The interactions between pathogen proteins and their hosts allow pathogens to manipulate host cellular mechanisms to their advantage. The identification of host proteins that are targeted by virulent pathogen proteins is crucial to increase our understanding of infection mechanisms and to propose new therapeutics that target pathogens. Understanding the virulence mechanisms of pathogens requires a detailed molecular description of the proteins involved, but acquiring this knowledge is time consuming and prohibitively expensive. Therefore, we develop a statistical method based on hypothesis testing to compare the time series obtained from conversion of the physicochemical characteristics of the amino acids that form the primary structure of proteins and thus to propose potential functional relation between proteins. We called this algorithm the multiple spectral comparison algorithm (MSCA), the MSCA was inspired by the BLASTP tool and was implemented in R code. The algorithm compares and relates multiple time series according to their spectral similarities, and the biological relation between them could be interpreted as either a similar function or protein-protein interaction (PPI).