DeepSP

DeepSP is an antibody-specific surrogate model that can generate 30 spatial properties of an antibody solely based on their sequence.

How to generate descriptors (features) using DeepSP

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Pipeline Workflow

1️⃣ Feature Preparation

Prepare a CSV file named:

DeepSP_input.csv

This file must contain the variable region sequences of the mAbs to be analyzed.

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2️⃣ Generate Spatial Properties (DeepSP)

Run:

DeepSP_predictor.ipynb

DeepSP generates 30 spatial descriptors from antibody sequences.

Output:

DeepSP_descriptors_anarci2_Abdev.csv

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🔄 Update: Migration from ANARCI to ANARCII

AbDev has transitioned from ANARCI to ANARCII for antibody sequence numbering.

Install via:

pip install anarcii

Why this change?

• pip installable
• Improved compatibility with modern Python environments
• Simplified installation (no legacy HMMER dependency)
• Active maintenance

Important Note

Due to differences in numbering logic and backend implementation, minor variations in IMGT residue assignments may occur.

Citation

Kalejaye, L., Wu, I.E., Terry, T., & Lai, P.K.
DeepSP: Deep Learning-Based Spatial Properties to Predict Monoclonal Antibody Stability
Computational and Structural Biotechnology Journal, 23:2220–2229, 2024.
https://www.csbj.org/article/S2001-0370(24)00173-9/fulltext