1,577 research outputs found
Inferring Microscopic Kinetic Rates from Stationary State Distributions.
We present a principled approach for estimating the matrix of microscopic transition probabilities among states of a Markov process, given only its stationary state population distribution and a single average global kinetic observable. We adapt Maximum Caliber, a variational principle in which the path entropy is maximized over the distribution of all possible trajectories, subject to basic kinetic constraints and some average dynamical observables. We illustrate the method by computing the solvation dynamics of water molecules from molecular dynamics trajectories
Generalized Approximate Survey Propagation for High-Dimensional Estimation
In Generalized Linear Estimation (GLE) problems, we seek to estimate a signal
that is observed through a linear transform followed by a component-wise,
possibly nonlinear and noisy, channel. In the Bayesian optimal setting,
Generalized Approximate Message Passing (GAMP) is known to achieve optimal
performance for GLE. However, its performance can significantly degrade
whenever there is a mismatch between the assumed and the true generative model,
a situation frequently encountered in practice. In this paper, we propose a new
algorithm, named Generalized Approximate Survey Propagation (GASP), for solving
GLE in the presence of prior or model mis-specifications. As a prototypical
example, we consider the phase retrieval problem, where we show that GASP
outperforms the corresponding GAMP, reducing the reconstruction threshold and,
for certain choices of its parameters, approaching Bayesian optimal
performance. Furthermore, we present a set of State Evolution equations that
exactly characterize the dynamics of GASP in the high-dimensional limit
Analyzing Machupo virus-receptor binding by molecular dynamics simulations
In many biological applications, we would like to be able to computationally
predict mutational effects on affinity in protein-protein interactions.
However, many commonly used methods to predict these effects perform poorly in
important test cases. In particular, the effects of multiple mutations,
non-alanine substitutions, and flexible loops are difficult to predict with
available tools and protocols. We present here an existing method applied in a
novel way to a new test case; we interrogate affinity differences resulting
from mutations in a host-virus protein-protein interface. We use steered
molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike
glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then
approximate affinity using the maximum applied force of separation and the area
under the force-versus-distance curve. We find, even without the rigor and
planning required for free energy calculations, that these quantities can
provide novel biophysical insight into the GP1/hTfR1 interaction. First, with
no prior knowledge of the system we can differentiate among wild type and
mutant complexes. Moreover, we show that this simple SMD scheme correlates well
with relative free energy differences computed via free energy perturbation.
Second, although the static co-crystal structure shows two large
hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate
that one of them may not be important for tight binding. Third, one viral site
known to be critical for infection may mark an important evolutionary
suppressor site for infection-resistant hTfR1 mutants. Finally, our approach
provides a framework to compare the effects of multiple mutations, individually
and jointly, on protein-protein interactions.Comment: 33 pages, 8 figures, 5 table
Universality and predictability in molecular quantitative genetics
Molecular traits, such as gene expression levels or protein binding
affinities, are increasingly accessible to quantitative measurement by modern
high-throughput techniques. Such traits measure molecular functions and, from
an evolutionary point of view, are important as targets of natural selection.
We review recent developments in evolutionary theory and experiments that are
expected to become building blocks of a quantitative genetics of molecular
traits. We focus on universal evolutionary characteristics: these are largely
independent of a trait's genetic basis, which is often at least partially
unknown. We show that universal measurements can be used to infer selection on
a quantitative trait, which determines its evolutionary mode of conservation or
adaptation. Furthermore, universality is closely linked to predictability of
trait evolution across lineages. We argue that universal trait statistics
extends over a range of cellular scales and opens new avenues of quantitative
evolutionary systems biology
Errors in energy landscapes measured with particle tracking
Tracking Brownian particles is often employed to map the energy landscape they explore. Such measurements have been exploited to study many biological processes and interactions in soft materials. Yet, video tracking is irremediably contaminated by localization errors originating from two imaging artifacts: the ``static'' errors come from signal noise, and the ``dynamic'' errors arise from the motion blur due to finite frame acquisition time. We show that these errors result in systematic and non-trivial biases in the measured energy landscapes. We derive a relationship between the true and the measured potential that elucidates, among other aberrations, the presence of false double-well minima in the apparent potentials reported in recent studies. We further assess several canonical trapping and pair-interaction potentials, by using our analytically derived results and Brownian dynamics simulations. In particular, we show that the apparent spring stiffness of harmonic potentials (such as optical traps) is increased by dynamic errors, but decreased by static errors. Our formula allows for the development of efficient corrections schemes, and we also present in this paper a provisional method for reconstructing true potentials from the measured ones
- …