4.8 Article

WALTZ-DB 2.0: an updated database containing structural information of experimentally determined amyloid-forming peptides

期刊

NUCLEIC ACIDS RESEARCH
卷 48, 期 D1, 页码 D389-D393

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OXFORD UNIV PRESS
DOI: 10.1093/nar/gkz758

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资金

  1. European Research Council under the European Union 2020 Framework Programme ERC Grant Agreement [647458]
  2. Flanders institute for Biotechnology (VIB)
  3. University of Leuven
  4. Fund for Scientific Research Flanders (FWO)
  5. Fund for Scientific Research Flanders Post-doctoral Fellowship (FWO) [12P0919N]

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Transition of soluble proteins into insoluble amyloid fibrils is driven by self-propagating short sequence stretches. However, accurate prediction of aggregation determinants remains challenging. Here, we describe WALTZ-DB 2.0, an updated and significantly expanded open-access database providing information on experimentally determined amyloid-forming hexapeptide sequences (http://waltzdb.switchlab.org/). We have updated WALTZ-DB 2.0 with new entries, including: (i) experimental validation of an in-house developed dataset of 229 hexapeptides, using electron microscopy and Thioflavin-T binding assays; (ii) manual curation of 98 amyloid-forming peptides isolated from literature. Furthermore, the content has been expanded by adding novel structural information for peptide entries, including sequences of the previous version. Using a computational methodology developed in the Switch lab, we have generated 3D-models of the putative amyloid fibril cores of WALTZ-DB 2.0 entries. Structural models, coupled with information on the energetic contributions and fibril core stabilities, can be accessed through individual peptide entries. Customized filtering options for subset selections and new modelling graphical features were added to upgrade online accessibility, providing a user-friendly interface for browsing, downloading and updating. WALTZ-DB 2.0 remains the largest open-access repository for amyloid fibril formation determinants and will continue to enhance the development of new approaches focused on accurate prediction of aggregation prone sequences.

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