Learning Path: T Cell Receptor Engineering

T Cell receptors (TCRs) bind to major histocompatibility complex (MHC) proteins presenting peptide antigens and have large therapeutic potential. While many computational tools and workflows designed for antibody engineering are also applicable to T cell receptors, there are challenges and tools particular to this class of immunotherapeutics. This learning path provides an overview of Schrödinger tools to aid your TCR engineering efforts.

TutorialIntroduction to T Cell Receptor Modeling with BioLuminateis a guided hands-on example of many of the workflows included in this learning path.

Sequence Analysis

Sequence-based analysis can be helpful for analyzing large amounts of data for both TCRs and MHCs.

TutorialIntroduction to Bioluminate and the Multiple Sequence Viewer(MSV) for the basics
DocumentationSequence Alignment Optionsin the MSV
DocumentationIdentify Family Features
DocumentationProtein Sequence Descriptors

Structure Prediction and Refinement

Regardless whether you have experimental structures of the proteins of interest or need to predict them based on the sequence, you will need to prepare and refine them for modeling. This step lists generic resources for structure preparation and assessing structure quality.

Specialized tools for structure prediction are available for the different components of the TCR-pMHC complex and listed in the next steps of this path.

TutorialIntroduction to Structure Preparation and Visualizationtutorial
DocumentationProtein Reliability Report Documentation
Quick Reference SheetInterpreting the Protein Reliability Reportquick reference sheet.
DocumentationBest Practices for Protein Preparation

For which part of the system do you want to predict a structure?

Note that these steps are not exclusive -- you may need to predict structures for both proteins separately and bring them together to obtain the ternary complex structure.

TCR Structure Prediction

You can predict TCR structures by using the machine-learning based TCR structure prediction panel or classical homology modeling.

DocumentationTCR Structure prediction panel
PublicationImmuneBuilder: Deep-Learning models for predicting the structures of immune proteinsThe original publication describing the method used in the TCR structure prediction panel.
TutorialHeteromultimer Homology Modeling using the Multiple Sequence Viewer/EditorAs an alternative to using the ML-based TCR structure prediction panel, you can also use homology modeling.
DocumentationRefine Loops panelimproves the predictions for the CDR loop positions.
TutorialDisulfide Bond Engineering

pMHC Structure Prediction

The structure of the peptide-MHC complex can be predicted by first using homology modeling to obtain a structure for the "bare" MHC and then finding a pose for the peptide in the binding groove.

TutorialBuilding Homology Models with the Multiple Sequence Viewer/EditorA general introduction to homology modeling.
TutorialHeteromultimer Homology Modeling using the Multiple Sequence Viewer/EditorAs the MHC consists of two chains, you will need to use the heteromultimer mode for homology modeling.
TutorialPeptide Modeling with BioLuminateCommon workflows for working with peptides in BioLuminate.
DocumentationRefine Loops panelfor improving the experimental or computationally predicted pose of a peptide in the MHC groove.
DocumentationProtein Linker DesignThis panel can help you design linkers to covalently connect the MHC chains to each other or the peptide in the binding groove. This can help reduce assay noise due to dissociation of the pMHC complex.
TutorialDisulfide Bond Engineering

Ternary complex prediction

Development and validation of more specialized tools for predicting the TCR-pMHC ternary complex is ongoing. At the moment, the only recommended method is using PIPER.

TutorialIntroduction to T Cell Receptor Modeling with BioLuminatefor a guided hands-on example of common workflows.
DocumentationProtein-Protein Docking with PIPER

Protein Engineering

Once you have the ternary structure, you can use generic protein design tools to introduce mutations and predict their impact on the affinity of the complex.

DocumentationNext-generation protein engineering with physics: Predicting affinity, stability, selectivity, and pH-dependent bindingoverview webinar
TutorialImproving the Thermostability of T4 Lysozyme Using Protein FEP+ Guided Design
TutorialValidating a Protein Free Energy Perturbation Model for Thermostability Predictions for Single Point Mutations
TutorialDisulfide Bond Engineering
TutorialIdentifying impactful mutations using FEP+ residue scanning
Quick Reference SheetMM-GBSA Residue Scanning
Commanddocking_angles.pyscript which calculates geometric metrics describing the relative orientation of the TCR and pMHC in a ternary complex.

Developability Assessment

Identifying and designing out liabilities before moving to the experimental stage can significantly reduce costs and speed up project timelines.

TutorialLiability Analysis for BiologicsThis tutorial highlights a few different tools and techniques for identifying liabilities.
TutorialProtein pKa Prediction with Constant pH Molecular DynamicsThe most accurate method to determine how your protein reacts to pH changes.
DocumentationProtein Titration Curve PanelA faster, less accurate method to predict pH-dependent protein behavior.
DocumentationReactive Protein Residues Panelscans the solvent-exposed residues for reactive sites.
DocumentationProtein Surface Analyzercan identify both aggregation or reactivity hotspots.

Conclusion and Next Steps

This concludes the overview of Schrödinger tools and workflows which have been validated for use on or with TCRs and MHCs. For more in-depth guidance on particular scientific workflows, consult our other learning resources.

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Online courseSchrödinger Online Certification Courses

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