Section 9 of 17
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¶The prediction of protein structures has had a long and varied development, which is extensively covered in a number of reviews14,40–43. Despite the long history of applying neural networks to structure prediction14,42,43, they have only recently come to improve structure prediction10,11,44,45. These approaches effectively leverage the rapid improvement in computer vision systems46 by treating the problem of protein structure prediction as converting an ‘image’ of evolutionary couplings22–24 to an ‘image’ of the protein distance matrix and then integrating the distance predictions into a heuristic system that produces the final 3D coordinate prediction. A few recent studies have been developed to predict the 3D coordinates directly47–50, but the accuracy of these approaches does not match traditional, hand-crafted structure prediction pipelines51. In parallel, the success of attention-based networks for language processing52 and, more recently, computer vision31,53 has inspired the exploration of attention-based methods for interpreting protein sequences54–56.