The rapid evolution of protein structure prediction tools has
significantly broadened access to protein structural data. Although
predicted structure models have the potential to accelerate and impact
fundamental and translational research significantly, it is essential to
note that they are not validated and cannot be considered the ground
truth. Thus, challenges persist, particularly in capturing protein
dynamics, predicting multi-chain structures, interpreting protein
function, and assessing model quality. Interdisciplinary collaborations
are crucial to overcoming these obstacles. Databases like the AlphaFold
Protein Structure Database, the ESM Metagenomic Atlas, and initiatives
like the 3D-Beacons Network provide FAIR access to these data, enabling
their interpretation and application across a broader scientific
community. Whilst substantial advancements have been made in protein
structure prediction, further progress is required to address the
remaining challenges. Developing training materials, nurturing
collaborations, and ensuring open data sharing will be paramount in this
pursuit. The continued evolution of these tools and methodologies will
deepen our understanding of protein function and accelerate disease
pathogenesis and drug development discoveries.