PROTEIN FOLDING PROBLEM & BETA FOLD

Protein folding represents a central challenge in structural biology. While AlphaFold demonstrates remarkable predictive accuracy, its underlying machine learning architecture demands substantial computational resources for both model development and training on extensive datasets. In contrast, BetaFold employs an alternative methodology grounded in physicochemical principles, utilizing amino acid charge distributions and integrating the Chou-Fasman and Ramachandran models. This approach combines predictions of secondary and tertiary structure with analyses of binding sites and domains. The tool further provides three-dimensional structural visualizations accompanied by confidence metrics, including predicted Local Distance Difference Test (pLDDT) scores and Predicted Aligned Error (PAE) plots.