Published Paper Details:

This study has been undertaken because as we know Proteins are central to biology, and their functions rely inextricably on their three-dimensional conformations. Although technologies such as AlphaFold have dramatically improved the prediction of structure using dazzling accuracy, they rely on Large-scale datasets and massive computing capacity, which frequently puts them inaccessibly outside of smaller labs, schools, and low-resource environments. To bridge this gap, we present Beta-Fold, an efficient and easy-to-use framework for the prediction of protein secondary structures specifically ?-helices, ?-sheets, and coils--directly from amino acid sequences. Utilizing a hybrid CNN-BiLSTM approach, Beta-Fold obviates the requirement for multiple sequence alignments (MSAs) and GPU-based computation. This allows for real-time prediction as well as interactive visualization via a simple web interface, making protein analysis more accessible and convenient in various research environments. Through reduced computational requirements, Beta-Fold has the potential to expedite applications in molecular diagnostics, drug discovery, and biomedical research. Directions for future work involve disease-specific case studies and incorporation into clinical bioinformatics pipelines with the ultimate aim of improving translational reach and enabling equitable access to protein modeling tools.