Please contact Dr. Works (keworks@fsu.eu) for additional help: Submission navigation links for Research Symposium Program Portal WF ‹ Previous submission Next submission › 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 Primary Student Contact Last Name Pronouns Primary Student Contact FSU Student Email Photo of all individuals presenting this work Zaczz Medium_0.png641.9 KB Remove Upload requirementsOne file only.2 MB limit. Major(s) of all individuals presenting this work 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 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 Research Mentor's College (or High School) Research Mentor's Department (or Subject) Research Mentor's Email Additional Research Mentor(s) Co-presenter(s) Keywords Poster Session/Number Work Complete Exploratory (the research question has been identified and design of approach is outlined) Presentation Modality Face to Face Poster session Synchronous Online Presentation Asynchronous Online Presentation Poster PDF Upload Upload requirementsOne file only.100 MB limit. Poster Thumbnail Please take a screenshot of your poster to be a thumbnail on your Symposium Program Profile. Upload Upload requirementsOne file only.2 MB limit. I will be printing my poster CAPTCHA What code is in the image? Enter the characters shown in the image. This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Save Leave this field blank
