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arXiv · 2312.05273

Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation

Divya Nori·Simon V. Mathis·Amir Shanehsazzadeh
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Abstract

The success of therapeutic antibodies relies on their ability to selectively bind antigens. AI-based antibody design protocols have shown promise in generating epitope-specific designs. Many of these protocols use an inverse folding step to generate diverse sequences given a backbone structure. Due to prohibitive screening costs, it is key to identify candidate sequences likely to bind in vitro. Here, we compare the efficacy of 8 common scoring paradigms based on open-source models to classify antibody designs as binders or non-binders. We evaluate these approaches on a novel surface plasmon resonance (SPR) dataset, spanning 5 antigens. Our results show that existing methods struggle to detect binders, and performance is highly variable across antigens. We find that metrics computed on flexibly docked antibody-antigen complexes are more robust, and ensembles scores are more consistent than individual metrics. We provide experimental insight to analyze current scoring techniques, highlighting that the development of robust, zero-shot filters is an important research gap.

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Divya Nori, Simon V. Mathis, Amir Shanehsazzadeh. 2023-12-07. Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation. https://arxiv.org/abs/2312.05273

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