Equation 8 · Part 2 · Structure from Sequence: What Protein Folding Prediction Did and Did Not Settle

Symbol a_1

P(a_1,\ldots,a_L) = \frac{1}{Z}\exp\!\left[\sum_{i} h_i(a_i) + \sum_{i<j} e_{ij}(a_i,a_j)\right],

a_1

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$a_1$ is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

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$a_1$ is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.

Full expressionSymbol a_1Article meaning

The passage around this formula

The fix is to fit a global model that explains the observed column statistics with the smallest number of direct couplings. In the maximum-entropy formulation used in this literature, the probability of a full sequence takes a Potts form, $P(a_1,\ldots,a_L) = \frac{1}{Z}\exp\!\left[\sum_{i} h_i(a_i) + \sum_{i<j} e_{ij}(a_i,a_j)\right]$ . with single-site fields $h_i$ and pair couplings $e_{ij}$ fitted so that the model reproduces the single-column and pairwise frequencies of the alignment. Marks and colleagues reported that the strength of the inferred couplings is a strong predictor of residue proximity in the folded structure, and that the top-scoring couplings are accurate and well-distributed enough to define a three-dimensional fold [ 5 ] . Morcos and colleagues…

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