Levels and particle-size distributions of environmentally persistent free radicals (EPFRs) in haze-associated atmospheric particulate matter (PM) have not been highlighted, even though they may enter the human body along with PM and adversely affect human health. This study quantified the levels of EPFRs in airborne PM with different aerodynamic diameters (d) using electron paramagnetic resonance (EPR) spectroscopy. EPR spectra showed a single, unstructured signal from persistent semiquinone radicals. The average concentration of EPFRs in the airborne PM during haze events was 2.18 × 12 spins/g (range: 3.06 × 10-6.23 × 10 spins/g), approximately 2 orders of magnitude higher than that reported previously in the US atmosphere. Particle-size distributions of EPFRs in four different PM fractions (d > 10 μm, 10 μm < d < 2.5 μm, 2.5 μm

Environmentally persistent free radicals (EPFRs) are emerging pollutants that can adversely affect human health. Although the pivotal roles of metal oxides in EPFR formation have been identified, few studies have investigated the influence of the metal oxide species, size, or concentration on the formation of EPFRs. In this study, EPFR formation from a polyaromatic hydrocarbon with chlorine and hydroxyl substituents (2,4-dichloro-1-naphthol) was investigated using electron paramagnetic resonance spectroscopy. The effect of the metal oxide on the EPFR species and its lifetime and yield were evaluated. The spectra obtained with catalysis by CuO, Al 2 O 3 , ZnO, and NiO were obviously different, indicating that different EPFRs formed. The abilities of the metal oxides to promote EPFR formation were in the order Al 2 O 3 > ZnO > CuO > NiO, which were in accordance with the oxidizing strengths of the metal cations. A decay study showed that the generated radicals were persistent, with a maximum 1/e lifetime of 108 days on the surface of Al 2 O 3 . The radical yields were dependent on the concentration and particle size of the metal oxide. Metal oxide nanoparticles increased the EPFR concentrations more than micrometer-sized particles.

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The structure of an active mutant of (S)-mandelate dehydrogenase (MDH-GOX2) from Pseudomonas putida has been determined at 2.15 A resolution. The membrane-associated flavoenzyme (S)-mandelate dehydrogenase (MDH) catalyzes the oxidation of (S)-mandelate to give a flavin hydroquinone intermediate which is subsequently reoxidized by an organic oxidant residing in the membrane. The enzyme was rendered soluble by replacing its 39-residue membrane-binding peptide segment with a corresponding 20-residue segment from its soluble homologue, glycolate oxidase (GOX). Because of their amphipathic nature and peculiar solubilization properties, membrane proteins are notoriously difficult to crystallize, yet represent a large fraction of the proteins encoded by genomes currently being deciphered. Here we present the first report of such a structure in which an internal membrane-binding segment has been replaced, leading to successful crystallization of the fully active enzyme in the absence of detergents. This approach may have general application to other membrane-bound proteins. The overall fold of the molecule is that of a TIM barrel, and it forms a tight tetramer within the crystal lattice that has circular 4-fold symmetry. The structure of MDH-GOX2 reveals how this molecule can interact with a membrane, although it is limited by the absence of a membrane-binding segment. MDH-GOX2 and GOX adopt similar conformations, yet they retain features characteristic of membrane and globular proteins, respectively. MDH-GOX2 has a distinctly electropositive surface capable of interacting with the membrane, while the opposite surface is largely electronegative. GOX shows no such pattern. MDH appears to form a new class of monotopic integral membrane protein that interacts with the membrane through coplanar electrostatic binding surfaces and hydrophobic interactions, thus combining features of both the prostaglandin synthase/squaline-hopine cyclase and the C-2 coagulation factor domain classes of membrane proteins.

Extensive efforts have been devoted to determining the binding specificity of Src homology 3 (SH3) domains usually in a case-by-case manner. A generic structure-based model is necessary to decipher the protein recognition code of the entire domain family. In this study, we have developed a general framework that combines molecular modeling and a machine learning algorithm to capture the energetic characteristics of the domain-peptide interactions and predict the binding specificity of the SH3 domain family. Our model is not trained for individual SH3 domains; rather it is a generic model for the entire domain family. Our model not only achieved satisfactory prediction accuracy but also provided structural insights into which residues are important for the binding specificity 1 domain (4) that recognizes proline-rich peptides with a core motif of PXXP (P is a proline and X is any amino acid) (5, 6). Peptides can bind to SH3 domains in two opposite orientations and are referred as class I and II peptides, which often contain ϩXXPXXP and PXXPXϩ (where X refers to any residue and ϩ refers to a positively charged residue) motifs, respectively. The binding specificity of an SH3 domain is determined by the amino acids in the flanking regions of the core motif, which has been investigated extensively for individual domains. However, a universal model was lacking to decipher the protein recognition code of the SH3 domain family.A generic model for the entire domain family needs to 1) provide a general framework to characterize the domainpeptide interaction and 2) reliably predict the binding specificity of each member in the domain family. Previous experimental and computational studies can only satisfy one of these requirements. For example, peptide library and peptide or protein array technologies are commonly used to determine the peptide motifs recognized by a domain, often represented as a position-specific scoring matrix (7-13). These approaches have limited coverage of the peptide space because the peptides tested in the experiments usually only represent a small portion of all the possible peptides of a given length. In addition, the prediction power of a sequence motif on interacting partners of a domain is often unsatisfactory. Along that line, a survey of protein-protein interaction interfaces (14) also suggested that a sophisticated model, rather than a set of well defined rules, is needed to decipher the specificity of protein recognition.On the other hand, high throughput technologies, such as yeast two-hybrid assay and complex purification followed by mass spectrometry, have been used to identify protein-protein interactions. However, these methods often miss the weak and transient domain-peptide interactions (15). Various computational methods have also been developed to predict the interacting partners of modular domains (16 -20). For example, the SH3-SPOT method builds a position-specific contact frequency matrix based on the protein-peptide contacts in a number of crystal structures of SH3-peptide and ...