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SPIN2: Predicting sequence profiles from protein structures using deep neural networks
Author(s) -
O'Connell James,
Li Zhixiu,
Hanson Jack,
Heffernan Rhys,
Lyons James,
Paliwal Kuldip,
Dehzangi Abdollah,
Yang Yuedong,
Zhou Yaoqi
Publication year - 2018
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25489
Subject(s) - protein design , artificial neural network , sequence (biology) , protein structure prediction , computer science , fold (higher order function) , protein sequencing , algorithm , inverse , protein folding , folding (dsp implementation) , protein structure , artificial intelligence , pattern recognition (psychology) , mathematics , peptide sequence , biology , engineering , genetics , gene , biochemistry , geometry , electrical engineering , programming language
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.