Interface energy distribution obtained with different simulation protocols.

<p>The energies for each target and method have been normallized to the dynamic range of interface energies observed for the respective target across all methods. The interface energies are normalized by the absolute value of mean energy of the 10 lowest observed energies for this target (highest energy is always 0). The <i>x</i>-percentile energy is the scaled energy value that separates off <i>x</i>% of the lowest energy decoys for a given simulation result. Shown are the distributions of <i>x</i>-percentile energies across all 30 targets for a) the 5%-tile, b) the 1%-tile and c) the 0.1%-tile, respectively.</p

Kinetics of Protein Complex Dissociation Studied by Hydrogen/Deuterium Exchange and Mass Spectrometry

The growing importance of protein aggregation diseases requires the development of new methods to elucidate the molecular features that are responsible for the incipient protein–protein interactions. Kinetic information from protein–protein association/dissociation reactions is particularly valuable for revealing mechanistic insight, but robust tools that can provide this information are somewhat lacking. In this work, we describe a hydrogen/deuterium exchange (HDX)-based method that provides rate constant information for protein oligomer dissociation, using the well-studied β-lactoglobulin (βLG) dimer as a model system to validate our approach. By measuring the rate of exchange at different regions of the protein using top-down tandem mass spectrometry and fitting the resulting data to an appropriate mathematical model, we are able to extract the dimer’s dissociation rate constant. We exploit the fact that regions of the protein that are part of the protein–protein interface have exchange patterns that are distinct from noninterfacial regions. This observation indicates that the HDX/MS method not only provides kinetic information but also could provide structural insight about the interface at the same time, which would be very valuable for previously uncharacterized protein–protein complexes

Interface RMSD vs. Interface Energy after refinement on target 1ppf and 1mlc.

<p>A) and C) refinement of shotgun sampling generated ensembles, B) and D) refinement of ReplicaDock generated ensembles. The red dots represent the RelaxedNative ensembles (Results).</p

Summary of structure prediction accuracy in unbound docking.

<p>Clusters are ranked by size and represented by the lowest interface energy decoy. In column ‘CQ’ (CAPRI Quality),</p><p>‘0’ indicates that none of the top 10 models was of accetable quality,</p><p>‘*’, ‘**’ and ‘***’ indicates that at least one of the top 10 models is of acceptable, medium or high quality, respectively (Section 4.7).</p><p>Columns ‘L_rms’, ‘I_rms’, ‘<i>f_nat</i>’ and ‘<i>f_non-nat</i>’ record the respective information of the best model within these top 10 models.</p>1<p>CQ refers to CAPRI quality.</p

Qualitative classification in ability to sample native energy basin.

<p>At the end is the summary of each category achieved by each method.</p

The centroid energy function prefers an alternative binding modes for the bound docking target 1emv.

<p>A–B) shotgun and ReplicaDock sample the low-resolution docking stage with ‘capped’ centroid energy function. C–D) shotgun and ReplicaDock sample the low-resolution docking stage with centroid energy function with no cap. The structure indicated by the red circle will be shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0072096#pone-0072096-g008" target="_blank">Figure 8</a>.</p

Detailed analysis of individual docking stages on bound target 1sq2.

<p>A) Interface RMSD (I_rms) before and after the Monte-Carlo optimization in the low-resolution stage of RosettaDock's shotgun sampling, B–C) I_rms before and after all-atom refinement for shotgun and ReplicaDock sampled ensembles, respectively. The colorbar indicates the density of data points at given position of the scatter plot, A–C) use a same colorbar range. The insets show the distribution of differences between I_rms after and before the respective sampling stage has been applied (negative values reflect an improvement in I_rms).</p

Fluorine-Free Oil Absorbents Made from Cellulose Nanofibril Aerogels

Aerogels based on cellulose nanofibrils (CNFs) have been of great interest as absorbents due to their high absorption capacity, low density, biodegradability, and large surface area. Hydrophobic aerogels have been designed to give excellent oil absorption tendency from water. Herein, we present an in situ method for CNF surface modification and hydrophobic aerogel preparation. Neither solvent exchange nor fluorine chemical is used in aerogel preparations. The as-prepared hydrophobic aerogels exhibit low density (23.2 mg/cm^–3), high porosity (98.5%), good flexibility, and solvent-induced shape recovery property. Successful surface modification was confirmed through field emission scanning electron microscopy (FE-SEM), Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and water contact angle measurements. The hydrophobic aerogels show high absorption capacities for various oils, depending on liquid density, up to 47× their original weight but with low water uptake (<0.5 g/g aerogel)