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Protein folding - key to faster drugs development
Read more on - Polity | Economy | Schemes | S&T | Environment
- Life's building blocks: One ha to first understand proteins, to understand life. These molecular chains, each assembled from a menu of 20 types of chemical links called amino acids, do biology’s heavy lifting. As enzymes they catalyse the chemistry that keeps bodies running. Examples: (a) Actin and myosin, the proteins of muscles, permit those bodies to move around. (b) Keratin provides their skin and hair. (c) Haemoglobin carries their oxygen. (d) Insulin regulates their metabolism. (e) A protein called spike allows coronaviruses to invade human cells, thereby shutting down entire economies!
- Understanding them: To list a protein’s amino acids is easy, but to know what a protein does, and how it does it, depends also on how it folds up after its creation, into its final, intricate shape.
- Molecular biologists can probe proteins’ shapes experimentally, using techniques like X-ray crystallography. But this is time-consuming.
- Things are changing now, and in November 2020, researchers from DeepMind, an artificial-intelligence (AI) laboratory owned by Alphabet, Google’s parent company, presented results suggesting that they have made enormous progress on using a computer to predict a protein’s shape from just a list of its amino-acid components.
- Huge leap: Replacing months of experiments with a few hours of computing time could shed new light on the inner workings of cells. It can speed up drug development, and can suggest treatments for diseases like Alzheimer’s, in which misshapen proteins are thought to play a role. Until now, the machine-learning techniques which DeepMind’s team used to attack the protein-folding problem have been best known for powering things like face-recognition cameras and voice assistants, and for defeating human beings at tricky games like Go.
- Using computers: The idea of using computers to predict proteins’ shapes is half a century old. Progress has been real, but slow. These days a humbler field, protein-shape prediction now measures its progress by how well algorithms perform in something called Critical Assessment of Protein Structure Prediction (CASP). This is a biennial experiment-cum-competition which started in 1994 and is dubbed the “Olympics of protein-folding”. In it, algorithms are subjected to blind tests of their ability to predict the shapes of several proteins of known structure.
- DeepMind’s first entry to CASP, two years ago, was dubbed AlphaFold. It made waves by performing much better than any other then-existing program. The current version, AlphaFold 2, has stretched that lead still further.
- One measure of success within CASP is the global-distance test. This assigns algorithms a score between zero and 100 by comparing the predicted locations of atoms in a molecule’s structure with their location in reality. AlphaFold 2 had an average score of 92.4—an accuracy that CASP’s founder, John Moult, who is a biologist at the University of Maryland, says is roughly comparable with what can be obtained by techniques like X-ray crystallography.
- The real crux: It is impractical to attack this problem with computational brute force. One estimate is that a reasonably complex protein could, in principle, take any of as many as 10300 different shapes. The shape which it does eventually settle into is a result of a balance of various atom-scale forces that act within its amino-acid building blocks, between those building blocks, and between the building blocks and other, surrounding, molecules, particularly those of water.
- Summary: Around 180 million amino-acid sequences are known to science. But only some 1,70,000 of them have had their structures determined. Some think that boosting this number could help screen drug candidates to see which are likely to bind well to a particular protein. It could be used to reanalyse existing drugs to see what else they might do. And it could boost synthetic biology, by speeding up the creation of human-designed proteins intended to catalyse chemical reactions. AlphaFold 2 was able to predict the structures of several of the proteins used by the new coronavirus, including spike.
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