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Molecular modelling of peptides and protein-ligand complexes using knowledge-based potentials |
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Principal Investigator : Debasisa Mohanty Ph D Student Collaborator The main theme of the research project is to
understand the structural principles that govern folding of peptides to stable
conformations and binding of various ligands to proteins and use these
structural principles for developing computational approaches for structure
prediction of peptides and protein-ligand complexes. The specific objective of
the project is to investigate, whether knowledge-based potentials i.e. scoring
functions obtained from analysis of structural features in databases of known
protein structures can be used for predicting the (i) experimentally observed
monomeric hairpin structures for short peptides, (ii) bound conformation of
peptides in MHC-peptide complexes and ranking of peptides as per their binding
free energy and (iii) substrate specificity of b-Ketoacyl synthase like
condensing enzymes which catalyze carbon-carbon bond formation during
polyketide biosynthesis. A.
Prediction of b
hairpin structure The explicit solvent molecular dynamics protocol,
which had been used successfully to demonstrate strong tendency for structure
in the short peptide ITVNGKTY, was applied to few other peptides to check if
they can adopt b
hairpin structures in solution. Ensembles of structures were generated for
these sequences and energy vs RMSD (from b
hairpin conformation) plots were obtained using a residue based statistical
potential, to check if b
hairpin structures can be predicted as low energy conformations. From the
energy vs RMSD plots, it was found that residue based statistical potentials
could not unambiguously distinguish b
hairpin structures from other possible conformations. This may be attributed
to the relatively few contacts formed in a short b
hairpin. Hence, it was decided to use the rotamer library approach and
construct all atom models and rank various conformations using atom based
statistical potentials. B. MHC-peptide
interactions In order to test the predictive ability of the MHC-peptide
modelling software, it was decided to check, whether the program can rank the
sequence of the bound peptides in MHC-peptide complexes as low energy binders
from all possible overlapping 9 or 10 mers fragments obtained from the
sequences of the proteins from which the bound peptide originated. The results
of this analysis indicated that, using a residue based statistical potential,
in majority of the cases it is possible to rank the bound peptides within top
20%. Hence, the residue based statistical potential can serve as a quick
screening tool for identifying potential MHC binding sequences and detailed
atomic models have to be taken into consideration for more accurate prediction
of peptide specificity for MHC. Analysis of all the known MHC-peptide complex
crystal structures indicated that the peptides bind in a similar extended
conformation and hence it was decided to use the extended backbone
conformation as seen in MHC-peptide complexes and a backbone dependent rotamer
library for building all atom models for peptides in the MHC groove. The side
chains were attached to the extended backbone using their most probable
rotameric state and steric criteria. The accuracy of the generated
conformations were tested by comparison with the known MHC-peptide complexes.
The results indicate that 73% of the peptide side chains generated by the
rotamer library approach have c values within ± 30° of their respective
values in the crystal structure. This relatively simple approach is able to
predict peptide conformations in the MHC groove with a reasonable accuracy.
However, detailed comparison of the predicted conformations with crystal
structures are being carried out to see if prediction accuracy can be improved
further. The generated all atom MHC-peptide complexes are being ranked using
various atom based statistical potentials and combinations of molecular
mechanics and statistical potentials to achieve more accurate prediction of
peptide sequence specificity for MHC. C.
Substrate specificity of b
-ketoacyl synthase (KS) like condensing enzymes The formation of carbon-carbon bond during polyketide
biosynthesis is catalyzed by KS domains of modular polyketide synthases or by
chalcone synthase like single mono-functional enzymes. Such domains or single
enzymes have also been found in genomes of M.tb and other microbes, though
their substrates are unknown. We have initiated molecular modelling studies to
complement the experimental approaches pursued in Dr. Gokhale’s group for
identifying possible substrates for such enzymes. For computational prediction
of possible substrates, we are making use of the available knowledge base of
the crystal structures of similar condensing enzymes, enzyme-substrate
complexes and sequence information of a large number KS domains or chalcone
synthase like proteins with known substrates. Since, sequences of 83 different
KS domains with known substrate were available, a sequence based phylogenetic
analysis was carried out to see if variation in length of the substrate and
chemical substitutions on these substrates can be correlated with the sequence
variations in the KS domains. However, no such correlation was obvious from
phylogenetic analysis as KS domains show a high degree of sequence variations
among themselves. Hence, it was decided to build 3D structures for these
sequences by homology modelling approach using crystal structure of a
condensing enzyme from fatty acid biosynthetic pathway as template and look
for sequence variations among the residues lining the active site cavity.
Homology models have been built for several KS domains and chalcone synthase
like proteins using MODELLER package. Analysis of the shape and chemical
nature of the active site cavity is being carried out to correlate them with
the substrate types and the results of this analysis would help us in
predicting unknown substrates. |