Computational

Understanding the three-dimensional (3D) structure of proteins is essential for deciphering their functions and interactions. However, determining protein structures experimentally through methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy can be time-consuming and costly. To overcome these limitations, computational methods have been developed to predict protein structures from their amino acid sequences. We will explore the different approaches used for protein structure prediction, including homology modeling, threading, ab initio methods, and the emerging field of deep learning.