Faculty Advisor/Mentor
Jing He
Location
Virginia Modeling, Analysis and Simulation Center, Room 2100
Conference Title
Modeling, Simulation and Visualization Student Capstone Conference 2023
Conference Track
Medical Simulation
Document Type
Paper
Abstract
Protein modeling is a rapidly expanding field with valuable applications in the pharmaceutical industry. Accurate protein structure prediction facilitates drug design, as extensive knowledge about the atomic structure of a given protein enables scientists to target that protein in the human body. However, protein structure identification in certain types of protein images remains challenging, with medium resolution cryogenic electron microscopy (cryo-EM) protein density maps particularly difficult to analyze. Recent advancements in computational methods, namely deep learning, have improved protein modeling. To maximize its accuracy, a deep learning model requires copious amounts of up-to-date training data.
This project explores DeepSSETracer, a software tool that uses deep learning to predict protein secondary structures in medium resolution cryo-EM density maps of protein samples. Python scripts were created to automate data acquisition tasks for DeepSSETracer. Furthermore, the Python library PDBx was used to parse mmCIF protein files. mmCIF is a relatively new file type that stores experimentally derived atomic models of proteins, and they have begun to replace the conventional PDB file type as the standard for atomic models. This project culminated in making ChainChopper, a program in DeepSSETracer, compatible with the mmCIF file type.
Keywords:
Alpha helix, Beta sheet, DeepSSETracer, ChainChopper, cryo-EM, mmCIF file
Start Date
4-20-2023
End Date
4-20-2023
Recommended Citation
Shen, Ruoming, "Enhancement of Deep Learning Protein Structure Prediction" (2023). Modeling, Simulation and Visualization Student Capstone Conference. 2.
https://digitalcommons.odu.edu/msvcapstone/2023/medicalsimulation/2
DOI
10.25776/syb2-pg49
Included in
Amino Acids, Peptides, and Proteins Commons, Bioimaging and Biomedical Optics Commons, Computational Engineering Commons, Computer Sciences Commons
Enhancement of Deep Learning Protein Structure Prediction
Virginia Modeling, Analysis and Simulation Center, Room 2100
Protein modeling is a rapidly expanding field with valuable applications in the pharmaceutical industry. Accurate protein structure prediction facilitates drug design, as extensive knowledge about the atomic structure of a given protein enables scientists to target that protein in the human body. However, protein structure identification in certain types of protein images remains challenging, with medium resolution cryogenic electron microscopy (cryo-EM) protein density maps particularly difficult to analyze. Recent advancements in computational methods, namely deep learning, have improved protein modeling. To maximize its accuracy, a deep learning model requires copious amounts of up-to-date training data.
This project explores DeepSSETracer, a software tool that uses deep learning to predict protein secondary structures in medium resolution cryo-EM density maps of protein samples. Python scripts were created to automate data acquisition tasks for DeepSSETracer. Furthermore, the Python library PDBx was used to parse mmCIF protein files. mmCIF is a relatively new file type that stores experimentally derived atomic models of proteins, and they have begun to replace the conventional PDB file type as the standard for atomic models. This project culminated in making ChainChopper, a program in DeepSSETracer, compatible with the mmCIF file type.