In this study, we explore the space of different families of homologous protein sequences through the lens of dimensionality reduction, specifically employing Principal Components Analysis (PCA), a technique known for its effectiveness in handling such data. Our primary objective is to establish a mapping from the sequence space to a reduced dimensional space defined by the principal components of the data (protein family) covariance matrix. This reduction aids in streamlining the analysis while preserving the relevant statistical (and hopefully biological) information. To accomplish this, we utilize the Expectation Propagation (EP) algorithm. This method allows us to estimate the probability distribution of a sequence that fits the imposed projection constraints, driving the resolution of the corresponding inverse problem.