Advancing the accuracy of protein fold recognition by utilizing profiles from hidden Markov models

Protein fold recognition is an important step towards solving protein function and tertiary structure prediction problems. Among a wide range of approaches proposed to solve this problem, pattern recognition based techniques have achieved the best results. The most effective pattern recognition-based techniques for solving this problem have been based on extracting evolutionary-based features. Most studies have relied on thePosition Specific Scoring Matrix (PSSM) to extract these features. However it is known that profile-profile sequence alignment techniques can identify more remote homologs than sequence-profile approaches like PSIBLAST. In this study we use a profile-profile sequence alignment technique, namely HHblits, to extract HMM profiles.We will show that unlike previous studies, using the HMM profile to extract evolutionary information can significantly enhance the protein fold prediction accuracy. We develop a new pattern recognition based system called HMMFold which extracts HMM based evolutionary information and captures remote homology information better than previous studies. Using HMMFold we achieve up to 93.8% and 86.0% prediction accuracies when the sequential similarity rates are less than 40% and 25%, respectively. These results are up to 10% better than previously reported results for this task. Our results show significant enhancement especially for benchmarks with sequential similarity as low as 25% which highlights the effectiveness of HMMFold to address this problem and its superiority over previously proposed approaches found in the literature

Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking

P311 downregulates TGF-β1 and TGF-β2 expression but not TGF-β3 during myofibroblast transformation

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Fatty infiltration of the cervical multifidus musculature and their clinical correlates in spondylotic myelopathy.

This work was supported by the National Institute of Health, National Institute of Neurological Disorders and Stroke (US), (NIH-NINDS), grant number 1K23NS091430-01A1Peer reviewe

Pressure dependent electronic properties of MgO polymorphs: A first-principles study of Compton profiles and autocorrelation functions

The first-principles periodic linear combination of atomic orbitals method within the framework of density functional theory implemented in the CRYSTAL06 code has been applied to explore effect of pressure on the Compton profiles and autocorrelation functions of MgO. Calculations are performed for the B1, B2, B3, B4, B8_1 and h-MgO polymorphs of MgO to compute lattice constants and bulk moduli. The isothermal enthalpy calculations predict that B4 to B8_1, h-MgO to B8_1, B3 to B2, B4 to B2 and h-MgO to B2 transitions take place at 2, 9, 37, 42 and 64 GPa respectively. The high pressure transitions B8_1 to B2 and B1 to B2 are found to occur at 340 and 410 GPa respectively. The pressure dependent changes are observed largely in the valence electrons Compton profiles whereas core profiles are almost independent of the pressure in all MgO polymorphs. Increase in pressure results in broadening of the valence Compton profiles. The principal maxima in the second derivative of Compton profiles shifts towards high momentum side in all structures. Reorganization of momentum density in the B1 to B2 structural phase transition is seen in the first and second derivatives before and after the transition pressure. Features of the autocorrelation functions shift towards lower r side with increment in pressure.Comment: 19 pages, 8 figures, accepted for publication in Journal of Materials Scienc

Ultrasound-Enhanced Drug Transport and Distribution in the Brain

Drug delivery in the brain is limited by slow drug diffusion in the brain tissue. This study tested the hypothesis that ultrasound can safely enhance the permeation of drugs in the brain. In vitro exposure to ultrasound at various frequencies (85 kHz, 174 kHz, and 1 MHz) enhanced the permeation of tritium-labeled molecules with molecular weight up to 70 kDa across porcine brain tissue. A maximum enhancement of 24-fold was observed at 85 kHz and 1,200 J/cm2. In vivo exposure to 1-MHz ultrasound further demonstrated the ability of ultrasound to facilitate molecule distribution in the brain of a non-human primate. Finally, ultrasound under conditions similar to those used in vivo was shown to cause no damage to plasmid DNA, siRNA, adeno-associated virus, and fetal rat cortical neurons over a range of conditions. Altogether, these studies demonstrate that ultrasound can increase drug permeation in the brain in vitro and in vivo under conditions that did not cause detectable damage

Rapid Sampling of Molecules via Skin for Diagnostic and Forensic Applications

Skin provides an excellent portal for diagnostic monitoring of a variety of entities; however, there is a dearth of reliable methods for patient-friendly sampling of skin constituents. This study describes the use of low-frequency ultrasound as a one-step methodology for rapid sampling of molecules from the skin. Sampling was performed using a brief exposure of 20 kHz ultrasound to skin in the presence of a sampling fluid. In vitro sampling from porcine skin was performed to assess the effectiveness of the method and its ability to sample drugs and endogenous epidermal biomolecules from the skin. Dermal presence of an antifungal drug—fluconazole and an abused substance, cocaine—was assessed in rats. Ultrasonic sampling captured the native profile of various naturally occurring moisturizing factors in skin. A high sampling efficiency (79 ± 13%) of topically delivered drug was achieved. Ultrasound consistently sampled greater amounts of drug from the skin compared to tape stripping. Ultrasonic sampling also detected sustained presence of cocaine in rat skin for up to 7 days as compared to its rapid disappearance from the urine. Ultrasonic sampling provides significant advantages including enhanced sampling from deeper layers of skin and high temporal sampling sensitivity