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

• URL: http://arxiv.org/abs/2312.05273v1
• Date: Thu, 7 Dec 2023 23:34:03 GMT
• Title: Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation
• Authors: Divya Nori and Simon V. Mathis and Amir Shanehsazzadeh
• Abstract summary: We compare 8 common scoring paradigms based on open-source models to classify antibody designs as binders or non-binders. Results show that existing methods struggle to detect binders, and performance is highly variable across antigens.
• Score: 0.08968838300743379
• License: http://creativecommons.org/licenses/by/4.0/
• 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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