HIV-1 evolves rapidly, which is thought to result from one or more error-prone steps in the virus life cycle. Because HIV-1 reverse transcriptase (RT) does not possess 3-to 5-exonucleolytic proofreading activity and because RT has been shown to be error-prone in cell free systems, it should be an important contributor to the high rate of HIV-1 mutation. However, because RNA polymerase II (pol II) synthesizes viral RNA, it might also contribute significantly to HIV-1 mutagenesis. To assess the relative contributions of RT and RNA pol II to HIV-1 mutagenesis, a system was established to study the rate and nature of mutations in HIV-1 long terminal repeats (LTRs). Owing to the unique nature of replication at the ends of the viral genome, mutational analysis of retroviral LTRs provides an opportunity to evaluate the relative contribution of HIV-1 RT and RNA pol II to viral mutagenesis. Mutational analysis was performed on both LTRs of 215 proviruses, restricted to a single cycle of replication, employing single-stranded conformational polymorphism and DNA sequencing allowing direct identification of mutations in the absence of selection and within autologous viral sequences. A total of 21 independent mutations was identified. Ten mutations were observed in both LTRs, which could have been introduced by either RT or RNA pol II, whereas the other eleven mutations were only present in a single LTR and could only have been introduced by RT. This provides the first direct evidence that HIV-1 RT contributes significantly to HIV-1 mutagenesis and is likely to be the primary engine for HIV-1 mutagenesis. Moreover, mutations were observed at the U3-R border, but the nature of the mutations and their frequency differed from experiments performed using cell-free systems suggesting that other viral and/or cellular factors contribute to fidelity at the ends of the viral genome.

β-barrel membrane proteins are found in the outer membranes of mitochondria, chloroplasts, and Gram-negative bacteria; however, exactly how they are folded and inserted remains unknown. Over the past decade, both functional and structural studies have greatly contributed to addressing this elusive mechanism. It is known that a conserved core machinery is required for each organelle, though, the overall composition varies significantly. The vast majority of studies to understand the biogenesis of β-barrel membrane proteins has been done in Gram-negative bacteria. Here, it is the task of a multi-component complex known as the β-barrel assembly machinery (BAM) complex to fold and insert new β-barrel membrane proteins into the outer membrane. In this review, we will discuss recent discoveries with the goal of utilizing all reported structural and functional studies to piece together a current structural model for the fully assembled BAM complex.

Protein trafficking across membranes is an essential function in cells; however, the exact mechanism for how this occurs is not well understood. In the endosymbionts, mitochondria and chloroplasts, the vast majority of proteins are synthesized in the cytoplasm as preproteins and then imported into the organelles via specialized machineries. In chloroplasts, protein import is accomplished by the TOC (translocon on the outer chloroplast membrane) and TIC (translocon on the inner chloroplast membrane) machineries in the outer and inner envelope membranes, respectively. TOC mediates initial recognition of preproteins at the outer membrane and includes a core membrane channel, Toc75, and two receptor proteins, Toc33/34 and Toc159, each containing GTPase domains that control preprotein binding and translocation. Toc75 is predicted to have a β-barrel fold consisting of an N-terminal intermembrane space (IMS) domain and a C-terminal 16-stranded β-barrel domain. Here we report the crystal structure of the N-terminal IMS domain of Toc75 from Arabidopsis thaliana, revealing three tandem polypeptide transportassociated (POTRA) domains, with POTRA2 containing an additional elongated helix not observed previously in other POTRA domains. Functional studies show an interaction with the preprotein, preSSU, which is mediated through POTRA2-3. POTRA2-3 also was found to have chaperone-like activity in an insulin aggregation assay, which we propose facilitates preprotein import. Our data suggest a model in which the POTRA domains serve as a binding site for the preprotein as it emerges from the Toc75 channel and provide a chaperonelike activity to prevent misfolding or aggregation as the preprotein traverses the intermembrane space.protein import | chloroplast | outer membrane | Toc75 | POTRA domain

During studies examining the rate of human immunodeficiency virus type 1 (HIV-1) mutation in a single cycle of replication, the 5 long terminal repeat of one progeny provirus was found to contain an insertion of 147 bp including an entire tRNA 3 Lys sequence as well as an additional 66 bp insertion of nonviral origin. Database searches revealed that 65 of 66 bp aligned with the human CpG island sequence found on chromosomes 6, 14, and 17. Therefore it seems probable that it is of human cellular sequence origin and was transduced by HIV-1. This is the first demonstration that HIV-1 can capture a cellular sequence. The site of integration of the parental provirus was mapped to chromosome 1p32.1. Sequence with homology to the transduced CpG island was not found on chromosome 1, suggesting that the transduced cellular sequence was not linked to the site of viral integration.

Emerging high-throughput techniques for the characterization of protein and protein-complex structures yield noisy data with sparse information content, placing a significant burden on computation to properly interpret the experimental data. One such technique uses cross-linking (chemical or by cysteine oxidation) to confirm or select among proposed structural models (e.g., from fold recognition, ab initio prediction, or docking) by testing the consistency between cross-linking data and model geometry. This paper develops a probabilistic framework for analyzing the information content in cross-linking experiments, accounting for anticipated experimental error. This framework supports a mechanism for planning experiments to optimize the information gained. We evaluate potential experiment plans using explicit trade-offs among key properties of practical importance: discriminability, coverage, balance, ambiguity, and cost. We devise a greedy algorithm that considers those properties and, from a large number of combinatorial possibilities, rapidly selects sets of experiments expected to discriminate pairs of models efficiently. In an application to residuespecific chemical cross-linking, we demonstrate the ability of our approach to plan experiments effectively involving combinations of cross-linkers and introduced mutations. We also describe an experiment plan for the bacteriophage Tfa chaperone protein in which we plan dicysteine mutants for discriminating threading models by disulfide formation. Preliminary results from a subset of the planned experiments are consistent and demonstrate the practicality of planning. Our methods provide the experimenter with a valuable tool (available from the authors) for understanding and optimizing cross-linking experiments.Keywords: protein structure prediction; protein-protein complexes; experiment design; cross-linking mass spectrometry; disulfide trapping; structural genomics A growing number of groups have demonstrated the utility of cross-linking for identifying geometric features of protein and complex structure. In contrast to other biophysical techniques, such as FRET (Dong et al. 2000) and EPR spin labeling (Voss et al. 1995;Gaponenko et al. 2000), which yield a number of approximate distances, cross-linking generally provides only the information that some pairs of residues are closer than a maximal cross-linking distance at some point during the reaction. That this information can be sufficient for discriminating among predicted structural models has been demonstrated (Cohen and Sternberg 1980;Swaney 1986;Haniu et al. 1993;Scaloni et al. 1998;Kwaw et al. 2000;Young et al. 2000;Chen et al. 2001;Schilling et al. 2003;Trester-Zedlitz et al. 2003). As illustrated in Figure 1, initial computational analyses provide possible models of a protein's structure, for example, by fold recognition (Godzik 2003;Kurowski and Bujnicki 2003) (Simons et al. 1997;Kihara et al. 2001), or a complex's interaction, for example, by docking (Smith and Sternberg 2002). Specific sites ...