The importance of residue-level filtering and the Top2018 best-parts dataset of high-quality protein residues.

Journal Article (Journal Article)

We have curated a high-quality, "best-parts" reference dataset of about 3 million protein residues in about 15,000 PDB-format coordinate files, each containing only residues with good electron density support for a physically acceptable model conformation. The resulting prefiltered data typically contain the entire core of each chain, in quite long continuous fragments. Each reference file is a single protein chain, and the total set of files were selected for low redundancy, high resolution, good MolProbity score, and other chain-level criteria. Then each residue was critically tested for adequate local map quality to firmly support its conformation, which must also be free of serious clashes or covalent-geometry outliers. The resulting Top2018 prefiltered datasets have been released on the Zenodo online web service and are freely available for all uses under a Creative Commons license. Currently, one dataset is residue filtered on main chain plus Cβ atoms, and a second dataset is full-residue filtered; each is available at four different sequence-identity levels. Here, we illustrate both statistics and examples that show the beneficial consequences of residue-level filtering. That process is necessary because even the best of structures contain a few highly disordered local regions with poor density and low-confidence conformations that should not be included in reference data. Therefore, the open distribution of these very large, prefiltered reference datasets constitutes a notable advance for structural bioinformatics and the fields that depend upon it.

Full Text

Duke Authors

Cited Authors

  • Williams, CJ; Richardson, DC; Richardson, JS

Published Date

  • January 2022

Published In

Volume / Issue

  • 31 / 1

Start / End Page

  • 290 - 300

PubMed ID

  • 34779043

Pubmed Central ID

  • PMC8740842

Electronic International Standard Serial Number (EISSN)

  • 1469-896X

Digital Object Identifier (DOI)

  • 10.1002/pro.4239


  • eng

Conference Location

  • United States