Predicting the Real‐Valued Inter‐Residue Distances for Proteins

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 ceil...

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Bibliographic Details
Published in:Advanced science 2020-10, Vol.7 (19), p.2001314-n/a
Main Authors: Ding, Wenze, Gong, Haipeng
Format: Article
Language:English
Subjects:
Classification
Deep learning
Design
generative adversarial networks
Mapping
Neural networks
Protein folding
protein inter‐residue distance
protein structure prediction
real‐valued distance prediction
Simulation
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Summary: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. A lightweight GAN‐based protein inter‐residue distance prediction method is reported. This method is capable of capturing the delicate geometric relationship between residue pairs and thus could predict the continuous, real‐valued distance rapidly and satisfactorily. Pipelined with structure modeling software like the CNS suite, this method could construct protein structure models with the state‐of‐the‐art quality.
ISSN:2198-3844
2198-3844
DOI:10.1002/advs.202001314