Google DeepMind AlphaFold 3 Could Accelerate Drug Discovery For Parkinson’s and Alzheimer’s
Photo by National Cancer Institute on Unsplash.
ad Key Takeaways
- Google DeepMind has developed AlphaFold 3, a new AI tool for drug discovery.
- Compared to previous software, the platform can more accurately predict the structure of proteins and protein complexes.
- This could accelerate the discovery of treatments for neurodegenerative diseases like Parkinson’s and Alzheimer’s.
Since its release in 2021, thousands of biomedical researchers have used AlphaFold 2 to predict protein structures, leading to the discovery of new potential medicines ranging from malaria vaccines to cancer treatments. Now, its successor AlphaFold 3 promises to go even further.
Whereas its predecessor was mostly limited to predicting standalone protein structures, the new software can predict the structure of more intricate protein complexes. This could lead to the discovery of new treatments for a greater range of illnesses.
Machine Learning and Drug Discovery
In the field of drug discovery, machine learning models are used to scan vast libraries of compounds for potentially useful ones.
The Protein Folding Problem
Proteins are essentially made up of long chains of amino acids, but they only function properly when folded into specific 3D shapes.
The protein folding problem asks: how does a long, chain-like protein know how to fold itself into its exact shape, and how can we predict that shape just by knowing the amino acid sequence?
Imagine a string with 100 beads (amino acids) that can fold in countless ways. Trying every single possibility would take years, yet living organisms fold proteins in a few seconds. Researchers know this contradiction as Levinthal’s paradox, and understanding the intricate origami of protein folding remains one of the biggest challenges of modern medicine.
AlphaFold 3 Improves Accuracy
Although the biological mechanisms that underscore protein folding in organisms remain something of a mystery, AI tools can (more or less accurately) predict the 3-dimensional structure of proteins from their amino acid sequence. This information is extremely useful for researchers, who look for molecular structures that will interact with physiological systems in a desired way.
AlphaFold3 outperforms existing software tools, including AlphaFold2 and RoseTTAFold All-Atom, which was previously the most advanced protein prediction engine.
In terms of accuracy, AlphaFold 3’s biggest improvement on previous solutions is on protein-ligand complexes, which are observed in almost all cellular processes.
Modeling these interactions may help researchers better target diseases caused by protein misfolding, i.e when the normal process by which proteins are assembled breaks down.
Protein Misfolding Diseases
Protein misfolding is believed to be the primary cause of neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, Creutzfeldt-Jakob disease, cystic fibrosis and Gaucher’s disease. It also plays a role in other illnesses including diabetes and HIV.
What connects these diseases is that the way amino acid chains fold into complex proteins doesn’t follow the normal pattern.
AlphaFold 3’s more accurate protein-ligand complex modeling could help researchers develop new drugs to slow or inhibit these pathological processes, marking a huge milestone for AI drug discovery.
