XPSI: X-ray Free Electron Laser-Based Protein Structure Identifier

Authors: Paula Fernanda Olaya García (University of Tennessee); Michael R. Wyatt II (University of Delaware; University of Tennessee, Knoxville); Silvina Caino-Lores (University of Tennessee, Knoxville); Florence Tama (RIKEN, Nagoya University); Osamu Miyashita (RIKEN); Piotr Luszczek (University of Tennessee, Knoxville; University of Tennessee, Innovative Computing Laboratory); and Michela Taufer (University of Tennessee, Knoxville)

Abstract: A protein's structure determines its function. Different proteins have different structures; proteins in the same family share similar substructures and thus may share similar functions. Additionally, one protein may exhibit several structural states, also named conformations. Identifying different proteins and their conformations can help solve problems such as determining the cause of diseases and designing drugs. X-ray Free Electron Laser (XFEL) beams are used to create diffraction patterns (images) that can reveal protein structure and function. The translation from diffraction patterns in the XFEL images to protein structures and functionalities is non-trivial. In this poster, we present the first steps into the design and assessment of a software framework for the identification of XFEL images. We call the framework XPSI (XFEL-based Protein Structure Identifier). We quantify the identification accuracy and performance of XPSI for protein diffraction imaging datasets including different protein orientations and conformations with realistic noise incorporated.

Best Poster Finalist (BP): no

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