Describe the main approaches to protein structure prediction.

Protein structure prediction approaches include: 1) Homology/Comparative modeling - uses known structures of homologous proteins as templates, most accurate when sequence identity >30%. Tools: SWISS-MODEL, MODELLER. 2) Threading/Fold recognition - matches query sequence to known fold library even without detectable sequence similarity. Tools: I-TASSER, Phyre2. 3) Ab initio/De novo - predicts structure from physical principles without templates, computationally intensive. Uses energy minimization and fragment assembly. 4) Deep learning methods - AlphaFold2 and RoseTTAFold use neural networks trained on known structures, achieving near-experimental accuracy. They revolutionized the field and can predict structures for most proteins with high confidence.