Hybrid nanoparticles based on organized protein immobilization on fullerenes

Nanoscale carbon materials (i.e., fullerenes and nanotubes) are an attractive platform for applications in biotransformations and biosensors. The interesting properties displayed by nanoparticles demand new strategies for the manipulation of these materials on the nanoscale. Controlled modification of their surface with biomolecules is required to fully realize their potential in bionanotechnology. In this work, immobilization of a fullerene derivative with a mutant subtilisin is demonstrated, and the effect of the fullerene on the protein activity is determined. The fullerene-conjugated enzyme had improved catalytic properties in comparison to subtilisin immobilized on nonporous silica. Further, the pH profile of free and fullerene-conjugated subtilisin were almost identical.     

Diversity of locations for Bacillus thuringiensis crystal protein genes.

The location of crystal protein genes in 22 crystalliferous Bacillus thuringiensis strains representing 14 subspecies was investigated by hybridization of an intragenic restriction fragment from a cloned crystal protein gene to whole plasmid preparations. Hybridization was found to a single plasmid in eight strains, to more than one plasmid in seven strains, and to one or both of two large, unresolved plasmids in two strains. The sizes of the hybridized plasmids ranged from 33 to over 150 megadaltons. In one additional subspecies, hybridization was only to linear DNA fragments, suggesting a chromosomal crystal protein gene, and for four other subspecies, not reported to be toxic to lepidopteran insects, no hybridization was found to either plasmids or to total cell DNA. Hybridization to restriction digests of plasmids and total cell DNA of several strains of subspecies thuringiensis and kurstaki revealed that all homology to the cloned crystal protein gene was plasmid associated and that several of these strains contained multiple regions of homology, implying the presence of multiple crystal protein genes.     

A tissue-specific code based on the abundance of SDS-solubilized proteins.

Proteins were extracted from different tissues using sodium dodecyl sulfate and were resolved on polyacrylamide gels. Soluble- and particulate-fraction proteins were listed by molecular weights in order of abundance to yield a three-component code that is tissue specific.     

Cellular proteomes have broad distributions of protein stability

Biological cells are extremely sensitive to temperature. What is the mechanism? We compute the thermal stabilities of the whole proteomes of Escherichia coli, yeast, and Caenorhabditis elegans using an analytical model and an extensive database of stabilities of individual proteins. Our results support the hypothesis that a cell''s thermal sensitivities arise from the collective instability of its proteins. This model shows a denaturation catastrophe at temperatures of 49–55°C, roughly the thermal death point of mesophiles. Cells live on the edge of a proteostasis catastrophe. According to the model, it is not that the average protein is problematic; it is the tail of the distribution. About 650 of E. coli''s 4300 proteins are less than 4 kcal mol⁻¹ stable to denaturation. And upshifting by only 4° from 37° to 41°C is estimated to destabilize an average protein by nearly 20%. This model also treats effects of denaturants, osmolytes, and other physical stressors. In addition, it predicts the dependence of cellular growth rates on temperature. This approach may be useful for studying physical forces in biological evolution and the role of climate change on biology.     

Prediction of protein submitochondria locations based on data fusion of various features of sequences

In this study, the predictors are developed for protein submitochondria locations based on various features of sequences. Information about the submitochondria location for a mitochondria protein can provide much better understanding about its function. We use ten representative models of protein samples such as pseudo amino acid composition, dipeptide composition, functional domain composition, the combining discrete model based on prediction of solvent accessibility and secondary structure elements, the discrete model of pairwise sequence similarity, etc. We construct a predictor based on support vector machines (SVMs) for each representative model. The overall prediction accuracy by the leave-one-out cross validation test obtained by the predictor which is based on the discrete model of pairwise sequence similarity is 1% better than the best computational system that exists for this problem. Moreover, we develop a method based on ordered weighted averaging (OWA) which is one of the fusion data operators. Therefore, OWA is applied on the 11 best SVM-based classifiers that are constructed based on various features of sequence. This method is called Mito-Loc. The overall leave-one-out cross validation accuracy obtained by Mito-Loc is about 95%. This indicates that our proposed approach (Mito-Loc) is superior to the result of the best existing approach which has already been reported.     

Prediction of membrane protein types and subcellular locations.

Membrane proteins are classified according to two different schemes. In scheme 1, they are discriminated among the following five types: (1) type I single-pass transmembrane, (2) type II single-pass transmembrane, (3) multipass transmembrane, (4) lipid chain-anchored membrane, and (5) GPI-anchored membrane proteins. In scheme 2, they are discriminated among the following nine locations: (1) chloroplast, (2) endoplasmic reticulum, (3) Golgi apparatus, (4) lysosome, (5) mitochondria, (6) nucleus, (7) peroxisome, (8) plasma, and (9) vacuole. An algorithm is formulated for predicting the type or location of a given membrane protein based on its amino acid composition. The overall rates of correct prediction thus obtained by both self-consistency and jackknife tests, as well as by an independent dataset test, were around 76-81% for the classification of five types, and 66-70% for the classification of nine cellular locations. Furthermore, classification and prediction were also conducted between inner and outer membrane proteins; the corresponding rates thus obtained were 88-91%. These results imply that the types of membrane proteins, as well as their cellular locations and other attributes, are closely correlated with their amino acid composition. It is anticipated that the classification schemes and prediction algorithm can expedite the functionality determination of new proteins. The concept and method can be also useful in the prioritization of genes and proteins identified by genomics efforts as potential molecular targets for drug design.     

Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

Background  

The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction.     

A redesigned genetic code for selective labeling in protein NMR

The outline of a universal cell-free translation system capable of site-specific insertion of any types of labeled amino acids is presented. The system could be an invaluable tool for NMR spectroscopy by making the exclusive and exact labeling of the segments of interest possible. Although the development of such a system requires considerable efforts and can not be expected to be available in the next few years, we argue that recent findings concerning the translation apparatus provide clues for overcoming the major difficulties that might arise. We propose a genetic code and a reactor expected to fulfill the specific requirements. Importantly, incomplete systems could also be useful to study selected functional aspects of a number of proteins, examples of which are also given.     

A versatile method for deciphering plant membrane proteomes

Proteomics is a very powerful approach to link the information contained in sequenced genomes, like that of Arabidopsis, to the functional knowledge provided by studies of plant cell compartments. This article summarizes the different steps of a versatile strategy that has been developed to decipher plant membrane proteomes. Initiated with envelope membranes from spinach chloroplasts, this strategy has been adapted to thylakoids, and further extended to a series of membranes from the model plant Arabidopsis: chloroplast envelope membranes, plasma membrane, and mitochondrial membranes. The first step is the preparation of highly purified membrane fractions from plant tissues. The second step in the strategy is the fractionation of membrane proteins on the basis of their physico-chemical properties. Chloroform/methanol extraction and washing of membranes with NaOH, NaCl or any other agent led to the simplification of the protein content of the fraction to be analysed. The next step is the genuine proteomic step, i.e. the separation of proteins by 1D-gel electrophoresis followed by in-gel proteolytic digestion of the polypeptides, analysis of the proteolytic peptides using mass spectrometry, and protein identification by searching through databases. The last step is the validation of the procedure by checking the subcellular location. The results obtained by using this strategy demonstrate that a combination of different proteomics approaches, together with bioinformatics, indeed provide a better understanding of the biochemical machinery of the different plant membranes at the molecular level.     

A microassay for protein glycation based on the periodate method.

The periodate assay for glycated protein has been adapted for use with a microplate reader. Up to 94 samples can be read in 40 s, and sensitivity has been improved so that only 2-40 nmol of protein-bound sugar are needed per well. Yields have been increased to over 90%, allowing in vitro glycation of human serum albumin to be assayed on 0.1-mg aliquots.     

A protein taxonomy based on secondary structure.

Does a protein's secondary structure determine its three-dimensional fold? This question is tested directly by analyzing proteins of known structure and constructing a taxonomy based solely on secondary structure. The taxonomy is generated automatically, and it takes the form of a tree in which proteins with similar secondary structure occupy neighboring leaves. Our tree is largely in agreement with results from the structural classification of proteins (SCOP), a multidimensional classification based on homologous sequences, full three-dimensional structure, information about chemistry and evolution, and human judgment. Our findings suggest a simple mechanism of protein evolution.     

A better fluorescent protein for whole-body imaging

Whole-body imaging with fluorescent proteins is a powerful technology with many applications in small animals. Brighter, red-shifted proteins can make whole-body imaging more sensitive owing to reduced absorption by tissues and less scatter. A new protein called Katushka has been isolated. It is the brightest known protein with emission at wavelengths longer than 620 nm. This new protein offers the potential for noninvasive whole-body imaging of numerous cellular and molecular processes in live animals.     

The toxin-agglutinin fold. A new group of small protein structures organized around a four-disulfide core

The three-dimensional structures of the snake venom postsynaptic neurotoxins and of the domains in wheat germ agglutinin show a remarkably similar overall folding pattern, consisting of equivalently placed, but variably sized loops which are held together by four similarly positioned disulfide bonds. Furthermore, occurrence of this wheat germ agglutinin-neurotoxin domain fold is predicted not only in the snake venom cardiotoxins and cytotoxins with neurotoxin-matched half-cystine sequence positions, but also for two small plant proteins, hevein and ragweed pollen allergen Ra5, on the basis of a nearly exact match of their half-cystine, sequence positions with those of the wheat germ agglutinin domain.     

A Darwinian evolutionary system. II. Experiments on protein evolution and evolutionary aspects of the genetic code

On the basis of the principles of Darwinian evolutionary systems laid out earlier, a system is constructed which simulates protein evolution. Two types of situations are studied: adaptation to highest possible alkalinity (“alkalinity model”), and adaptation to an arbitrary sequence (“sequence model”). No restrictions in adaptability were found for the (comparably special) alkalinity model, but severe restrictions were found for the sequence model. Approximately 15% of all possible evolutionary paths from one amino acid to another turned out to be impossible, in the sense that no chain of intermediate steps exists which leads to a higher fitness level, in this case an increased chemical similarity of the two amino acids.The evolutionary efficiency of the natural genetic code was also investigated by comparing it with two classes of artificially constructed codes: semi-random and random codes. It was found that the natural code possesses the highest evolutionary efficiency, given by the mean number of generations required to reach identity in 5 of 10 sites, if originally all 10 were different. Closest to the natural code in evolutionary efficiency were the random codes, next, the semi-random codes.This pattern could be explained by a theoretical measure, called the code efficiency. The most important component of the code efficiency is the percentage of impossible paths. The natural code is far superior to the other code types in this respect. However, the random codes are superior to the natural code with respect to the mean shortest path length of the possible paths, the other important component of the code efficiency.It is suggested that the natural genetic code might have arisen from a semi-random code during a process of optimizing several of its features, of which the evolutionary efficiency is a very important one; or that the natural code is the most efficient edition of a large variety of semi-random codes which originated by chance.     

A new computational model for protein folding based on atomic solvation.

A new model for calculating the solvation energy of proteins is developed and tested for its ability to identify the native conformation as the global energy minimum among a group of thousands of computationally generated compact non-native conformations for a series of globular proteins. In the model (called the WZS model), solvation preferences for a set of 17 chemically derived molecular fragments of the 20 amino acids are learned by a training algorithm based on maximizing the solvation energy difference between native and non-native conformations for a training set of proteins. The performance of the WZS model confirms the success of this learning approach; the WZS model misrecognizes (as more stable than native) only 7 of 8,200 non-native structures. Possible applications of this model to the prediction of protein structure from sequence are discussed.     

Comparative bioinformatic analysis of complete proteomes and protein parameters for cross-species identification in proteomics

Peptide mass fingerprinting (PMF) remains the most amenable technique for protein identification in proteomics, using mass spectrometry as the primary analytical technique coupled with bioinformatics. This relies on the presence of the amino acid sequence of the protein in the current databanks. Despite this, it is desirable to be able to use the technique for organisms whose genomes are not yet fully sequenced and apply cross-species protein identification. In this study, we have re-examined the feasibility of such approaches by considering the extent of protein similarity between genome sequences using a data set of 29 complete bacterial and two eukaryotic genomes. A range of protein and peptide features are considered, including protein isoelectric focussing point, protein mass, and amino acid conservation. The effectiveness of PMF approaches has then been tested with a series of computer simulations with varying peptide number and mass accuracy for several cross-species tests. The results show that PMF alone is unsuitable in general for divergent species jumps, or when protein similarity is less than 70% identity. Despite this, there exists a considerable enrichment above random of tryptic peptide conservation and PMF promises to remain useful when combined with other data than just peptide masses for cross-species protein identification.