67,361 research outputs found
Critical assessment of methods of protein structure prediction: Progress and new directions in round XI
Modeling of protein structure from amino acid sequence now plays a major role in structural biology. Here we report new
developments and progress from the CASP11 community experiment, assessing the state of the art in structure modeling.
Notable points include the following: (1) New methods for predicting three dimensional contacts resulted in a few spectacular
template free models in this CASP, whereas models based on sequence homology to proteins with experimental structure
continue to be the most accurate. (2) Refinement of initial protein models, primarily using molecular dynamics related
approaches, has now advanced to the point where the best methods can consistently (though slightly) improve nearly all
models. (3) The use of relatively sparse NMR constraints dramatically improves the accuracy of models, and another type of
sparse data, chemical crosslinking, introduced in this CASP, also shows promise for producing better models. (4) A new
emphasis on modeling protein complexes, in collaboration with CAPRI, has produced interesting results, but also shows the
need for more focus on this area. (5) Methods for estimating the accuracy of models have advanced to the point where they
are of considerable practical use. (6) A first assessment demonstrates that models can sometimes successfully address biological
questions that motivate experimental structure determination. (7) There is continuing progress in accuracy of modeling
regions of structure not directly available by comparative modeling, while there is marginal or no progress in some other
areas
Classical nucleation theory for the nucleation of the solid phase of spherical particles with a short-ranged attraction
Classical nucleation theory is used to estimate the free-energy barrier to
nucleation of the solid phase of particles interacting via a potential which
has a short-ranged attraction. Due to the high interfacial tension between the
fluid and solid phases, this barrier is very large, much larger than in hard
spheres. It is divergent in the limit that the range of the attraction tends to
zero. We predict an upper limit on nucleation in good agreement with the
results of experiments on the crystallisation of proteins.Comment: 10 pages including 5 figure
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
Sequence-based protein homology detection has been extensively studied and so
far the most sensitive method is based upon comparison of protein sequence
profiles, which are derived from multiple sequence alignment (MSA) of sequence
homologs in a protein family. A sequence profile is usually represented as a
position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and
accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This
paper presents a new homology detection method MRFalign, consisting of three
key components: 1) a Markov Random Fields (MRF) representation of a protein
family; 2) a scoring function measuring similarity of two MRFs; and 3) an
efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning
two MRFs. Compared to HMM that can only model very short-range residue
correlation, MRFs can model long-range residue interaction pattern and thus,
encode information for the global 3D structure of a protein family.
Consequently, MRF-MRF comparison for remote homology detection shall be much
more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that
MRFalign outperforms several popular HMM or PSSM-based methods in terms of both
alignment accuracy and remote homology detection and that MRFalign works
particularly well for mainly beta proteins. For example, tested on the
benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM
succeed on 48% and 52% of proteins, respectively, at superfamily level, and on
15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign
succeeds on 57.3% and 42.5% of proteins at superfamily and fold level,
respectively. This study implies that long-range residue interaction patterns
are very helpful for sequence-based homology detection. The software is
available for download at http://raptorx.uchicago.edu/download/.Comment: Accepted by both RECOMB 2014 and PLOS Computational Biolog
Phase equilibria and glass transition in colloidal systems with short-ranged attractive interactions. Application to protein crystallization
We have studied a model of a complex fluid consisting of particles
interacting through a hard core and a short range attractive potential of both
Yukawa and square-well form. Using a hybrid method, including a self-consistent
and quite accurate approximation for the liquid integral equation in the case
of the Yukawa fluid, perturbation theory to evaluate the crystal free energies,
and mode-coupling theory of the glass transition, we determine both the
equilibrium phase diagram of the system and the lines of equilibrium between
the supercooled fluid and the glass phases. For these potentials, we study the
phase diagrams for different values of the potential range, the ratio of the
range of the interaction to the diameter of the repulsive core being the main
control parameter. Our arguments are relevant to a variety of systems, from
dense colloidal systems with depletion forces, through particle gels,
nano-particle aggregation, and globular protein crystallization.Comment: 20 pages, 10 figure
Protein Structure Prediction Using Basin-Hopping
Associative memory Hamiltonian structure prediction potentials are not overly
rugged, thereby suggesting their landscapes are like those of actual proteins.
In the present contribution we show how basin-hopping global optimization can
identify low-lying minima for the corresponding mildly frustrated energy
landscapes. For small systems the basin-hopping algorithm succeeds in locating
both lower minima and conformations closer to the experimental structure than
does molecular dynamics with simulated annealing. For large systems the
efficiency of basin-hopping decreases for our initial implementation, where the
steps consist of random perturbations to the Cartesian coordinates. We
implemented umbrella sampling using basin-hopping to further confirm when the
global minima are reached. We have also improved the energy surface by
employing bioinformatic techniques for reducing the roughness or variance of
the energy surface. Finally, the basin-hopping calculations have guided
improvements in the excluded volume of the Hamiltonian, producing better
structures. These results suggest a novel and transferable optimization scheme
for future energy function development
- …