Research in protein structure modeling:
1.
Protein folding and structure prediction
Sampling method was suggested recently as the
bottleneck in protein structure prediction. We developed a new sampling
algorithm for protein structure prediction and tested it on a popular protein
folding model, HP model. The new method performed significantly better than all
previous methods. As a powerful global optimization algorithm, the new method
will also find application in other problems.
Since a protein's dynamic fluctuation inside
cells affects the protein's biological properties, we present a novel method to
study the ensemble of near-native structures (NNS) of proteins, namely, the
conformations that are very similar to the experimentally determined native
structure. We show that this method enables us to (i) quantify the difficulty
of predicting a protein's structure, (ii) choose appropriate simplified
representations of protein structures, and (iii) assess the effectiveness of
knowledge-based potential functions. We found that well designed simple
representations of protein structures are likely as accurate as those more
complex ones for certain potential functions. We also found that the widely
used contact potential functions stabilize NNS poorly, whereas potential
functions incorporating local structure information significantly increase the stability
of NNS.
An effective potential function is critical for protein
structure prediction and folding simulation. Simplified protein models such as
those requiring only Ca or backbone atoms are attractive because they enable
efficient search of the conformational space. We show residue-specific reduced
discrete-state models can represent the backbone conformations of proteins with
small RMSD values. However, no potential functions exist that are designed for
such simplified protein models. In this study, we develop optimal potential
functions by combining contact interaction descriptors and local
sequence-structure descriptors. The performance of the potential function in a
test of discriminating native protein structures from decoys is evaluated using
several benchmark decoy sets. Our potential function requiring only backbone
atoms or Ca atoms have comparable or better performance than several
residue-based potential functions that require additional coordinates of
side-chain centers or coordinates of all side-chain atoms.
2.
Protein packing and stability.
The role of side-chain entropy (SCE) in protein
folding has long been speculated but is still not fully understood. Utilizing a
newly developed Monte Carlo method, we conducted a systematic investigation on
how the SCE relates to the size of the protein and how it differs among a protein's
X-ray, NMR, and decoy structures. For a set of 675 non-homologous proteins, we
observed that there is significant SCE for both exposed and buried residues --
the contribution of buried residues approaches ~40% of the overall SCE.
Furthermore, the SCE can be quite different for structures with similar
compactness or even similar conformations. As a striking example, we found that
proteins' X-ray structures appear to pack more "cleverly" than their
NMR or decoy counterparts in the sense of retaining higher SCE while achieving
comparable compactness, which suggests that the SCE plays an important role in
favouring native protein structures. By including a SCE term in a simple free
energy function, we can significantly improve the discrimination of native
protein structures from decoys.
There are only 20 natural amino acids. What make
them so special? We study side-chains using two-dimensional square lattice and
three-dimensional tetrahedral lattice models, with explicitly constructed
side-chains formed by two atoms of different chirality and flexibility. With
enumeration and sequential Monte Carlo
technique, we found that both chirality and reduced side-chain flexibility
lower the folding entropy significantly for globally compact conformations,
suggesting that they are important properties of residues to ensure fast
folding and stable native structure. This corresponds well with our finding
that natural amino acid residues have reduced effective flexibility, as
evidenced by statistical analysis of rotamer libraries and side-chain rotatable
bonds. We also found that among compact backbones with maximum side-chain
entropy, helical structures emerge as the dominating configurations. Our
results suggest that compactness and conformational entropy are important
factors contributing to the formation of helices.
·
Voids in proteins.
Voids exist in proteins as packing defects and
are often associated with protein functions. Using lattice and off-lattice
simplified protein models, we found that packing density for single domain
proteins decreases with chain length. We further demonstrate that protein-like
scaling relationship between packing density and chain length is observed in
off-lattice self-avoiding walks. Our studies suggest that maintaining high
packing density is only characteristic of short chain proteins. We found that
the scaling behavior of packing density with chain length of proteins is a
generic feature of random polymers satisfying loose constraint in compactness.
3.
Atom-level free energy functions for structure
modeling.
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