DeepMind’s gigantic leap: Solving 50 years old quest of biology
By: Hamza Abdullah
Tags: Artificial Intelligence, Machine Learning, Biology, Technology

DeepMind, an AI technology company offshoot of Google AI, has already amazed the scientific community showcasing AIs that have learned to play a variety of complex games with superhuman skill, from Go and StarCraft to Atari’s entire back catalog. It has now made an astronomical leap in solving previously unsolvable biological challenge. Determining the protein’s structure through its amino acid sequence.

Christian Anfinsen, 1972 Nobel laureate in Chemistry has postulated the theory that a Protein’s amino acid sequence should fully determine its structure. It sparked a long 50 years swedge to correctly model protein structure computationally based on one-dimensional amino-sequence. Which took a half-century to correctly 3D model protein structure because theoretically protein folding takes astronomical random shifts before synthesizing into a final 3D structure.

In 1969, Levinthal's paradox, a thought experiment which estimated 10^300 possible conformations for a typical protein if modeled using brute force technique probably take the age of the known universe to correctly configure all protein’s structure.

We have been stuck on this one problem — how do proteins fold up — for nearly 50 years. To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment. — PROFESSOR JOHN MOULT , Co-founder & Chair of CASP, University of Maryland

The latest version of DeepMind’s deep learning framework “AlphaFold” can accurately predict the protein structure within the width of an atom. CASP uses the Global distance test (GDT) to measure the accuracy of the protein’s structure. GDT is a percentage of amino acid residues (beads in the protein chain) within a threshold distance from the correct position.

The 14th version of CASP results shows AlphaFold scoring at 92.4 GDT across all targets. This means that the latest version of DeepMind’s algorithm has an average error of approximately 1.6 Angstroms, which is comparable to the width of an atom (or 0.1 of a nanometer). It scored 87.0 GDT for the hardest protein targets in the most challenging free-modeling category. Which is quite promising.

This exciting advancement in the field of AI opens up new doors for the biologist to test computational modeling to unfold some of the challenging biological quests.

One of few drawbacks concerning to AlphaFold is the time taken to successfully predict the structure as compared to other rival computational modelling techniques i.e. Al-Quraishi’s model which is millions of times faster but with least accuracy as of “AlphaFold”. But it is preferable where time is money.

I was also a part of CASP 14, but my model far from reaching the maturity of AlphaFold or Al-Quraishi’s model. They’re astonishing, computational beast solving the unsolvable biological challenge.

I'm along with other fellow scientists are highly optimistic about advancements in AI technology in solving several biological challenges. i.e. Drug discovery, understanding of diseases, and in future pandemic response efforts to synthesizing the vaccine.

DeepMind’s significant contribution in field of AI as well as in Biology still needs tetra folds of improvement. Not every structure predicted is perfect. There’s still much to unfold, including how multiple proteins form complex structures, how they interact with DNA, RNA, or small molecules, and how we can determine the precise position of all amino acid side chains.

AlphaFold shows the promising potential of AI as a toolbox to further experiment with the mysteries of life. It is also a gigantic leap for DeepMind’s corporate race to become benchmark setter in the community.