SMQ-V043 N-001_ligas_size.pdf - Journal of the Mexican Chemical ...

State-of-the-art Protein Structural Biology 27
amide I band and H D exchange studies revealed bands originating
from polypeptide backbone conforination. The conclusions
of FT-IR and laser Raman studies therefore were in concurrence.
are progressing rnuch more rapidly. A set of proton-proton
NOE contact distances were derived and used as an input in
molecular dynamics simulation of amelogenin structure
described below.
7. 2D and 3D NMR Spectroscopic Studies
of Bovine Amelogenin
2D and 3D [1] H NMR [1] spectroscopy provides a blue print
of the proton resonances and their spatial disposition through
nuclear Overhauser enhancement (NOE) phenomenon which
arises from spatial proximity of protons in a protein which
give rise to a NOE cross-peak in 2D NOESY spectra, with an
intensity proportional to the inverse sixth power of the distance
between the two protons (l/r 6 ).Usually NOE’s are classified
into strong, intermediate, and weak, which implies that
the inter-proton distances are < 2.5 Å, < 3.5 Å, or < 5 Å. NOE
data of bovine amelogenin is what led to the derivation of the
3D structure of bovine amelogenin. Recently 2D NMR studies
of bovine amelogenin [l4] were focused on the elucidation of
amelogenin secondary structure in solution. The complex task
of assigment of proton resonances using 2D COSY, selected
NOE experiments, aided by automated assigment computer
routines (FELIX, XEASY) has now been completed and will
appear in the literature in 1999 [l4]. The hydrophobicity of
bovine amelogenin presents formidable challenge in 1D and
2D NMR studies due to its poor solubility, whereas the 179-
residue mouse amelogenin is more soluble in aqueous buffers
and therefore 2D and 3D NMR studies of the mouse variant
Fig. 3. Polypeptide Backbone Structure of the 170 residue bovine
tooth amelogenin from NMR and Molecular Dynamics Simulations
(Prabhakaran et al., 1991).
8. Theoretical Derivation of the Secondary
Structure of Bovine Amelogenin from 2D NMR
Studies
Recent advances have made it possible to explore the multidimensional
energy surface of a protein by the calculation of the
elassical energy of a complex system like protein. The energy
surfaces so obtained forrn the basis of molecular dynamics
and Monte Carlo studies [15] Another important method for
exploring the energy surfaces is to find configurations for
which the energy is a minimum at which point the molecular
system is assumed to exist in a stable conformation. By
adding external forces to the molecule in the form of restraints
and constraints, a wide range of modeling strategies can be
developed using minimization techniques as the foundation to
answer specific questions. The pursuit of a global minumum
for a protein is riddled with difficulties. Neverthless, the minimized
structure provides complimentary information to physical
methods in structural biology e.g. NMR. In Monte Carlo
or Dynamic minimization, ensembles of structures can be generated
to estimate thermodynarnic averages. A sirnple but
powerful restraint is to add a term restraining the distance
between two atoms e.g. two protons, an estimate of which is
obtainable from 2D NOESY experiments. Inclusion of NOE
distance restraint with the energy function can yield a family
of representative structures and constitutes a powerful method
for solving structures of proteins in solution. Most application
of NOE distance restraints to solving protein structures use
either distance geometry algorithms or molecular dynamics
method. Minimization is resorted to only in the final stages of
the refinement of the 3D structure of a protein. Molecular
dynamics method is linked to the solution of Newton equations
of motion for all atoms in a protein, care should be exercised
in establishing the initial conditions of thermodynamic
equilibrium. In such a process many conformations of a protein
may be obtained wich are close in energy. To further
explore the fluctuations of such structures at any given temperature,
it is required that the simulations should explore a
time window of the order of several picoseconds. An alternative
approach is a search for the global minimum in energy of
a protein. The search sometimes may end in a local minimum
trap. A number of stochastic methods e.g. simulated annealing,
threshold accepting, genetic algorithms TABU search
[16] have been developed to circumvent the stalling of the
search for a global minimum in a local minimum trap. The
application of the methods described here to amelogenin is
described below.
A combination of both approaches have been applied to
amelogenin structure prediction. Amelogenin is a protein rich
in β-sheet: β-turn segments [17]. Extensive protein secondary