Rational design of proteins and peptides is a growing field that requires knowledge of protein function down to the amino acid level. Understanding protein geometry and amino acid composition is crucial for understanding molecular and supramolecular chemistry in protein design. In this thesis, the focus is put on active sites of natural enzymes and the drivers of catalytic function in said active sites. The analysis of natural enzyme active sites was done with the purpose of having a set of criteria for building synthetic peptides with identical catalytic function and to broaden enzyme understanding. A data set was built to analyze enzymes from the EC 3.1. (Hydrolases acting on ester bonds) subclass looking at both amino acid content and geometry of active site residues. Patterns in existing data were searched by statistically analyzing 96 esterases. In this process, 22 enzymes with known catalytic triads were selected for further evaluation. Based on the primary structure of the selected enzymes, the composition profiles were created for: (1) the full sequence, (2) the “long active site” including the residues between the first and the last active amino acid, (3) the “short active site” containing only active residues and (4) the “catalytic loop” which looked at 4 residues from each side of the sequence from the catalytic residues. To analyze the dataset two approaches were used. The first approach was based on composition and chemical property analysis. The second approach consisted in using crystal structure PDB files obtained via X-ray, only considering a resolution below 3,0 Å and in measuring the residue geometries in PyMol. The results showed that the distances within which natural enzymes function are less than 1Å in variation and that the angles conform within a 10% variation in the interquartile range compared to the average angle. Moreover, the qualitative amino acid analysis showed an increase of non-polar and a decrease in basic, hydroxylic and polar residues near the catalytically active residues. This suggests a strict functional geometry for the optimal enzymatic activity as well as a specific local amino acid content tolerated in active sites. These findings will allow the development of algorithms for prediction and optimization of enzyme functions and of new theoretical models of modular peptide design.