From sequence to mechanobiology? Promises and challenges for AlphaFold 3

Interactions between macromolecules orchestrate many mechanobiology processes. However, progress in the field has often been hindered by the monetary and time costs of obtaining reliable experimental structures. In recent years, deep-learning methods, such as AlphaFold, have democratized access to high-quality predictions of the structural properties of proteins and other macromolecules. The newest implementation, AlphaFold 3, significantly expands the applications of its predecessor, AlphaFold 2, by incorporating reliable models for small molecules and nucleic acids and enhancing the prediction of macromolecular complexes. While several limitations still exist, the continuous improvement of machine learning methods like AlphaFold is producing a significant revolution in the field. The possibility of easily accessing structural predictions of biomolecular complexes may create substantial impacts in mechanobiology. Indeed, structural studies are at the basis of several applications in the field, such as drug discovery for mechanosensing proteins, development of mechanotherapy, understanding the mechanotransduction mechanisms and the mechanistic basis of diseases, or designing biomaterials for tissue engineering.