Frequency Of Amino Acid Residues Research Articles

Computational Analysis Predicts Correlations among Amino Acids in SARS-CoV-2 Proteomes.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious global challenge requiring urgent and permanent therapeutic solutions. These solutions can only be engineered if the patterns and rate of mutations of the virus can be elucidated. Predicting mutations and the structure of proteins based on these mutations have become necessary for early drug and vaccine design purposes in anticipation of future viral mutations. The amino acid composition (AAC) of proteomes and individual viral proteins provide avenues for exploitation since AACs have been previously used to predict structure, shape and evolutionary rates. Herein, the frequency of amino acid residues found in 1637 complete proteomes belonging to 11 SARS-CoV-2 variants/lineages were analyzed. Leucine is the most abundant amino acid residue in the SARS-CoV-2 with an average AAC of 9.658% while tryptophan had the least abundance of 1.11%. The AAC and ranking of lysine and glycine varied in the proteome. For some variants, glycine had higher frequency and AAC than lysine and vice versa in other variants. Tryptophan was also observed to be the most intolerant to mutation in the various proteomes for the variants used. A correlogram revealed a very strong correlation of 0.999992 between B.1.525 (Eta) and B.1.526 (Iota) variants. Furthermore, isoleucine and threonine were observed to have a very strong negative correlation of -0.912, while cysteine and isoleucine had a very strong positive correlation of 0.835 at p < 0.001. Shapiro-Wilk normality test revealed that AAC values for all the amino acid residues except methionine showed no evidence of non-normality at p < 0.05. Thus, AACs of SARS-CoV-2 variants can be predicted using probability and z-scores. AACs may be beneficial in classifying viral strains, predicting viral disease types, members of protein families, protein interactions and for diagnostic purposes. They may also be used as a feature along with other crucial factors in machine-learning based algorithms to predict viral mutations. These mutation-predicting algorithms may help in developing effective therapeutics and vaccines for SARS-CoV-2.

Computational studies suggest compounds restoring function of p53 cancer mutants can bind SARS-CoV-2 spike protein

It is reasonable to think that cancer patients undergoing chemotherapy or immunotherapy may have a more aggressive course if they are positive for the novel coronavirus disease. Their compulsive condition requires investigation into effective drugs. We applied computational techniques to a series of compounds known for restoring the function of p53 cancer mutant p53R175H and p53G245S. Two potent inhibitors, 1-(3-chlorophenyl)-3-(1, 3 -thiazol-2-yl) urea (CTU, PubChem NSC321792) with the highest binding affinity −6.92 kcal/mol followed by a thiosemicarbazone compound N’-(1-(Pyridin-2-yl)ethylidene) azetidine − 1 -carbothiohydrazide (NPC, PubChem NSC319726) with −6.75 kcal/mol were subjected to Molecular Dynamics simulation with receptor binding domain (RBD) and compared with control ligand dexamethasone. In particular, CTU adheres to pocket 1 with an average free energy of binding −21.65 2.89 kcal/mol at the RBD - angiotensin-converting enzyme 2 binding region with the highest frequency of amino acid residues after reaching a local equilibrium in 100 ns MD simulation trajectory. A significant enthalpy contribution from the independent simulations unfolds the possibility of dual binding sites for NPC as shifted pocket 1 (−15.59 5.98 kcal/mol) and pocket 2 (−18.90 5.02 kcal/mol). The obtained results for these two compounds are in good agreement with dexamethasone (−18.45 2.42 kcal/mol). Taken together our findings could facilitate the discovery of small molecules that restore the function of p53 cancer mutants newly against COVID-19 in cancer patients.

Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria

There is a pressing need for new therapeutics to combat multidrug- and carbapenem-resistant bacterial pathogens. This challenge prompted us to use a long short-term memory (LSTM) language model to understand the underlying grammar, i.e. the arrangement and frequencies of amino acid residues, in known antimicrobial peptide sequences. According to the output of our LSTM network, we synthesized 10 peptides and tested them against known bacterial pathogens. All of these peptides displayed broad-spectrum antimicrobial activity, validating our LSTM-based peptide design approach. Our two most effective antimicrobial peptides displayed activity against multidrug-resistant clinical isolates of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and coagulase-negative staphylococci strains. High activity against extended-spectrum β-lactamase, methicillin-resistant S. aureus, and carbapenem-resistant strains was also observed. Our peptides selectively interacted with and disrupted bacterial cell membranes and caused secondary gene-regulatory effects. Initial structural characterization revealed that our most effective peptide appeared to be well folded. We conclude that our LSTM-based peptide design approach appears to have correctly deciphered the underlying grammar of antimicrobial peptide sequences, as demonstrated by the experimentally observed efficacy of our designed peptides.

Use of AAScatterPlot tool for monitoring the evolution of the hemagglutinin cleavage site in H9 avian influenza viruses.

Viruses rapidly evolve due to their error-prone genome replication, and identifying which mutations are selected for during evolution is critical for virus surveillance efforts. Here we introduce a scatter plot tool (AAScatterPlot) that easily shows the selection and avoidance of certain protein mutations based on biochemical properties. We demonstrate its utility for monitoring the evolution of H9 avian influenza viruses from China between 2005 and 2015, particularly at the hemagglutinin (HA) proteolytic cleavage site (PCS) that can affect virus activation and pathogenicity. Given genome sequences, the AAScatterPlot tool compacts into a single plot, information about the hydropathy index, Van der Waals volume, chemical property and occurrence frequency of amino acid residues. The tool also shows the range of residues that could arise from a single point mutation in the genome, which can then be compared against the observed residues to identify mutation constraints. Through this approach, we found that the 2nd position towards the N-terminus side of the HA PCS (P2 position) avoided hydrophobic residues, whereas the P3 position avoided hydrophilic residues. AAScatterPlot is available at https://github.com/WhittakerLab/AAScatterPlot. gary.whittaker@cornell.edu. Supplementary data are available at Bioinformatics online.

Relationship between structure and amino acid sequence of strongly twisted and coiled β-hairpins in globular proteins

β-Hairpins are widespread in proteins, and it is possible to find them both within β-sheets and separately. In this work, a comparative analysis of amino acid sequences of β-strands within strongly twisted β-hairpins from different structural protein subclasses has been conducted. Strongly twisted and coiled β-hairpin generates in the space a right double helix out of β-strands that are connected by a loop region (connections). The frequencies of amino acid residues on the internal (concave) and external (convex) surfaces of strongly twisted β-hairpins have been determined (220 β-hairpins from nonhomologous proteins were studied). The concave surface of these β-hairpins is mainly generated by hydrophobic residues, while the convex surface by hydrophilic residues; accordingly, the alternation of hydrophobic internal and hydrophilic external residues is observed in their amino acid sequences. Amino acid residues of glycine and alanine (especially in places of the largest twisting of the strands) were anomalously frequently found in internal positions of strongly twisted and coiled β-hairpins. It was established that internal positions never contain the proline residues, while external positions in the twisting region contain them in a relatively large amount. It was demonstrated that at least one amino acid residue in αL- or ε-conformation is required for generation of relatively short (up to 7 amino acid residues) connection. As a rule, these positions are occupied by glycines. Thus, not only the alternation of hydrophobic and hydrophilic amino acid residues, but also the presence of one or two glycine residues in the connection region and the excess of glycines and alanines in the places of the largest strand twisting on the concave surface, as well as the presence of prolines on the convex surface, are required to generate a strongly twisted and coiled β-hairpin.

Characterization of an exo-inulinase from Arthrobacter: A novel NaCl-tolerant exo-inulinase with high molecular mass

A glycoside hydrolase family 32 exo-inulinase gene was cloned from Arthrobacter sp. HJ7 isolated from saline soil located in Heijing town. The gene encodes an 892-residue polypeptide with a calculated mass of 95.1 kDa and a high total frequency of amino acid residues G, A, and V (30.0%). Escherichia coli BL21 (DE3) cells were used as hosts to express the exo-inulinase gene. The recombinant exo-inulinase (rInuAHJ7) showed an apparently maximal activity at pH 5.0–5.5 and 40–45°C. The addition of 1.0 and 10.0 mM Zn2+ and Pb2+ had little or no effect on the enzyme activity. rInuAHJ7 exhibited good salt tolerance, retaining more than 98% inulinase activity at a concentration of 3.0%–20.0% (w/v) NaCl. Fructose was the main product of inulin, levan, and Jerusalem artichoke tubers hydrolyzed by the enzyme. The present study is the first to report the identification and characterization of an Arthrobacter sp exo-inulinase showing a high molecular mass of 95.1 kDa and NaCl tolerance. These results suggest that the exo-inulinase might be an alternative material for potential applications in processing seafood and other foods with high saline contents, such as marine algae, pickles, and sauces.

Peptide‐binding motifs and characteristics for HLA ‐B*13:01 molecule

Trichloroethylene (TCE)-induced hypersensitivity dermatitis is one of the critical occupational diseases among workers in China. Our previous studies have identified a strong linkage between the disease and the HLA-B*13:01 allele. In this study, we searched for peptides bound to the HLA-B*13:01 molecule; 57 HLA-B*13:01-bound peptides in total were identified and 54 peptides were used to calculate frequency of amino acid residues to obtain binding motifs of HLA-B*13:01 molecule. The results showed P2, P3, and P9 were the primary binding anchor positions with the dominant anchor motifs of L, Q at P2, L at P9, D at P3. HLA-B*13:01-bound peptides were identified for the first time in our research, the results of which could contribute to the human leukocyte antigen (HLA)-binding peptides database.

Classification of Bacteria Based on the Biases of Terminal Amino Acid Residues

The frequencies of amino acid residues are known to be biased at both terminal regions of amino acid sequences deduced from bacterial genomic DNA. To investigate whether or not the features of biases of amino acid residues at the terminal regions are related to the bacterial phylogeny, we calculated the normalized amino acid compositions at both terminal regions, and used these compositions to classify 144 bacteria by hierarchical clustering analysis. Our results showed that most of these bacteria were classified into taxonomic classes by the hierarchical clustering analysis that was based on the normalized amino acid compositions at the N-terminal region. Therefore, we concluded that the features of biases of the N-terminal amino acid residues were related to the bacterial phylogeny.

Bacterial proteins fold faster than eukaryotic proteins with simple folding kinetics

Protein domain frequency and distribution among kingdoms was statistically analyzed using the SCOP structural database. It appeared that among chosen protein domains with the best resolution, eukaryotic proteins more often belong to α-helical and β-structural proteins, while proteins of bacterial origin belong to α/β structural class. Statistical analysis of folding rates of 73 proteins with known experimental data revealed that bacterial proteins with simple kinetics (23 proteins) exhibit a higher folding rate compared to eukaryotic proteins with simple folding kinetics (27 proteins). Analysis of protein domain amino acid composition showed that the frequency of amino acid residues in proteins of eukaryotic and bacterial origin is different for proteins with simple and complex folding kinetics.

HIV-1 Subtype C Phylodynamics in the Global Epidemic

The diversity of HIV-1 and its propensity to generate escape mutants present fundamental challenges to control efforts, including HIV vaccine design. Intra-host diversification of HIV is determined by immune responses elicited by an HIV-infected individual over the course of the infection. Complex and dynamic patterns of transmission of HIV lead to an even more complex population viral diversity over time, thus presenting enormous challenges to vaccine development. To address inter-patient viral evolution over time, a set of 653 unique HIV-1 subtype C gag sequences were retrieved from the LANL HIV Database, grouped by sampling year as <2000, 2000, 2001–2002, 2003, and 2004–2006, and analyzed for the site-specific frequency of translated amino acid residues. Phylogenetic analysis revealed that a total of 289 out of 653 (44.3%) analyzed sequences were found within 16 clusters defined by aLRT of more than 0.90. Median (IQR) inter-sample diversity of analyzed gag sequences was 8.7% (7.7%; 9.8%). Despite the heterogeneous origins of analyzed sequences, the gamut and frequency of amino acid residues in wild-type Gag were remarkably stable over the last decade of the HIV-1 subtype C epidemic. The vast majority of amino acid residues demonstrated minor frequency fluctuation over time, consistent with the conservative nature of the HIV-1 Gag protein. Only 4.0% (20 out of 500; HXB2 numbering) amino acid residues across Gag displayed both statistically significant (p<0.05 by both a trend test and heterogeneity test) changes in amino acid frequency over time as well as a range of at least 10% in the frequency of the major amino acid. A total of 59.2% of amino acid residues with changing frequency of 10%+ were found within previously identified CTL epitopes. The time of the most recent common ancestor of the HIV-1 subtype C was dated to around 1950 (95% HPD from 1928 to 1962). This study provides evidence for the overall stability of HIV-1 subtype C Gag among viruses circulating in the epidemic over the last decade. However selected sites across HIV-1C Gag with changing amino acid frequency are likely to be under selection pressure at the population level.

Substrate Recognition of Anthrax Lethal Factor Examined by Combinatorial and Pre-steady-state Kinetic Approaches

Lethal factor (LF), a zinc-dependent protease of high specificity produced by Bacillus anthracis, is the effector component of the binary toxin that causes death in anthrax. New therapeutics targeting the toxin are required to reduce systemic anthrax-related fatalities. In particular, new insights into the LF catalytic mechanism will be useful for the development of LF inhibitors. We evaluated the minimal length required for formation of bona fide LF substrates using substrate phage display. Phage-based selection yielded a substrate that is cleaved seven times more efficiently by LF than the peptide targeted in the protein kinase MKK6. Site-directed mutagenesis within the metal-binding site in the LF active center and within phage-selected substrates revealed a complex pattern of LF-substrate interactions. The elementary steps of LF-mediated proteolysis were resolved by the stopped-flow technique. Pre-steady-state kinetics of LF proteolysis followed a four-step mechanism as follows: initial substrate binding, rearrangement of the enzyme-substrate complex, a rate-limiting cleavage step, and product release. Examination of LF interactions with metal ions revealed an unexpected activation of the protease by Ca(2+) and Mn(2+). Based on the available structural and kinetic data, we propose a model for LF-substrate interaction. Resolution of the kinetic and structural parameters governing LF activity may be exploited to design new LF inhibitors.

Detection of low-frequency pretherapy chemokine (CXC motif) receptor 4 (CXCR4)-using HIV-1 with ultra-deep pyrosequencing

Identification of low-frequency variants is of clinical importance in the identification of preexisting drug resistance. Using 'ultra-deep' sequencing, we address the detection of potential resistance to the chemokine (C-C motif) receptor 5 (CCR5) antagonist, maraviroc, due to the pretreatment presence of low levels of chemokine (CXC motif) receptor 4 (CXCR4)-using virus. We present a novel protocol for the phenotyping of HIV based on '454' pyrosequence data and apply this to two large data sets comprised of 104 628 (before treatment, day 1) and 191 637 (after treatment, day 11) reads from the envelope region. We study resistance in the context of the evolutionary history of the intrapatient viral population. Variation was also investigated both within and outside the V3 region, the region associated with the receptor switch. CXCR4-using virus can be detected at low frequency prior to maraviroc treatment ( approximately 0.5%) and at high frequency after failure of monotherapy ( approximately 81%). Inferring an evolutionary tree from the 1674 unique reads that span the V3 region confirms that the CXCR4-using population emerged from low-frequency CXCR4-using variants present before treatment. Changes in the frequency of amino acid residues used at individual sites were found in regions outside the V3 region, indicative of other potential sites associated with receptor usage. We have provided a high-resolution snapshot of intrapatient viral variation, prior and after treatment with maraviroc, and detected preexisting CXCR4-using variants present at an extremely low frequency. The evolutionary analysis demonstrates the extent of diversity present at a single time point within an infected individual and the rapid effect of drug pressure on the structure of a viral population.

Evolutionarily Conserved Allosteric Network in the Cys Loop Family of Ligand-gated Ion Channels Revealed by Statistical Covariance Analyses

The Cys loop family of ligand-gated ion channels mediate fast synaptic transmission for communication between neurons. They are allosteric proteins, in which binding of a neurotransmitter to its binding site in the extracellular amino-terminal domain triggers structural changes in distant transmembrane domains to open a channel for ion flow. Although the locations of binding site and channel gating machinery are well defined, the structural basis of the activation pathway coupling binding and channel opening remains to be determined. In this paper, by analyzing amino acid covariance in a multiple sequence alignment, we have identified an energetically interconnected network in the Cys loop family of ligand-gated ion channels. Statistical coupling and correlated mutational analyses along with clustering revealed a highly coupled cluster. Mapping the positions in the cluster onto a three-dimensional structural model demonstrated that these highly coupled positions form an interconnected network linking experimentally identified binding domains through the coupling region to the gating machinery. In addition, these highly coupled positions are also condensed in the transmembrane domains, which are a recent focus for the sites of action of many allosteric modulators. Thus, our results revealed a genetically interconnected network that potentially plays an important role in the allosteric activation and modulation of the Cys loop family of ligand-gated ion channels.

Life on Earth. Extremophiles Continue to Move the Goal Posts

Environmental Context.Since the discovery of a diverse array of microbial life associated with hydrothermal vents on the ocean floor, where conditions are hot, reducing and acidic, scientists have been seeking insight into the mechanisms used by ‘extremophilic’ organisms (those that reside permanently under environmental extremes of temperature (hot or cold), pH (acid or alkaline), salinity, or pressure) to thrive under such seemingly inhospitable conditions. Abstract.The discovery of an abundance of microorganisms that flourish in a diverse range of environments, from the frigid waters of the Antarctic, to the superheated waters of the hydrothermal vents, at the bottom of 11-km deep ocean trenches and in salt-saturated lakes, has fuelled research aimed to understand the novel survival strategies evolved by these extreme-loving (extremophilic) organisms. Adaptations of biomolecules (proteins, nucleic acids, membranes and small molecules) evolved by extremophiles are wide ranging. Compared with a protein from a ‘regular’ organism, the extremophilic analogue might feature changes to the relative frequencies of amino acid residues that modulate the properties (e.g. conformational flexibility and stability) of the protein under conditions of the specific environmental challenge. The integrity of RNA and DNA from extremophiles may be maintained by subtle structural changes to RNA nucleobases and, in the case of (hyper)thermophiles, the expression of the enzyme reverse gyrase, which catalyses positive DNA supercoiling. The expression of small molecular weight heat-shock or related caretaker proteins also features as a common adaptive strategy for maintaining cell viability at environmental extremes. Membrane architecture in extremophiles can be modulated by the environmental temperature, with additional thermal stability in membranes from some hyperthermophiles conferred by novel (cyclised) lipid chains. In addition, a selection of osmolytes and small molecules are biosynthesised or sequestered by extremophilic organisms that have adapted to conditions of high salt and/or micronutrient deprivation.

Use of Variable Selection in Modeling the Secondary Structural Content of Proteins from Their Composition of Amino Acid Residues.

The possibility of prediction of protein secondary structure content from composition of their amino acid residues can help in bridging the gap between proteins of known primary sequence having an unknown secondary structure. Almost all recently published models for understanding the relationship between composition (frequency of occurrence) of amino acid residues and secondary structure content of proteins involved composition of all 20 amino acid residues. However, it is well-known that many amino acid residues are mutually similar according to their physicochemical properties (hydrophobicity, hydrophilicity, charge, size, etc.). Because of that, we were motivated to investigate the possibility of reduction of the total number of terms (frequencies of amino acid residues) in the models for describing the relation between the composition of amino acid residues and the percentage of residues belonging to alpha, beta, and coil secondary structure. For this purpose, the CROMRsel algorithm (J. Chem. Inf. Comput. Sci. 1999, 39, 121-132) for selection of a small subset of the most important variables/descriptors into the multiregression (MR) models, i.e., frequency of occurrence of amino acid residues in proteins, was used. Analysis was performed on a data set containing 475 proteins, taken from Proteins 1996, 25, 157-168. A complete data set was partitioned into a 317-protein training set and 158-protein test set. The best possible linear models containing I=1, ..., 20 frequencies were selected among all 20 frequencies of occurrence of amino acid residues on the 317-protein training set, and were used for performing prediction of the corresponding percentage of secondary structure content on the 158-protein test set. For the 317-protein data set the best selected concise models for the alpha, beta, and coil secondary structure contain only 9, 5, and 8 frequencies, respectively. Selected concise models are of the same or better fitted, cross-validated, and predictive statistical parameters than the models containing all 20 frequencies. Additionally, for each I (I=1, ...., 20) 30 the best possible random models were selected. In each case, the best possible real models are much better than each of the best possible random models, showing clearly that there is no risk of a chance correlation (what one could expect due to the application of an exhaustive search for the best model having I frequencies among all 20!/I!(20-I)! possible models). Finally, the best selected models on the complete 475-protein data set for the alpha, beta, and coil secondary structure contain only 7, 4, and 7 frequencies of amino acid residues, respectively. These models are much simpler and have better fitted and cross-validated errors than the corresponding models from the literature, that were obtained without using a procedure for selection of the most important frequencies of amino acid residues in proteins.

Use of variable selection in modeling the secondary structural content of proteins from their composition of amino acid residues.

The possibility of prediction of protein secondary structure content from composition of their amino acid residues can help in bridging the gap between proteins of known primary sequence having an unknown secondary structure. Almost all recently published models for understanding the relationship between composition (frequency of occurrence) of amino acid residues and secondary structure content of proteins involved composition of all 20 amino acid residues. However, it is well-known that many amino acid residues are mutually similar according to their physicochemical properties (hydrophobicity, hydrophilicity, charge, size, etc.). Because of that, we were motivated to investigate the possibility of reduction of the total number of terms (frequencies of amino acid residues) in the models for describing the relation between the composition of amino acid residues and the percentage of residues belonging to alpha, beta, and coil secondary structure. For this purpose, the CROMRsel algorithm (J. Chem. Inf. Comput. Sci. 1999, 39, 121-132) for selection of a small subset of the most important variables/descriptors into the multiregression (MR) models, i.e., frequency of occurrence of amino acid residues in proteins, was used. Analysis was performed on a data set containing 475 proteins, taken from Proteins 1996, 25, 157-168. A complete data set was partitioned into a 317-protein training set and 158-protein test set. The best possible linear models containing I=1, ..., 20 frequencies were selected among all 20 frequencies of occurrence of amino acid residues on the 317-protein training set, and were used for performing prediction of the corresponding percentage of secondary structure content on the 158-protein test set. For the 317-protein data set the best selected concise models for the alpha, beta, and coil secondary structure contain only 9, 5, and 8 frequencies, respectively. Selected concise models are of the same or better fitted, cross-validated, and predictive statistical parameters than the models containing all 20 frequencies. Additionally, for each I (I=1, ...., 20) 30 the best possible random models were selected. In each case, the best possible real models are much better than each of the best possible random models, showing clearly that there is no risk of a chance correlation (what one could expect due to the application of an exhaustive search for the best model having I frequencies among all 20!/I!(20-I)! possible models). Finally, the best selected models on the complete 475-protein data set for the alpha, beta, and coil secondary structure contain only 7, 4, and 7 frequencies of amino acid residues, respectively. These models are much simpler and have better fitted and cross-validated errors than the corresponding models from the literature, that were obtained without using a procedure for selection of the most important frequencies of amino acid residues in proteins.

Predicting Reliable Regions in Protein Alignments from Sequence Profiles

For applications such as comparative modelling one major issue is the reliability of sequence alignments. Reliable regions in alignments can be predicted using sub-optimal alignments of the same pair of sequences. Here we show that reliable regions in alignments can also be predicted from multiple sequence profile information alone. Alignments were created for a set of remotely related pairs of proteins using five different test methods. Structural alignments were used to assess the quality of the alignments and the aligned positions were scored using information from the observed frequencies of amino acid residues in sequence profiles pre-generated for each template structure. High-scoring regions of these profile-derived alignment scores were a good predictor of reliably aligned regions. These profile-derived alignment scores are easy to obtain and are applicable to any alignment method. They can be used to detect those regions of alignments that are reliably aligned and to help predict the quality of an alignment. For those residues within secondary structure elements, the regions predicted as reliably aligned agreed with the structural alignments for between 92% and 97.4% of the residues. In loop regions just under 92% of the residues predicted to be reliable agreed with the structural alignments. The percentage of residues predicted as reliable ranged from 32.1% for helix residues to 52.8% for strand residues. This information could also be used to help predict conserved binding sites from sequence alignments. Residues in the template that were identified as binding sites, that aligned to an identical amino acid residue and where the sequence alignment agreed with the structural alignment were in highly conserved, high scoring regions over 80% of the time. This suggests that many binding sites that are present in both target and template sequences are in sequence-conserved regions and that there is the possibility of translating reliability to binding site prediction.

On the Informational Content of Overlapping Genes in Prokaryotic and Eukaryotic Viruses

. In genetic language a peculiar arrangement of biological information is provided by overlapping genes in which the same region of DNA can code for functionally unrelated messages. In this work, the informational content of overlapping genes belonging to prokaryotic and eukaryotic viruses was analyzed. Using information theory indices, we identified in the regions of overlap a first pattern, exhibiting a more uniform base composition and more severe constraints in base ordering with respect to the nonoverlapping regions. This pattern was found to be peculiar to coliphage, avian hepatitis B virus, human lentivirus, and plant luteovirus families. A second pattern, characterized by the occurrence of similar compositional constraints in both types of coding regions, was found to be limited to plant tymoviruses. At the level of codon usage, a low degree of correlation between overlapping and nonoverlapping coding regions characterized the first pattern, whereas a close link was found in tymoviruses, indicating a fine adaptation of the overlapping frame to the original codon choice of the virus. As a result of codon usage correlation analysis, deductions concerning the origin and evolution of several overlapping frames were also proposed. Comparison of amino acid composition revealed an increased frequency of amino acid residues with a high level of degeneracy (arginine, leucine, and serine) in the proteins encoded by overlapping genes; this peculiar feature of overlapping genes can be viewed as a way with which they may expand their coding ability and gain new, specialized functions.

Refined Structure of the Porin from Rhodopseudomonas blastica: Comparison with the Porin from Rhodobacter capsulatus

The structure of the membrane channel porin from the phototrophic bacteria Rhodopseudomonas blastica has been refined at 1.96 Å resolution yielding an R-factor of 17.6%. The final model consists of all 289 amino acid residues, 247 water molecules and three detergent molecules modelled as n -octyltetraoxyethylene. One of these detergent molecules binds together with its two symmetry-related molecules tightly in a pocket at the molecular 3-fold axis. This pocket may bind three alkyl chains of a lipopolysaccharide which in turn would stabilize the trimer and could possibly play a rôle in membrane insertion. The overall shape of this porin resembles OmpF of Escherichia coli more than the only known sequence-related porin from Rhodobacter capsulatus . The membrane contacting surface is similar in all structurally known porins; it shows exceptional frequencies of amino acid residues and side-chain rotamers. The 46-residue loop β5-β6 of the porin is shown to be tightly fastened to the β-barrel, excluding an in vivo loop movement that closes the pore. The trimer interface region has the structure of a water-soluble protein with an extensive non-polar core and numerous hydrogen bonds at the surface. The loops at the external end of the barrel are long and rigid whereas those at the periplasmic barrel end are short and mobile. The crystal packing is discussed.

Predicting the Probability of Helper T Cell Immunodominant Sites through Discriminant Analysis

Bayesian discriminant analysis is used to predict whether or not a given protein segment will activate helper T cells. The predictor variables are drawn from the products of frequencies of amino acid residues. The model's predictive validity compares favourably with that of alternative modelling strategies, suggesting that this approach merits further investigation.