SPIN2: Predicting sequence profiles from protein structures using deep neural networks

Designing protein sequences that can fold into a given structure is a well‐known inverse protein‐folding problem. One important characteristic to attain for a protein design program is the ability to recover wild‐type sequences given their native backbone structures. The highest average sequence ide...

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Bibliographic Details
Published in:Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2018-06, Vol.86 (6), p.629-633
Main Authors: O'Connell, James, Li, Zhixiu, Hanson, Jack, Heffernan, Rhys, Lyons, James, Paliwal, Kuldip, Dehzangi, Abdollah, Yang, Yuedong, Zhou, Yaoqi
Format: Article
Language:English
Subjects:
Amino acid sequence
Artificial neural networks
bioinformatics
deep learning
Depth profiling
Design
fold recognition
Neural networks
Protein folding
Proteins
Sequences
structure prediction
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Summary:Designing protein sequences that can fold into a given structure is a well‐known inverse protein‐folding problem. One important characteristic to attain for a protein design program is the ability to recover wild‐type sequences given their native backbone structures. The highest average sequence identity accuracy achieved by current protein‐design programs in this problem is around 30%, achieved by our previous system, SPIN. SPIN is a program that predicts sequences compatible with a provided structure using a neural network with fragment‐based local and energy‐based nonlocal profiles. Our new model, SPIN2, uses a deep neural network and additional structural features to improve on SPIN. SPIN2 achieves over 34% in sequence recovery in 10‐fold cross‐validation and independent tests, a 4% improvement over the previous version. The sequence profiles generated from SPIN2 are expected to be useful for improving existing fold recognition and protein design techniques. SPIN2 is available at http://sparks-lab.org.
ISSN:0887-3585
1097-0134
DOI:10.1002/prot.25489