The intricate genotype-phenotype relationship has been a long-standing issue in biology, important both from the fundamental and applied points of view. One of the major irregularities hindering progress in establishing these links is epistasis - the complex and elusive interaction between mutations. Despite the vast accumulated genetic data and progress in this area, epistasis is still far from being completely understood. Epistasis can be studied quantitatively in combinatorially complete datasets, which form hypercubes in protein sequence space, where connected sequences are one mutation away from each other. However, this might be insufficient to portray the full picture of epistatic interactions. To extend the repertoire of the methods for exploring epistasis, we propose here to consider hyperrectangles, where some edges connect sequences being two or more mutations away from each other. The present work formalizes the theoretical knowledge about these novel structures and compares the amount of epistasis identified in hypercubes and hyperrectangles constructed from experimental datasets. A new algorithm, CuboidME, was developed for calculating hyperrectangles, which were then compared to hypercubes. In the experimental datasets, there were four orders of magnitude more hyperrectangles than hypercubes for the same sample size. Subsequently, we showed that for the studied datasets there is an increase in epistasis measured by epistatic coefficients in hyperrectangles compared to hypercubes. For the same datasets, hyperrectangles could find more sign epistasis than using hypercubes alone. It was also shown that there is a trend for increase in epistasis with increasing number of mutations being considered in a hyperrectangle. The results indicate that hyperrectangles can be used to reveal more information on epistasis in a fitness landscape, especially if it is combinatorially incomplete.