Additional Supporting Information may be found in the online version of this article: Figure S1 . MS/MS analysis of pAtPSK4-myc. Figure S2 . RT-PCR analysis of AtSBT1.1 transcripts in wild-type and sbt1.1-1 and sbt1.1-2 mutants. Figure S3 . pAtPSK4-myc processing is restored in F 1 hybrids resulting from out-crossing of 35S:ppAtPSK4-myc sbt1.1-1 to wildtype. Figure S4 . AtSBT1.1-YFP and AtPSK4-YFP are located in the apoplast. Figure S5 . MS/MS spectrum for the cleaved N-terminal fluorogenic peptide. Table S1 . Primer sequences used. Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article. Protein Domain Boundary Predictions: A Structural Biology Perspective One of the important fields to apply computational tools for domain boundaries prediction is structural biology. They can be used to design protein constructs that must be expressed in a stable and functional form and must produce diffraction-quality crystals. However, prediction of protein domain boundaries on the basis of amino acid sequences is still very problematical. In present study the performance of several computational approaches are compared. It is observed that the statistical significance of most of the predictions is rather poor. Nevertheless, when the right number of domains is correctly predicted, domain boundaries are predicted within very few residues from their real location. It can be concluded that prediction methods cannot be used yet as routine tools in structural biology, though some of them are rather promising. Computational/mathematical approaches, such as structural bioinformatics [1] , structural class prediction [2, 3] , molecular docking [4] [5] [6] [7] [8] [9] , molecular packing [10, 11] , pharmacophore modelling [12] , Mote Carlo simulated annealing approach [13] , diffusion-controlled reaction simulation [14] , graph/diagram approach [15] [16] [17] [18] [19] [20] [21] , bio-macromolecular internal collective motion simulation [22] , QSAR [23] [24] [25] , protein subcellular location prediction [26] [27] [28] [29] [30] , protein structural class prediction [31, 32] , identification of membrane proteins and their types [33] , identification of enzymes and their functional classes [34] , identification of proteases and their types [35] , protein cleavage site prediction [36] [37] [38] , and signal peptide prediction [39, 40] can timely provide very useful information and insights for both basic research and drug design and hence are widely welcome by science community.