Non-proline Cis peptide bonds in proteins

Abstract

In a non-redundant set of 571 proteins from the Brookhaven Protein Data Base, a total of 43 non-proline cis peptide bonds were identified. Average geometrical parameters of the well-defined cis peptide bonds in proteins determined at high resolution show that some parameters, most notably the bond angle at the amide bond nitrogen, deviate significantly from the corresponding one in the trans conformation. Since the same feature was observed in cis amide bonds in small molecule structures found in the Cambridge Structural Data Base, a new set of parameters for the refinement of protein structures containing non-Pro cis peptide bonds is proposed. A striking preference was observed for main-chain dihedral angles of the residues involved in cis peptide bonds. All residues N-terminal and most residues C-terminal to a non-Pro cis peptide bond (except Gly) are located in the β-region of a φ/ψ plot. Also, all of the few C-terminal residues (except Gly) located in the α-region of the φ/ψ plot constitute the start of an α-helix in the respective structure. In the majority of cases, an intimate side-chain/side-chain interaction was observed between the flanking residues, often involving aromatic side-chains. Interestingly, most of the cases found occur in functionally important regions such as close to the active site of proteins. It is intriguing that many of the proteins containing non-proline cis peptide bonds are carbohydrate-binding or processing proteins. The occurrence of these unusual peptide bonds is significantly more frequent in structures determined at high resolution than in structures determined at medium and low resolution, suggesting that these bonds may be more abundant than previously thought. On the basis of our experience with the structure determination of coagulation factor XIII, we developed an algorithm for the identification of possibly overlooked cis peptide bonds that exploits the deviations of geometrical parameters from ideality. A few likely candidates based on our algorithm have been identified and are discussed.