Screening of Human Class-A GPCRs Using Ligand Profiles Built on Multiple Ligand-Receptor Interactions.

G protein-coupled receptors (GPCRs) are a large family of integral membrane proteins responsible for cellular signal transductions. Identification of therapeutic compounds to regulate physiological processes is an important first step of drug discovery. We proposed MAGELLAN, a novel hierarchical virtual-screening (VS) pipeline, which starts with low-resolution protein structure prediction and structure-based binding-site identification, followed by homologous GPCR detections through structure and orthosteric binding-site comparisons. Ligand profiles constructed from the homologous ligand-GPCR complexes are then used to thread through compound databases for VS. The pipeline was first tested in a large-scale retrospective screening experiment against 224 human Class A GPCRs, where MAGELLAN achieved a median enrichment factor (EF) of 14.38, significantly higher than that using individual ligand profiles. Next, MAGELLAN was examined on 5 and 20 GPCRs from two public VS databases (DUD-E and GPCR-Bench) and resulted in an average EF of 9.75 and 13.70, respectively, which compare favorably with other state-of-the-art docking- and ligand-based methods, including AutoDock Vina (with EF = 1.48/3.16 in DUD-E and GPCR-Bench), DOCK 6 (2.12/3.47 in DUD-E and GPCR-Bench), PoLi (2.2 in DUD-E), and FINDSITECcomb2.0 (2.90 in DUD-E). Detailed data analyses show that the major advantage of MAGELLAN is attributed to the power of ligand profiling, which integrates complementary methods for ligand-GPCR interaction recognition and thus significantly improves the coverage and sensitivity of VS models. Finally, cases studies on opioid and motilin receptors show that new connections between functionally related GPCRs can be visualized in the minimum spanning tree built on the similarities of predicted ligand-binding ensembles, suggesting a novel use of MAGELLAN for GPCR deorphanization.

N-linked glycosylation of proline-rich membrane anchor (PRiMA) is not required for assembly and trafficking of globular tetrameric acetylcholinesterase.

Acetylcholinesterase (AChE) is organized into globular tetramers (G(4)) by a structural protein called proline-rich membrane anchor (PRiMA), anchoring it into the cell membrane of neurons in the brain. The assembly of AChE tetramers with PRiMA requires the presence of a C-terminal "t-peptide" in the AChE catalytic subunit (AChE(T)). The glycosylation of AChE(T) is known to be required for its proper assembly and trafficking; however, the role of PRiMA glycosylation in the oligomer assembly has not been revealed. PRiMA is a glycoprotein containing two putative N-linked glycosylation sites. By using site-directed mutagenesis, the asparagine-43 was identified to be the N-linked glycosylation site of PRiMA. Abolishing glycosylation on mouse PRiMA appeared not to affect its assembly with AChE(T), the enzymatic properties of AChE, and the membrane trafficking of PRiMA-linked AChE tetramers. This result is contrary to the reports that glycosylation is essential for conformation and trafficking of membrane glycoproteins.

Molecular Assembly and Biosynthesis of Acetylcholinesterase in Brain and Muscle: the Roles of t-peptide, FHB Domain, and N-linked Glycosylation.

Acetylcholinesterase (AChE) is responsible for the hydrolysis of the neurotransmitter, acetylcholine, in the nervous system. The functional localization and oligomerization of AChE T variant are depending primarily on the association of their anchoring partners, either collagen tail (ColQ) or proline-rich membrane anchor (PRiMA). Complexes with ColQ represent the asymmetric forms (A(12)) in muscle, while complexes with PRiMA represent tetrameric globular forms (G(4)) mainly found in brain and muscle. Apart from these traditional molecular forms, a ColQ-linked asymmetric form and a PRiMA-linked globular form of hybrid cholinesterases (ChEs), having both AChE and BChE catalytic subunits, were revealed in chicken brain and muscle. The similarity of various molecular forms of AChE and BChE raises interesting question regarding to their possible relationship in enzyme assembly and localization. The focus of this review is to provide current findings about the biosynthesis of different forms of ChEs together with their anchoring proteins.

An induction effect of heat shock on the transcript of globular acetylcholinesterase in NG108-15 cells.

Heat shock response, an induced transcription of a set of genes in response to high temperature, occurs in all organisms. In neurons, the catalytic subunit of acetylcholinesterase (AChE(T)) interacts with proline-rich membrane anchor (PRiMA) to form a globular tetrameric form (G(4) form). In this study, we examined the effects of heat shock on the transcription and protein assembly of AChE(T) in cultured NG108-15 cells. The transcription of AChE(T) was rapidly induced by heat shock at 40 degrees C, reaching a 15-fold increase in 3h and decreasing thereafter. On the other hand, the level of PRiMA mRNA was not affected after the heat shock. In parallel with AChE(T) mRNA, the enzymatic activity of cellular AChE, in terms of G(1) and G(2) forms, was increased after heat shock; however, the PRiMA-linked G(4) remained unchanged. These results suggest that heat shock can induce the expression level of AChE(T) by the regulation of AChE(T) transcripts in NG108-15 cells.