Protein-carbohydrate (PC) interactions play a key role in various biological processes. In particular, PC interactions govern infected erythrocyte (IE) adhesion on placental cells during placental malaria (PM) leading to severe pathological conditions. Experimental description of PC interfaces remains very challenging. The main goal of SugarPred is the development of structure- and sequence-based carbohydrate binding site prediction tools through implementation of the most recent machine learning approaches on the basis of the available structural data. We will apply the developed tools to VAR2CSA, the protein responsible for IE adhesion during PM, and will verify our predictions in direct experiment. This interdisciplinary approach will allow identification of VAR2CSA sugar-binding residues, and thus fill an important knowledge gap currently limiting the improvement of PM vaccines. A set of machine learning tools for the carbohydrate binding site prediction will be made available to the scientific community.