Amino acid sequence autocorrelation vectors and Bayesian-regularized genetic neural networks for modeling protein conformational stability: gene V protein mutants.

Abstract excerpt

Development of novel computational approaches for modeling protein properties from their primary structure is the main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino acid sequence autocorrelation (AASA) vectors were calculated by measuring the...

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