English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The Protein Data Bank (PDB) undergoes an exponential expansion in terms of the number of macromolecular structures deposited every year. A pivotal question is how this rapid growth of structural information improves the quality of three-dimensional models constructed by contemporary bioinformatics approaches. To address this problem, we performed a retrospective analysis of the structural coverage of a representative set of proteins using remote homology detected by COMPASS and HHpred. We show that the number of proteins whose structures can be confidently predicted increased during a 9-year period between 2005 and 2014 on account of the PDB growth alone. Nevertheless, this encouraging trend slowed down noticeably around the year 2008 and has yielded insignificant improvements ever since. At the current pace, it is unlikely that the protein structure prediction problem will be solved in the near future using existing template-based modeling techniques. Therefore, further advances in experimental structure determination, qualitatively better approaches in fold recognition, and more accurate template-free structure prediction methods are desperately needed.

Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling?