5,806 research outputs found
Exploring cavity dynamics in biomolecular systems
Background The internal cavities of proteins are dynamic structures and their
dynamics may be associated with conformational changes which are required for
the functioning of the protein. In order to study the dynamics of these
internal protein cavities, appropriate tools are required that allow rapid
identification of the cavities as well as assessment of their time-dependent
structures. Results In this paper, we present such a tool and give results
that illustrate the applicability for the analysis of molecular dynamics
trajectories. Our algorithm consists of a pre-processing step where the
structure of the cavity is computed from the Voronoi diagram of the van der
Waals spheres based on coordinate sets from the molecular dynamics trajectory.
The pre-processing step is followed by an interactive stage, where the user
can compute, select and visualize the dynamic cavities. Importantly, the tool
we discuss here allows the user to analyze the time-dependent changes of the
components of the cavity structure. An overview of the cavity dynamics is
derived by rendering the dynamic cavities in a single image that gives the
cavity surface colored according to its time-dependent dynamics. Conclusion
The Voronoi-based approach used here enables the user to perform accurate
computations of the geometry of the internal cavities in biomolecules. For the
first time, it is possible to compute dynamic molecular paths that have a
user-defined minimum constriction size. To illustrate the usefulness of the
tool for understanding protein dynamics, we probe the dynamic structure of
internal cavities in the bacteriorhodopsin proton pump
Computational structure‐based drug design: Predicting target flexibility
The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant
from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft
A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins
Membrane proteins constitute a large portion of the human proteome and
perform a variety of important functions as membrane receptors, transport
proteins, enzymes, signaling proteins, and more. The computational studies of
membrane proteins are usually much more complicated than those of globular
proteins. Here we propose a new continuum model for Poisson-Boltzmann
calculations of membrane channel proteins. Major improvements over the existing
continuum slab model are as follows: 1) The location and thickness of the slab
model are fine-tuned based on explicit-solvent MD simulations. 2) The highly
different accessibility in the membrane and water regions are addressed with a
two-step, two-probe grid labeling procedure, and 3) The water pores/channels
are automatically identified. The new continuum membrane model is optimized (by
adjusting the membrane probe, as well as the slab thickness and center) to best
reproduce the distributions of buried water molecules in the membrane region as
sampled in explicit water simulations. Our optimization also shows that the
widely adopted water probe of 1.4 {\AA} for globular proteins is a very
reasonable default value for membrane protein simulations. It gives an overall
minimum number of inconsistencies between the continuum and explicit
representations of water distributions in membrane channel proteins, at least
in the water accessible pore/channel regions that we focus on. Finally, we
validate the new membrane model by carrying out binding affinity calculations
for a potassium channel, and we observe a good agreement with experiment
results.Comment: 40 pages, 6 figures, 5 table
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Digging into Lipid Membrane Permeation for Cardiac Ion Channel Blocker d-Sotalol with All-Atom Simulations.
Interactions of drug molecules with lipid membranes play crucial role in their accessibility of cellular targets and can be an important predictor of their therapeutic and safety profiles. Very little is known about spatial localization of various drugs in the lipid bilayers, their active form (ionization state) or translocation rates and therefore potency to bind to different sites in membrane proteins. All-atom molecular simulations may help to map drug partitioning kinetics and thermodynamics, thus providing in-depth assessment of drug lipophilicity. As a proof of principle, we evaluated extensively lipid membrane partitioning of d-sotalol, well-known blocker of a cardiac potassium channel Kv11.1 encoded by the hERG gene, with reported substantial proclivity for arrhythmogenesis. We developed the positively charged (cationic) and neutral d-sotalol models, compatible with the biomolecular CHARMM force field, and subjected them to all-atom molecular dynamics (MD) simulations of drug partitioning through hydrated lipid membranes, aiming to elucidate thermodynamics and kinetics of their translocation and thus putative propensities for hydrophobic and aqueous hERG access. We found that only a neutral form of d-sotalol accumulates in the membrane interior and can move across the bilayer within millisecond time scale, and can be relevant to a lipophilic channel access. The computed water-membrane partitioning coefficient for this form is in good agreement with experiment. There is a large energetic barrier for a cationic form of the drug, dominant in water, to cross the membrane, resulting in slow membrane translocation kinetics. However, this form of the drug can be important for an aqueous access pathway through the intracellular gate of hERG. This route will likely occur after a neutral form of a drug crosses the membrane and subsequently re-protonates. Our study serves to demonstrate a first step toward a framework for multi-scale in silico safety pharmacology, and identifies some of the challenges that lie therein
Visualization of large molecular trajectories
The analysis of protein-ligand interactions is a time-intensive task. Researchers have to analyze multiple physico-chemical properties of the protein at once and combine them to derive conclusions about the protein-ligand interplay. Typically, several charts are inspected, and 3D animations can be played side-by-side to obtain a deeper understanding of the data. With the advances in simulation techniques, larger and larger datasets are available, with up to hundreds of thousands of steps. Unfortunately, such large trajectories are very difficult to investigate with traditional approaches. Therefore, the need for special tools that facilitate inspection of these large trajectories becomes substantial. In this paper, we present a novel system for visual exploration of very large trajectories in an interactive and user-friendly way. Several visualization motifs are automatically derived from the data to give the user the information about interactions between protein and ligand. Our system offers specialized widgets to ease and accelerate data inspection and navigation to interesting parts of the simulation. The system is suitable also for simulations where multiple ligands are involved. We have tested the usefulness of our tool on a set of datasets obtained from protein engineers, and we describe the expert feedback.Peer ReviewedPostprint (author's final draft
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