27,404 research outputs found
High-throughput Binding Affinity Calculations at Extreme Scales
Resistance to chemotherapy and molecularly targeted therapies is a major
factor in limiting the effectiveness of cancer treatment. In many cases,
resistance can be linked to genetic changes in target proteins, either
pre-existing or evolutionarily selected during treatment. Key to overcoming
this challenge is an understanding of the molecular determinants of drug
binding. Using multi-stage pipelines of molecular simulations we can gain
insights into the binding free energy and the residence time of a ligand, which
can inform both stratified and personal treatment regimes and drug development.
To support the scalable, adaptive and automated calculation of the binding free
energy on high-performance computing resources, we introduce the High-
throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block
approach in order to attain both workflow flexibility and performance. We
demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage
binding affinity calculation pipelines. This permits a rapid time-to-solution
that is essentially invariant of the calculation protocol, size of candidate
ligands and number of ensemble simulations. As such, HTBAC advances the state
of the art of binding affinity calculations and protocols
Physics-based visual characterization of molecular interaction forces
Molecular simulations are used in many areas of biotechnology, such as drug design and enzyme engineering. Despite the development of automatic computational protocols, analysis of molecular interactions is still a major aspect where human comprehension and intuition are key to accelerate, analyze, and propose modifications to the molecule of interest. Most visualization algorithms help the users by providing an accurate depiction of the spatial arrangement: the atoms involved in inter-molecular contacts. There are few tools that provide visual information on the forces governing molecular docking. However, these tools, commonly restricted to close interaction between atoms, do not consider whole simulation paths, long-range distances and, importantly, do not provide visual cues for a quick and intuitive comprehension of the energy functions (modeling intermolecular interactions) involved. In this paper, we propose visualizations designed to enable the characterization of interaction forces by taking into account several relevant variables such as molecule-ligand distance and the energy function, which is essential to understand binding affinities. We put emphasis on mapping molecular docking paths obtained from Molecular Dynamics or Monte Carlo simulations, and provide time-dependent visualizations for different energy components and particle resolutions: atoms, groups or residues. The presented visualizations have the potential to support domain experts in a more efficient drug or enzyme design process.Peer ReviewedPostprint (author's final draft
Investigation of Structural Dynamics of Enzymes and Protonation States of Substrates Using Computational Tools.
This review discusses the use of molecular modeling tools, together with existing experimental findings, to provide a complete atomic-level description of enzyme dynamics and function. We focus on functionally relevant conformational dynamics of enzymes and the protonation states of substrates. The conformational fluctuations of enzymes usually play a crucial role in substrate recognition and catalysis. Protein dynamics can be altered by a tiny change in a molecular system such as different protonation states of various intermediates or by a significant perturbation such as a ligand association. Here we review recent advances in applying atomistic molecular dynamics (MD) simulations to investigate allosteric and network regulation of tryptophan synthase (TRPS) and protonation states of its intermediates and catalysis. In addition, we review studies using quantum mechanics/molecular mechanics (QM/MM) methods to investigate the protonation states of catalytic residues of β-Ketoacyl ACP synthase I (KasA). We also discuss modeling of large-scale protein motions for HIV-1 protease with coarse-grained Brownian dynamics (BD) simulations
First-principles calculation of DNA looping in tethered particle experiments
We calculate the probability of DNA loop formation mediated by regulatory
proteins such as Lac repressor (LacI), using a mathematical model of DNA
elasticity. Our model is adapted to calculating quantities directly observable
in Tethered Particle Motion (TPM) experiments, and it accounts for all the
entropic forces present in such experiments. Our model has no free parameters;
it characterizes DNA elasticity using information obtained in other kinds of
experiments. [...] We show how to compute both the "looping J factor" (or
equivalently, the looping free energy) for various DNA construct geometries and
LacI concentrations, as well as the detailed probability density function of
bead excursions. We also show how to extract the same quantities from recent
experimental data on tethered particle motion, and then compare to our model's
predictions. [...] Our model successfully reproduces the detailed distributions
of bead excursion, including their surprising three-peak structure, without any
fit parameters and without invoking any alternative conformation of the LacI
tetramer. Indeed, the model qualitatively reproduces the observed dependence of
these distributions on tether length (e.g., phasing) and on LacI concentration
(titration). However, for short DNA loops (around 95 basepairs) the experiments
show more looping than is predicted by the harmonic-elasticity model, echoing
other recent experimental results. Because the experiments we study are done in
vitro, this anomalously high looping cannot be rationalized as resulting from
the presence of DNA-bending proteins or other cellular machinery. We also show
that it is unlikely to be the result of a hypothetical "open" conformation of
the LacI tetramer.Comment: See the supplement at
http://www.physics.upenn.edu/~pcn/Ms/TowlesEtalSuppl.pdf . This revised
version accepted for publication at Physical Biolog
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
Open Boundary Simulations of Proteins and Their Hydration Shells by Hamiltonian Adaptive Resolution Scheme
The recently proposed Hamiltonian Adaptive Resolution Scheme (H-AdResS)
allows to perform molecular simulations in an open boundary framework. It
allows to change on the fly the resolution of specific subset of molecules
(usually the solvent), which are free to diffuse between the atomistic region
and the coarse-grained reservoir. So far, the method has been successfully
applied to pure liquids. Coupling the H-AdResS methodology to hybrid models of
proteins, such as the Molecular Mechanics/Coarse-Grained (MM/CG) scheme, is a
promising approach for rigorous calculations of ligand binding free energies in
low-resolution protein models. Towards this goal, here we apply for the first
time H-AdResS to two atomistic proteins in dual-resolution solvent, proving its
ability to reproduce structural and dynamic properties of both the proteins and
the solvent, as obtained from atomistic simulations.Comment: This document is the Accepted Manuscript version of a Published Work
that appeared in final form in Journal of Chemical Theory and Computation,
copyright \c{opyright} American Chemical Society after peer review and
technical editing by the publishe
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