P
Philip E. Bourne
Researcher at University of Virginia
Publications - 357
Citations - 64294
Philip E. Bourne is an academic researcher from University of Virginia. The author has contributed to research in topics: Protein Data Bank & Structural genomics. The author has an hindex of 68, co-authored 331 publications receiving 54563 citations. Previous affiliations of Philip E. Bourne include University of Sheffield & University of California, Los Angeles.
Papers
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Book ChapterDOI
Other structure-based databases.
Helge Weissig,Philip E. Bourne +1 more
TL;DR: The single repository for experimentally derived macromolecular structures is the Protein DataBank (PDB), which currently releases primary structure data once per week as requested by the depositor, whereupon a number of sites worldwide acquire these data via the Internet, derive additional information, and constitute a set of secondary resources.
Journal ArticleDOI
The structure and heavy-metal-ion-binding sites of horse spleen apoferritin.
Posted ContentDOI
The Small β-barrel Domain: A Survey-based Structural Analysis
Philippe Youkharibache,Stella Veretnik,Qingliang Li,Kimberly A. Stanek,Cameron Mura,Philip E. Bourne +5 more
TL;DR: The small beta-barrel is an ancient protein structural domain characterized by extremes: it features an extremely broad range of structural varieties, a deeply intricate evolutionary history, and it is associated with a bewildering array of biomolecular pathways and physiological functions as discussed by the authors.
Journal ArticleDOI
Ten simple rules for approaching a new job.
TL;DR: Ten simple rules as you prepare for a job interview are offered, which while the general principles are universal, how they are applied depends somewhat on the seniority of the position.
Posted ContentDOI
Exploration of Dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVID-19 Anti-Infective Polypharmacology
TL;DR: In this article, a new deep learning framework, called Portal Learning, has been developed to explore dark chemical and biological space to predict chemical-protein interactions on a genome-wide scale.