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Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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Application of protein language models for antibody developability prediction.

Samad Amini¹, Yimin Huang¹, Mark Julian¹

¹Research, Biogen, Cambridge, MA, USA.

|March 20, 2026

Summary

Protein language models (PLMs) enhance antibody discovery by improving developability assessments. Fine-tuning PLMs on antibody sequence data boosts predictive performance for critical developability assays.

Keywords:

Developability machine learning protein language models

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Biotechnology
Computational Biology
Immunology

Background:

Protein language models (PLMs) show promise for predicting antibody sequence-property relationships.
Their real-world utility in industrial antibody discovery pipelines needs further investigation.

Purpose of the Study:

To systematically evaluate state-of-the-art PLMs for antibody developability assessment.
To determine the effectiveness of domain-adaptive fine-tuning and sequence likelihoods for predicting developability risks.

Main Methods:

Evaluated multiple PLMs using internal datasets of antibody sequences and developability assay measurements from 33 therapeutic programs.
Assessed developability across three dimensions: polyspecificity reagent (PSR), hydrophobic interaction chromatography (HIC), and AC-SINS.
Compared performance of fine-tuned PLMs against pretrained representations and analyzed sequence likelihoods as unsupervised indicators.

Main Results:

Domain-adaptive fine-tuning consistently improved PLM predictive performance across all evaluated developability assays.
Sequence likelihoods from pretrained PLMs served as useful, albeit varied, unsupervised indicators of developability risk.
PLMs demonstrated robust and complementary signals for antibody developability assessment.

Conclusions:

PLMs, particularly when fine-tuned, offer significant improvements in predicting antibody developability.
These models support practical applications in early-stage antibody candidate optimization and selection.
PLMs are valuable tools for enhancing the efficiency and success rate of therapeutic antibody discovery.