Palmitoylation-dependent regulation of cardiomyocyte Rac1 signaling activity and minor effects on cardiac hypertrophy

S-palmitoylation is a reversible lipid modification catalyzed by 23 S-acyltransferases with a conserved zinc finger aspartate-histidine-histidine-cysteine (zDHHC) domain that facilitates targeting of proteins to specific intracellular membranes. Here we performed a gain-of-function screen in the mouse and identified the Golgi-localized enzymes zDHHC3 and zDHHC7 as regulators of cardiac hypertrophy. Cardiomyocyte-specific transgenic mice overexpressing zDHHC3 show cardiac disease, and S-acyl proteomics identified the small GTPase Rac1 as a novel substrate of zDHHC3. Notably, cardiomyopathy and congestive heart failure in zDHHC3 transgenic mice is preceded by enhanced Rac1 S-palmitoylation, membrane localization, activity, downstream hypertrophic signaling, and concomitant induction of all Rho family small GTPases whereas mice overexpressing an enzymatically dead zDHHC3 mutant show no discernible effect. However, loss of Rac1 or other identified zDHHC3 targets Gαq/11 or galectin-1 does not diminish zDHHC3-induced cardiomyopathy, suggesting multiple effectors and pathways promoting decompensation with sustained zDHHC3 activity. Genetic deletion of Zdhhc3 in combination with Zdhhc7 reduces cardiac hypertrophy during the early response to pressure overload stimulation but not over longer time periods. Indeed, cardiac hypertrophy in response to 2 weeks of angiotensin-II infusion is not diminished by Zdhhc3/7 deletion, again suggesting other S-acyltransferases or signaling mechanisms compensate to promote hypertrophic signaling. Taken together, these data indicate that the activity of zDHHC3 and zDHHC7 at the cardiomyocyte Golgi promote Rac1 signaling and maladaptive cardiac remodeling, but redundant signaling effectors compensate to maintain cardiac hypertrophy with sustained pathological stimulation in the absence of zDHHC3/7.

In-Situ Growth of CoS Nanoparticles Onto Electrospun Graphitized Carbon Nanofibers as an Efficient Counter Electrode for Dye-Sensitized Solar Cells

One-dimensional graphitized carbon nanofibers (G-CNFs) were prepared by employing facile electrospinning technique using 10 wt% of polyacrylonitrile (PAN) solution in N,N-dimethyl formamide (DMF) as precursor followed by successive stabilization, carbonization and purification processes. Cobalt sulfide (CoS) nanoparticles were grown onto G-CNFs by hydrothermal method using cobalt chloride and L-cysteine as precursors. The results of X-ray diffraction (XRD) and Raman spectroscopy confirmed the phase formation and degree of graphitization, respectively. Field-emission scanning electron microscope (FE-SEM) and transmission electron microscope (TEM) images confirmed the morphology, growth and distribution of CoS nanoparticles over G-CNFs (CoS/G-CNFs). The electrochemical studies such as cyclic voltammetry (CV), electrochemical impedance and Tafel polarization revealed that CoS/G-CNFs have lower overpotential, low charge transfer resistance and higher exchange current density for triiodide (I− 3 reduction reaction. The superior electrocat- alytic activity of CoS/G-CNFs than std. Pt is due to combined contribution of interconnected pore structure with high surface area of G-CNFs and excellent electrocatalytic activity of CoS. In addition, the dye sensitized solar cell (DSSC) based on platinum-free CoS/G-CNFs has exhibited higher photo-conversion efficiency (PCE) under a simulated solar light irradiation of 100 mW cm−2 when compared to standard platinum (std. Pt) which is attributed to the synergistic effect of CoS with G-CNFs.

Structure prediction of loops with fixed and flexible stems

The prediction of loop structures is considered one of the main challenges in the protein folding problem. Regardless of the dependence of the overall algorithm on the protein data bank, the flexibility of loop regions dictates the need for special attention to their structures. In this article, we present algorithms for loop structure prediction with fixed stem and flexible stem geometry. In the flexible stem geometry problem, only the secondary structure of three stem residues on either side of the loop is known. In the fixed stem geometry problem, the structure of the three stem residues on either side of the loop is also known. Initial loop structures are generated using a probability database for the flexible stem geometry problem, and using torsion angle dynamics for the fixed stem geometry problem. Three rotamer optimization algorithms are introduced to alleviate steric clashes between the generated backbone structures and the side chain rotamers. The structures are optimized by energy minimization using an all-atom force field. The optimized structures are clustered using a traveling salesman problem-based clustering algorithm. The structures in the densest clusters are then utilized to refine dihedral angle bounds on all amino acids in the loop. The entire procedure is carried out for a number of iterations, leading to improved structure prediction and refined dihedral angle bounds. The algorithms presented in this article have been tested on 3190 loops from the PDBSelect25 data set and on targets from the recently concluded CASP9 community-wide experiment.

ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction

The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles-based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented.