1,286,341 research outputs found
Biophysical characterization of a protein for structure comparison : methods for identifying insulin structural changes
Although protein structure has been studied for many decades it remains the case that we cannot state with confidence whether two samples have the same molecular structure, particularly in solution. The increasing number of biosimilar biopharmaceutical drugs that are being tested means this is not an academic exercise. In this work we consider how four well-established techniques: dynamic light scattering (DLS), circular dichroism (CD), nuclear magnetic resonance spectroscopy (NMR), and molecular modelling can be combined to provide information about the supposedly well-understood protein insulin. A goal of this work was to establish a systematic means of detecting differences between insulin samples as a function of pH, temperature, and the presence or absence of zinc, all of which are known to change the oligomerisation state and to affect molecular structure. We used the recently developed Secondary Structure Neural Network (SSNN) circular dichroism algorithm to facilitate analysis of the CD spectra
Introduction to Protein Structure Prediction
This chapter gives a graceful introduction to problem of protein three-
dimensional structure prediction, and focuses on how to make structural sense
out of a single input sequence with unknown structure, the 'query' or 'target'
sequence. We give an overview of the different classes of modelling techniques,
notably template-based and template free. We also discuss the way in which
structural predictions are validated within the global com- munity, and
elaborate on the extent to which predicted structures may be trusted and used
in practice. Finally we discuss whether the concept of a sin- gle fold
pertaining to a protein structure is sustainable given recent insights. In
short, we conclude that the general protein three-dimensional structure
prediction problem remains unsolved, especially if we desire quantitative
predictions. However, if a homologous structural template is available in the
PDB model or reasonable to high accuracy may be generated
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
Protein Structure Prediction: The Next Generation
Over the last 10-15 years a general understanding of the chemical reaction of
protein folding has emerged from statistical mechanics. The lessons learned
from protein folding kinetics based on energy landscape ideas have benefited
protein structure prediction, in particular the development of coarse grained
models. We survey results from blind structure prediction. We explore how
second generation prediction energy functions can be developed by introducing
information from an ensemble of previously simulated structures. This procedure
relies on the assumption of a funnelled energy landscape keeping with the
principle of minimal frustration. First generation simulated structures provide
an improved input for associative memory energy functions in comparison to the
experimental protein structures chosen on the basis of sequence alignment
Towards Reliable Automatic Protein Structure Alignment
A variety of methods have been proposed for structure similarity calculation,
which are called structure alignment or superposition. One major shortcoming in
current structure alignment algorithms is in their inherent design, which is
based on local structure similarity. In this work, we propose a method to
incorporate global information in obtaining optimal alignments and
superpositions. Our method, when applied to optimizing the TM-score and the GDT
score, produces significantly better results than current state-of-the-art
protein structure alignment tools. Specifically, if the highest TM-score found
by TMalign is lower than (0.6) and the highest TM-score found by one of the
tested methods is higher than (0.5), there is a probability of (42%) that
TMalign failed to find TM-scores higher than (0.5), while the same probability
is reduced to (2%) if our method is used. This could significantly improve the
accuracy of fold detection if the cutoff TM-score of (0.5) is used.
In addition, existing structure alignment algorithms focus on structure
similarity alone and simply ignore other important similarities, such as
sequence similarity. Our approach has the capacity to incorporate multiple
similarities into the scoring function. Results show that sequence similarity
aids in finding high quality protein structure alignments that are more
consistent with eye-examined alignments in HOMSTRAD. Even when structure
similarity itself fails to find alignments with any consistency with
eye-examined alignments, our method remains capable of finding alignments
highly similar to, or even identical to, eye-examined alignments.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
Protein Structure Determination Using Chemical Shifts
In this PhD thesis, a novel method to determine protein structures using
chemical shifts is presented.Comment: Univ Copenhagen PhD thesis (2014) in Biochemistr
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