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AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
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While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant challenges. Particularly, the lack of large-scale standardized benchmark and poor accuray hinder the research progress. In order to standardize comparisons and draw more research interest, we use AlphaFold DB, one of the world's largest protein structure databases, to establish a new graph-based benchmark -- AlphaDesign. Based on AlphaDesign, we propose a new method called ADesign to improve accuracy by introducing protein angles as new features, using a simplified graph transformer encoder (SGT), and proposing a confidence-aware protein decoder (CPD). Meanwhile, SGT and CPD also improve model efficiency by simplifying the training and testing procedures. Experiments show that ADesign significantly outperforms previous graph models, e.g., the average accuracy is improved by 8\%, and the inference speed is 40+ times faster than before.
Forward citations
Cited by 5 Pith papers
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AlphaFold Database Debiasing for Robust Inverse Folding
A denoising autoencoder trained on experimentally determined structures removes AlphaFold-specific geometric bias from database structures and recovers most of the inverse folding accuracy lost when training on raw Al...
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ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Search
A reward-guided tree search over a frozen protein language model designs diverse sequences that score higher on ESMFold-based self-consistency benchmarks than existing inverse folding methods.
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EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
A Markov-bridge inverse folding model fine-tuned with energy-based preference pairs and an explicit ΔΔG loss designs lower-energy protein complex sequences while keeping sequence recovery close to state-of-the-art.
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From Sentences to Sequences: Rethinking Languages in Biological System
A new RNA inverse folding model (RiFold) using stochastic-order decoding and structure-aware metrics outperforms prior methods, and the paper shows sequence recovery and structural recovery are correlated but not equivalent.
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Protein Inverse Folding From Structure Feedback
DPO fine-tuning with ESMFold TM-Score preferences raises sequence recovery and predicted TM-Score of inverse folding models, and multi-round refinement produces large gains on hard targets.
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