AI Guided Mutation Prediction and Vaccine Development Using Inverse Folding

Ali, Muhammad A.

Permalink

https://hdl.handle.net/2142/130489 Copy

Description

Title

AI Guided Mutation Prediction and Vaccine Development Using Inverse Folding

Author(s)

Ali, Muhammad A.

Issue Date

2025-12-10

Keyword(s)

• Protein
• Artificial Intelligence
• Deep Learning
• Machine Learning
• Inverse Folding
• Bioinformatics
• Entropy
• Data Science
• Exploratory Data Analysis
• Vaccine
• Mutation
• Evolution
• Biology
• Protein Sequence
• Protein Structure
• Computational Biology
• AI Workflow

Date of Ingest

2025-12-10T12:44:20-06:00

Abstract

Vaccine and binder design relies on structural stability of the target protein. Being able to predict mutations or identify regions along the protein that do not mutate can help us better prepare for future pandemics. Since most mutations combined do not show extreme structural deviations (RMSD between original and mutant structure is under 3Å) we can use Inverse Folding techniques to identify regions that are stable or highly mutable based on positional entropies calculated from the resulting inverse folded sequences.

Publisher

https://www.ideals.illinois.edu/

Has Part

• https://github.com/0asif/InverseFolding-ProteinStructuralStability
• https://0asif.github.io/projects.html

Series/Report Name or Number

Crop Sciences Student Poster Symposium, Fall 2025

Type of Resource

• Poster
• text
• still image

Genre of Resource

• conference poster
• presentation/lecture/speech
• data

Language

eng

DOI

https://hdl.handle.net/2142/130489

Copyright and License Information

Muhammad Asif Ali (2025)

Owning Collections