Protein Structure Prediction Tools and Stem Cell Research
Protein structure prediction tools are becoming indispensable in stem cell biology, providing molecular insights into the mechanisms that regulate pluripotency, differentiation, and cell signaling. The function of key transcription factors—such as OCT4, SOX2, and NANOG depends heavily on their 3D structural conformations, which determine DNA-binding affinity and interaction with cofactors. Advanced computational tools like AlphaFold2, RoseTTAFold, and I-TASSER now allow researchers to accurately predict these protein structures from amino acid sequences, accelerating discoveries in stem cell signaling and reprogramming pathways. By integrating proteomic modeling with genomic and transcriptomic data, scientists can elucidate how mutations, post-translational modifications, or small molecules influence stem cell fate decisions. Moreover, structure-based drug design using these predictive models supports the development of compounds that selectively modulate stem cell behavior, enhancing their therapeutic potential in regenerative medicine and disease modeling. The combination of AI-driven protein modeling and stem cell research thus represents a powerful approach to understanding and manipulating cellular processes at the molecular level.