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Article ; Online: Modeling and Viewing T Cell Receptors Using TCRmodel and TCR3d.

Gowthaman, Ragul / Pierce, Brian G

Methods in molecular biology (Clifton, N.J.)

2020 Volume 2120, Page(s) 197–212

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Amino Acid Sequence ; Animals ; Databases, Protein ; Humans ; Models, Molecular ; Protein Conformation ; Receptors, Antigen, T-Cell/chemistry ; Software
Chemical Substances	Receptors, Antigen, T-Cell
Language	English
Publishing date	2020-03-02
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-0327-7_14
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.

Yin, Rui / Ribeiro-Filho, Helder V / Lin, Valerie / Gowthaman, Ragul / Cheung, Melyssa / Pierce, Brian G

Nucleic acids research

2023 Volume 51, Issue W1, Page(s) W569–W576

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 ... ...

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.
MeSH term(s)	Humans ; Deep Learning ; Receptors, Antigen, T-Cell/chemistry ; Peptides/chemistry ; Computer Simulation ; Antigens
Chemical Substances	Receptors, Antigen, T-Cell ; Peptides ; Antigens
Language	English
Publishing date	2023-05-04
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkad356
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Article ; Online: TCR3d: The T cell receptor structural repertoire database.

Gowthaman, Ragul / Pierce, Brian G

Bioinformatics (Oxford, England)

2019 Volume 35, Issue 24, Page(s) 5323–5325

Abstract: 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 ... ...

Abstract	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/.
MeSH term(s)	Antigens ; Databases, Factual ; Peptides ; Receptors, Antigen, T-Cell
Chemical Substances	Antigens ; Peptides ; Receptors, Antigen, T-Cell
Language	English
Publishing date	2019-06-26
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz517
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Article ; Online: TCRmodel: high resolution modeling of T cell receptors from sequence.

Gowthaman, Ragul / Pierce, Brian G

Nucleic acids research

2018 Volume 46, Issue W1, Page(s) W396–W401

Abstract: 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 ... ...

Abstract	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/.
MeSH term(s)	Algorithms ; Amino Acid Sequence ; Antigens/chemistry ; Antigens/immunology ; Benchmarking ; Binding Sites ; Databases, Protein ; Humans ; Internet ; Models, Molecular ; Protein Binding ; Protein Interaction Domains and Motifs ; Protein Structure, Secondary ; Receptors, Antigen, T-Cell/chemistry ; Receptors, Antigen, T-Cell/immunology ; Software ; Structural Homology, Protein ; T-Lymphocytes/chemistry ; T-Lymphocytes/immunology ; Time Factors
Chemical Substances	Antigens ; Receptors, Antigen, T-Cell
Language	English
Publishing date	2018-05-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gky432
Database	MEDical Literature Analysis and Retrieval System OnLINE

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ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Structural basis for oligoclonal T cell recognition of a shared p53 cancer neoantigen

Daichao Wu / D. Travis Gallagher / Ragul Gowthaman / Brian G. Pierce / Roy A. Mariuzza

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 12

Abstract: Developing broadly applicable neoantigen-directed adoptive cell therapies (ACTs) is challenging because each cancer patient has an unique neoantigen repertoire. Here, the authors present the crystal structures of tumor-specific T cell receptors (TCRs) ... ...

Abstract	Developing broadly applicable neoantigen-directed adoptive cell therapies (ACTs) is challenging because each cancer patient has an unique neoantigen repertoire. Here, the authors present the crystal structures of tumor-specific T cell receptors (TCRs) that recognize a shared neoepitope arising from the R175H driver mutation in the p53 oncogene (p53R175H) alone and bound to p53R175H–HLA-A2, which are of interest for the structure-guided design of TCRs to improve T cell potency for ACT.
Keywords	Science ; Q
Language	English
Publishing date	2020-06-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Structural basis for oligoclonal T cell recognition of a shared p53 cancer neoantigen

Daichao Wu / D. Travis Gallagher / Ragul Gowthaman / Brian G. Pierce / Roy A. Mariuzza

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 12

Abstract	Developing broadly applicable neoantigen-directed adoptive cell therapies (ACTs) is challenging because each cancer patient has an unique neoantigen repertoire. Here, the authors present the crystal structures of tumor-specific T cell receptors (TCRs) that recognize a shared neoepitope arising from the R175H driver mutation in the p53 oncogene (p53R175H) alone and bound to p53R175H–HLA-A2, which are of interest for the structure-guided design of TCRs to improve T cell potency for ACT.
Keywords	Science ; Q
Language	English
Publishing date	2020-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Structural basis for oligoclonal T cell recognition of a shared p53 cancer neoantigen.

Wu, Daichao / Gallagher, D Travis / Gowthaman, Ragul / Pierce, Brian G / Mariuzza, Roy A

Nature communications

2020 Volume 11, Issue 1, Page(s) 2908

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 ... ...

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.
MeSH term(s)	Antigens, Neoplasm/chemistry ; Binding Sites ; Biotinylation ; Codon ; Crystallography, X-Ray ; Epitopes ; Escherichia coli/metabolism ; HLA-A2 Antigen/chemistry ; Humans ; Immunotherapy, Adoptive ; Ligands ; Lymphocytes, Tumor-Infiltrating/immunology ; Mutation ; Neoplasms/metabolism ; Peptides/chemistry ; Protein Binding ; Protein Conformation ; Protein Folding ; Receptors, Antigen, T-Cell/metabolism ; Software ; Surface Plasmon Resonance ; T-Lymphocytes/immunology ; Tumor Suppressor Protein p53/chemistry
Chemical Substances	Antigens, Neoplasm ; Codon ; Epitopes ; HLA-A2 Antigen ; Ligands ; Peptides ; Receptors, Antigen, T-Cell ; TP53 protein, human ; Tumor Suppressor Protein p53
Language	English
Publishing date	2020-06-09
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-020-16755-y
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Article ; Online: Structural and energetic profiling of SARS-CoV-2 receptor binding domain antibody recognition and the impact of circulating variants.

Yin, Rui / Guest, Johnathan D / Taherzadeh, Ghazaleh / Gowthaman, Ragul / Mittra, Ipsa / Quackenbush, Jane / Pierce, Brian G

PLoS computational biology

2021 Volume 17, Issue 9, Page(s) e1009380

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Antibodies, Viral/chemistry ; Antibodies, Viral/metabolism ; Binding Sites ; COVID-19/virology ; Cluster Analysis ; Computational Biology ; Humans ; Models, Molecular ; Protein Binding ; SARS-CoV-2/genetics ; Spike Glycoprotein, Coronavirus/chemistry ; Spike Glycoprotein, Coronavirus/genetics ; Spike Glycoprotein, Coronavirus/metabolism
Chemical Substances	Antibodies, Viral ; Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2
Language	English
Publishing date	2021-09-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1009380
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Article: Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry.

Bai, Nan / Adeshina, Yusuf / Bychkov, Igor / Xia, Yan / Gowthaman, Ragul / Miller, Sven A / Gupta, Abhishek K / Johnson, David K / Lan, Lan / Golemis, Erica A / Makhov, Petr B / Xu, Liang / Pillai, Manoj M / Boumber, Yanis / Karanicolas, John

Research square

2023

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- ...

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.
Language	English
Publishing date	2023-01-10
Publishing country	United States
Document type	Preprint
DOI	10.21203/rs.3.rs-2395172/v1
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Article: Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry.

bioRxiv : the preprint server for biology

2023

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.
Language	English
Publishing date	2023-01-10
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.01.09.523326
Database	MEDical Literature Analysis and Retrieval System OnLINE

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