AlphaFold Spreads through Protein Science

More on a topic we worked on,   A yet better approach emerges for use.

AlphaFold Spreads through Protein Science

By Chris Edwards

Communications of the ACM, May 2023, Vol. 66 No. 5, Pages 10-12  10.1145/3586582

AlphaFold-predicted structure of estrogen receptor protein, seen binding to DNA

Two years ago, as the COVID-19 pandemic swept across the world, researchers at DeepMind, the artificial intelligence (AI) and research laboratory subsidiary of Alphabet Inc., demonstrated how it could use machine learning to achieve a breakthrough in the ability to predict how proteins, the work-horses of the living cell, fold into the intricate shapes they take on. The work gave hope to biologists that they could use this kind of tool to tackle diseases such as the SARS-CoV-2 coronavirus much more quickly in the future.

Researchers were able to assess the abilities of DeepMind's AlphaFold2 thanks to its inclusion in the 14th Critical Assessment of Structure Prediction (CASP14), a benchmarking competition that ran through 2020 and which added a parallel program to uncover the structures of key proteins from the SARS-CoV2 virus to try to accelerate vaccine and drug development. The organizers of CASP14 declared the tool represented "an almost complete solution to the problem of computing three-dimensional structure from amino-acid sequences," though some caveats lie behind that statement.

Figure. An AlphaFold protein prediction with a very high (greater than 90 out of 100) per-residue confidence score.

In principle, quantum mechanical simulations can predict which collection of folds leads to the lowest combined energy of all the chemical bonds in the shape and the water and other molecules around it. However, this remains beyond the capacity of even today's computers and may not even be practical in most cases.

John Jumper, senior staff research scientist at DeepMind, points out that to perform a full molecular-dynamic simulation is not just computationally complex; it requires a complete specification of the environment around the protein in question. "Proteins are exquisitely sensitive machines and extremely finely balanced. We can't write down really good energy functions for them. Even small changes, like getting the salt concentration wrong or not specifying some condition, can cause them not to fold at all. And you have no hope of writing down all the correct conditions of every protein in the human cell," he says.

When biologists produce structures for proteins experimentally, they find ways to fix the molecule in what they hope is a representative conformation. One method is to isolate and crystallize the protein and then use X-ray diffraction to estimate the positions of atoms in the complex structure. Another increasingly common method is cryogenic electron microscopy (cryo-EM): freezing the isolated protein and then using the scattering of electron beams by the atoms to work out how the protein chain bends and folds. Years of effort have populated publicly accessible databases such as the Protein Data Bank (PDB) set up by a group of U.K. and U.S. laboratories in 1971. Though painstaking to create, this data has proven crucial to the growing efficacy of AI-based models.... '