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During her\u00a0chemistry Nobel Prize lecture<\/a>\u00a0in 2018, Frances Arnold said, \u201cToday we can for all practical purposes read, write and edit any sequence of DNA, but we cannot compose it.\u201d That isn\u2019t true anymore.<\/p>\n Since then, science and technology have progressed so much that artificial intelligence has learned to compose DNA, and with genetically modified bacteria, scientists are on their way to designing and making bespoke proteins.<\/p>\n The goal is that with AI\u2019s designing talents and gene editing\u2019s engineering abilities, scientists can modify bacteria to act as mini factories producing new proteins that can reduce greenhouse gases, digest plastics or act as species-specific pesticides.<\/p>\n As a\u00a0chemistry professor and computational chemist<\/a>\u00a0who studies molecular science and environmental chemistry, I believe that advances in AI and gene editing make this a realistic possibility.<\/p>\n All living things contain genetic materials \u2013 DNA and RNA \u2013 that provide the hereditary information needed to replicate themselves and make proteins. Proteins constitute 75% of human dry weight. They make up muscles, enzymes, hormones, blood, hair and cartilage. Understanding proteins means understanding much of biology. The order of nucleotide bases in DNA, or RNA in some viruses, encodes this information, and genomic sequencing technologies identify the order of these bases.<\/p>\n The\u00a0Human Genome Project<\/a>\u00a0was an international effort that sequenced the entire human genome from 1990 to 2003. Thanks to rapidly improving technologies, it took seven years to sequence the first 1% of the genome and another seven years for the remaining 99%. By 2003, scientists had the complete sequence of the 3 billion nucleotide base pairs coding for 20,000 to 25,000 genes in the human genome.<\/p>\n However, understanding the functions of most proteins and correcting their malfunctions remained a challenge.<\/p>\n Each protein\u2019s shape is critical to its function and is determined by the sequence of its amino acids, which is in turn determined by the gene\u2019s nucleotide sequence. Misfolded proteins have the wrong shape and\u00a0can cause illnesses<\/a>\u00a0such as neurodegenerative diseases, cystic fibrosis and Type 2 diabetes. Understanding these diseases and developing treatments requires knowledge of protein shapes.<\/p>\n Before 2016, the only way to determine the shape of a protein was through\u00a0X-ray crystallography<\/a>, a laboratory technique that uses the diffraction of X-rays by single crystals to determine the precise arrangement of atoms and molecules in three dimensions in a molecule. At that time, the structure of about 200,000 proteins had been determined by crystallography, costing billions of dollars.<\/p>\n AlphaFold, a machine learning program<\/a>, used these crystal structures as a training set to determine the shape of the proteins from their nucleotide sequences. And in less than a year, the program\u00a0calculated the protein structures<\/a>\u00a0of all 214 million genes that have been sequenced and published. The protein structures AlphaFold determined have all been released in a\u00a0freely available database<\/a>.<\/p>\n To effectively address noninfectious diseases and design new drugs, scientists need more detailed knowledge of how proteins, especially enzymes, bind small molecules. Enzymes are protein catalysts that enable and regulate biochemical reactions.Gene sequencing \u2013 reading life\u2019s recipes<\/strong><\/h4>\n
AI learns proteins<\/strong><\/h4>\n
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