The cornerstone to the next revolution — DeepMind’s AlphaFold 2
By: Sukanta Saha
Tags: AI, Data Science, Biotechnology, Deep Learning, Machine Learning

For a quick Biology 101; all living things on earth are made of 21 different organic molecules called amino acids. These molecules mix and match to form the various different kinds of biological structures that build us, our dogs, our cats, our plants, our food, and everything in nature. What differs from one structure to another due to these different combinations of amino acids, is their shape, which ultimately plays a major role in dictating their functions. The specific sequences of these amino acids are determined by the DNA patterns in which our genome is made, and therefore, theoretically, if we know the genetic code, we know the amino acid sequence it will lead to, and if we know that, we should have a somewhat accurate prediction of its shape, and by a little more research, what its functions are. Abnormalities in these protein structures due to faulty mutations in our gene are often the cause of many health disorders.

Getting the amino acid sequence from its DNA was as straightforward as translating a very simple cryptograph. Every three DNA (called a codon) corresponds to a specific amino acid. However, what really is the more complex part is getting to the accurate protein shape from this chain of amino acid sequence. Because there are so many possible combinations of bond angles and intermolecular interactions like electrostatic forces, Hydrogen bond interactions, hydrophobic interactions, and disulfide bonds, it was like looking for a needle in a haystack. There are 10¹⁴³ possible combinations, roughly, in which a sequence could fold, according to Levinthal’s Paradox. It has been labeled as one of the fundamental scientific problems.

There are experimental methods to determine the structure of a protein like X-Ray Crystallography, NMR Spectroscopy, or Electron Microscopy. But these are meticulous and highly expensive and time-consuming processes. These processes generate other pieces of information like the bond angles, density, general 2D and 3D shapes, which are then combined to deduce the original structure. And so just the amino acid sequence is not enough. Around 180 million amino acid sequences are currently available, compared to a tiny 170,000 protein structures so far determined by experimentalists using these techniques. With DNA sequencing being exponentially more accessible, if coupled with an equally accessible method to generate protein models using just those sequences, the implications could be massive.

A generic Protein model with alpha helices and beta-sheet structures. Photo by National Cancer Institute on Unsplash

Every two years since 1994, the Critical Assessment of Protein Structure Prediction (CASP) administers the global competition for the best protein model predictors. About 100 teams line up to predict the structure of proteins previously unknown to the community, using just the amino acid sequences. Only a handful of people, who assess the performance of these predictors know the true structure of these proteins. Until 2018, the best predictors could barely even cross the 50% accuracy threshold. When Google’s DeepMind was done with its AlphaGo project though, this was about to change forever.

DeepMind’s next big project, AlphaFold introduced itself as the state-of-the-art predictor, with a median accuracy of about 60% for free modeling of protein structures in the 13th CASP in 2018. This predictor was based on the all mighty Machine Learning algorithms that have taken over the world by storm.

The model can be segmented into two sections. One section predicted the probability distribution of the relative distance between each of the two amino acids in a chain in the form of a heatmap using very deep residual neural networks. This is called the distance map. Another section predicted the specific angles between them. These two sections were combined to predict the most likely structure of the protein from the amino acid sequence, based on the optimization of local Gibbs free energy using Gradient Descent.

Other mathematical and physical manipulations were done to increase the quality of the data like filtering out less relevant information or accounting for the biases that come with the training data to increase the prediction accuracy. Evolutionarily related known protein structures were also integrated into the 3D model prediction process. 29,000 protein samples were used to train the model. In spite of being the most accurate predictor though, this was far from actually being applied in practice.