flowchart TD
			    A["Antibody Modeling"] --> B("Sequence Analysis")
                B --> C
                C{{"Do you have a structure of the antibody-antigen complex of interest?"}}
                C -- Yes --> G("Structure Quality Assessment and Refinement")
                C -- No --> D{{"For which part of the system do you want to predict a structure?"}}
                D -- Antibody -->E("Antibody Structure Prediction")
                D -- Antigen -->F("Antigen Structure Prediction")
                D -- "Antibody-Antigen Complex" -->H("Antibody-Antigen Complex Structure Prediction")

                E --> G
                F --> G
                G --> H

                H --> I("Antibody Engineering")
                I --> J("Developability Assessment")
                J --> K("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 A path_title
        class B,E,F,G,H,I,J,K simple_step
		class C,D decision_step

Learning Path: Antibody Modeling

Antibodies are a major class of biologics with broad therapeutic applications. Computational modeling plays an important role in different stages of antibody discovery and optimization, spanning from initial structure modeling and refinement to final developability assessment. This learning path provides an overview of Schrödinger tools and workflows for modeling antibodies, with a focus on choosing appropriate approaches based on available information and research goals.

The Introduction to computational antibody engineering online course provides an overview of structure-based workflows for assessing and improving the stability, affinity, developability, and ‘humanness’ of antibody-therapeutics.

Next step: Sequence Analysis

Sequence Analysis

The first step is gathering the available relevant sequence data. Once you have the sequence data, you can perform sequence-based analysis that helps annotate antibody sequences by identifying frameworks, CDR regions, and germline origins. It also helps to flag sequence features that may warrant closer inspection in downstream modeling.

Next step: Do you have a structure of the antibody-antigen complex of interest?

Do you have a structure of the antibody-antigen complex of interest?

If you already have a structure of an antibody-antigen complex, you will need to prepare and refine the structure for downstream tasks.

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

There are two approaches to predict structures – homology modeling and machine learning-based (ML) structure prediction. Homology modeling uses closely related proteins to map structure motifs to a sequence, which is only possible if structures of homologs with sufficiently high sequence identity are available. ML structure prediction uses the similarity to known structures as well, but in a much more holistic fashion. It can be useful to use both approaches and compare the results. Note that the following steps are not exclusive -- you may need to predict structures for both proteins separately, refine them, and then bring them together to obtain the complex structure.

Antibody Structure Prediction

You can predict antibody structures by classical template-based homology modeling or by machine learning approach using the Antibody Structure Prediction panel.

Antibody Structure Prediction and Visualization with BioLuminate tutorial guides you through the steps to predict antibody structures via homology modeling approach and make necessary structural refinements.
Antibody Structure Prediction video guides you through different strategies for antibody structure prediction.

To learn more about the available methods, you can read more in following publications:

Next step: Structure Quality Assessment and Refinement

Antigen Structure Prediction

You can predict antigen structures by classical template-based homology modeling or by machine learning approach.

Computational Structure Prediction
A general introduction to computational structure prediction methods for protein targets.

Next step: Structure Quality Assessment and Refinement

Antibody-Antigen Complex Structure Prediction

Antibody – Antigen Docking with PIPER tutorial is a step-by-step guide for antibody-antigen docking.
PIPER: an FFT-based protein docking program with pairwise potentials publication that describes the PIPER docking program

Next step: Antibody Engineering

Structure Quality Assessment and Refinement

Assessing the structure quality of the predicted structure is an important prerequisite before proceeding to subsequent computational analysis, such as antibody-antigen docking.

Introduction to Structure Preparation and Visualization
tutorial shows the workflow for a generic protein.
Protein Reliability Report gives a graphical representation of various metrics that indicate reliability or quality of protein structures
While structure quality assessment follows common principles for all proteins, refinement strategies often differ for antibodies and antigens due to their distinct structural features. Antibodies have conserved frameworks with highly variable CDR loops (especially CDR-H3), which often require targeted loop refinement. Antigens can range from small, rigid domains to large, flexible or multi-domain proteins, and their refinement typically focuses on overall fold quality and local geometry.
Antibody Loop Modeling for refining CDR loop positions
Refining Protein Structures for refining antigen structures

Next step: Antibody-Antigen Complex Structure Prediction

Antibody Engineering

Antibody engineering covers computational and experimental strategies used to design, modify, and optimize antibodies for improved therapeutic performance. This includes improving antigen binding, ensuring structural stability, minimizing developability risks, and tailoring antibody formats and functions for development. Following is a list of some generic resources that introduce you to the protein design tools to introduce mutations and predict their impact on the affinity of the complex:

The Introduction to computational antibody engineering online course provides an overview of structure-based workflows for assessing and improving the stability, affinity, developability, and ‘humanness’ of antibody-therapeutics.
Improving Antibody Stability/Affinity Using MM-GBSA Residue Scanning
MM-GBSA Residue Scanning allows you to identify mutation hotspots (via alanine scanning) and improve the stability and affinity via affinity maturation/CDR mutagenesis.
MM-GBSA Residue Scanning quick reference sheet

Additionally, if your antibody structure is derived from a non-human source, you can perform antibody humanization via CDR grafting or residue scanning.

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 Surface Analyzer can identify both aggregation or reactivity hotspots.
Reactive Protein Residues Panel scans the solvent-exposed residues for reactive sites.
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.

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 antibodies and antibody-antigen complexes. For more in-depth guidance on particular scientific workflows, consult our other learning resources.