Low Surface Accessible Area NanoCoral TiO2 for the Reduction of Foreign Body Reaction During Implantation.

The entry of implants triggers the secretion of damage associated molecular patterns (DAMPs) that recruit dendritic cells (DCs) and results in subsequent foreign body reaction (FBR). Though several studies have illustrated that the surface accessible area (SAA) of implants plays a key role in the process of DAMPs release and absorption, the effect of SAA on the immune reaction still remains unknown. Here, a series of TiO2 plates with different SAA is fabricated to investigate the relationship between SAA and FBR. Compared with larger SAA surface, the aggregation of DC is significantly inhibited by lower SAA surface. Total internal reflection microscopy (TIRFM) and molecular dynamic (MD) simulation show that although high mobility group box 1 (HMGB1) is adsorbed more on plates with lower SAA, the exposure ratio of cysteine (CYS) residue in HMGB1 is significantly decreased in lower SAA group. The lower exposure of CYS reduces the activation of Toll-like receptors 4 (TLR4), which down-regulates the expression of myeloid differentiation factor (Myd88)-TNF receptor associated factor 6 (TRAF6) to inhibit nuclear factor kappa B (NF-κB) signaling. Generally, this study reveals the mechanism of how SAA, a nanoscale property, affects FBR from perspective of DAMPs, and provides a new direction for designing better biocompatible implants.

Side chain torsion dictates planarity and ionizability of green fluorescent protein's chromophore leading to spectral perturbations.

Spectral characteristics of fluorescent proteins (FPs) are well studied, and through protein engineering, several FP variants constituting entire visible spectrum have been created. One of the most common mechanisms attributed to spectral shifts in FP is excited state proton transfer (ESPT), hydroxyl moiety protonation and deprotonation, along with chromophore cis-trans isomerism. The most widely studied FPs are those derived from avGFP (Aequorea victoria GFP) and Dsred (Discosoma coral). Apart from the above mechanism, certain interacting residues are said to play a vital role in altering the proton transfer pathway leading to numerous spectral variants. Similarly, the hydrogen-bonded networks solely cannot dictate the energy landscape of FPs. Non-bonded interactions also can create secondary harmonic shifts by dipole-dipole inductions. Side chain contacts tend to alter the topological and torsional geometry, thereby disturbing the chromophore's planarity. Side chain torsional variations have almost been unaccounted for their distortions in FPs. We hypothesize the torsional landscape and altered residual interactions as prominent factors for the spectral shifts. Through our 200 ns molecular dynamics investigation, we prospect that van der Waals packing in Dsred is more compact than that of avGFP, thus creating a low solvent occupiable environment and reduced solvent interactions having higher red spectral shift. The torsional changes of wild avGFP, S65T avGFP and Dsred have been studied to comprehend the inter-residual contact distance and the geometrical descriptors. Communicated by Ramaswamy H. Sarma.

Calculation of distribution coefficients in the SAMPL5 challenge from atomic solvation parameters and surface areas.

In the context of SAMPL5, we submitted blind predictions of the cyclohexane/water distribution coefficient (D) for a series of 53 drug-like molecules. Our method is purely empirical and based on the additive contribution of each solute atom to the free energy of solvation in water and in cyclohexane. The contribution of each atom depends on the atom type and on the exposed surface area. Comparatively to similar methods in the literature, we used a very small set of atomic parameters: only 10 for solvation in water and 1 for solvation in cyclohexane. As a result, the method is protected from overfitting and the error in the blind predictions could be reasonably estimated. Moreover, this approach is fast: it takes only 0.5 s to predict the distribution coefficient for all 53 SAMPL5 compounds, allowing its application in virtual screening campaigns. The performance of our approach (submission 49) is modest but satisfactory in view of its efficiency: the root mean square error (RMSE) was 3.3 log D units for the 53 compounds, while the RMSE of the best performing method (using COSMO-RS) was 2.1 (submission 16). Our method is implemented as a Python script available at https://github.com/diogomart/SAMPL5-DC-surface-empirical .

Crystal structure and biochemical characterization of a manganese superoxide dismutase from Chaetomium thermophilum.

A manganese superoxide dismutase from the thermophilic fungus Chaetomium thermophilum (CtMnSOD) was expressed in Pichia pastoris and purified to homogeneity. Its optimal temperature was 60°C with approximately 75% of its activity retained after incubation at 70°C for 60min. Recombinant yeast cells carrying C. thermophilum mnsod gene exhibited higher stress resistance to salt and oxidative stress-inducing agents than control yeast cells. In an effort to provide structural insights, CtMnSOD was crystallized and its structure was determined at 2.0Å resolution. The overall architecture of CtMnSOD was found similar to other MnSODs with highest structural similarities obtained against a MnSOD from the thermotolerant fungus Aspergillus fumigatus. In order to explain its thermostability, structural and sequence analysis of CtMnSOD with other MnSODs was carried out. An increased number of charged residues and an increase in the number of intersubunit salt bridges and the Thr:Ser ratio were identified as potential reasons for the thermostability of CtMnSOD.

Evaluation and applications of antibody variable stability / 军事医学

Objective To study the intrinsic relationships between the binding energy of the antibody light and heavy chains and the conformational characteristics , physical and chemical properties , and to establish a corresponding mathemat-ical model and evaluate the thermal stability of the antibody molecules , which contribute to the antibody design , optimiza-tion and affinity maturation .Methods Based on bioinformatics and computational biology methods , the antibody′s structur-al information with the crystal diffraction data was analyzed .The conformational character of the variable domain of the antibody was studied using distance geometry and computer graphics technology .With the aid of the intermolecular hydrogen bond formation theory and the reaction free energy theory , the dynamic structure and energy characteristics be-tween the heavy and light chain variable regions of the antibody were studied .Furthermore , using nonlinear fitting and regression analysis, a mathematical model was set up .Results According to simulation and statistic analysis , there was a linear relationship between the binding energy and the number of the intermolecular hydrogen bonding , Van der Waals interaction of the heavy and light chains of the antibody .There was polynomial correlation between the binding energy and the physicochemical properties of the antibody .Using the frequency of amino acid position and the established model , the humanized anti-ricin antibody , which could not obtain the stable engineering cell line , was evaluated and optimized .The stable engineering cell line of the humanized anti-ricin antibody was obtained in the experiment .Conclusion The self structure of the antibody variable region ( conformation and physicochemical properties ) has much effect on its stability . The antibody stability can be improved by structural optimization .

Beta-decomposition for the volume and area of the union of three-dimensional balls and their offsets.

Given a set of spherical balls, called atoms, in three-dimensional space, its mass properties such as the volume and the boundary area of the union of the atoms are important for many disciplines, particularly for computational chemistry/biology and structural molecular biology. Despite many previous studies, this seemingly easy problem of computing mass properties has not been well-solved. If the mass properties of the union of the offset of the atoms are to be computed as well, the problem gets even harder. In this article, we propose algorithms that compute the mass properties of both the union of atoms and their offsets both correctly and efficiently. The proposed algorithms employ an approach, called the Beta-decomposition, based on the recent theory of the beta-complex. Given the beta-complex of an atom set, these algorithms decompose the target mass property into a set of primitives using the simplexes of the beta-complex. Then, the molecular mass property is computed by appropriately summing up the mass property corresponding to each simplex. The time complexity of the proposed algorithm is O(m) in the worst case where m is the number of simplexes in the beta-complex that can be efficiently computed from the Voronoi diagram of the atoms. It is known in ℝ(3) that m = O(n) on average for biomolecules and m = O(n(2)) in the worst case for general spheres where n is the number of atoms. The theory is first introduced in ℝ(2) and extended to ℝ(3). The proposed algorithms were implemented into the software BetaMass and thoroughly tested using molecular structures available in the Protein Data Bank. BetaMass is freely available at the Voronoi Diagram Research Center web site.

Identifying protein-protein interaction sites using covering algorithm.

Identification of protein-protein interface residues is crucial for structural biology. This paper proposes a covering algorithm for predicting protein-protein interface residues with features including protein sequence profile and residue accessible area. This method adequately utilizes the characters of a covering algorithm which have simple, lower complexity and high accuracy for high dimension data. The covering algorithm can achieve a comparable performance (69.62%, Complete dataset; 60.86%, Trim dataset with overall accuracy) to a support vector machine and maximum entropy on our dataset, a correlation coefficient (CC) of 0.2893, 58.83% specificity, 56.12% sensitivity on the Complete dataset and 0.2144 (CC), 53.34% (specificity), 65.59% (sensitivity) on the Trim dataset in identifying interface residues by 5-fold cross-validation on 61 protein chains. This result indicates that the covering algorithm is a powerful and robust protein-protein interaction site prediction method that can guide biologists to make specific experiments on proteins. Examination of the predictions in the context of the 3-dimensional structures of proteins demonstrates the effectiveness of this method.