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Predicting protein structure from the amino acid sequence has been achallenge with theoretical and practical significance in biophysics. Despitethe recent progresses elicited by improved residue-residue contact prediction,contact-based structure prediction has gradually reached the performanceceiling. New methods have been proposed to predict the residue-residuedistance, but unanimously by simplifying the real-valued distance predictioninto a multiclass classification problem. Here we show a regression-baseddistance prediction method, which adopts the generative adversarial network tocapture the delicate geometric relationship between residue pairs and thuscould predict the continuous, real-valued residue-residue distancesatisfactorily. The predicted residue distance map allows rapid structuremodeling by the CNS suite, and the constructed models approach at least thesame level of quality as the other state-of-the-art protein structureprediction methods when tested on available CASP13 targets. Moreover, thismethod can be used directly for the structure prediction of membrane proteinswithout transfer learning.

Predicting the real-valued distances between residue pairs for proteins