An Allosteric Pathway in Copper, Zinc Superoxide Dismutase Unravels the Molecular Mechanism of the G93A Amyotrophic Lateral Sclerosis-Linked Mutation

Several different mutations of the protein copper, zinc superoxide dismutase (SOD1) produce the neurodegenerative disorder amyotrophic lateral sclerosis (ALS). The molecular mechanism by which the diverse mutations converge to a similar pathology is currently unknown. The electrostatic loop (EL) of SOD1 is known to be affected in all of the studied ALS-linked mutations of SOD1. In this work, we employ a multiscale simulation approach to show that this perturbation corresponds to an increased probability of the EL detaching from its native position, exposing the metal site of the protein to water. From extensive atomistic and coarse-grained molecular dynamics (MD) simulations, we identify an allosteric pathway that explains the action of the distant G93A mutation on the EL. Finally, we employ quantum mechanics/molecular mechanics MD simulations to show that the opening of the EL decreases the Zn(II) affinity of the protein. As the loss of Zn(II) is at the center of several proposed pathogenic mechanisms in SOD1-linked ALS, the structural effect identified here not only is in agreement with the experimental data but also places the opening of the electrostatic loop as the possible main pathogenic effect for a significant number of ALS-linked SOD1 mutations

Perspectives on High-Throughput Ligand/Protein Docking With Martini MD Simulations

Molecular docking is central to rational drug design. Current docking techniques suffer, however, from limitations in protein flexibility and solvation models and by the use of simplified scoring functions. All-atom molecular dynamics simulations, on the other hand, feature a realistic representation of protein flexibility and solvent, but require knowledge of the binding site. Recently we showed that coarse-grained molecular dynamics simulations, based on the most recent version of the Martini force field, can be used to predict protein/ligand binding sites and pathways, without requiring any a priori information, and offer a level of accuracy approaching all-atom simulations. Given the excellent computational efficiency of Martini, this opens the way to high-throughput drug screening based on dynamic docking pipelines. In this opinion article, we sketch the roadmap to achieve this goal

Polyply:A python suite for facilitating simulations of macromolecules and nanomaterials

Molecular dynamics simulations play an increasingly important role in the rational design of (nano)-materials and in the study of biomacromolecules. However, generating input files and realistic starting coordinates for these simulations is a major bottleneck, especially for high throughput protocols and for complex multi-component systems. To eliminate this bottleneck, we present the polyply software suite that provides 1) a multi-scale graph matching algorithm designed to generate parameters quickly and for arbitrarily complex polymeric topologies, and 2) a generic multi-scale random walk protocol capable of setting up complex systems efficiently and independent of the target force-field or model resolution. We benchmark quality and performance of the approach by creating realistic coordinates for polymer melt simulations, single-stranded as well as circular single-stranded DNA. We further demonstrate the power of our approach by setting up a microphase-separated block copolymer system, and by generating a liquid-liquid phase separated system inside a lipid vesicle

An alternative conformation of ERβ bound to estradiol reveals H12 in a stable antagonist position

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOThe natural ligand 17β-estradiol (E2) is so far believed to induce a unique agonist-bound active conformation in the ligand binding domain (LBD) of the estrogen receptors (ERs). Both subtypes, ERα and ERβ, are transcriptionally activated in the presence o7FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO2014/22007-02013/08293-72012/24750-6301981/2011-6This work was supported by the São Paulo Research Foundation FAPESP (grants 2014/22007-0, 2013/08293-7, 2012/24750-6) and by CNPq (grant 301981/2011-6

Martini coarse-grained models of imidazolium-based ionic liquids:from nanostructural organization to liquid-liquid extraction

Ionic liquids (ILs) are remarkable green solvents, which find applications in many areas of nano- and biotechnology including extraction and purification of value-added compounds or fine chemicals. These liquid salts possess versatile solvation properties that can be tuned by modifications in the cation or anion structure. So far, in contrast to the great success of theoretical and computational methodologies applied to other fields, only a few IL models have been able to bring insights towards the rational design of such solvents. In this work, we develop coarse-grained (CG) models for imidazolium-based ILs using a new version of the Martini force field. The model is able to reproduce the main structural properties of pure ILs, including spatial heterogeneity and global densities over a wide range of temperatures. More importantly, given the high intermolecular compatibility of the Martini force field, this new IL CG model opens the possibility of large-scale simulations of liquid-liquid extraction experiments. As examples, we show two applications, namely the extraction of aromatic molecules from a petroleum oil model and the extraction of omega-3 polyunsaturated fatty acids from a fish oil model. In semi-quantitative agreement with the experiments, we show how the extraction capacity and selectivity of the IL could be affected by the cation chain length or addition of co-solvents

Sequential Voxel-Based Leaflet Segmentation of Complex Lipid Morphologies

[Image: see text] As molecular dynamics simulations increase in complexity, new analysis tools are necessary to facilitate interpreting the results. Lipids, for instance, are known to form many complicated morphologies, because of their amphipathic nature, becoming more intricate as the particle count increases. A few lipids might form a micelle, where aggregation of tens of thousands could lead to vesicle formation. Millions of lipids comprise a cell and its organelle membranes, and are involved in processes such as neurotransmission and transfection. To study such phenomena, it is useful to have analysis tools that understand what is meant by emerging entities such as micelles and vesicles. Studying such systems at the particle level only becomes extremely tedious, counterintuitive, and computationally expensive. To address this issue, we developed a method to track all the individual lipid leaflets, allowing for easy and quick detection of topological changes at the mesoscale. By using a voxel-based approach and focusing on locality, we forego costly geometrical operations without losing important details and chronologically identify the lipid segments using the Jaccard index. Thus, we achieve a consistent sequential segmentation on a wide variety of (lipid) systems, including monolayers, bilayers, vesicles, inverted hexagonal phases, up to the membranes of a full mitochondrion. It also discriminates between adhesion and fusion of leaflets. We show that our method produces consistent results without the need for prefitting parameters, and segmentation of millions of particles can be achieved on a desktop machine

Protein-ligand binding with the coarse-grained Martini model

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model

CHARMM Force Field Parameterization of Peroxisome Proliferator-Activated Receptor γ Ligands

The peroxisome proliferator-activated receptor γ (PPARγ) ligands are important therapeutic drugs for the treatment of type 2 diabetes, obesity and cardiovascular diseases. In particular, partial agonists and non-agonists are interesting targets to reduce glucose levels, presenting few side effects in comparison to full agonists. In this work, we present a set of CHARMM-based parameters of a molecular mechanics force field for two PPARγ ligands, GQ16 and SR1664. GQ16 belongs to the thiazolidinedione class of drugs and it is a PPARγ partial agonist that has been shown to promote the “browning” of white adipose tissue. SR1664 is the precursor of the PPARγ non-agonist class of ligands that activates PPARγ in a non-classical manner. Here, we use quantum chemical calculations consistent with the CHARMM protocol to obtain bonded and non-bonded parameters, including partial atomic charges and effective torsion potentials for both molecules. The newly parameterized models were evaluated by examining the behavior of GQ16 and SR1664 free in water and bound to the ligand binding pocket of PPARγ using molecular dynamics simulations. The potential parameters derived here are readily transferable to a variety of pharmaceutical compounds and similar PPARγ ligands

Multiscale modeling of molecular structure and optical properties of complex supramolecular aggregates

Supramolecular aggregates of synthetic dye molecules offer great perspectives to prepare biomimetic functional materials for light-harvesting and energy transport. The design is complicated by the fact that structure-property relationships are hard to establish, because the molecular packing results from a delicate balance of interactions and the excitonic properties that dictate the optics and excited state dynamics, in turn sensitively depend on this packing. Here we show how an iterative multiscale approach combining molecular dynamics and quantum mechanical exciton modeling can be used to obtain accurate insight into the packing of thousands of cyanine dye molecules in a complex double-walled tubular aggregate in close interaction with its solvent environment. Our approach allows us to answer open questions not only on the structure of these prototypical aggregates, but also about their molecular-scale structural and energetic heterogeneity, as well as on the microscopic origin of their photophysical properties. This opens the route to accurate predictions of energy transport and other functional properties

Analysis of non-TIR NBS-LRR resistance gene analogs in Musa acuminata Colla: Isolation, RFLP marker development, and physical mapping

<p>Abstract</p> <p>Background</p> <p>Many commercial banana varieties lack sources of resistance to pests and diseases, as a consequence of sterility and narrow genetic background. Fertile wild relatives, by contrast, possess greater variability and represent potential sources of disease resistance genes (R-genes). The largest known family of plant R-genes encode proteins with nucleotide-binding site (NBS) and C-terminal leucine-rich repeat (LRR) domains. Conserved motifs in such genes in diverse plant species offer a means for isolation of candidate genes in banana which may be involved in plant defence.</p> <p>Results</p> <p>A computational strategy was developed for unbiased conserved motif discovery in NBS and LRR domains in R-genes and homologues in monocotyledonous plant species. Degenerate PCR primers targeting conserved motifs were tested on the wild cultivar <it>Musa acuminata </it>subsp. <it>burmannicoides</it>, var. Calcutta 4, which is resistant to a number of fungal pathogens and nematodes. One hundred and seventy four resistance gene analogs (RGAs) were amplified and assembled into 52 contiguous sequences. Motifs present were typical of the non-TIR NBS-LRR RGA subfamily. A phylogenetic analysis of deduced amino-acid sequences for 33 RGAs with contiguous open reading frames (ORFs), together with RGAs from <it>Arabidopsis thaliana </it>and <it>Oryza sativa</it>, grouped most <it>Musa </it>RGAs within monocotyledon-specific clades. RFLP-RGA markers were developed, with 12 displaying distinct polymorphisms in parentals and F1 progeny of a diploid <it>M. acuminata </it>mapping population. Eighty eight BAC clones were identified in <it>M. acuminata </it>Calcutta 4, <it>M. acuminata </it>Grande Naine, and <it>M. balbisiana </it>Pisang Klutuk Wulung BAC libraries when hybridized to two RGA probes. Multiple copy RGAs were common within BAC clones, potentially representing variation reservoirs for evolution of new R-gene specificities.</p> <p>Conclusion</p> <p>This is the first large scale analysis of NBS-LRR RGAs in <it>M. acuminata </it>Calcutta 4. Contig sequences were deposited in GenBank and assigned numbers <ext-link ext-link-type="gen" ext-link-id="ER935972">ER935972</ext-link> – <ext-link ext-link-type="gen" ext-link-id="ER936023">ER936023</ext-link>. RGA sequences and isolated BACs are a valuable resource for R-gene discovery, and in future applications will provide insight into the organization and evolution of NBS-LRR R-genes in the <it>Musa </it>A and B genome. The developed RFLP-RGA markers are applicable for genetic map development and marker assisted selection for defined traits such as pest and disease resistance.</p