Protein Structure Prediction AlphaFold is a deep learning system predicting 3D structure of proteins from amino acid sequences, achieving unprecedented accuracy and revolutionizing structural biology — breakthrough solving 50-year-old grand challenge. AlphaFold transforms biology. Protein Folding Challenge proteins fold into specific 3D structures determining function. Prediction from sequence experimentally difficult (X-ray crystallography, cryo-EM expensive, slow). AlphaFold automates prediction. Evolutionary Information homologous proteins evolve from common ancestor. Multiple sequence alignment (MSA) captures evolutionary relationships. Covariation in multiple sequence alignment reveals structure: residues in contact coevolve. Transformer Architecture AlphaFold uses transformers adapted for sequence processing. Transformer attends over all sequence positions, captures long-range interactions. Pairwise Attention key innovation: attention on pairs of residues. Predicts how pairs interact (contact, distance). Pairwise features incorporated explicitly. Structure Modules predict distance and angle distributions between residues. Iterative refinement: initial prediction refined through multiple structure modules. Training Supervision trained on PDB (Protein Data Bank) structures. Objective: minimize distance to native structure. Coordinate regression with auxiliary losses on distance/angle predictions. Few-Shot and Zero-Shot Capabilities AlphaFold generalizes to sequences not in training data. Predicts structures for entire proteomes. Some structures more difficult (multimeric, disorder), accuracy varies. Multimer Predictions AlphaFold2 extended to predict protein complexes. Protein-protein interaction predictions. Biological relevance: understanding function requires knowing interactions. AlphaFold2 vs. Original original AlphaFold (CASP13 2018) used deep learning + template matching. AlphaFold2 (CASP14 2020) purely deep learning, much better. Transformers enable end-to-end learning. Confidence Metrics pAE (predicted aligned error) estimates per-residue prediction confidence. PAE visualized as heatmap showing uncertain regions. Intrinsically Disordered Regions some proteins lack fixed structure (functional in flexibility). AlphaFold struggles with disorder. Combining with disorder predictors. Validation and Comparison compared against experimental structures. RMSD (root mean square distance) measures deviation. AlphaFold predictions often validate via new experiments. Computational Efficiency prediction formerly O(2^n) exponential complexity (NP-hard). AlphaFold is polynomial time. Enables large-scale prediction. Open Source and Accessibility DeepMind released AlphaFold2 open-source. Community implementations (OmegaFold, OmegaFold2), fine-tuned versions. Dramatically democratized structure prediction. Applications in Drug Discovery structure enables rational drug design: target binding sites, predict ADMET properties. Structure-based virtual screening. Immunology Applications predict MHC-peptide interactions (immune presentation). Predict TCR-pMHC binding (T cell recognition). Mutational Studies predict effect of mutations on structure/stability. Structure-guided protein engineering. Biological Databases structures predicted for all known proteins. AlphaFoldDB public database. Resource for research community. Limitations structure alone insufficient for function prediction. Dynamics matter (protein motion). Allosteric effects, regulation. Future Directions predicting protein dynamics, RNA structures, nucleic acid-protein complexes. Predicting functional consequences of mutations. AlphaFold solved protein structure prediction enabling rapid structural biology discovery.