Discipline: Biological Sciences
Subcategory: Computer Science & Information Systems
Christopher Jones - Tennessee State University
Co-Author(s): Kamal Al Nasr, Bashar Aboona, and Abdulrahman Alanazi, Tennessee State University, Nashville, TN
Cryo-Electron Microscopy is a powerful biophysical technique that is capable to generate 3-dimensional volume images for macromolecular assemblies and machines. De novo protein modeling uses these images to model the biological molecules. In de novo modeling, many candidate structures are generated as an intermediate step. The candidates are evaluated conventionally by time-consuming approaches. We introduce an initial version of a geometrical screening method that uses the skeleton of the cryo-EM images to evaluate the candidate structures. A test of ten (10) proteins shows that our method was able to successfully detect good candidate structures in efficient way. Future development will increase computational efficiency and test the method’s ability to discern the best fit between very similar candidate structures.
Funder Acknowledgement(s): NSF grant: HBCU-UP RIA 1600919
Faculty Advisor: Kamal Al Nasr, email@example.com
Role: With some guidance from Al Nasr developed software to test candidate structures fit with cryo-EM volume data. We applied computer vision techniques (Iterative Closest Point) to rapidly test fit the candidate structures. Research was originally done as a part of a capstone senior project for undergraduate degree.