SPIN2: Predicting sequence profiles from protein structures using deep neural networks.
Proteins
; 86(6): 629-633, 2018 06.
Article
em En
| MEDLINE
| ID: mdl-29508448
ABSTRACT
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.
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Base de dados:
MEDLINE
Assunto principal:
Software
/
Proteínas
/
Redes Neurais de Computação
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Austrália