Artificial intelligence can predict 3D protein structures that may lead to cancer research
Researchers have found a way to use artificial intelligence (AI) for 3-dimensional protein structure predictions.
Currently, genomic technologies are used to figure out the amino acid sequence of a protein. Amino acids are the building blocks of proteins. Usually, trying to discover the 3D shape of protein structures is very expensive and time consuming. However, the technique that uses AI helps to understand the 3D shape of the protein folds efficiently and quickly. Researchers have made attempts to understand what makes proteins certain shapes, and how can the shape be predicted from amino acid sequences.
The study was recently published in the journal Nature Structural and Molecular Biology.
Alpha Fold 2
The research team incorporated a type of artificial intelligence called Alpha Fold 2 into the study. The team trained the AI to solve the 3D structure of proteins from the amino acid sequences. Alpha Fold 2 is a neural network created by Deep Mind, an artificial intelligence company owned by Google. Alpha Fold 2 has been accurate in its predictions of the sequences, leaving many researchers impressed, especially when the team presented the results at an annual assessment contest known as the Critical Assessment of protein Structure Prediction (CASP). The research team presented the full sets of proteins for 11 different species, including humans.
The Alpha Fold 2 currently has data from over 300,000 models. Researchers evaluated the new structures made available and compared them to the ones currently available. They concluded that Alpha Fold 2 had contributed an additional 25% of high-quality protein structures not previously included in the data.
Although the key role proteins play in disease such as cancer is known, applying AI to the information will allow for a deeper comprehension of the function of proteins at their molecular levels. The structural data about these proteins will help researchers understand the proteins better, and to know what other molecules may interact within the cell. This could allow for the creation of new medication that could interfere with the protein functions when they are altered.
Limitations within the study
The research team did find limitations with the capabilities of using Alpha Fold 2. The team noticed that the AI had a few problems regarding the algorithm, which had issues with recreating protein complexes, or the collection of proteins. Proteins normally work together collectively to get a biological function done. However, predicting how various proteins could stick together would be desirable, but was limited when using the algorithm.
Another limitation was the inability to show the structure of mutated proteins — proteins encoded with a gene mutation such as amino acid changes, that can cause destabilization of the protein — with amino acids on its sequence. These mutations result in abnormal functioning of the protein structure, often leading to diseases like cancer.
AI contribution to the research
Although there are limitations, the research team realizes the great effect that Alpha Fold 2 has on the study, and the overall contribution to the field of biomedical research. “The application of AlphaFold2 [and the coming tools] will have a transformative impact in life sciences,” the researchers stated.
The research includes the thousands of new 3D protein models, and the team is looking forward to the future impact artificial intelligence will have on the discovery of new proteins, which could lead to new treatments.
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