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3.3.3 Complementarity between Surfaces
3.3.3.1 Gap Index
One essential feature in receptor-ligand binding is the electrostatic and geometric
complementarity observed between associating molecules. Here, we introduce the
use of gap index (Jones and Thornton 1996) as a means to evaluate complementarity
of interacting interfaces:
gap volume between pMHC (Å
3
)
(1)
Gap index (Å) =
interface ASA (Å
2
) (per complex)
The mean gap indices for class I and class II pMHC complexes are 0.95 ± 0.24 Å
and 1.12 ± 0.20 Å, respectively (Kangueane et al. 2001). The results indicate that the
interacting surfaces in pMHC complexes are significantly complementary. On an
average, the gap index is higher in class II complexes than in class I complexes. This
implies that the interface area of class I complexes is greater than its corresponding
gap volume. On the contrary, the mean gap volume is greater than the interface area
in class II complexes. Not much difference can be identified in the gap index
between complexes of different alleles in both class I and class II complexes.
3.3.3.2 Gap Volume
The gap volume between the MHC and the peptide in each complex can be computed
using the SURFNET program (Laskowski 1991), which provides an estimate of the
volume enclosed by the two interacting molecular subunits. The algorithm places a
series of spheres (maximum radius 5.00 Å) midway between the surfaces of each pair
of subunit atoms, such that its surface is in contact with the surfaces of the atoms in the
pair. The size of each sphere is reduced accordingly whenever it is intercepted by other
atoms and subsequently discarded if it falls below a minimum allowed radius (1.00 Å).
The sizes of all the remaining allowable gap-spheres are subsequently used to compute
the gap volume between the two subunits.
3.4 Structural Prediction Techniques
3.4.1 Homology Modeling
The use of known homologous protein structure(s) to predict the unknown structure
of a related amino acid sequence represents one of the most reliable strategies for
model building of proteins (Swindells and Thornton 1991), often producing model
structures with accuracy to within 2.00 Å RMSD from the actual crystal structure.
Homology modeling involves a series of steps, with each step depending on the
success of the preceding one. A comprehensive coverage of the homology modeling
