The Future of Protein Secondary Structure Prediction Was Invented by Oleg Ptitsyn

When Oleg Ptitsyn and his group published the first secondary structure prediction for a protein sequence, they started a research field that is still active today. Oleg Ptitsyn combined fundamental rules of physics with human understanding of protein structures. Most followers in this field, howeve...

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Bibliographic Details
Published in:	Biomolecules (Basel, Switzerland) Switzerland), 2020-06, Vol.10 (6), p.910
Main Authors:	Rademaker, Daniel, van Dijk, Jarek, Titulaer, Willem, Lange, Joanna, Vriend, Gert, Xue, Li
Format:	Article
Language:	eng
Subjects:	Amino acid sequence Biochemistry - history Biochemistry - methods Biochemistry - trends Bioinformatics Computational Biology - history Computational Biology - methods Computational Biology - trends Deep learning History, 20th Century History, 21st Century Learning algorithms Methods Neural networks Oleg Ptitsyn Predictions Protein folding Protein structure Protein structure prediction Protein Structure, Secondary Proteins - chemistry Ptitsyn Review Secondary structure secondary structure prediction Software Structure-Activity Relationship
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Summary:	When Oleg Ptitsyn and his group published the first secondary structure prediction for a protein sequence, they started a research field that is still active today. Oleg Ptitsyn combined fundamental rules of physics with human understanding of protein structures. Most followers in this field, however, use machine learning methods and aim at the highest (average) percentage correctly predicted residues in a set of proteins that were not used to train the prediction method. We show that one single method is unlikely to predict the secondary structure of all protein sequences, with the exception, perhaps, of future deep learning methods based on very large neural networks, and we suggest that some concepts pioneered by Oleg Ptitsyn and his group in the 70s of the previous century likely are today's best way forward in the protein secondary structure prediction field.
ISSN:	2218-273X 2218-273X