Creating Artificial Proteins

Researchers have presented an unusual artificial intelligence method for developing entirely new proteins that have never previously been seen in nature. The team has used machine learning to derive “musical scores” from the structures of proteins, that can be used to train deep learning neural networks to design completely novel proteins. Historically, scientists have developed new proteins by copying existing proteins or by altering the amino acids that a protein is composed of. However, this process is time-consuming and predicting the impact that altering amino acids has on protein structure is challenging. However, computational modeling techniques such as physiochemical simulations have been developed that can generate models of 3D protein structure based on the amino acid sequence. Scientists have used musical theory concepts to translate the chemical structure of proteins into sounds that can be used in machine learning to design completely new proteins. Machine learning is a type of artificial intelligence where computers are used to automatically analyze and learn from data, identify patterns and make decisions, without requiring preprogramming and with only minimal human input needed. Since each of the twenty amino acids that form a protein has its own distinct vibrational frequency, the whole protein chemical structure can be represented audibly using key aspects of musical theory such as melody and rhythm. Alternatively, “perhaps you find an enzyme in nature and want to improve how it catalyzes or come up with new variations of proteins altogether,” he suggests. By altering a condition such as temperature, for example, the algorithm can be prompted to create more mutations, which could then be quantified to assess which ones contribute to making up the most effective enzymes.

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