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A working example of relative solvent accessibility (RSA) prediction for proteins is presented. Novel logistic regression models with various qualitative descriptors that include amino acid type and quantitative descriptors that include 20‐ and six‐term sequence entropy have been built and validated. A domain‐complete learning set of over 1300 proteins is used to fit initial models with various sequence...
Prediction of relative solvent accessibility (RSA) is a standard first-approach in predicting three-dimensional protein structures. Here we have applied linear regression methods that include various sequence homology values for each residue as well as query residue qualitative predictors, corresponding to each of the twenty canonical amino acids. We fit the 268-protein learning set with a variety...
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