been uploaded as supplementary information (please open uploaded file Figure S1) where the name of all pdb files has been listed. This file should be open with WordPad. The associated .ent and .hssp files are publically available. Solvent shell Each protein was treated as being made of shells with different solvent accessibilities. The coordinates of all atoms were extracted from the corresponding .ent file and used to calculate the geometric midpoint (M) of each residue: Maa,s(j)~ XN si i N s~x,y,z where N is the number of atoms in residue number j, and s is the x, y and z coordinates of atom number i in residue j. The geometric midpoint of all residues was calculated by: XNR Maa,s(j) Ms~ NR j s~x,y,z where NR is the number of amino acids in the protein. This defined the geometric midpoint of the protein, which is the center of the protein when assuming that the protein is globular. The Euclidian distance from this center to the geometric midpoint of each amino acid residue was then calculated. The solvent accessible surface area of each residue was read from the corresponding .hssp files and converted to a percentage of solvent accessible area by dividing the value with the total surface area of the residue side-chain (calculated from a Gly-X-Gly tripeptide [61]). The secondary structure information of each amino acid was also extracted. The protein residues were binned in solvent shells ranging from totally buried to full solvent exposure in steps of 10%, with 0% and .100% treated explicitly. The average distance to the protein center was calculated for all residues in every bin, thereby obtaining a ‘‘thickness’’ of each solvent accessible shell (Fig. 1B). This can only be considered as an approximation since many proteins differ significantly from spherical structure. Amino acid solvent accessibility distributions Each type of amino acid in the dataset was binned according to its solvent accessibility and secondary structure. This provided information about the abundance of the different amino acids in the different solvent shells and their secondary structure preferences (Fig. 3). In order to retrieve the data displayed in Figure 3 we have written the following files: Hssp caller.m, HsspRead.m, rotateticklabel.m, SAplot.m, SolventAcc.mat. These codes have been uploaded as supplementary information in Figure S2 as ‘‘Hssp caller.m’’, ‘‘HsspRead.m’’, ‘‘rotateticklabel.m’’, ‘‘SAplot.m’’, and ‘‘SolventAcc.mat’’. Furthermore, a description on how to install and run the software used to retrieve the solvent accessibility data displayed in Figure 3 can be found in the uploaded file ‘‘Figure S2 SAplots_User instructions.doc’’. ProExtract ProExtract combined the data from .ent and .hssp files into MATLAB structures, which were saved in .mat format (a MATLAB data file). For each .ent file, the atoms’ coordinates and chain information were loaded into ProExtract, while information on residue type, secondary structure and solvent Amino Acid Pairs in Proteins accessibility (SA) was loaded from the corresponding .hssp file. Since many .ent and .hssp files were found to contain errors, ProExtract included a validation routine, where residues as a minimum were required to have information on the Ca and functional atoms coordinates (vide infra), residue type, secondary structure, solvent accessibility (SA) and chain length. Furthermore, .hssp entries were required to have information about which residue and chain they corresponded to in the .ent file, as numbering in .hssp and .ent files might differ. Residues that did not have all the required information were discarded, while the rest of the chain information was retained. Those that were accepted were added to the MATLAB structure file for that protein. As a result, a file for each protein was created containing combined information on atom coordinates and chains, residue types, secondary structure and SA. As output, ProExtract created a database in the form of an 8D tensor from the .mat files. The tensor contained information about pairs of amino acids present in the different SA protein shells. Two amino acids were considered a pair if they belonged to the same chain, were within the same SA-bin and had a distance less than 8.25 A˚ between their functional atoms (vide infra). Each of the eight tensor directions was binned according to: N Type of the first amino acid (AA1) (20 bins) N Type of the second amino acid (AA2) (20 bins) N Solvent accessibility of the amino acid pair (SA) (12 bins) N Distance between atoms in functional groups (D) (14 bins) N Secondary structure for the first amino acid (SS1) (4 bins) N Secondary structure for the second amino acid (SS2) (4 bins) N Chain length (CL) (12 bins) N Sequence distance between AA1 and AA2 (SD) (6 bins) A pair of amino acid residues is composed of two amino acid residues. The first dimension tells us what is the type of the first amino acid (AA1) out of 20 possible amino acid types and the second dimension tells us what is the type of the second amino acid (AA2) in a pair (once again out of 20 possible amino acid types). So, the two first dimensions give us information about the nature of each amino acid in an amino acid pair in the protein. The fifth dimension is simply telling us in which secondary structural element the first amino acid is located (a-helix, b-strand, turn or coil). This information is retrieved from the downloaded .hssp file associated to each .pdb file. The same is valid for the sixth dimension: it tells us in which secondary structural element the second amino acid in a pair is located (a-helix, b-strand, turn or coil). This information is also retrieved from the .hssp file. See bin definitions in section ‘‘Bin definitions and functional atoms’’. 8272 .mat files were processed successively. All possible combina- tions of two residues were carried out to test if the two residues would constitute a pair (vide supra). When a pair was identified, the count in the data tensor cell with the coordinates (AA1, AA2, SA, D, SS1, SS2, CL, SD) was increased by one. A total of 5.211.796 pairs were identified. These were distributed between 1.756.714 cells in the tensor. In order to establish the significance of the coordinates of each of the pairs in a protein, ProExtract shuffled the amino acids in accordance to the amino acid distribution of the protein. This process was repeated 10 times for each of the 8272 proteins, and the resulting 8D tensors were averaged. The average 8D tensor was used as a reference dataset. For a given pair, the ratio between the actual count in a cell in the observed 8D tensor and the average count in the reference dataset was a measure of the significance of the cell. We compute the ratio between the actual PLoS ONE | www.plosone.org 4 December 2011 | Volume 6 | Issue 12 | e25638