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Machine learning in protein science

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Open
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Over the past few years, deep-learning-based methods have revolutionized the field of protein structure prediction. Tools like AlphaFold can now reliably model the structure of a protein based solely on its amino acid sequence, even when few homologous sequences and structures are available. These advances promise to transform our understanding of individual proteins’ biological functions, with implications for drug discovery, de novo protein design, and protein-protein interaction research.

This Collection will feature Articles applying this exciting new technology in these and other areas.

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Illustration of protein

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Submitting a paper for consideration

 

To submit your manuscript for consideration at Scientific Reports as part of this Collection, please follow the steps detailed on this page. On the first page of our online submission system, under “I’m submitting:” select the option “any other article type”. Once logged in you can submit your manuscript to a Collection by selecting “Guest Edited Collection”, under the “Choose the appropriate manuscript type” message, and clicking “Continue”. Then when filling out the manuscript information, select the "Machine learning in protein science" Collection from the alphabetical list on the “Springer Nature Subject Category” tab. Authors should express their interest in the Collection in their cover letter.

Accepted papers are published on a rolling basis as soon as they are ready.

In addition to papers on Machine learning in protein science, Scientific Reports welcomes all original research in molecular biology. To browse our latest articles in molecular biology click here.

 

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