Genes set the order that amino acids (the chemical building blocks of proteins) appear in the proteins which they code for. But, working from the gene, the form which the protein's 3-D structure will take cannot as yet be predicted. The extremely complex shapes in which the protein 'folds' has a profound effect on the properties it has within an organism.
The so-called 'protein folding problem' first described more than half a century ago, means that pharmaceutical and bio-informatic researchers (for example) are faced with very extensive problems when trying to design new medicines and enzymes - and also in understanding how currently known examples actually work.
We have little experimental knowledge of protein-folding energy landscapes.
We cannot consistently predict the structures of proteins to high accuracy. We do not have a quantitative microscopic understanding of the folding routes or transition states for arbitrary amino acid sequences. We cannot predict a protein’s propensity to aggregate, which is important for aging and folding diseases. We do not have algorithms that accurately give the binding affinities of drugs and small molecules to proteins. We do not understand why a cellular proteome does not precipitate, because of the high density inside a cell. We know little about how folding diseases happen, or how to intervene.“
Source : The protein-folding problem, 50 years on in Science, vol. 338, 2012
Of the genes which are known to code for the generation of proteins, around 20% produce proteins which have functions that are as yet unknown. Oddly, this 20% figure seems to remain fairly constant for most of the organisms which have been studied. From the simplest yeast up to and including humans. See: The Royal Society Open Biology, 2019
Researchers using the DeepMind neural-network computing system have shown that it's possible to predict the folded protein shape with good accuracy (typically around 70%) on about half of the proteins that were tested.
There is, however, a lack of understanding about how artificialactually operate, and therefore how the results were achieved.
We have shown that the deep distance prediction neural network achieves high accuracy, but we would like to understand how the network arrives at its distance predictions and—in particular—to understand how the inputs to the model affect the final prediction.
Source : Nature Jan. 2020
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