The researchers used neural networks to analyze GFP sequence data and their brightness to create a “compatibility map” of the proteins. After “correcting” this map, they trained the model to predict improved protein sequences. This approach has been shown to be effective in generating new sequences for the AAV capsid, which could have important implications for medical applications.

The researchers plan to use this method to improve protein indicators of stress; This could accelerate the development of tools for neuroscience research and medical applications.

Source: Ferra

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