.png){kind=icon}
.png){kind=icon}
Return
SPIN2: Predicting sequence profiles from protein structures using deep neural networks
.png){kind=icon}
OA
AI
DOI：10.1002/prot.25489.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 .
Keywords:
bioinformatics
deep learning
fold recognition
neural networks
structure prediction
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
P
IF:
2.8
Papers:
6.6K
Citations:
1.4W