How many membrane proteins are there?

One of the basic issues that arises in functional genomics is the ability to predict the subcellular location of proteins that are deduced from gene and genome sequencing. In particular, one would like to be able to readily specify those proteins that are soluble and those that are inserted in a membrane. Traditional methods of distinguishing between these two locations have relied on extensive, time-consuming biochemical studies. The alternative approach has been to make inferences based on a visual search of the amino acid sequences of presumed gene products for stretches of hydrophobic amino acids. This numerical, sequence-based approach is usually seen as a first approximation pending more reliable biochemical data. The recent availability of large and complete sequence data sets for several organisms allows us to determine just how accurate such a numerical approach could be, and to attempt to minimize and quantify the error involved. We have optimized a statistical approach to protein location determination. Using our approach, we have determined that surprisingly few proteins are misallocated using the numerical method. We also examine the biological implications of the success of this technique.     

The SecY translocation complex: convergence of genetics and structure

All organisms share a requirement for translocation of proteins across membranes. The major mechanism for this process is the universally conserved SecY/Sec61 pathway. Many years of extensive genetic and biochemical analyses identified the components of the SecY/Sec61 pathway, demonstrated that most exported proteins use this route for translocation, and led to understanding of many functions of the components. Recently, structural predictions based on genetic analyses in Escherichia coli were confirmed, in a striking and satisfying manner, by the solution of an X-ray crystal structure from an archaeal SecY complex. This review discusses the genetic background that led to those hypotheses and the convergence of genetic studies with structural data.     

An evaluation of electrophoresis as a taxonomic method using comparative data from the macropodidae (Marsupialia)

Comparative electrophoretic data collected for seven proteins from thirty-four species of marsupial have been examined to define the advantages and shortcomings of electrophoresis as a taxonomic method. The problems of relative rates of change, convergence and parallelism are examined and the range of taxonomic ranks which can usefully be studied with this technique is determined. The relative merits of this type of data, when combined with various methods of taxonomic analysis, are discussed.     

Evolution and character congruence in some western Indian Ocean Phelsuma: Numerical analysis of biochemistry, shape and scalation

The evolution of five island populations of Green gecko, representing inter- and intra-specific divergence, was studied using biochemical data, scalation and shape. The data were numerically analysed using ordination analyses for the phenetic classification and Wagner trees to hypothesize the phylogeny. These studies revealed three phenetic groups corresponding to three mono-phyletic lineages. The numerical analysis of morphological data agreed with the numerical analysis of biochemical data. It is concluded that the classification based on biochemical affinities differed from the previous classification based on conventional analysis of morphology due to methodological and philosophical differences rather than differences between morphological and biochemical evolution.
The ordination analyses were very congruent between data sets (biochemical, shape, scalation, total) and the Wagner trees were generally congruent between data sets. Some Wagner trees based on scalation data were incongruent. The phenetic and cladistic classifications corresponded to each other but differed from the conventional classification. The phylogenetic analysis of the total data set indicated that the three specific lineages showed relatively equal anagenesis. However, anagenic divergence differed markedly between character types. It is suggested that a range of character types be used when studying anagenesis.     

Quantitative modeling of biochemical networks

Today different database systems for molecular structures (genes and proteins) and metabolic pathways are available. All these systems are characterized by the static data representation. For progress in biotechnology the dynamic representation of this data is important. The metabolism can be characterized as a complex biochemical network. Different models for the quantitative simulation of biochemical networks are discussed, but no useful formalization is available. This paper shows that the theory of Petrinets is useful for the quantitative modeling of biochemical networks.     

Explicit formulation of titration models for isothermal titration calorimetry

Isothermal titration calorimetry (ITC) produces a differential heat signal with respect to the total titrant concentration. This feature gives ITC excellent sensitivity for studying the thermodynamics of complex biomolecular interactions in solution. Currently, numerical methods for data fitting are based primarily on indirect approaches rooted in the usual practice of formulating biochemical models in terms of integrated variables. Here, a direct approach is presented wherein ITC models are formulated and solved as numerical initial value problems for data fitting and simulation purposes. To do so, the ITC signal is cast explicitly as a first-order ordinary differential equation (ODE) with total titrant concentration as independent variable and the concentration of a bound or free ligand species as dependent variable. This approach was applied to four ligand-receptor binding and homotropic dissociation models. Qualitative analysis of the explicit ODEs offers insights into the behavior of the models that would be inaccessible to indirect methods of analysis. Numerical ODEs are also highly compatible with regression analysis. Since solutions to numerical initial value problems are straightforward to implement on common computing platforms in the biochemical laboratory, this method is expected to facilitate the development of ITC models tailored to any experimental system of interest.     

Characterization and primary structures of DNA-binding HU-type proteins from Rhizobiaceae

The DNA-binding HU-type proteins from several species of Rhizobiaceae including Rhizobium meliloti, two strains of Rhizobium leguminosarum with highly different phenotypic characters and Agrobacterium tumefaciens, were characterized and their amino acid sequences were determined. HU-type proteins isolated from R. leguminosarum L18 and A. tumefaciens are identical and show slight differences with the R. meliloti HU-type protein. On the other hand the R. leguminosarum L53 HU-type protein is quite different from the proteins cited above; several amino acid substitutions encountered in this protein result in significant changes in the folding of the polypeptide chain. The biochemical characteristics of these proteins are in good agreement with the respective position of these bacteria in the phylogeny determined by numerical taxonomy.     

Proteome Profile of American Hybrid Grape cv. Blanc du Bois during Ripening Reveals Proteins Associated with Flavor Volatiles and Ethylene Production

The study of key control points in ripening is essential to improve grape wine quality. Molecular basis of ripening is still far from being understood from the Pierce's disease (PD)‐tolerant grapes predominantly grown in the southeastern United States. To identify proteins expressed during Blanc du Bois grape berry green and ripening stages, proteome analysis from five different stages revealed 1091, 1131, 1078, 1042, and 1066 proteins. Differential expression analysis revealed 551 common proteins across different stages of maturity that are involved in various biochemical and metabolic pathways. The proteins identified were associated with phenylpropanoids, isoquinoline alkaloids, fatty acids, unsaturated fatty acids, and furanones. Our data provide the first step to understand the complex biochemical changes during ripening of PD‐tolerant American hybrid grapes that are popular for their aroma and flavor profile in the southeastern United States. Proteomics data are deposited to the ProteomeXchange PXD004157.     

The numerical solution of stiff differential equations

This paper first discusses the conditions in which a set of differential equations should give stable solutions, starting with linear systems assuming that these do not differ greatly in this respect from non-linear systems. Methods of investigating the stability of particular systems are briefly discussed. Most real biochemical systems are known from observation to be stable, but little is known of the regions over which stability persists; moreover, models of biochemical systems may not be stable, because of inaccurate choice of parameter values.The separate problem of stability and accuracy in numerical methods of approximating the solution of systems of non-linear equations is then treated. Stress is laid on the consistently unsatisfactory results given by explicit methods for systems containing "stiff" equations, and implicit multistep methods are particularly recommended for this class of problem, which is likely to include many biochemical model systems. Finally, an iteration procedure likely to give convergence both in multistep methods and in the steady-state approach is recommended, and areas in which improvement in methods is likely to occur are outlined.     

Enzyme classification by ligand binding

The problem of assigning a biochemical function to newly discovered proteins has been traditionally approached by expert enzymological analysis, sequence analysis, and structural modeling. In recent years, the appearance of databases containing protein-ligand interaction data for large numbers of protein classes and chemical compounds have provided new ways of investigating proteins for which the biochemical function is not completely understood. In this work, we introduce a method that utilizes ligand-binding data for functional classification of enzymes. The method makes use of the existing Enzyme Commission (EC) classification scheme and the data on interactions of small molecules with enzymes from the BRENDA database. A set of ligands that binds to an enzyme with unknown biochemical function serves as a query to search a protein-ligand interaction database for enzyme classes that are known to interact with a similar set of ligands. These classes provide hypotheses of the query enzyme's function and complement other computational annotations that take advantage of sequence and structural information. Similarity between sets of ligands is computed using point set similarity measures based upon similarity between individual compounds. We present the statistics of classification of the enzymes in the database by a cross-validation procedure and illustrate the application of the method on several examples.     

Pseudo-Lyapunov exponents and predictability of Hodgkin-Huxley neuronal network dynamics

We present a numerical analysis of the dynamics of all-to-all coupled Hodgkin-Huxley (HH) neuronal networks with Poisson spike inputs. It is important to point out that, since the dynamical vector of the system contains discontinuous variables, we propose a so-called pseudo-Lyapunov exponent adapted from the classical definition using only continuous dynamical variables, and apply it in our numerical investigation. The numerical results of the largest Lyapunov exponent using this new definition are consistent with the dynamical regimes of the network. Three typical dynamical regimes—asynchronous, chaotic and synchronous, are found as the synaptic coupling strength increases from weak to strong. We use the pseudo-Lyapunov exponent and the power spectrum analysis of voltage traces to characterize the types of the network behavior. In the nonchaotic (asynchronous or synchronous) dynamical regimes, i.e., the weak or strong coupling limits, the pseudo-Lyapunov exponent is negative and there is a good numerical convergence of the solution in the trajectory-wise sense by using our numerical methods. Consequently, in these regimes the evolution of neuronal networks is reliable. For the chaotic dynamical regime with an intermediate strong coupling, the pseudo-Lyapunov exponent is positive, and there is no numerical convergence of the solution and only statistical quantifications of the numerical results are reliable. Finally, we present numerical evidence that the value of pseudo-Lyapunov exponent coincides with that of the standard Lyapunov exponent for systems we have been able to examine.     

Functional analysis of the Kunitz trypsin inhibitor family in poplar reveals biochemical diversity and multiplicity in defense against herbivores

We investigated the functional and biochemical variability of Kunitz trypsin inhibitor (KTI) genes of Populus trichocarpa x Populus deltoides. Phylogenetic analysis, expressed sequence tag databases, and western-blot analysis confirmed that these genes belong to a large and diverse gene family with complex expression patterns. Five wound- and herbivore-induced genes representing the diversity of the KTI gene family were selected for functional analysis and shown to produce active KTI proteins in Escherichia coli. These recombinant KTI proteins were all biochemically distinct and showed clear differences in efficacy against trypsin-, chymotrypsin-, and elastase-type proteases, suggesting functional specialization of different members of this gene family. The in vitro stability of the KTIs in the presence of reducing agents and elevated temperature also varied widely, emphasizing the biochemical differences of these proteins. Significantly, the properties of the recombinant KTI proteins were not predictable from primary amino acid sequence data. Proteases in midgut extracts of Malacosoma disstria, a lepidopteran pest of Populus, were strongly inhibited by at least two of the KTI gene products. This study suggests that the large diversity in the poplar (Populus spp.) KTI family is important for biochemical and functional specialization, which may be important in the maintenance of pest resistance in long-lived plants such as poplar.     

Frequentist model averaging for undirected Gaussian graphical models

Advances in information technologies have made network data increasingly frequent in a spectrum of big data applications, which is often explored by probabilistic graphical models. To precisely estimate the precision matrix, we propose an optimal model averaging estimator for Gaussian graphs. We prove that the proposed estimator is asymptotically optimal when candidate models are misspecified. The consistency and the asymptotic distribution of model averaging estimator, and the weight convergence are also studied when at least one correct model is included in the candidate set. Furthermore, numerical simulations and a real data analysis on yeast genetic data are conducted to illustrate that the proposed method is promising.     

The plasma proteins of some albatrosses and petrels as an index of relationship in the Procellariiformes

Abstract

The plasma proteins of 29 species of albatrosses and petrels were electrophoretically separated in acrylamide gels to clarify relationships at the species-group to family-group levels. Little in the resulting data from 472 birds seriously contests the present classification of the Procellariiformes; much of the biochemical evidence supports, confirms, and clarifies the proposals of conventional taxonomic methodology. The biochemical data give fresh insights into the interrelationships of procellariiform taxa, and highlight intriguing new problems. Sex, season, age, and other sources of non-genetic protein variation are insignificant for taxonomie purposes. Proteins of value for comparison include the transferrins, some α and β globulins, albumins, prealbumins, and non-specific esterases. Genetic variations in the mobility of these proteins are useful at the genus-group level and below. Other proteins are monomorphic at genus and family level, and three are monomorphic in both number and mobility throughout the Procellariiformes; these are useful reference points for calibrating samples on different gels. One conspicuous α protein is absent in the Hydrobatidae but present in all other families; the implications of this are discussed. Polymorphic proteins at the population or species level were not detected; this conspicuous phylogenetic conservatism is discussed with regard to its possible evolutionary significance. Following a summary of the protein data, three categories of defined probability statements, based on the biochemical and other evidence, allow speculative comment on the evolutionary relationships and history of the taxa within the Procellariiformes. The value of further biochemical research into the marine birds is emnhasised.     

Review: nucleotide-dependent conformational changes of the chaperonin containing TCP-1

Current biochemical and structural studies on the conformational changes induced by the nature of nucleotide bound to the chaperonin containing testis complex polypeptide 1 (CCT) are examined to see how consistent the data are. This exercise suggests that the biochemical and structural data are in good agreement. CCT clearly appears as a folding nano-machine fueled by ATP. A careful comparison of the biochemical and structural data, however, highlights a number of points that remain to be carefully documented in order to better understand the nature of the conformational changes in CCT that yield folded target proteins. Special effort should be made to clearly answer the points listed at the end of this review in order to obtain the dynamic sequence of events yielding folded proteins in the eukaryotic cytoplasm similar to what has been obtained for prokaryotes.     

Cuvier meets Watson and Crick: the utility of molecules as classical homologies

Much phylogenetic information has been derived from the analysis of sequence similarity in genes and proteins. These data are generally considered to be more reliable than an examination of the phylogenetic distribution of similar biologically active molecules. However, molecules can provide significant phylogenetic information when accompanied by a careful analysis of their structure, synthesis, genetics and function. Molecules may be highly structurally and functionally constrained. Thus, similar or even chemically identical molecules may be unrelated, and this may be discernible only by examination of information beyond the simple structure of the molecule. Phylogenetic variation in the synthesis of tyrosine and lysine demonstrates that chemical identity of molecules may be brought about by unrelated synthetic pathways. The widespread distribution of collagen triple helices is shown to result from convergence under structural and functional constraints. This is demonstrated by an examination of the steric constraints upon collagens and the presence of several independent families of collagen genes. Lysyl oxidase crosslinking occurs in several unrelated proteins, indicating that similarity in the post-translational modification of proteins is not evidence of homology.     

Conformational sampling and dynamics of membrane proteins from 10-nanosecond computer simulations

In the current report, we provide a quantitative analysis of the convergence of the sampling of conformational space accomplished in molecular dynamics simulations of membrane proteins of duration in the order of 10 nanoseconds. A set of proteins of diverse size and topology is considered, ranging from helical pores such as gramicidin and small beta-barrels such as OmpT, to larger and more complex structures such as rhodopsin and FepA. Principal component analysis of the C(alpha)-atom trajectories was employed to assess the convergence of the conformational sampling in both the transmembrane domains and the whole proteins, while the time-dependence of the average structure was analyzed to obtain single-domain information. The membrane-embedded regions, particularly those of small or structurally simple proteins, were found to achieve reasonable convergence. By contrast, extra-membranous domains lacking secondary structure are often markedly under-sampled, exhibiting a continuous structural drift. This drift results in a significant imprecision in the calculated B-factors, which detracts from any quantitative comparison to experimental data. In view of such limitations, we suggest that similar analyses may be valuable in simulation studies of membrane protein dynamics, in order to attach a level of confidence to any biologically relevant observations.     

Information content of protein sequences

The complexity of large sets of non-redundant protein sequences is measured. This is done by estimating the Shannon entropy as well as applying compression algorithms to estimate the algorithmic complexity. The estimators are also applied to randomly generated surrogates of the protein data. Our results show that proteins are fairly close to random sequences. The entropy reduction due to correlations is only about 1%. However, precise estimations of the entropy of the source are not possible due to finite sample effects. Compression algorithms also indicate that the redundancy is in the order of 1%. These results confirm the idea that protein sequences can be regarded as slightly edited random strings. We discuss secondary structure and low-complexity regions as causes of the redundancy observed. The findings are related to numerical and biochemical experiments with random polypeptides.     

Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks

A novel extraction protocol is described with which metabolites, proteins and RNA are sequentially extracted from the same sample, thereby providing a convenient procedure for the analysis of replicates as well as exploiting the inherent biological variation of independent samples for multivariate data analysis. A detection of 652 metabolites, 297 proteins and clear RNA bands in a single Arabidopsis thaliana leaf sample was validated by analysis with gas chromatography coupled to a time of flight mass spectrometer for metabolites, two-dimensional liquid chromatography coupled to mass spectrometry for proteins, and Northern blot analysis for RNA. A subset of the most abundant proteins and metabolites from replicate analysis of different Arabidopsis accessions was merged to form an integrative dataset allowing both classification of different genotypes and the unbiased analysis of the hierarchical organization of proteins and metabolites within a real biochemical network.