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De novo design of high-affinity protein binders with AlphaProteo
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Computational design of protein-binding proteins is a fundamental capability with broad utility in biomedical research and biotechnology. Recent methods have made strides against some target proteins, but on-demand creation of high-affinity binders without multiple rounds of experimental testing remains an unsolved challenge. This technical report introduces AlphaProteo, a family of machine learning models for protein design, and details its performance on the de novo binder design problem. With AlphaProteo, we achieve 3- to 300-fold better binding affinities and higher experimental success rates than the best existing methods on seven target proteins. Our results suggest that AlphaProteo can generate binders "ready-to-use" for many research applications using only one round of medium-throughput screening and no further optimization.
Forward citations
Cited by 7 Pith papers
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pTMEnergy converts AlphaFold pAE confidence logits into an energy-like score that improves computational binder design success and virtual screening over ipTM-based and generative baselines.
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Diffusion Sequence Models for Enhanced Protein Representation and Generation
Masked diffusion retrofitted onto ESM2 produces a pLM that matches representation benchmarks and generates protein-like sequences, with an in-silico binder design case study.
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The paper describes a production antibody design platform built on HelixFold3 and reports that sampling more candidates improves top-ranked metrics, a claim that reduces to trivial sampling statistics.
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HelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold3
The paper presents a scalable, fully automated binder design pipeline and benchmarks it on six targets using predicted structural and energetic scores.
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