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De novo protein structure prediction from amino acid sequence is one of themost challenging problems in computational biology. As one of the extensivelyexplored mathematical models for protein folding, Hydrophobic-Polar (HP) modelenables thorough investigation of protein structure formation and evolution.Although HP model discretizes the conformational space and simplifies thefolding energy function, it has been proven to be an NP-complete problem. Inthis paper, we propose a novel protein folding framework FoldingZero,self-folding a de novo protein 2D HP structure from scratch based on deepreinforcement learning. FoldingZero features the coupled approach of a two-head(policy and value heads) deep convolutional neural network (HPNet) and aregularized Upper Confidence Bounds for Trees (R-UCT). It is trained solely bya reinforcement learning algorithm, which improves HPNet and R-UCT iterativelythrough iterative policy optimization. Without any supervision and domainknowledge, FoldingZero not only achieves comparable results, but also learnsthe latent folding knowledge to stabilize the structure. Without exponentialcomputation, FoldingZero shows promising potential to be adopted for real-worldprotein properties prediction.

FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model