444 research outputs found
Transient hydrophobic exposure in the molecular dynamics of Abeta peptide at low water concentration
Abeta is a disordered peptide central to Alzheimer's Disease. Aggregation of
Abeta has been widely explored, but its molecular crowding less so. The
synaptic cleft where Abeta locates only holds 60-70 water molecules along its
width. We subjected Abeta40 to 100 different simulations with variable water
cell size. We show that even for this disordered aggregation-prone peptide,
many properties are not cell-size dependent, i.e. a small cell is easily
justified. The radius of gyration, intra-peptide, and peptide-water hydrogen
bonds are well-sampled by short (50 ns) time scales at any cell size. Abeta is
mainly disordered with 0-30% alpha helix but undergoes consistent alpha-beta
transitions up to 14% strand in 5-10% of the simulations regardless of cell
size. The similar prevalence in long and short simulations indicate small
diffusion barriers for structural transitions in contrast to folded globular
proteins, which we suggest is a defining hallmark of intrinsically disordered
proteins. Importantly, the hydrophobic surface increases significantly in small
cells (confidence level 95%, two-tailed t-test), as does the variation in
exposure and backbone conformations (>40% and >27% increased standard
deviations). Whereas hydrophilic exposure dominates hydrophobic exposure in
large cells, this tendency breaks down at low water concentration. We interpret
these findings as a concentration-dependent hydrophobic effect, with the small
water layer unable to keep the protein unexposed, an effect mainly caused by
the layered water-water interactions, not by the peptide dynamics. The exposure
correlates with radius of gyration (R2 0.35-0.50) and could be important in
crowded environments, e.g. the synaptic cleft
Survival of the cheapest: How proteome cost minimization drives evolution
Darwin's theory of evolution emphasized that positive selection of functional
proficiency provides the fitness that ultimately determines the structure of
life, a view that has dominated biochemical thinking of enzymes as perfectly
optimized for their specific functions. The 20th-century modern synthesis,
structural biology, and the central dogma explained the machinery of evolution,
and nearly neutral theory explained how selection competes with random fixation
dynamics that produce molecular clocks essential e.g. for dating evolutionary
histories. However, the quantitative proteomics revealed that fitness effects
not related to functional proficiency play much larger roles on long
evolutionary time scales than previously thought, with particular evidence that
some universal biophysical selection pressures act via protein expression
levels. This paper first summarizes recent progress in the 21st century towards
recovering this universal selection pressure. Then, the paper argues that
proteome cost minimization is the dominant, underlying "non-function" selection
pressure controlling most of the evolution of already functionally adapted
living systems. A theory of proteome cost minimization is described and argued
to have consequences for understanding evolutionary trade-offs, aging, cancer,
and neurodegenerative protein-misfolding diseases
Using Electronegativity and Hardness to Test Density Functional Universality
Density functional theory (DFT) is used in thousands of papers each year, yet
lack of universality reduces DFT's predictive capacity, and functionals may
produce energy-density imbalances. The absolute electronegativity (\chi) and
hardness (\eta) directly reflect the energy-density relationship via the
chemical potential dE/dN and we thus hypothesized that they probe universality.
We studied \chi and \eta for atoms Z = 1-36 using 50 diverse functionals
covering all major classes. Very few functionals describe both \chi and \eta
well. \eta benefits from error cancelation whereas \chi is marred by error
propagation from IP and EA; thus almost all standard GGA and hybrid functionals
display a plateau in the MAE at 0.2-0.3 eV for \eta. In contrast, variable
performance for \chi indicates problems in describing the chemical potential by
DFT. The accuracy and precision of a functional is far from linearly related,
yet for a universal functional we expect linearity. Popular functionals such as
B3LYP, PBE, and revPBE, perform poorly for both properties. Density sensitivity
calculations indicate large density-derived errors as occupation of degenerate
p- and d-orbitals causes "non-universality" and large dependency on exact
exchange. Thus, we argue that performance for \chi for the same systems is a
hallmark of universality by probing dE/dN. With this metric, B98, B97-1,
PW6B95D3, APFD are the most "universal" tested functionals. B98 and B97-1 are
accurate for very diverse metal-ligand bonds, supporting that a balanced
description of dE/dN and dE2/dN2, via \chi and \eta, is probably a first simple
probe of universality
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A base measure of precision for protein stability predictors: structural sensitivity.
BACKGROUND: Prediction of the change in fold stability (ÎÎG) of a protein upon mutation is of major importance to protein engineering and screening of disease-causing variants. Many prediction methods can use 3D structural information to predict ÎÎG. While the performance of these methods has been extensively studied, a new problem has arisen due to the abundance of crystal structures: How precise are these methods in terms of structure input used, which structure should be used, and how much does it matter? Thus, there is a need to quantify the structural sensitivity of protein stability prediction methods. RESULTS: We computed the structural sensitivity of six widely-used prediction methods by use of saturated computational mutagenesis on a diverse set of 87 structures of 25 proteins. Our results show that structural sensitivity varies massively and surprisingly falls into two very distinct groups, with methods that take detailed account of the local environment showing a sensitivity ofâ~â0.6 to 0.8Â kcal/mol, whereas machine-learning methods display much lower sensitivity (~â0.1Â kcal/mol). We also observe that the precision correlates with the accuracy for mutation-type-balanced data sets but not generally reported accuracy of the methods, indicating the importance of mutation-type balance in both contexts. CONCLUSIONS: The structural sensitivity of stability prediction methods varies greatly and is caused mainly by the models and less by the actual protein structural differences. As a new recommended standard, we therefore suggest that ÎÎG values are evaluated on three protein structures when available and the associated standard deviation reported, to emphasize not just the accuracy but also the precision of the method in a specific study. Our observation that machine-learning methods deemphasize structure may indicate that folded wild-type structures alone, without the folded mutant and unfolded structures, only add modest value for assessing protein stability effects, and that side-chain-sensitive methods overstate the significance of the folded wild-type structure
The Influence of Selection for Protein Stability on dN/dS Estimations
Understanding the relative contributions of various evolutionary processesâpurifying selection, neutral drift, and adaptationâis fundamental to evolutionary biology. A common metric to distinguish these processes is the ratio of nonsynonymous to synonymous substitutions (i.e., dN/dS) interpreted from the neutral theory as a null model. However, from biophysical considerations, mutations have non-negligible effects on the biophysical properties of proteins such as folding stability. In this work, we investigated how stability affects the rate of protein evolution in phylogenetic trees by using simulations that combine explicit protein sequences with associated stability changes. We first simulated myoglobin evolution in phylogenetic trees with a biophysically realistic approach that accounts for 3D structural information and estimates of changes in stability upon mutation. We then compared evolutionary rates inferred directly from simulation to those estimated using maximum-likelihood (ML) methods. We found that the dN/dS estimated by ML methods (ÏML) is highly predictive of the per gene dN/dS inferred from the simulated phylogenetic trees. This agreement is strong in the regime of high stability where protein evolution is neutral. At low folding stabilities and under mutation-selection balance, we observe deviations from neutrality (per gene dN/dS > 1 and dN/dS 1. Altogether, we show how protein biophysics affects the dN/dS estimations and its subsequent interpretation. These results are important for improving the current approaches for detecting positive selection
Positively selected sites in cetacean myoglobins contribute to protein stability.
Since divergence âŒ50 Ma ago from their terrestrial ancestors, cetaceans underwent a series of adaptations such as a âŒ10â20 fold increase in myoglobin (Mb) concentration in skeletal muscle, critical for increasing oxygen storage capacity and prolonging dive time. Whereas the -binding affinity of Mbs is not significantly different among mammals (with typical oxygenation constants of âŒ0.8â1.2 ), folding stabilities of cetacean Mbs are âŒ2â4 kcal/mol higher than for terrestrial Mbs. Using ancestral sequence reconstruction, maximum likelihood and Bayesian tests to describe the evolution of cetacean Mbs, and experimentally calibrated computation of stability effects of mutations, we observe accelerated evolution in cetaceans and identify seven positively selected sites in Mb. Overall, these sites contribute to Mb stabilization with a conditional probability of 0.8. We observe a correlation between Mb folding stability and protein abundance, suggesting that a selection pressure for stability acts proportionally to higher expression. We also identify a major divergence event leading to the common ancestor of whales, during which major stabilization occurred. Most of the positively selected sites that occur later act against other destabilizing mutations to maintain stability across the clade, except for the shallow divers, where late stability relaxation occurs, probably due to the shorter aerobic dive limits of these species. The three main positively selected sites 66, 5, and 35 undergo changes that favor hydrophobic folding, structural integrity, and intra-helical hydrogen bonds.Chemistry and Chemical Biolog
Chemical Bond Energies of 3d Transition Metals Studied by Density Functional Theory
Despite their vast
importance to inorganic chemistry, materials
science, and catalysis, the accuracy of modeling the formation or
cleavage of metalâligand (MâL) bonds depends greatly
on the chosen functional and the type of bond in a way that is not
systematically understood. In order to approach a state of high-accuracy
DFT for rational prediction of chemistry and catalysis, such system-dependencies
need to be resolved. We studied 30 different density functionals applied
to a âbalanced data setâ of 60 experimental diatomic
MâL bond energies; this data set has no bias toward any d<sup>q</sup> configuration, metal, bond type, or ligand as all of these
occur to the same extent, and we can therefore identify accuracy bottlenecks.
We show that the performance of a functional is very dependent on
data set choice, and we dissect these effects into system type. In
addition to the use of balanced data sets, we also argue that the
precision (rather than just accuracy) of a functional is of interest,
measured by standard deviations of the errors. There are distinct
system dependencies both in the ligand and metal series: Hydrides
are best described by a very large HF exchange percentage, possibly
due to self-interaction error, whereas halides are best described
by very small (0â10%) HF exchange fractions, and double-bond
enforcing oxides and sulfides favor 10â25% HF exchange, as
is also average for the full data set. Thus, average HF requirements
hide major system-dependent requirements. For late transition metals
CoâZn, HF percentage of 0â10% is favored, whereas for
the early transition metals ScâFe hybrid functionals with 20%
HF exchange or higher are commonly favored. Accordingly, B3LYP is
an excellent choice for early d-block but a poor choice for late transition
metals. We conclude that DFT intrinsically underestimates the bond
strengths of late vs early transition metals, correlating with increased
effective nuclear charge. Thus, the revised RPBE, which reduces the
overbinding tendency of PBE, is mainly an advantage for the early
and
mid transition metals and not very much for the late transition metals,
i.e. there is a metal-dependent effect of the relative performance
of RPBE vs PBE, which are widely used to study adsorption energetics
on metal surfaces. Overall, the best performing functionals are PW6B95,
the MN15 and MN15-L functionals, and the double hybrid B2PLYP
Different forms of African cassava mosaic virus capsid protein within plants and virions
One geminiviral gene encodes the capsid protein (CP), which can appear as several bands after electrophoresis depending on virus and plant. African cassava mosaic virus-Nigeria CP in Nicotiana benthamiana, however, yielded one band (~âŻ30âŻkDa) in total protein extracts and purified virions, although its expression in yeast yielded two bands (~âŻ30, 32âŻkDa). Mass spectrometry of the complete protein and its tryptic fragments from virions is consistent with a cleaved start M1, acetylated S2, and partial phosphorylation at T12, S25 and S62. Mutants for additional potentially modified sites (N223A; C235A) were fully infectious and formed geminiparticles. Separation in triton acetic acid urea gels confirmed charge changes of the CP between plants and yeast indicating differential phosphorylation. If the CP gene alone was expressed in plants, multiple bands were observed like in yeast. A high turnover rate indicates that post-translational modifications promote CP decay probably via the ubiquitin-triggered proteasomal pathway
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