Computational Antibody Humanization

ISEF Category: Biomedical and Health Sciences

Subcategory: Immunology  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A tiny sequence change can make an antibody safer, or make it miss its target. Your job is to ask which humanization design keeps the binding shape closest to the original mouse antibody. That turns a drug design problem into a data project you can run on public structures.

What Is It?

Antibody humanization means editing a mouse antibody so the human immune system is less likely to treat it like a foreign protein. Think of the antibody like a glove. You want to swap the fabric so it looks human, but keep the finger shape that grabs the antigen.

Your project uses public mouse antibodies from Thera-SAbDab and compares them with real humanized versions. AlphaFold-Multimer predicts how the antibody and antigen may fit together, so you can build an affinity proxy, a score that stands in for binding strength. You are not measuring wet-lab affinity directly. You are testing whether the computer score tracks the known humanized counterpart better than the starting mouse sequence does.

Why This Is a Good Topic

This topic works well because you can test real design choices with public data and repeatable code. It connects to drug development, where humanization can lower immune reactions without wrecking binding. You can learn sequence alignment, structure prediction, dataset curation, and basic model evaluation without needing a wet lab.

Research Questions

• How does the choice of humanization strategy change the AlphaFold-Multimer affinity proxy for the same mouse antibody?
• What is the effect of back-mutation count on predicted interface preservation after humanization?
• Does CDR grafting keep the modeled antigen-contact residues closer to the clinical humanized counterpart than framework resurfacing does?
• To what extent do predicted interface scores agree with the published humanized sequences in Thera-SAbDab?
• Which sequence changes, such as framework identity or CDR similarity, best predict loss of modeled binding after humanization?
• How does heavy-chain versus light-chain humanization affect the affinity proxy when only one chain changes?

Basic Materials

• Laptop or desktop computer with internet access and at least 16 GB RAM.
• Free Google account for Colab and file storage.
• Python 3.11 with Jupyter Notebook or Google Colab.
• Spreadsheet software for tracking sequences, scores, and annotations.
• Web access to Thera-SAbDab, RCSB PDB, and PubMed.
• FASTA files and PDB files for the antibody pairs you select.

Advanced Materials

• Workstation with an NVIDIA GPU or access to a university compute cluster.
• Python environment with Biopython, pandas, NumPy, SciPy, and matplotlib.
• Structural analysis tools such as PyMOL and MMseqs2.
• Multiple sequence alignment software for antibody framework comparison.
• Local copy of antibody numbering and annotation resources.
• Shared storage for predicted structures and run logs.

Software & Tools

• Python: Scripts sequence cleaning, alignment, scoring, and plotting.
• Jupyter Notebook: Keeps the workflow readable and makes each analysis step easy to rerun.
• Google Colab: Runs notebook code in a browser if your laptop is slow.
• Biopython: Parses antibody sequences, PDB files, and alignment output.
• ColabFold or AlphaFold-Multimer: Predicts antibody complex structure for your binding proxy.

Experiment Steps

1. Choose a matched antibody set from Thera-SAbDab, including the mouse parent and the clinically humanized version.
2. Decide how you will define each humanization method so every antibody gets the same treatment rules.
3. Build a scoring plan that converts each predicted complex into an affinity proxy and a structure check metric.
4. Plan controls that compare the original mouse antibody, the published humanized counterpart, and any redesigned variants.
5. Set up analysis plots and statistics that show when humanization helps, hurts, or leaves binding unchanged.
6. Predefine how you will handle failed predictions or missing chain pairs so the dataset stays fair.

Common Pitfalls

• Mixing antibodies that bind different antigens, which makes the score comparisons unfair.
• Using the wrong heavy-light chain pairing from the database, which changes the modeled interface.
• Treating one AlphaFold-Multimer run as final, which hides prediction noise.
• Comparing humanized sequences without matching their published clinical counterpart, which removes your real benchmark.
• Letting sequence numbering drift between mouse, humanized, and benchmark files, which breaks your alignment and residue counts.

What Makes This Competitive

A class-level project shows one or two examples. A stronger project compares several humanization rules across a matched antibody set and checks whether the proxy score agrees with known clinical outcomes. Add controls for chain pairing, framework choice, and sequence distance, and you start to measure the method itself, not just the output. That kind of analysis can reveal when humanization helps binding and when it quietly hurts it.

Project Variations

• Test only antibodies with solved antigen complexes, then compare how the proxy changes after humanization.
• Compare CDR grafting with framework resurfacing to see which keeps the interface score closer to the mouse parent.
• Focus on one antibody family, such as anti-TNF or anti-VEGF, and ask whether one design rule works across related targets.

Learn More

• Thera-SAbDab: Search therapeutic antibody structures, humanization records, and matched clinical counterparts in the Therapeutic Structural Antibody Database.
• RCSB PDB: Download antibody-antigen complex structures and inspect their experimental metadata.
• IMGT, the international ImMunoGeneTics information system: Find antibody numbering, germline, and sequence annotation references.
• PubMed: Search review articles on antibody humanization, affinity, and structure-guided design.
• NIH NCBI Bookshelf: Read free textbook chapters on antibodies, protein structure, and immunology.