Researchers map hidden water architecture surrounding proteins

For decades, the scientific community has characterized proteins primarily by two fundamental attributes: the linear order of their amino acids and the three‑dimensional shapes those chains adopt. This dual framework has underpinned major breakthroughs across biology, biotechnology and medicine, enabling researchers to elucidate enzyme mechanisms, design therapeutic antibodies and engineer novel biomolecules.

Recent advances in artificial intelligence have amplified the impact of this paradigm. Deep‑learning models now predict protein structures from sequence data with unprecedented accuracy, accelerating the discovery pipeline and expanding the range of proteins that can be studied without labor‑intensive laboratory methods. The integration of AI‑driven structural prediction promises to deepen understanding of protein function and to spur further innovation in health‑related applications.