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The “protein folding problem” consists of three closely related puzzles: (a) What is the folding code? (b) What is the folding mechanism? (c) Can we predict the

The “protein folding problem” consists of three closely related puzzles: (a) What is the folding code? (b) What is the folding mechanism? (c) Can we predict the native structure of a protein from its amino acid sequence? (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2443096/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2443096/). It seems the purist definition of solving protein folding means specifying the sequence of actions to arrive at the native structure for a given input sequence. This implies your solution is grounded in a physical reality. Such approaches (i.e MD simulations) have not been very successful to my knowledge and are computationally intractable for most proteins. Even if you are able to do this, how do you verify that your folding mechanism is in fact correct? We don't have ground truth experimental data at the temporal resolution one would want to verify such a prediction. For now, the only practical framing of the protein folding problem is to predict a native structure given an input sequence. This is simply because we have the requisite data...

YeGoblynQueenne 5y ago
We have a lot of data we don't understand and we train a model to represent that data. The model represents the data very well, but we don't understand the model. So now we have a lot of data we don't understand and a model we don't understand. What new knowledge have we acquired? The OP is right to complain that there is no scientific knowledge gained by the mere act of modelling a poorly understood process, when the modelling is performed automatically and its results are also poorly understood. The goal of science is to gain knowledge of the processes in nature, not merely to predict their results. That's how science has advanced in the last few thousands of years and it's startling to see Nature and Science standing behind such a different idea of "science".

YeGoblynQueenne 5y ago

We have a lot of data we don't understand and we train a model to represent that data. The model represents the data very well, but we don't understand the model. So now we have a lot of data we don't understand and a model we don't understand. What new knowledge have we acquired? The OP is right to complain that there is no scientific knowledge gained by the mere act of modelling a poorly understood process, when the modelling is performed automatically and its results are also poorly understood. The goal of science is to gain knowledge of the processes in nature, not merely to predict their results. That's how science has advanced in the last few thousands of years and it's startling to see Nature and Science standing behind such a different idea of "science".