News Excerpt:
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.
More about News:
- The goal is that with AI’s designing talents and gene editing’s 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.
Gene sequencing – reading life’s recipes
- All living things contain genetic materials – DNA and RNA – that provide the hereditary information needed to replicate themselves and make proteins.
- Proteins constitute 75 percent of human dry weight and 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 the information for making proteins.
- Genomic sequencing technologies identify the order of these bases.
- It took seven years to sequence the first 1 percent of the human genome and another seven years for the remaining 99 percent.
- 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.
- Understanding the functions of most proteins and correcting their malfunctions remained a challenge even after sequencing the human genome.
AI learns proteins
- Each protein's shape is critical to its function and is determined by the sequence of its amino acids, which is determined by the gene's nucleotide sequence.
- Misfolded proteins with the wrong shape can cause illnesses such as neurodegenerative diseases, cystic fibrosis, and Type 2 diabetes.
- Before 2016, the only way to determine a protein's shape was through X-ray crystallography, a laboratory technique that is costing billions of dollars.
- 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.
Alfafold
- AlphaFold, a machine learning program, used the crystal structures as a training set to determine the shape of proteins from their nucleotide sequences.
- AlphaFold calculated the protein structures of all 214 million sequenced and published genes, which were released in a freely available database.
- To effectively address non-infectious diseases and design new drugs, scientists need detailed knowledge of how proteins, especially enzymes, bind small molecules.
- AlphaFold3 can predict protein shapes and the locations where small molecules can bind to these proteins.
- In rational drug design, small molecule drugs are designed to bind to proteins involved in disease pathways, modulating their activity and influencing the disease path.
- By predicting protein binding sites, AlphaFold3 will enhance researchers' capabilities in drug development.
AI + CRISPR: composing new proteins
- Around 2015, the development of CRISPR technology revolutionized gene editing, allowing specific gene modifications with ease and at a low cost.
- In 2020, Jennifer Doudna and Emmanuelle Charpentier received the Nobel Prize in Chemistry for developing the CRISPR gene-editing method.
- The dream is to design proteins using AI and produce them through CRISPR-modified bacteria, potentially creating enzymes that can convert carbon dioxide, methane, or plastics into useful products.
- Two groups have successfully created functioning enzymes from scratch using AI-designed proteins.
- David Baker's Institute for Protein Design used a deep-learning strategy called "family-wide hallucination" to make a new light-emitting enzyme.
- Biotech startup Profluent used an AI trained on CRISPR-Cas knowledge to design new functioning genome editors.
Conclusion:
The combination of AI and CRISPR holds promise for designing new enzymes and could potentially help tackle climate change. However, powerful technologies like AI and CRISPR also pose risks, and engineering nature has often led to unintended consequences due to the complexity of natural systems.
