Spatial structures of transmembrane proteins are difficult to obtain either experimentally or by computational methods. Recognition of helix-helix contacts conformations, which provide structural skeleton of many transmembrane proteins, is essential in the modeling. Majority of helix-helix interactions in transmembrane proteins can be accurately clustered into a few classes on the basis of their 3D shape. We propose a Stochastic Context Free Grammars framework, combined with evolutionary algorithm, to represent sequence level features of these classes. The descriptors were tested using independent test sets and typically achieved the areas under ROC curves 0.60-0.70; some reached 0.77.