8,267 research outputs found
Recoverable One-dimensional Encoding of Three-dimensional Protein Structures
Protein one-dimensional (1D) structures such as secondary structure and
contact number provide intuitive pictures to understand how the native
three-dimensional (3D) structure of a protein is encoded in the amino acid
sequence. However, it has not been clear whether a given set of 1D structures
contains sufficient information for recovering the underlying 3D structure.
Here we show that the 3D structure of a protein can be recovered from a set of
three types of 1D structures, namely, secondary structure, contact number and
residue-wise contact order which is introduced here for the first time. Using
simulated annealing molecular dynamics simulations, the structures satisfying
the given native 1D structural restraints were sought for 16 proteins of
various structural classes and of sizes ranging from 56 to 146 residues. By
selecting the structures best satisfying the restraints, all the proteins
showed a coordinate RMS deviation of less than 4\AA{} from the native
structure, and for most of them, the deviation was even less than 2\AA{}. The
present result opens a new possibility to protein structure prediction and our
understanding of the sequence-structure relationship.Comment: Corrected title. No Change In Content
Cooperative "folding transition" in the sequence space facilitates function-driven evolution of protein families
In the protein sequence space, natural proteins form clusters of families
which are characterized by their unique native folds whereas the great majority
of random polypeptides are neither clustered nor foldable to unique structures.
Since a given polypeptide can be either foldable or unfoldable, a kind of
"folding transition" is expected at the boundary of a protein family in the
sequence space. By Monte Carlo simulations of a statistical mechanical model of
protein sequence alignment that coherently incorporates both short-range and
long-range interactions as well as variable-length insertions to reproduce the
statistics of the multiple sequence alignment of a given protein family, we
demonstrate the existence of such transition between natural-like sequences and
random sequences in the sequence subspaces for 15 domain families of various
folds. The transition was found to be highly cooperative and two-state-like.
Furthermore, enforcing or suppressing consensus residues on a few of the
well-conserved sites enhanced or diminished, respectively, the natural-like
pattern formation over the entire sequence. In most families, the key sites
included ligand binding sites. These results suggest some selective pressure on
the key residues, such as ligand binding activity, may cooperatively facilitate
the emergence of a protein family during evolution. From a more practical
aspect, the present results highlight an essential role of long-range effects
in precisely defining protein families, which are absent in conventional
sequence models.Comment: 13 pages, 7 figures, 2 tables (a new subsection added
On the optimal contact potential of proteins
We analytically derive the lower bound of the total conformational energy of
a protein structure by assuming that the total conformational energy is well
approximated by the sum of sequence-dependent pairwise contact energies. The
condition for the native structure achieving the lower bound leads to the
contact energy matrix that is a scalar multiple of the native contact matrix,
i.e., the so-called Go potential. We also derive spectral relations between
contact matrix and energy matrix, and approximations related to one-dimensional
protein structures. Implications for protein structure prediction are
discussed.Comment: 5 pages, text onl
Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks
Prediction of one-dimensional protein structures such as secondary structures
and contact numbers is useful for the three-dimensional structure prediction
and important for the understanding of sequence-structure relationship. Here we
present a new machine-learning method, critical random networks (CRNs), for
predicting one-dimensional structures, and apply it, with position-specific
scoring matrices, to the prediction of secondary structures (SS), contact
numbers (CN), and residue-wise contact orders (RWCO). The present method
achieves, on average, accuracy of 77.8% for SS, correlation coefficients
of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS
prediction is comparable to other state-of-the-art methods, and that of the CN
prediction is a significant improvement over previous methods. We give a
detailed formulation of critical random networks-based prediction scheme, and
examine the context-dependence of prediction accuracies. In order to study the
nonlinear and multi-body effects, we compare the CRNs-based method with a
purely linear method based on position-specific scoring matrices. Although not
superior to the CRNs-based method, the surprisingly good accuracy achieved by
the linear method highlights the difficulty in extracting structural features
of higher order from amino acid sequence beyond that provided by the
position-specific scoring matrices.Comment: 20 pages, 1 figure, 5 tables; minor revision; accepted for
publication in BIOPHYSIC
Immune evasion of the CD1d/NKT cell axis
Many reviews on the CD1d/NKT cell axis focus on the ability of CD1d-restricted NKT cells to serve as effector cells in a variety of disorders, be they infectious diseases, cancer or autoimmunity. In contrast, here, we discuss the ways that viruses, bacteria and tumor cells can evade the CD1d/NKT cell axis. As a result, these disease states have a better chance to establish a foothold and potentially cause problems for the subsequent adaptive immune response, as the host tries to rid itself of infections or tumors
Generic transport coefficients of a confined electrolyte solution
Physical parameters characterising electrokinetic transport in a confined
electrolyte solution are reconstructed from the generic transport coefficients
obtained within the classical non-equilibrium statistical thermodynamic
framework. The electro-osmotic flow, the diffusio-osmotic flow, the osmotic
current, as well as the pressure-driven Poiseuille-type flow, the electric
conduction, and the ion diffusion, are described by this set of transport
coefficients. The reconstruction is demonstrated for an aqueous NaCl solution
between two parallel charged surfaces with a nanoscale gap, by using the
molecular dynamic (MD) simulations. A Green-Kubo approach is employed to
evaluate the transport coefficients in the linear-response regime, and the
fluxes induced by the pressure, electric, and chemical potential fields are
compared with the results of non-equilibrium MD simulations. Using this
numerical scheme, the influence of the salt concentration on the transport
coefficients is investigated. Anomalous reversal of diffusio-osmotic current,
as well as that of electro-osmotic flow, is observed at high surface charge
densities and high added-salt concentrations.Comment: 6 pages with 6 figure
Comprehensive structural classification of ligand binding motifs in proteins
Comprehensive knowledge of protein-ligand interactions should provide a
useful basis for annotating protein functions, studying protein evolution,
engineering enzymatic activity, and designing drugs. To investigate the
diversity and universality of ligand binding sites in protein structures, we
conducted the all-against-all atomic-level structural comparison of over
180,000 ligand binding sites found in all the known structures in the Protein
Data Bank by using a recently developed database search and alignment
algorithm. By applying a hybrid top-down-bottom-up clustering analysis to the
comparison results, we determined approximately 3000 well-defined structural
motifs of ligand binding sites. Apart from a handful of exceptions, most
structural motifs were found to be confined within single families or
superfamilies, and to be associated with particular ligands. Furthermore, we
analyzed the components of the similarity network and enumerated more than 4000
pairs of ligand binding sites that were shared across different protein folds.Comment: 13 pages, 8 figure
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