Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Quantitative Proteomic Analysis of Host-virus Interactions Reveals a Role for Golgi Brefeldin A Resistance Factor 1 (GBF1) in Dengue Infection

Dengue virus is considered to be the most important mosquito-borne virus worldwide and poses formidable economic and health care burdens on many tropical and subtropical countries. Dengue infection induces drastic rearrangement of host endoplasmic reticulum membranes into complex membranous structures housing replication complexes; the contribution(s) of host proteins and pathways to this process is poorly understood but is likely to be mediated by protein-protein interactions. We have developed an approach for obtaining high confidence protein-protein interaction data by employing affinity tags and quantitative proteomics, in the context of viral infection, followed by robust statistical analysis. Using this approach, we identified high confidence interactors of NS5, the viral polymerase, and NS3, the helicase/protease. Quantitative proteomics allowed us to exclude a large number of presumably nonspecific interactors from our data sets and imparted a high level of confidence to our resulting data sets. We identified 53 host proteins reproducibly associated with NS5 and 41 with NS3, with 13 of these candidates present in both data sets. The host factors identified have diverse functions, including retrograde Golgi-to-endoplasmic reticulum transport, biosynthesis of long-chain fatty-acyl-coenzyme As, and in the unfolded protein response. We selected GBF1, a guanine nucleotide exchange factor responsible for ARF activation, from the NS5 data set for follow up and functional validation. We show that GBF1 plays a critical role early in dengue infection that is independent of its role in the maintenance of Golgi structure. Importantly, the approach described here can be applied to virtually any organism/system as a tool for better understanding its molecular interactions.Viruses modify the intracellular environment of infected host cells in a number of important ways, including subverting the antiviral response, reorganizing host membranes, and manipulating host signaling pathways to create an environment more favorable for infection. For example, some viral proteins co-opt host proteins to degrade host interferon signaling components, thus antagonizing the antiviral response (1, 2); other viral proteins recruit metabolic enzymes that are potentially involved in the biogenesis of replication complexes (RCs)¹ (3); and some viral proteins interact with host regulatory proteins to block the cellular stress response (4). These examples illustrate only a few of the ways in which viral-host protein-protein interactions (PPIs) enable the viral life cycle and drive pathogenicity. Because of the limited coding capacity of many viral genomes, in particular RNA virus genomes, viral-host PPIs generally occur between a remarkably small number of viral proteins and a much larger number of host proteins (5). The study of these extensive interactions necessitates comprehensive and quantitative approaches, the development and validation of which will potentially contribute to: 1) our understanding of the mechanisms by which viruses subvert cellular pathways to their own advantage; 2) our understanding of fundamental cell biology; 3) the choice of potential drug targets and the rational design of such drugs; and 4) our understanding of the host response to infection.Dengue virus (DENV) is a positive-sense, single stranded RNA virus in the family Flaviviridae that is transmitted by the bite of an infected Aedes mosquito (6). DENV is an important emerging pathogen that is the causative agent of dengue fever, dengue hemorrhagic fever, and dengue shock syndrome, diseases which cumulatively pose formidable economic and health care burdens in many tropical and subtropical countries worldwide (7). Recent estimates of the global burden of DENV infection have revealed that DENV infection is ∼threefold more prevalent than previously estimated, with ∼400 million annual incidences worldwide (8). Moreover, development of an anti-DENV vaccine has been hindered by the existence of four antigenically distinct DENV serotypes (DENV-1, -2, -3, and -4), each of which is capable of producing the full spectrum of DENV-induced disease (9). DENV is also related to other flaviviruses that cause significant human disease, including yellow fever virus, West Nile virus, and Japanese encephalitis virus (10). Thus, insights into DENV biology may be applicable to other flaviviruses of medical importance.The flavivirus genome encodes only three structural (C, pr/M, and E) and seven nonstructural (NS) proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5), and is translated as a single polyprotein, which is later cleaved into the mature viral proteins (6). The three structural proteins, capsid (C), membrane (M), and envelope (E) comprise the virion, whereas the NS proteins are mainly responsible for carrying out genome replication in infected cells. Among the seven NS proteins, NS5 and NS3 are the two largest and most highly conserved proteins (11); moreover, each possesses multiple enzymatic activities. NS5 contains an RNA-dependent RNA polymerase domain as well as a nucleoside-2′-O-methyltransferase domain; both of these activities are essential for replication (12, 13). NS3, on the other hand, possesses an N-terminal serine protease domain, which is responsible for cleaving the viral polyprotein at several sites (along with its cofactor, NS2B) (14). The C-terminal domain of NS3 has 5′ RNA triphosphatase, nucleoside triphosphatase, and helicase activities (15–17). NS5 and NS3 have been shown to interact in infected cells (18), most likely in the RC. The precise composition and biogenesis mechanisms of RCs are poorly understood, but likely involve host proteins as well as viral proteins. As with other viruses, DENV-host PPIs have been interrogated by a number of high-throughput yeast two-hybrid assays (19–31) and approaches coupling either affinity purification (AP), immunoprecipitation, or immunoaffinity purification (IP) with MS (32–35). These approaches have yielded a number of putative DENV-host PPIs; however, considering the large repertoire of interactions undertaken by other viruses (36–41), our knowledge of DENV-host PPIs is likely incomplete. One advantage of IP/MS approaches is their potential to comprehensively reveal bona fide time-resolved interactions from the environment of an infected cell; however, the extremely high sensitivity of modern mass spectrometers highlights the need to develop IP/MS workflows capable of reliably discriminating between genuine interactors and nonspecific contaminants (42). Here, we present a workflow incorporating immunoaffinity purification and quantitative proteomics from infected cells, followed by robust statistical analysis to identify high confidence interactors of virtually any protein of interest, and apply this workflow to DENV NS5 and NS3.