76 research outputs found
Protein and DNA sequence determinants of thermophilic adaptation
Prokaryotes living at extreme environmental temperatures exhibit pronounced
signatures in the amino acid composition of their proteins and nucleotide
compositions of their genomes reflective of adaptation to their thermal
environments. However, despite significant efforts, the definitive answer of
what are the genomic and proteomic compositional determinants of Optimal Growth
Temperature of prokaryotic organisms remained elusive. Here the authors
performed a comprehensive analysis of amino acid and nucleotide compositional
signatures of thermophylic adaptation by exhaustively evaluating all
combinations of amino acids and nucleotides as possible determinants of Optimal
Growth Temperature for all prokaryotic organisms with fully sequences genomes..
The authors discovered that total concentration of seven amino acids in
proteomes, IVYWREL, serves as a universal proteomic predictor of Optimal Growth
Temperature in prokaryotes. Resolving the old-standing controversy the authors
determined that the variation in nucleotide composition (increase of purine
load, or A+G content with temperature) is largely a consequence of thermal
adaptation of proteins. However, the frequency with which A and G nucleotides
appear as nearest neighbors in genome sequences is strongly and independently
correlated with Optimal Growth Temperature. as a result of codon bias in
corresponding genomes. Together these results provide a complete picture of
proteomic and genomic determinants of thermophilic adaptation.Comment: in press PLoS Computational Biology; revised versio
Entropic stabilization of proteins and its proteomic consequences
We report here a new entropic mechanism of protein thermostability due to
residual dynamics of rotamer isomerization in native state. All-atom
simulations show that Lysines have much greater number of accessible rotamers
than Arginines in folded states of proteins. This finding suggests that Lysines
would preferentially entropically stabilize the native state. Indeed we show in
computational experiments that Arginine-to-Lysine amino acid substitutions
result in noticeable stabilization of proteins. We then hypothesize that if
evolution uses this physical mechanisms in its strategies of thermophilic
adaptation then hyperthermostable organisms would have much greater content of
Lysines in their proteomes than of comparable in size and similarly charged
Arginines.. Consistent with that, high-throughput comparative analysis of
complete proteomes shows extremely strong bias towards Arginine-to-Lysine
replacement in hyperthermophilic organisms and overall much greater content of
Lysines than Arginines in hyperthermophiles. This finding cannot be explained
by GC compositional biases. Our study provides an example of how analysis of a
delicate physical mechanism of thermostability helps to resolve a puzzle in
comparative genomics as to why aminoacid compositions of hyperthermophilic
proteomes are significantly biased towards Lysines but not Arginine
Spin dynamics and level structure of quantum-dot quantum wells
We have characterized CdS/CdSe/CdS quantum-dot quantum wells using
time-resolved Faraday rotation (TRFR). The spin dynamics show that the electron
g-factor varies as a function of quantum well width and the transverse spin
lifetime of several nano-seconds is robust up to room temperature. As a
function of probe energy, the amplitude of the TRFR signal shows pronounced
resonances, which allow one to identify individual exciton transitions. While
the TRFR data are inconsistent with the conduction and valence band level
scheme of spherical quantum-dot quantum wells, a model in which broken
spherical symmetry is taken into account captures the essential features.Comment: 5 pages, 3 figure
ИСПОЛЬЗОВАНИЕ ВТОРИЧНЫХ ГОРЮЧИХ ЭНЕРГЕТИЧЕСКИХ РЕСУРСОВ ДЛЯ ПРОИЗВОДСТВА КОММУНАЛЬНО-БЫТОВОГО ТОПЛИВА
The paper shows an advantage to utilize secondary power resources (lignin, wastes of fine coal with its dressing, sawdust) in mixture with local types of fuel (peat) in order to fulfill power supply purpose, namely: obtaining hot water in boilers of small capacity and obtaining household fuel.Показано преимущество использования вторичных энергоресурсов (лигнин, отходы мелких углей при их обогащении, опилки) в смеси с местными видами топлива (торф) в энергетических целях, в частности при получении горячей воды в котлах малой производительности и при получении бытового топлива.
Interplay between pleiotropy and secondary selection determines rise and fall of mutators in stress response
Dramatic rise of mutators has been found to accompany adaptation of bacteria
in response to many kinds of stress. Two views on the evolutionary origin of
this phenomenon emerged: the pleiotropic hypothesis positing that it is a
byproduct of environmental stress or other specific stress response mechanisms
and the second order selection which states that mutators hitchhike to fixation
with unrelated beneficial alleles. Conventional population genetics models
could not fully resolve this controversy because they are based on certain
assumptions about fitness landscape. Here we address this problem using a
microscopic multiscale model, which couples physically realistic molecular
descriptions of proteins and their interactions with population genetics of
carrier organisms without assuming any a priori fitness landscape. We found
that both pleiotropy and second order selection play a crucial role at
different stages of adaptation: the supply of mutators is provided through
destabilization of error correction complexes or fluctuations of production
levels of prototypic mismatch repair proteins (pleiotropic effects), while rise
and fixation of mutators occur when there is a sufficient supply of beneficial
mutations in replication-controlling genes. This general mechanism assures a
robust and reliable adaptation of organisms to unforeseen challenges. This
study highlights physical principles underlying physical biological mechanisms
of stress response and adaptation
Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
<p>Abstract</p> <p>Background</p> <p>By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations.</p> <p>Results</p> <p>First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly – or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms.</p> <p>Conclusion</p> <p>By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy – in some cases exceeding 95%.</p
Quantum Computing
Quantum mechanics---the theory describing the fundamental workings of
nature---is famously counterintuitive: it predicts that a particle can be in
two places at the same time, and that two remote particles can be inextricably
and instantaneously linked. These predictions have been the topic of intense
metaphysical debate ever since the theory's inception early last century.
However, supreme predictive power combined with direct experimental observation
of some of these unusual phenomena leave little doubt as to its fundamental
correctness. In fact, without quantum mechanics we could not explain the
workings of a laser, nor indeed how a fridge magnet operates. Over the last
several decades quantum information science has emerged to seek answers to the
question: can we gain some advantage by storing, transmitting and processing
information encoded in systems that exhibit these unique quantum properties?
Today it is understood that the answer is yes. Many research groups around the
world are working towards one of the most ambitious goals humankind has ever
embarked upon: a quantum computer that promises to exponentially improve
computational power for particular tasks. A number of physical systems,
spanning much of modern physics, are being developed for this task---ranging
from single particles of light to superconducting circuits---and it is not yet
clear which, if any, will ultimately prove successful. Here we describe the
latest developments for each of the leading approaches and explain what the
major challenges are for the future.Comment: 26 pages, 7 figures, 291 references. Early draft of Nature 464, 45-53
(4 March 2010). Published version is more up-to-date and has several
corrections, but is half the length with far fewer reference
Neuer Kopf, alte Ideen? : "Normalisierung" des Front National unter Marine Le Pen
In this article, it is investigated
whether vibrational entropy
(VE) is an important contribution to the free energy of globular proteins
at ambient conditions. VE represents the major configurational-entropy
contribution of these proteins. By definition, it is an average of
the configurational entropies of the protein within single minima
of the energy landscape, weighted by their occupation probabilities.
Its large part originates from thermal motion of flexible torsion
angles giving rise to the finite peak widths observed in torsion angle
distributions. While VE may affect the equilibrium properties of proteins,
it is usually neglected in numerical calculations as its consideration
is difficult. Moreover, it is sometimes believed that all well-packed
conformations of a globular protein have similar VE anyway. Here, we measure explicitly the VE for six different conformations from simulation data of a test protein. Estimates
are obtained using the quasi-harmonic approximation for three coordinate
sets, Cartesian, bond-angle-torsion (BAT), and a new set termed rotamer-degeneracy
lifted BAT coordinates by us. The new set gives improved estimates
as it overcomes a known shortcoming of the quasi-harmonic approximation
caused by multiply populated rotamer states, and it may serve for
VE estimation of macromolecules in a very general context. The obtained
VE values depend considerably on the type of coordinates used. However,
for all coordinate sets we find large entropy differences between
the conformations, of the order of the overall stability of the protein.
This result may have important implications on the choice of free
energy expressions used in software for protein structure prediction,
protein design, and NMR refinement
Computational Design of a PDZ Domain Peptide Inhibitor that Rescues CFTR Activity
The cystic fibrosis transmembrane conductance regulator (CFTR) is an epithelial chloride channel mutated in patients with cystic fibrosis (CF). The most prevalent CFTR mutation, ΔF508, blocks folding in the endoplasmic reticulum. Recent work has shown that some ΔF508-CFTR channel activity can be recovered by pharmaceutical modulators (“potentiators” and “correctors”), but ΔF508-CFTR can still be rapidly degraded via a lysosomal pathway involving the CFTR-associated ligand (CAL), which binds CFTR via a PDZ interaction domain. We present a study that goes from theory, to new structure-based computational design algorithms, to computational predictions, to biochemical testing and ultimately to epithelial-cell validation of novel, effective CAL PDZ inhibitors (called “stabilizers”) that rescue ΔF508-CFTR activity. To design the “stabilizers”, we extended our structural ensemble-based computational protein redesign algorithm to encompass protein-protein and protein-peptide interactions. The computational predictions achieved high accuracy: all of the top-predicted peptide inhibitors bound well to CAL. Furthermore, when compared to state-of-the-art CAL inhibitors, our design methodology achieved higher affinity and increased binding efficiency. The designed inhibitor with the highest affinity for CAL (kCAL01) binds six-fold more tightly than the previous best hexamer (iCAL35), and 170-fold more tightly than the CFTR C-terminus. We show that kCAL01 has physiological activity and can rescue chloride efflux in CF patient-derived airway epithelial cells. Since stabilizers address a different cellular CF defect from potentiators and correctors, our inhibitors provide an additional therapeutic pathway that can be used in conjunction with current methods
Photonic transistor and router using a single quantum-dotconfined spin in a single-sided optical microcavity
The future Internet is very likely the mixture of all-optical Internet with low power consumption and quantum Internet with absolute security guaranteed by the laws of quantum mechanics. Photons would be used for processing, routing and com-munication of data, and photonic transistor using a weak light to control a strong light is the core component as an optical analogue to the electronic transistor that forms the basis of modern electronics. In sharp contrast to previous all-optical tran-sistors which are all based on optical nonlinearities, here I introduce a novel design for a high-gain and high-speed (up to terahertz) photonic transistor and its counterpart in the quantum limit, i.e., single-photon transistor based on a linear optical effect: giant Faraday rotation induced by a single electronic spin in a single-sided optical microcavity. A single-photon or classical optical pulse as the gate sets the spin state via projective measurement and controls the polarization of a strong light to open/block the photonic channel. Due to the duality as quantum gate for quantum information processing and transistor for optical information processing, this versatile spin-cavity quantum transistor provides a solid-state platform ideal for all-optical networks and quantum networks
- …