Mask blast with a new chemical logic of amino acids for improved protein function prediction

With the exponential increase in protein sequence data, there is an urgency to acquire a knowledge of function of the millions of sequences, using automated methods with high reliability. Conventional methods for annotating a protein sequence transfer the function of a homologous sequence with known functions based on evolutionary information. Here, we present a newer way of classifying amino acids based on chemical measures and demonstrate that, when integrated with mask BLAST, the chemical properties identified outperform current classifications of amino acids as well as evolutionary measures in function detection.