News Excerpt:
The three-dimensional structure of the protein-RNA ion PDB 8AW3, as predicted by AlphaFold 3, showcases a significant advancement in the field of protein structure prediction.
Structure of protein and its study:
- Proteins are essential molecules that regulate almost every biological function from birth to death.
- Each protein is composed of a sequence of amino acids, which contain all the information needed to transform a protein from a linear sequence to a folded, functional 3D structure.
- The process of protein folding, from its straight form to its final 3D structure, is complex and has long posed a challenge to scientists, known as the protein-folding problem.
- It is essential to know how proteins acquire their shape to understand the molecular foundations of cellular function, organismal biology, and life itself.
AlphaFold:
- AlphaFold employs machine learning and artificial intelligence (AI) to predict protein structures from amino acid sequences, effectively solving the protein-folding problem without delving into the deeper physical principles underlying this biological process.
- The landscape of protein structure prediction changed dramatically with the advent of Google DeepMind’s AlphaFold in 2020, and even more so with AlphaFold 2 in 2021.
Introduction of AlphaFold 3 and its advancement:
- In a groundbreaking paper published in Nature in May 2024, DeepMind scientists introduced AlphaFold 3. This new iteration builds on its predecessors with even more transformative capabilities.
- AlphaFold 3 not only predicts protein structures with greater accuracy but also can predict protein-protein interactions and the structures of other molecules like DNA and RNA, along with their interactions.
- AlphaFold 2 predicted the structure of proteins with revolutionary levels of accuracy. AlphaFold 3 is even more accurate for proteins, but can also predict the structure of DNA, RNA, and all the other molecular components that make up biology.
- AlphaFold 3 is also more user-friendly, making it accessible to scientists who are not experts in machine learning. One can upload protein sequences to the DeepMind server, and 10 minutes later you get the results.
How does it works?
- The original AlphaFold was trained on thousands of sequences and protein structures from the Protein Data Bank, a vast repository of experimentally determined protein structures.
- AlphaFold is completely ignoring all the fundamental physics and thermodynamics. It models based on learning what real structures tend to look like, taking advantage of tendencies of protein structures that are too subtle for humans to realize.
- AlphaFold 3 uses a diffusion model, similar to those used in image-generating software. This model trains on protein structures by adding noise to the data and then attempting to de-noise it.
- This approach enables the model to reconstruct a real protein structure from a noisy one, enhancing its ability to handle larger datasets.
Scope of improvement:
- While AlphaFold 3 excels at predicting protein-protein interactions, its reliability in predicting interactions between small molecules and proteins is less robust.
- Proteins use a language of 20 amino acids, whereas small molecule ligands(Any substance that binds specifically and reversibly to a biomacromolecule to form a larger complex and alters its activity or function is called a ligand.) have a much larger vocabulary.
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- This complexity can lead to the model generating plausible but incorrect predictions.
- Adding more training data can help mitigate this issue but not entirely eliminate it.
- Researchers can upload protein sequences to the AlphaFold server, but many are frustrated by the lack of access to the model's full code, which limits modifications for specific uses.
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- An open letter signed by over 600 researchers criticized this restriction, arguing it hinders scientific progress.
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- In response, DeepMind has promised to release the full code within six months. Meanwhile, they have increased the daily job limit on the server.
Significance of AlphaFold 3:
- AlphaFold 3 IS superior to other models in predicting protein structures and interactions.
- It holds significant potential for drug discovery, with DeepMind’s spin-off company, Isomorphic Labs, already using it for this purpose. However, this capability is not yet available to the broader scientific community.
- Despite its limitations, AlphaFold 3 remains one of the most advanced AI-based protein structure prediction models, now capable of predicting interactions with various biological structures.
- While its predictions are highly accurate, they serve as a starting point for further experiments and expert analysis.
