Student Research Symposium Program Portal: Submission #207
Submission information
Submission Number: 207
Submission ID: 9055
Submission UUID: ef93c6d6-f557-477a-85c2-07b0941c0278
Submission URI: /student-research/symposium/research-symposium-program-portal
Submission Update: /student-research/symposium/research-symposium-program-portal?token=w4WZjkT7KN-w2dmOJJpe2thTjlQv8a9MmYMOYm4mBdk
Created: Fri, 01/30/2026 - 09:54 AM
Completed: Fri, 01/30/2026 - 09:55 AM
Changed: Fri, 01/30/2026 - 09:55 AM
Remote IP address: 146.201.10.32
Submitted by: Anonymous
Language: English
Is draft: No
Webform: Research Symposium Program Portal WF
Submitted to: Student Research Symposium Program Portal
Primary Student Contact First Name: Zachariah
Primary Student Contact Last Name: Zawahry
Pronouns: {Empty}
Primary Student Contact FSU Student Email: zz23f@fsu.edu
Photo of all individuals presenting this work: https://pc.fsu.edu/system/files/webform/research_portal/9055/Zaczz%20%20Medium_0.png
Major(s) of all individuals presenting this work: intended major biology
Bio of all individuals presenting this work:
I am a dual-enrolled student at the FSU collegiate school. I was born and raised in Panama City, Florida. I volunteer for multiple organizations, such as AMAL and Florida Springs Watch, as well as play varsity soccer for Bay High School. I am deeply intrigued by microbiology and plan to go into the medical field.
Poster Title: Machine learning in respect to P53 protien
Abstract:
The p53 protein, or the “guardian of the genome,” is one of the body's biggest natural defenses against cancer. Therefore, understanding this protein could lead to big improvements in cancer research. But P53 is an incredibly dynamic protein, constantly moving and changing into different conformational states, because of this its extremely hard to model with traditional methods. Modeling the P53 protein using machine learning technology could be the thing to get accurate and insightful models. To research whether machine learning could create more accurate models than traditional methods, I will utilize AlphaFold to generate multiple models of the p53 protein and compare them to p53 models created using traditional methods that are found online in large databases such as ChimerX. If the models are as accurate or more accurate than traditional models, then I will use AlphaFold to create rarer conformational states of p53 and compare them to rare models created using traditional methods. If these models are as accurate or more accurate than traditional ones, I will finally attempt to create extremely rare or unknown conformational states. The real world significance of my research is not only a huge leap in cancer research but also a huge leap in complex protein modeling, which has a significant number of applications in many different fields.
Research Mentor Name: Madyson Flamia
Research Mentor's College (or High School): FSU Collegiate School
Research Mentor's Department (or Subject): Research departmant
Research Mentor's Email: mjf24@fsu.edu
Additional Research Mentor(s): {Empty}
Co-presenter(s): {Empty}
Keywords: Machine learning, Micro biology, protein modeling
Poster Session/Number: {Empty}
Work: Exploratory (the research question has been identified and design of approach is outlined)
Presentation Modality: Face to Face Poster session
Poster PDF: {Empty}
Poster Thumbnail: {Empty}
I will be printing my poster: No
Year: 2026
Annual description: 5th annual Undergraduate Research Symposium, April 17, 2025
Update URL: https://pc.fsu.edu/student-research/symposium/research-symposium-program-portal?element_parents=elements/student_photo&ajax_form=1&_wrapper_format=drupal_ajax&token=w4WZjkT7KN-w2dmOJJpe2thTjlQv8a9MmYMOYm4mBdk
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