This is my Final Year project where i was a part in backend for AI along with Research Paper . Protein structure prediction plays a critical role in understanding biological mechanisms and developing new therapeutic strategies. Accurate structural information helps researchers identify functional regions within proteins, study disease-causing mutations, and design effective drugs. However, many existing prediction systems require substantial computational resources, which makes them difficult to use in resource-limited settings.
The motivation behind the development of BetaFold arises from the need to create an accessible and efficient solution for protein secondary structure prediction. Many academic institutions and small research laboratories lack access to high-performance computing clusters or GPUs required by modern protein prediction tools.
By designing a lightweight deep learning framework that eliminates the need for complex preprocessing techniques such as Multiple Sequence Alignments (MSAs), BetaFold provides a practical alternative for rapid and accessible protein analysis.
The project aims to bridge the gap between advanced computational biology tools and their availability to researchers with limited resources. By providing a simple web-based interface and efficient prediction model, BetaFold encourages broader participation in computational bioinformatics research and educational applications