The Evolution of the AlphaFold Architecture

For decades, biologists have been struggling to determine the structure of proteins efficiently. They were experimenting to find a method to accurately predict protein structures from their amino acid sequence. As a result of this research, Google DeepMind was able to introduce a Deep Learning approach to predict protein structures. It is AlphaFold2. AlphaFold2 could grab the attention of biologists because of its high impact on human beings. Also, the Nobel Prize for chemistry in 2024 was offered for this breakthrough invention. However, even though AlphaFold2’s exceptional accuracy, it has some limitations. This comprehensive review provides a systematic analysis of the main limitations of the AlphaFold 2 framework. The AlphaFold2 struggles to predict multiple conformations for the same sequence, the effects of point mutations, and antigen-antibody interactions. And AlphaFold2 fails to predict protein-DNA and protein-RNA complexes, nucleic acid structure, ligand and ion binding, post-translational modifications, and membrane plane for transmembrane domains. By systematically reviewing these limitations, we can use this review as a roadmap for future research to improve the legendary AlphaFold2 framework for the well-being of human beings.