Protein Folding: Recent Advances and Analysis in Computing

Protein folding is a fundamental yet elusive problem: a protein's three-dimensional structure determines its function, but misfolding underlies disorders such as Alzheimer's. Experimental techniques like X-ray crystallography and NMR resolve structures but cannot keep pace with the explosive growth of sequence data. Consequently, computational approaches - from simplified hydrophobic-polar (HP) lattice models to deep neural networks - have become indispensable. This paper reviews recent advances in computational protein folding, using the HP model as a conceptual test bed. It surveys classic heuristics, modern deep reinforcement learning, variational generative techniques and emerging quantum algorithms for NP-hard lattice models, and compares them with breakthroughs in all-atom structure prediction exemplified by AlphaFold and RosettaFold. Benchmark datasets, evaluation metrics and ongoing challenges - such as data bias, dynamic folding and integration of physical constraints - are discussed. The review concludes that future progress will likely come from hybrid methods that combine machine-learning flexibility with physics-based priors, expanded and more diverse structural data sets, and algorithmic innovations, including quantum-inspired heuristics and efficient hardware. Such advances could enable more accurate folding predictions, facilitate rational drug design and deepen our understanding of protein misfolding diseases.