35 research outputs found
Pathways and kinetic barriers in mechanical unfolding and refolding of RNA and proteins
Using self-organized polymer models, we predict mechanical unfolding and
refolding pathways of ribo-zymes, and the green fluorescent protein. In
agreement with experiments, there are between six and eight unfolding
transitions in the Tetrahymena ribozyme. Depending on the loading rate, the
number of rips in the force-ramp unfolding of the Azoarcus ribozymes is between
two and four. Force-quench refolding of the P4-P6 subdomain of the Tetrahymena
ribozyme occurs through a compact intermediate. Subsequent formation of
tertiary contacts between helices P5b-P6a and P5a/P5c-P4 leads to the native
state. The force-quench refolding pathways agree with ensemble experiments. In
the dominant unfolding route, the N-terminal a helix of GFP unravels first,
followed by disruption of the N terminus b strand. There is a third
intermediate that involves disruption of three other strands. In accord with
experiments, the force-quench refolding pathway of GFP is hierarchic, with the
rate-limiting step being the closure of the barrel.Comment: 33 pages 7 figure
Size, shape, and flexibility of RNA structures
Determination of sizes and flexibilities of RNA molecules is important in
understanding the nature of packing in folded structures and in elucidating
interactions between RNA and DNA or proteins. Using the coordinates of the
structures of RNA in the Protein Data Bank we find that the size of the folded
RNA structures, measured using the radius of gyration, , follows the Flory
scaling law, namely, \AA where N is the number of
nucleotides. The shape of RNA molecules is characterized by the asphericity
and the shape parameters that are computed using the eigenvalues
of the moment of inertia tensor. From the distribution of , we find
that a large fraction of folded RNA structures are aspherical and the
distribution of values shows that RNA molecules are prolate (). The
flexibility of folded structures is characterized by the persistence length
. By fitting the distance distribution function to the worm-like
chain model we extracted the persistence length . We find that \AA. The dependence of on implies the average length of
helices should increases as the size of RNA grows. We also analyze packing in
the structures of ribosomes (30S, 50S, and 70S) in terms of , ,
, and . The 70S and the 50S subunits are more spherical compared to
most RNA molecules. The globularity in 50S is due to the presence of an
unusually large number (compared to 30S subunit) of small helices that are
stitched together by bulges and loops. Comparison of the shapes of the intact
70S ribosome and the constituent particles suggests that folding of the
individual molecules might occur prior to assembly.Comment: 28 pages, 8 figures, J. Chem. Phys. in pres
Tubulin bond energies and microtubule biomechanics determined from nanoindentation in silico
Microtubules, the primary components of the chromosome segregation machinery,
are stabilized by longitudinal and lateral non-covalent bonds between the
tubulin subunits. However, the thermodynamics of these bonds and the
microtubule physico-chemical properties are poorly understood. Here, we explore
the biomechanics of microtubule polymers using multiscale computational
modeling and nanoindentations in silico of a contiguous microtubule fragment. A
close match between the simulated and experimental force-deformation spectra
enabled us to correlate the microtubule biomechanics with dynamic structural
transitions at the nanoscale. Our mechanical testing revealed that the
compressed MT behaves as a system of rigid elements interconnected through a
network of lateral and longitudinal elastic bonds. The initial regime of
continuous elastic deformation of the microtubule is followed by the transition
regime, during which the microtubule lattice undergoes discrete structural
changes, which include first the reversible dissociation of lateral bonds
followed by irreversible dissociation of the longitudinal bonds. We have
determined the free energies of dissociation of the lateral (6.9+/-0.4
kcal/mol) and longitudinal (14.9+/-1.5 kcal/mol) tubulin-tubulin bonds. These
values in conjunction with the large flexural rigidity of tubulin
protofilaments obtained (18,000-26,000 pN*nm^2), support the idea that the
disassembling microtubule is capable of generating a large mechanical force to
move chromosomes during cell division. Our computational modeling offers a
comprehensive quantitative platform to link molecular tubulin characteristics
with the physiological behavior of microtubules. The developed in silico
nanoindentation method provides a powerful tool for the exploration of
biomechanical properties of other cytoskeletal and multiprotein assemblie
Mechanism of Fibrin(ogen) Forced Unfolding
SummaryFibrinogen, upon enzymatic conversion to monomeric fibrin, provides the building blocks for fibrin polymer, the scaffold of blood clots and thrombi. Little has been known about the force-induced unfolding of fibrin(ogen), even though it is the foundation for the mechanical and rheological properties of fibrin, which are essential for hemostasis. We determined mechanisms and mapped the free energy landscape of the elongation of fibrin(ogen) monomers and oligomers through combined experimental and theoretical studies of the nanomechanical properties of fibrin(ogen), using atomic force microscopy-based single-molecule unfolding and simulations in the experimentally relevant timescale. We have found that mechanical unraveling of fibrin(ogen) is determined by the combined molecular transitions that couple stepwise unfolding of the γ chain nodules and reversible extension-contraction of the α-helical coiled-coil connectors. These findings provide important characteristics of the fibrin(ogen) nanomechanics necessary to understand the molecular origins of fibrin viscoelasticity at the fiber and whole clot levels
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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation