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Patterns of Ligands Coordinated to Metallocofactors Extracted from the Protein Data Bank.

Luca BelmonteSheref S Mansy
Published in: Journal of chemical information and modeling (2017)
A new R tool is described that rapidly identifies, ranks, and clusters sequence patterns coordinated to metallocofactors. This tool, PdPDB, fills a void because, unlike currently available tools, PdPDB searches through sequences with metal coordination as the primary determinant and can identify patterns consisting of amino acids, nucleotides, and small molecule ligands at once. PdPDB was tested by analyzing structures that coordinate Fe2+/3+, [2Fe-2S], [4Fe-4S], Zn2+, and Mg2+ cofactors. PdPDB confirmed previously identified sequence motifs and revealed which residues are enriched (e.g., glycine) and are under-represented (e.g., glutamine) near ligands to metal centers. The data show the similarities and differences between different metal-binding sites. The patterns that coordinate metallocofactors vary, depending upon whether the metal ions play a structural or catalytic role, with catalytic metal centers exhibiting partial coordination by small molecule ligands. PdPDB 2.0.1 is freely available as a CRAN package.
Keyphrases
  • small molecule
  • amino acid
  • protein protein
  • big data
  • high resolution
  • genome wide
  • heavy metals
  • machine learning
  • quantum dots
  • metal organic framework
  • deep learning