Predicting the Real‐Valued Inter‐Residue Distances for Proteins

Abstract

Abstract Predicting protein structure from the amino acid sequence has been a challenge with theoretical and practical significance in biophysics. Despite the recent progresses elicited by improved inter‐residue contact prediction, contact‐based structure prediction has gradually reached the performance ceiling. New methods have been proposed to predict the inter‐residue distance, but unanimously by simplifying the real‐valued distance prediction into a multiclass classification problem. Here, a lightweight regression‐based distance prediction method is shown, which adopts the generative adversarial network to capture the delicate geometric relationship between residue pairs and thus could predict the continuous, real‐valued inter‐residue distance rapidly and satisfactorily. The predicted residue distance map allows quick structure modeling by the CNS suite, and the constructed models approach the same level of quality as the other state‐of‐the‐art protein structure prediction methods when tested on CASP13 targets. Moreover, this method can be used directly for the structure prediction of membrane proteins without transfer learning.

Keywords

• deep learning
• generative adversarial networks
• protein inter‐residue distance
• protein structure prediction
• real‐valued distance prediction