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- W3041027842 abstract "Predicting the secondary structure of a single protein sequence in the absence of homology information has remained a challenge for several decades. Although not as performant as their homology-based counterparts, single-sequence methods are not constrained by the requirement of evolutionary information. More accurate single-sequence approaches have the potential to improve structural modelling across the vast majority of sequence space, especially in areas of great scientific interest like viral proteins, the dark proteome, and de novo protein design. Here we introduce S4PRED, the successor to the open-source PSIPRED-Single method, which utilizes semi-supervised learning to achieve a Q3 score of 75.3% on the standard CB513 test set, taking only single sequences as input. Not only does this result represent a leap in performance for single-sequence methods, it also provides a blueprint for the development of future tools, beyond waiting for larger structural datasets and more powerful neural networks." @default.
- W3041027842 created "2020-07-16" @default.
- W3041027842 creator A5065020771 @default.
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- W3041027842 date "2020-07-14" @default.
- W3041027842 modified "2023-09-25" @default.
- W3041027842 title "Semi-Supervised Learning of Protein Secondary Structure from Single Sequences" @default.
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