The tridimensional structure of a protein is constrained or stabilized by some local interactions between distant residues of the protein, such as disulfide bonds, electrostatic interactions or hydrogen links. The in silico prediction of the disulfide connectivity has been widely studied: most re- sults were based on few amino-acids around bonded cys- teines, which we call local environments of cysteines. In order to evaluate the impact of such local information onto residue pairing, we propose a machine learning based pro- tocol, independent from the type of contact, to detect affini- ties between local environments which would contribute to residues pairing. Finally, we experiment our protocol on proteins that feature disulfide or salt bridges. The results show that local environments contribute to the formation of salt bridges. However, results on disulfide bridges are not significantly positive with the class of linear functions used by the perceptron-type algorithm we propose.