AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder
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Abstract
Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using…
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Keywords
- Robustness (evolution)
- Biology
- Web server
- Deep learning
- Benchmark (surveying)
- Computer science
- Artificial intelligence
- Machine learning
UN Sustainable Development Goals
- Affordable and clean energy
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