Introduction: Deep learning has changed the way we predict the structure of proteins. Models such as AlphaFold are close to experimental accuracy. However, the current methods rely on sequence coevolutionary signals, which lead to poor prediction performance in proteins with low homology, heterogeneous assemblies, and flexible systems. Integrating experimental evidence can provide physical and dynamic constraints, which can help solve these problems to a certain extent. Methods: We searched the articles published from 2020 to 2025 in PubMed, Web of Science, bioRxiv, and Google Scholar. The keywords used include deep learning, protein structure prediction, and experimental data such as X-ray crystallography, mass spectrometry (MS), and cryo-electron microscopy (cryo-EM). We also consulted the list of references and evaluated the studies to compare their methods, advantages, and limitations. Results: The evidence shows that the integration of experimental data can improve prediction accuracy, physical plausibility, and conformational diversity. Each technique offers distinct advantages: X-ray crystallography helps to refine the atomic structure; mass spectrometry (MS) provides distance and interface restraints for structural modeling; and cryo-EM density maps can show conformational variability more clearly. Discussion: Multimodal strategies can reduce dependence on evolutionary information and achieve dynamic modeling. However, due to the uneven quality of data, the high cost of computing, and the uncertain ability to generalize across protein categories, they still face many challenges. Conclusion: Deep learning assisted by experimental data can improve the accuracy and robustness of protein structure prediction. Future progress will depend on a unified differentiable framework, uncertainty-aware learning, and systematic benchmarking to clarify the specific scenarios that can bring maximum benefits after integration.
Yang et al. (2026) studied this question.