A Nearest Neighbour-Based Approach for Viral Protein Structure Prediction

Abstract

Protein tertiary structure prediction consists of determining the three-dimensional conformation of a protein based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using in formation extracted from known protein structures. Several existing pro tein tertiary structure prediction methods produce contact maps as their output. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. In addition, many existing approaches use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, we used three different physicochemical prop erties of amino acids obtained from the literature. Using this method, we performed tertiary structure predictions on 63 viral capsid proteins with a maximum identity of 30% obtained from the Protein Data Bank. We achieved a precision of 0.75 with an 8-angstrom cut-off and a minimum sequence separation of 7 amino acids. Thus, for the studied proteins, our results provide a notable improvement over those of other methods