ABSTRACT
The cellular immune system, which is a critical component of human immunity, uses T cell receptors (TCRs) to recognize antigenic proteins in the form of peptides presented by major histocompatibility complex (MHC) proteins. Accurate definition of the structural basis of TCRs and their engagement of peptide-MHCs can provide major insights into normal and aberrant immunity, and can help guide the design of vaccines and immunotherapeutics. Given the limited amount of experimentally determined TCR-peptide-MHC structures and the vast amount of TCRs within each individual as well as antigenic targets, accurate computational modeling approaches are needed. Here, we report a major update to our web server, TCRmodel, which was originally developed to model unbound TCRs from sequence, to now model TCR-peptide-MHC complexes from sequence, utilizing several adaptations of AlphaFold. This method, named TCRmodel2, allows users to submit sequences through an easy-to-use interface and shows similar or greater accuracy than AlphaFold and other methods to model TCR-peptide-MHC complexes based on benchmarking. It can generate models of complexes in 15 minutes, and output models are provided with confidence scores and an integrated molecular viewer. TCRmodel2 is available at https://tcrmodel.ibbr.umd.edu.
Subject(s)
Deep Learning , Humans , Receptors, Antigen, T-Cell/chemistry , Peptides/chemistry , Computer Simulation , AntigensABSTRACT
RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.
ABSTRACT
RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.
ABSTRACT
T cells play a vital role in combatting SARS-CoV-2 and forming long-term memory responses. Whereas extensive structural information is available on neutralizing antibodies against SARS-CoV-2, such information on SARS-CoV-2-specific T-cell receptors (TCRs) bound to their peptide-MHC targets is lacking. Here we determine the structures of a public and a private TCR from COVID-19 convalescent patients in complex with HLA-A2 and two SARS-CoV-2 spike protein epitopes (YLQ and RLQ). The structures reveal the basis for selection of particular TRAV and TRBV germline genes by the public but not the private TCR, and for the ability of the TCRs to recognize natural variants of RLQ but not YLQ. Neither TCR recognizes homologous epitopes from human seasonal coronaviruses. By elucidating the mechanism for TCR recognition of an immunodominant yet variable epitope (YLQ) and a conserved but less commonly targeted epitope (RLQ), this study can inform prospective efforts to design vaccines to elicit pan-coronavirus immunity.
Subject(s)
COVID-19/immunology , Epitopes, T-Lymphocyte/immunology , HLA-A2 Antigen/immunology , Receptors, Antigen, T-Cell/immunology , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , CD4-Positive T-Lymphocytes/immunology , CD4-Positive T-Lymphocytes/metabolism , CD4-Positive T-Lymphocytes/virology , CD8-Positive T-Lymphocytes/immunology , CD8-Positive T-Lymphocytes/metabolism , CD8-Positive T-Lymphocytes/virology , COVID-19/virology , Epitopes, T-Lymphocyte/metabolism , HLA-A2 Antigen/chemistry , HLA-A2 Antigen/metabolism , Humans , Immunodominant Epitopes/immunology , Immunodominant Epitopes/metabolism , Jurkat Cells , K562 Cells , Peptides/chemistry , Peptides/immunology , Peptides/metabolism , Protein Binding , Protein Conformation , Receptors, Antigen, T-Cell/chemistry , Receptors, Antigen, T-Cell/metabolism , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Spike Glycoprotein, Coronavirus/metabolism , Surface Plasmon Resonance/methodsABSTRACT
The SARS-CoV-2 pandemic highlights the need for a detailed molecular understanding of protective antibody responses. This is underscored by the emergence and spread of SARS-CoV-2 variants, including Alpha (B.1.1.7) and Delta (B.1.617.2), some of which appear to be less effectively targeted by current monoclonal antibodies and vaccines. Here we report a high resolution and comprehensive map of antibody recognition of the SARS-CoV-2 spike receptor binding domain (RBD), which is the target of most neutralizing antibodies, using computational structural analysis. With a dataset of nonredundant experimentally determined antibody-RBD structures, we classified antibodies by RBD residue binding determinants using unsupervised clustering. We also identified the energetic and conservation features of epitope residues and assessed the capacity of viral variant mutations to disrupt antibody recognition, revealing sets of antibodies predicted to effectively target recently described viral variants. This detailed structure-based reference of antibody RBD recognition signatures can inform therapeutic and vaccine design strategies.
Subject(s)
Antibodies, Viral , COVID-19/virology , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus , Antibodies, Viral/chemistry , Antibodies, Viral/metabolism , Binding Sites , Cluster Analysis , Computational Biology , Humans , Models, Molecular , Protein Binding , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics , Spike Glycoprotein, Coronavirus/metabolismABSTRACT
SARS-CoV-2, the etiologic agent of COVID-19, exemplifies the general threat to global health posed by coronaviruses. The urgent need for effective vaccines and therapies is leading to a rapid rise in the number of high resolution structures of SARS-CoV-2 proteins that collectively reveal a map of virus vulnerabilities. To assist structure-based design of vaccines and therapeutics against SARS-CoV-2 and other coronaviruses, we have developed CoV3D, a database and resource for coronavirus protein structures, which is updated on a weekly basis. CoV3D provides users with comprehensive sets of structures of coronavirus proteins and their complexes with antibodies, receptors, and small molecules. Integrated molecular viewers allow users to visualize structures of the spike glycoprotein, which is the major target of neutralizing antibodies and vaccine design efforts, as well as sets of spike-antibody complexes, spike sequence variability, and known polymorphisms. In order to aid structure-based design and analysis of the spike glycoprotein, CoV3D permits visualization and download of spike structures with modeled N-glycosylation at known glycan sites, and contains structure-based classification of spike conformations, generated by unsupervised clustering. CoV3D can serve the research community as a centralized reference and resource for spike and other coronavirus protein structures, and is available at: https://cov3d.ibbr.umd.edu.
Subject(s)
Computational Biology , Coronavirus/metabolism , Databases, Protein , Spike Glycoprotein, Coronavirus/metabolism , Amino Acid Sequence , Antibodies, Neutralizing/immunology , Antibodies, Neutralizing/metabolism , Antibodies, Viral/immunology , Antibodies, Viral/metabolism , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19/virology , Epidemics , Humans , Internet , Models, Molecular , Protein Structure, Tertiary , SARS-CoV-2/chemistry , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/geneticsABSTRACT
RNA-binding protein Musashi-1 (MSI1) is a key regulator of several stem cell populations. MSI1 is involved in tumor proliferation and maintenance, and it regulates target mRNAs at the translational level. The known mRNA targets of MSI1 include Numb, APC, and P21WAF-1, key regulators of Notch/Wnt signaling and cell cycle progression, respectively. In this study, we aim to identify small molecule inhibitors of MSI1-mRNA interactions, which could block the growth of cancer cells with high levels of MSI1. Using a fluorescence polarization (FP) assay, we screened small molecules from several chemical libraries for those that disrupt the binding of MSI1 to its consensus RNA. One cluster of hit compounds is the derivatives of secondary metabolites from Aspergillus nidulans. One of the top hits, Aza-9, from this cluster was further validated by surface plasmon resonance and nuclear magnetic resonance spectroscopy, which demonstrated that Aza-9 binds directly to MSI1, and the binding is at the RNA binding pocket. We also show that Aza-9 binds to Musashi-2 (MSI2) as well. To test whether Aza-9 has anti-cancer potential, we used liposomes to facilitate Aza-9 cellular uptake. Aza-9-liposome inhibits proliferation, induces apoptosis and autophagy, and down-regulates Notch and Wnt signaling in colon cancer cell lines. In conclusion, we identified a series of potential lead compounds for inhibiting MSI1/2 function, while establishing a framework for identifying small molecule inhibitors of RNA binding proteins using FP-based screening methodology.
ABSTRACT
SARS-CoV-2, the etiologic agent behind COVID-19, exemplifies the general threat to global health posed by coronaviruses. The urgent need for effective vaccines and therapies is leading to a rapid rise in the number of high resolution structures of SARS-CoV-2 proteins that collectively reveal a map of virus vulnerabilities. To assist structure-based design of vaccines and therapeutics against SARS-CoV-2 and other coronaviruses, we have developed CoV3D, a database and resource for coronavirus protein structures, which is updated on a weekly basis. CoV3D provides users with comprehensive sets of structures of coronavirus proteins and their complexes with antibodies, receptors, and small molecules. Integrated molecular viewers allow users to visualize structures of the spike glycoprotein, which is the major target of neutralizing antibodies and vaccine design efforts, as well as sets of spike-antibody complexes, spike sequence variability, and known polymorphisms. In order to aid structure-based design and analysis of the spike glycoprotein, CoV3D permits visualization and download of spike structures with modeled N-glycosylation at known glycan sites, and contains structure-based classification of spike conformations, generated by unsupervised clustering. CoV3D can serve the research community as a centralized reference and resource for spike and other coronavirus protein structures, and is available at: https://cov3d.ibbr.umd.edu.
ABSTRACT
Adoptive cell therapy (ACT) with tumor-specific T cells can mediate cancer regression. The main target of tumor-specific T cells are neoantigens arising from mutations in self-proteins. Although the majority of cancer neoantigens are unique to each patient, and therefore not broadly useful for ACT, some are shared. We studied oligoclonal T-cell receptors (TCRs) that recognize a shared neoepitope arising from a driver mutation in the p53 oncogene (p53R175H) presented by HLA-A2. Here we report structures of wild-type and mutant p53-HLA-A2 ligands, as well as structures of three tumor-specific TCRs bound to p53R175H-HLA-A2. These structures reveal how a driver mutation in p53 rendered a self-peptide visible to T cells. The TCRs employ structurally distinct strategies that are highly focused on the mutation to discriminate between mutant and wild-type p53. The TCR-p53R175H-HLA-A2 complexes provide a framework for designing TCRs to improve potency for ACT without sacrificing specificity.
Subject(s)
Antigens, Neoplasm/chemistry , HLA-A2 Antigen/chemistry , Mutation , T-Lymphocytes/immunology , Tumor Suppressor Protein p53/chemistry , Binding Sites , Biotinylation , Codon , Crystallography, X-Ray , Epitopes , Escherichia coli/metabolism , Humans , Immunotherapy, Adoptive , Ligands , Lymphocytes, Tumor-Infiltrating/immunology , Neoplasms/metabolism , Peptides/chemistry , Protein Binding , Protein Conformation , Protein Folding , Receptors, Antigen, T-Cell/metabolism , Software , Surface Plasmon ResonanceABSTRACT
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
Subject(s)
Macromolecular Substances/chemistry , Models, Molecular , Proteins/chemistry , Software , Molecular Docking Simulation , Peptidomimetics/chemistry , Protein ConformationABSTRACT
Patients diagnosed with metastatic breast cancer have a dismal 5-year survival rate of only 24%. The RNA-binding protein Hu antigen R (HuR) is upregulated in breast cancer, and elevated cytoplasmic HuR correlates with high-grade tumors and poor clinical outcome of breast cancer. HuR promotes tumorigenesis by regulating numerous proto-oncogenes, growth factors, and cytokines that support major tumor hallmarks including invasion and metastasis. Here, we report a HuR inhibitor KH-3, which potently suppresses breast cancer cell growth and invasion. Furthermore, KH-3 inhibits breast cancer experimental lung metastasis, improves mouse survival, and reduces orthotopic tumor growth. Mechanistically, we identify FOXQ1 as a direct target of HuR. KH-3 disrupts HuR-FOXQ1 mRNA interaction, leading to inhibition of breast cancer invasion. Our study suggests that inhibiting HuR is a promising therapeutic strategy for lethal metastatic breast cancer.
Subject(s)
Antineoplastic Agents/pharmacology , Breast Neoplasms/drug therapy , Cell Movement/drug effects , ELAV-Like Protein 1/antagonists & inhibitors , Forkhead Transcription Factors/metabolism , Lung Neoplasms/prevention & control , Animals , Breast Neoplasms/genetics , Breast Neoplasms/metabolism , Breast Neoplasms/pathology , Cell Line, Tumor , Cell Proliferation/drug effects , ELAV-Like Protein 1/genetics , ELAV-Like Protein 1/metabolism , Female , Forkhead Transcription Factors/genetics , Humans , Lung Neoplasms/genetics , Lung Neoplasms/metabolism , Lung Neoplasms/secondary , Mice, Inbred BALB C , Mice, Nude , Middle Aged , Neoplasm Invasiveness , Signal Transduction , Tumor Burden/drug effects , Xenograft Model Antitumor AssaysABSTRACT
Primary cutaneous γδ T cell lymphomas (PCGDTLs) represent a heterogeneous group of uncommon but aggressive cancers. Herein, we perform genome-wide DNA, RNA, and T cell receptor (TCR) sequencing on 29 cutaneous γδ lymphomas. We find that PCGDTLs are not uniformly derived from Vδ2 cells. Instead, the cell-of-origin depends on the tissue compartment from which the lymphomas are derived. Lymphomas arising from the outer layer of skin are derived from Vδ1 cells, the predominant γδ cell in the epidermis and dermis. In contrast, panniculitic lymphomas arise from Vδ2 cells, the predominant γδ T cell in the fat. We also show that TCR chain usage is non-random, suggesting common antigens for Vδ1 and Vδ2 lymphomas respectively. In addition, Vδ1 and Vδ2 PCGDTLs harbor similar genomic landscapes with potentially targetable oncogenic mutations in the JAK/STAT, MAPK, MYC, and chromatin modification pathways. Collectively, these findings suggest a paradigm for classifying, staging, and treating these diseases.
Subject(s)
Lymphoma, T-Cell, Cutaneous/genetics , Lymphoma, T-Cell, Cutaneous/pathology , Receptors, Antigen, T-Cell, gamma-delta/metabolism , Skin Neoplasms/genetics , Skin Neoplasms/pathology , Amino Acid Sequence , Antigens, CD1d/metabolism , Chromatin Assembly and Disassembly , Epitopes/immunology , Genome, Human , HEK293 Cells , Humans , Lymph Nodes/pathology , Models, Biological , Mutation/genetics , Phenotype , Principal Component Analysis , Signal Transduction , Skin/pathology , Transcription, Genetic , Transcriptome/geneticsABSTRACT
The past decade has seen a rapid increase in T cell receptor (TCR) sequences from single cell cloning and repertoire-scale high throughput sequencing studies. Many of these TCRs are of interest as potential therapeutics or for their implications in autoimmune disease or effective targeting of pathogens. As it is impractical to characterize the structure or targeting of the vast majority of these TCRs experimentally, advanced computational methods have been developed to predict their 3D structures and gain mechanistic insights into their antigen binding and specificity. Here, we describe the use of a TCR modeling web server, TCRmodel, which generates models of TCRs from sequence, and TCR3d, which is a weekly-updated database of all known TCR structures. Additionally, we describe the use of RosettaTCR, which is a protocol implemented in the Rosetta framework that serves as the command-line backend to TCRmodel. We provide an example where these tools are used to analyze and model a therapeutically relevant TCR based on its amino acid sequence.
Subject(s)
Receptors, Antigen, T-Cell/chemistry , Amino Acid Sequence , Animals , Databases, Protein , Humans , Models, Molecular , Protein Conformation , SoftwareABSTRACT
Recognition of antigenic peptides bound to major histocompatibility complex (MHC) proteins by αß T cell receptors (TCRs) is a hallmark of T cell mediated immunity. Recent data suggest that variations in TCR binding geometry may influence T cell signaling, which could help explain outliers in relationships between physical parameters such as TCR-pMHC binding affinity and T cell function. Traditionally, TCR binding geometry has been described with simple descriptors such as the crossing angle, which quantifies what has become known as the TCR's diagonal binding mode. However, these descriptors often fail to reveal distinctions in binding geometry that are apparent through visual inspection. To provide a better framework for relating TCR structure to T cell function, we developed a comprehensive system for quantifying the geometries of how TCRs bind peptide/MHC complexes. We show that our system can discern differences not clearly revealed by more common methods. As an example of its potential to impact biology, we used it to reveal differences in how TCRs bind class I and class II peptide/MHC complexes, which we show allow the TCR to maximize access to and "read out" the peptide antigen. We anticipate our system will be of use in not only exploring these and other details of TCR-peptide/MHC binding interactions, but also addressing questions about how TCR binding geometry relates to T cell function, as well as modeling structural properties of class I and class II TCR-peptide/MHC complexes from sequence information. The system is available at https://tcr3d.ibbr.umd.edu/tcr_com or for download as a script.
Subject(s)
Histocompatibility Antigens Class II/chemistry , Histocompatibility Antigens Class I/chemistry , Receptors, Antigen, T-Cell, alpha-beta/chemistry , Binding Sites , Crystallography, X-Ray , Histocompatibility Antigens Class I/immunology , Histocompatibility Antigens Class I/metabolism , Histocompatibility Antigens Class II/immunology , Histocompatibility Antigens Class II/metabolism , Humans , Models, Molecular , Principal Component Analysis , Protein Binding , Protein Conformation, alpha-Helical , Protein Conformation, beta-Strand , Protein Interaction Domains and Motifs , Receptors, Antigen, T-Cell, alpha-beta/immunology , Receptors, Antigen, T-Cell, alpha-beta/metabolism , T-Lymphocytes/chemistry , T-Lymphocytes/immunology , T-Lymphocytes/metabolism , ThermodynamicsABSTRACT
SUMMARY: T cell receptors (TCRs) are critical molecules of the adaptive immune system, capable of recognizing diverse antigens, including peptides, lipids and small molecules, and represent a rapidly growing class of therapeutics. Determining the structural and mechanistic basis of TCR targeting of antigens is a major challenge, as each individual has a vast and diverse repertoire of TCRs. Despite shared general recognition modes, diversity in TCR sequence and recognition represents a challenge to predictive modeling and computational techniques being developed to predict antigen specificity and mechanistic basis of TCR targeting. To this end, we have developed the TCR3d database, a resource containing all known TCR structures, with a particular focus on antigen recognition. TCR3d provides key information on antigen binding mode, interface features, loop sequences and germline gene usage. Users can interactively view TCR complex structures, search sequences of interest against known structures and sequences, and download curated datasets of structurally characterized TCR complexes. This database is updated on a weekly basis, and can serve the community as a centralized resource for those studying T cell receptors and their recognition. AVAILABILITY AND IMPLEMENTATION: The TCR3d database is available at https://tcr3d.ibbr.umd.edu/.
Subject(s)
Databases, Factual , Antigens , Peptides , Receptors, Antigen, T-CellABSTRACT
T cells generate adaptive immune responses mediated by the T cell receptor (TCR)-CD3 complex comprising an αß TCR heterodimer noncovalently associated with three CD3 dimers. In early T cell activation, αß TCR engagement by peptide-major histocompatibility complex (pMHC) is first communicated to the CD3 signaling apparatus of the TCR-CD3 complex, but the underlying mechanism is incompletely understood. It is possible that pMHC binding induces allosteric changes in TCR conformation or dynamics that are then relayed to CD3. Here, we carried out NMR analysis and molecular dynamics (MD) simulations of both the α and ß chains of a human antiviral TCR (A6) that recognizes the Tax antigen from human T cell lymphotropic virus-1 bound to the MHC class I molecule HLA-A2. We observed pMHC-induced NMR signal perturbations in the TCR variable (V) domains that propagated to three distinct sites in the constant (C) domains: 1) the Cß FG loop projecting from the Vß/Cß interface; 2) a cluster of Cß residues near the Cß αA helix, a region involved in interactions with CD3; and 3) the Cα AB loop at the membrane-proximal base of the TCR. A biological role for each of these allosteric sites is supported by previous mutational and functional studies of TCR signaling. Moreover, the pattern of long-range, ligand-induced changes in TCR A6 revealed by NMR was broadly similar to that predicted by the MD simulations. We propose that the unique structure of the TCR ß chain enables allosteric communication between the TCR-binding sites for pMHC and CD3.
Subject(s)
Gene Products, tax/metabolism , HLA-A2 Antigen/metabolism , Receptor-CD3 Complex, Antigen, T-Cell/metabolism , Receptors, Antigen, T-Cell, alpha-beta/metabolism , Allosteric Regulation , Animals , Binding Sites , Gene Products, tax/chemistry , HLA-A2 Antigen/chemistry , Human T-lymphotropic virus 1/chemistry , Humans , Mice , Molecular Dynamics Simulation , Protein Binding , Protein Conformation , Receptor-CD3 Complex, Antigen, T-Cell/chemistry , Receptors, Antigen, T-Cell, alpha-beta/chemistryABSTRACT
BACKGROUND: The Musashi (MSI) family of RNA-binding proteins is best known for the role in post-transcriptional regulation of target mRNAs. Elevated MSI1 levels in a variety of human cancer are associated with up-regulation of Notch/Wnt signaling. MSI1 binds to and negatively regulates translation of Numb and APC (adenomatous polyposis coli), negative regulators of Notch and Wnt signaling respectively. METHODS: Previously, we have shown that the natural product (-)-gossypol as the first known small molecule inhibitor of MSI1 that down-regulates Notch/Wnt signaling and inhibits tumor xenograft growth in vivo. Using a fluorescence polarization (FP) competition assay, we identified gossypolone (Gn) with a > 20-fold increase in Ki value compared to (-)-gossypol. We validated Gn binding to MSI1 using surface plasmon resonance, nuclear magnetic resonance, and cellular thermal shift assay, and tested the effects of Gn on colon cancer cells and colon cancer DLD-1 xenografts in nude mice. RESULTS: In colon cancer cells, Gn reduced Notch/Wnt signaling and induced apoptosis. Compared to (-)-gossypol, the same concentration of Gn is less active in all the cell assays tested. To increase Gn bioavailability, we used PEGylated liposomes in our in vivo studies. Gn-lip via tail vein injection inhibited the growth of human colon cancer DLD-1 xenografts in nude mice, as compared to the untreated control (P < 0.01, n = 10). CONCLUSION: Our data suggest that PEGylation improved the bioavailability of Gn as well as achieved tumor-targeted delivery and controlled release of Gn, which enhanced its overall biocompatibility and drug efficacy in vivo. This provides proof of concept for the development of Gn-lip as a molecular therapy for colon cancer with MSI1/MSI2 overexpression.
Subject(s)
Colonic Neoplasms/drug therapy , Gossypol/analogs & derivatives , Nerve Tissue Proteins/antagonists & inhibitors , RNA-Binding Proteins/antagonists & inhibitors , Animals , Apoptosis/drug effects , Biological Products/administration & dosage , Cell Line, Tumor , Colonic Neoplasms/genetics , Colonic Neoplasms/pathology , Gene Expression Regulation, Neoplastic/drug effects , Gossypol/administration & dosage , Humans , Liposomes/administration & dosage , Mice , Nerve Tissue Proteins/genetics , RNA-Binding Proteins/genetics , Signal Transduction/drug effects , Xenograft Model Antitumor AssaysABSTRACT
T cell receptors (TCRs), along with antibodies, are responsible for specific antigen recognition in the adaptive immune response, and millions of unique TCRs are estimated to be present in each individual. Understanding the structural basis of TCR targeting has implications in vaccine design, autoimmunity, as well as T cell therapies for cancer. Given advances in deep sequencing leading to immune repertoire-level TCR sequence data, fast and accurate modeling methods are needed to elucidate shared and unique 3D structural features of these molecules which lead to their antigen targeting and cross-reactivity. We developed a new algorithm in the program Rosetta to model TCRs from sequence, and implemented this functionality in a web server, TCRmodel. This web server provides an easy to use interface, and models are generated quickly that users can investigate in the browser and download. Benchmarking of this method using a set of nonredundant recently released TCR crystal structures shows that models are accurate and compare favorably to models from another available modeling method. This server enables the community to obtain insights into TCRs of interest, and can be combined with methods to model and design TCR recognition of antigens. The TCRmodel server is available at: http://tcrmodel.ibbr.umd.edu/.
Subject(s)
Algorithms , Antigens/chemistry , Models, Molecular , Receptors, Antigen, T-Cell/chemistry , Software , Structural Homology, Protein , Amino Acid Sequence , Antigens/immunology , Benchmarking , Binding Sites , Databases, Protein , Humans , Internet , Protein Binding , Protein Interaction Domains and Motifs , Protein Structure, Secondary , Receptors, Antigen, T-Cell/immunology , T-Lymphocytes/chemistry , T-Lymphocytes/immunology , Time FactorsABSTRACT
The E2 envelope glycoprotein is the primary target of human neutralizing antibody response against hepatitis C virus (HCV), and is thus a major focus of vaccine and immunotherapeutics efforts. There is emerging evidence that E2 is a highly complex, dynamic protein with residues across the protein that are modulating antibody recognition, local and global E2 stability, and viral escape. To comprehensively map these determinants, we performed global E2 alanine scanning with a panel of 16 human monoclonal antibodies (hmAbs), resulting in an unprecedented dataset of the effects of individual alanine substitutions across the E2 protein (355 positions) on antibody recognition. Analysis of shared energetic effects across the antibody panel identified networks of E2 residues involved in antibody recognition and local and global E2 stability, as well as predicted contacts between residues across the entire E2 protein. Further analysis of antibody binding hotspot residues defined groups of residues essential for E2 conformation and recognition for all 14 conformationally dependent E2 antibodies and subsets thereof, as well as residues that enhance antibody recognition when mutated to alanine, providing a potential route to engineer E2 vaccine immunogens. By incorporating E2 sequence variability, we found a number of E2 polymorphic sites that are responsible for loss of neutralizing antibody binding. These data and analyses provide fundamental insights into antibody recognition of E2, highlighting the dynamic and complex nature of this viral envelope glycoprotein, and can serve as a reference for development and rational design of E2-targeting vaccines and immunotherapeutics.
ABSTRACT
Protein-protein interactions represent an exciting and challenging target class for therapeutic intervention using small molecules. Protein interaction sites are often devoid of the deep surface pockets presented by "traditional" drug targets, and crystal structures reveal that inhibitors typically engage these sites using very shallow binding modes. As a consequence, modern virtual screening tools developed to identify inhibitors of traditional drug targets do not perform as well when they are instead deployed at protein interaction sites. To address the need for novel inhibitors of important protein interactions, here we introduce an alternate docking strategy specifically designed for this regime. Our method, termed DARC (Docking Approach using Ray-Casting), matches the topography of a surface pocket "observed" from within the protein to the topography "observed" when viewing a potential ligand from the same vantage point. We applied DARC to carry out a virtual screen against the protein interaction site of human antiapoptotic protein Mcl-1 and found that four of the top-scoring 21 compounds showed clear inhibition in a biochemical assay. The Ki values for these compounds ranged from 1.2 to 21 µM, and each had ligand efficiency comparable to promising small-molecule inhibitors of other protein-protein interactions. These hit compounds do not resemble the natural (protein) binding partner of Mcl-1, nor do they resemble any known inhibitors of Mcl-1. Our results thus demonstrate the utility of DARC for identifying novel inhibitors of protein-protein interactions.