flowchart TD
			step_T_Cell_Receptor_Engineering["T Cell Receptor Engineering"]
			style step_T_Cell_Receptor_Engineering stroke-width:2px,fill:#12122c,stroke:#12122c    
			step_Sequence_Analysis("Sequence Analysis")
			step_Structure_Prediction_and_Refinement("Structure Prediction and Refinement")
			step_For_which_part_of_the_system_do_you_want_to_predict_a_structure{{"For which part of the system do you want to predict a structure?"}}
			step_TCR_structure_prediction("TCR Structure Prediction")
			step_MHC_structure_prediction("pMHC Structure Prediction")
			step_Ternary_complex_prediction("Ternary Complex Prediction")
			step_Protein_Engineering("Protein Engineering")
            step_Developability_Assessment("Developability Assessment")
			step_Conclusion_and_Next_Steps("Conclusion and Next Steps")
    
			step_T_Cell_Receptor_Engineering --> step_Sequence_Analysis
			step_Sequence_Analysis --> step_Structure_Prediction_and_Refinement
			step_Structure_Prediction_and_Refinement --> step_For_which_part_of_the_system_do_you_want_to_predict_a_structure
			step_For_which_part_of_the_system_do_you_want_to_predict_a_structure --> |"TCR"| step_TCR_structure_prediction
			step_For_which_part_of_the_system_do_you_want_to_predict_a_structure --> |"MHC"| step_MHC_structure_prediction
			step_For_which_part_of_the_system_do_you_want_to_predict_a_structure --> |"ternary complex"| step_Ternary_complex_prediction
            step_TCR_structure_prediction --> step_Ternary_complex_prediction
            step_MHC_structure_prediction --> step_Ternary_complex_prediction
            step_Ternary_complex_prediction --> step_Protein_Engineering
			step_Protein_Engineering --> step_Developability_Assessment
            step_Developability_Assessment --> step_Conclusion_and_Next_Steps


		classDef path_title stroke-width:2px,fill:#12122c,stroke:#12122c
		classDef decision_step stroke-width:2px,fill:#005aaa,stroke:#005aaa
		classDef simple_step stroke-width:2px,fill:#12122c,stroke:#12122c
		class T_Cell_Receptor_Engineering path_title
        class step_Sequence_Analysis,step_Structure_Prediction_and_Refinement,step_TCR_structure_prediction,step_MHC_structure_prediction,step_Ternary_complex_prediction,step_Protein_Engineering,step_Developability_Assessment,step_Conclusion_and_Next_Steps simple_step
		class step_For_which_part_of_the_system_do_you_want_to_predict_a_structure decision_step

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.

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

Next step: Sequence Analysis

Sequence Analysis

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

Next step: Structure Prediction and Refinement

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.

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

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.

ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins
The original publication describing the method used in the TCR structure prediction panel.
Heteromultimer Homology Modeling using the Multiple Sequence Viewer/Editor
As an alternative to using the ML-based TCR structure prediction panel, you can also use homology modeling.
Refine Loops panel improves the predictions for the CDR loop positions.

Next step: Ternary complex prediction

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.

Building Homology Models with the Multiple Sequence Viewer/Editor
A general introduction to homology modeling.
Heteromultimer Homology Modeling using the Multiple Sequence Viewer/Editor
As the MHC consists of two chains, you will need to use the heteromultimer mode for homology modeling.
Peptide Modeling with BioLuminate
Common workflows for working with peptides in BioLuminate.
Refine Loops panel for improving the experimental or computationally predicted pose of a peptide in the MHC groove.
Protein Linker Design
This 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.

Next step: Ternary complex prediction

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.

Introduction to T Cell Receptor Modeling with BioLuminate for a guided hands-on example of common workflows.

Next step: Protein Engineering

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.

MM-GBSA Residue Scanning quick reference sheet
docking_angles.py script which calculates geometric metrics describing the relative orientation of the TCR and pMHC in a ternary complex.

Next step: Developability Assessment

Developability Assessment

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

Liability Analysis for Biologics
This tutorial highlights a few different tools and techniques for identifying liabilities.
Protein pKa Prediction with Constant pH Molecular Dynamics
The most accurate method to determine how your protein reacts to pH changes.
Protein Titration Curve Panel
A faster, less accurate method to predict pH-dependent protein behavior.
Reactive Protein Residues Panel scans the solvent-exposed residues for reactive sites.
Protein Surface Analyzer can identify both aggregation or reactivity hotspots.

Next step: Conclusion and Next Steps

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.