Evaluation of a Deep Learning Method for Protein Secondary Structure Segmentation from Cryo-Electron Microscopy Density Maps at Medium Resolution

Description/Abstract/Artist Statement

A protein’s atomic structure determines its function. Understanding the structure and function of a protein is fundamental in several different applications, including but not limited to drug design, disease treatment and diagnosis, and advancing biotechnology. Cryo-electron microscopy (cryo-EM) is an imaging process yielding three-dimensional density maps of proteins and other molecules that can be used to derive their atomic structures. While highly effective in high resolution, particular challenges arise when using a cryo-EM density map in medium resolution (5-10Å) to derive a protein’s atomic structure. In such cases, the segmentation of protein secondary structures (helices and β sheets) provides constraints for deriving the atomic structure. DeepSSETracer is a software bundle within ChimeraX which aims to accomplish that task. It is a deep learning model created using a U-net convolutional neural network architecture to detect protein secondary structures in medium-resolution cryo-EM density maps. A newly created ad hoc dataset will be combined with randomly sampled existing data to test the performance of DeepSSETracer in its segmentation of protein secondary structures. Performance will be determined using a program to calculate an F1 score by comparing the predicted secondary structures with known solution structures archived in the Protein Data Bank.

Presenting Author Name/s

Bryan Hawickhorst

Faculty Advisor/Mentor

Jing He

Faculty Advisor/Mentor Department

Computer Science

College Affiliation

College of Sciences

Presentation Type

Oral Presentation

Disciplines

Artificial Intelligence and Robotics | Bioinformatics | Data Science

Session Title

College of Sciences 4

Location

Learning Commons @Perry Library, Room 1310

Start Date

3-30-2024 12:00 PM

End Date

3-30-2024 1:00 PM

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Mar 30th, 12:00 PM Mar 30th, 1:00 PM

Evaluation of a Deep Learning Method for Protein Secondary Structure Segmentation from Cryo-Electron Microscopy Density Maps at Medium Resolution

Learning Commons @Perry Library, Room 1310

A protein’s atomic structure determines its function. Understanding the structure and function of a protein is fundamental in several different applications, including but not limited to drug design, disease treatment and diagnosis, and advancing biotechnology. Cryo-electron microscopy (cryo-EM) is an imaging process yielding three-dimensional density maps of proteins and other molecules that can be used to derive their atomic structures. While highly effective in high resolution, particular challenges arise when using a cryo-EM density map in medium resolution (5-10Å) to derive a protein’s atomic structure. In such cases, the segmentation of protein secondary structures (helices and β sheets) provides constraints for deriving the atomic structure. DeepSSETracer is a software bundle within ChimeraX which aims to accomplish that task. It is a deep learning model created using a U-net convolutional neural network architecture to detect protein secondary structures in medium-resolution cryo-EM density maps. A newly created ad hoc dataset will be combined with randomly sampled existing data to test the performance of DeepSSETracer in its segmentation of protein secondary structures. Performance will be determined using a program to calculate an F1 score by comparing the predicted secondary structures with known solution structures archived in the Protein Data Bank.