Prediction of protein structural classes by a new measure of information discrepancy

Since it was observed that the structural class of a protein is related to its amino acid composition, various methods based on amino acid composition have been proposed to predict protein structural classes. Though those methods are effective to some degree, their predictive quality is confined because amino acid composition cannot sufficiently include the information of protein sequences. In this paper, a measure of information discrepancy is applied to the prediction of protein structural classes; different from the previous methods, this new approach is based on the comparisons of subsequence distributions; therefore, the effect of residue order on protein structure is taken into account. The predictive results of the new approach on the same data set are better than those of the previous methods. As to a data set of 1401 sequences with no more than 30% redundancy, the overall correctness rates of resubstitution test and Jackknife test are 99.4 and 75.02%, respectively, and to other data sets the similar results are also obtained. All tests demonstrate that the residue order along protein sequences plays an important role on recognition of protein structural classes, especially for alpha/beta proteins and alpha+beta proteins. In addition, the tests also show that the new method is simple and efficient.     

Evaluation of sample extraction methods for proteomics analysis of green algae Chlorella vulgaris

Many protein extraction methods have been developed for plant proteome analysis but information is limited on the optimal protein extraction method from algae species. This study evaluated four protein extraction methods, i.e. direct lysis buffer method, TCA‐acetone method, phenol method, and phenol/TCA‐acetone method, using green algae Chlorella vulgaris for proteome analysis. The data presented showed that phenol/TCA‐acetone method was superior to the other three tested methods with regards to shotgun proteomics. Proteins identified using shotgun proteomics were validated using sequential window acquisition of all theoretical fragment‐ion spectra (SWATH) technique. Additionally, SWATH provides protein quantitation information from different methods and protein abundance using different protein extraction methods was evaluated. These results highlight the importance of green algae protein extraction method for subsequent MS analysis and identification.     

Protein inference: A protein quantification perspective

In mass spectrometry-based shotgun proteomics, protein quantification and protein identification are two major computational problems. To quantify the protein abundance, a list of proteins must be firstly inferred from the raw data. Then the relative or absolute protein abundance is estimated with quantification methods, such as spectral counting. Until now, most researchers have been dealing with these two processes separately. In fact, the protein inference problem can be regarded as a special protein quantification problem in the sense that truly present proteins are those proteins whose abundance values are not zero. Some recent published papers have conceptually discussed this possibility. However, there is still a lack of rigorous experimental studies to test this hypothesis.In this paper, we investigate the feasibility of using protein quantification methods to solve the protein inference problem. Protein inference methods aim to determine whether each candidate protein is present in the sample or not. Protein quantification methods estimate the abundance value of each inferred protein. Naturally, the abundance value of an absent protein should be zero. Thus, we argue that the protein inference problem can be viewed as a special protein quantification problem in which one protein is considered to be present if its abundance is not zero. Based on this idea, our paper tries to use three simple protein quantification methods to solve the protein inference problem effectively. The experimental results on six data sets show that these three methods are competitive with previous protein inference algorithms. This demonstrates that it is plausible to model the protein inference problem as a special protein quantification task, which opens the door of devising more effective protein inference algorithms from a quantification perspective. The source codes of our methods are available at: http://code.google.com/p/protein-inference/.     

Development and assessment of scoring functions for protein identification using PMF data

PMF is one of the major methods for protein identification using the MS technology. It is faster and cheaper than MS/MS. Although PMF does not differentiate trypsin-digested peptides of identical mass, which makes it less informative than MS/MS, current computational methods for PMF have the potential to improve its detection accuracy by better use of the information content in PMF spectra. We developed a number of new probability-based scoring functions for PMF protein identification based on the MOWSE algorithm. We considered a detailed distribution of matching masses in a protein database and peak intensity, as well as the likelihood of peptide matches to be close to each other in a protein sequence. Our computational methods are assessed and compared with other methods using PMF data of 52 gel spots of known protein standards. The comparison shows that our new scoring schemes have higher or comparable accuracies for protein identification in comparison to the existing methods. Our software is freely available upon request. The scoring functions can be easily incorporated into other proteomics software packages.     

A comparison of the Kjeldahl and Dumas methods for the determination of protein in foods,using data from a proficiency testing scheme

Both the Kjeldahl and the Dumas methods for the determination of protein in foodstuffs are currently in use, but the empirical nitrogen factors used to convert the determined nitrogen content to protein content are based on the Kjeldahl method alone. Non-equivalence between the two methods could therefore result in some laboratories reporting an incorrect protein content. We report here a study using data accumulated over several years in the results of a proficiency testing scheme. On average the Dumas method provided results that were relatively higher by about 1.4% than the Kjeldahl method, but the difference between the methods depended on the type of foodstuff. The methodology of looking for bias between analytical methods is critically discussed.     

Quantitative Estimation of Protein in Sprouts of Vigna radiate (Mung Beans), Lens culinaris (Lentils), and Cicer arietinum (Chickpeas) by Kjeldahl and Lowry Methods

Protein scarcity is the most vital cause of long-lasting diseases and even untimely deaths in some developing nations. The application of protein in food is advantageous from the point of view of non-toxicity, biocompatibility, and dietary benefits. This study aimed to determine the protein contents of the sprouts of Vigna radiates (mung beans), Lens culinaris (lentils), and Cicer arietinum (chickpeas) using the Kjeldahl and Lowry methods. The results obtained from the Kjeldahl method identified protein concentrations of 2.54, 2.63, and 2.19%, whereas the Lowry method results identified protein concentrations of 2.96%, 4.10%, and 1.6% in mung beans, lentils, and chickpeas, respectively. In both the methods, lentils were found to have the highest amount of protein followed by mung beans and chickpeas. Both the Kjeldahl and Lowry methods demonstrated good protein values and low variation in the protein amount in the analyzed samples. Furthermore, the methods had greater sensitivity and comparable experimental variability. The outcomes revealed that assays can be applied for protein analysis in legumes. In the context of a lack of suitable standard procedures for evaluating legumes’ compositions, the present study is suitable for food control laboratories. In addition, the studied samples represent a significant source of protein and can be used to fulfil the daily requirements for protein intake and other food applications.     

Mini-review: computational structure-based design of inhibitors that target protein surfaces

Finding drugs that inhibit protein-protein interactions is usually difficult. While computer-aided design is used widely to facilitate the drug discovery process for protein targets with well-defined binding pockets, its application to the design of inhibitors targeting a protein surface is very limited. In this mini-review we address two aspects of this issue: firstly, we overview the current state of design methodology for inhibitors specifically targeting protein surfaces, and secondly, we briefly outline recent advances in computational methods for structure-based drug design. These methods are closely related to protein docking and protein recognition, the difference being that in ligand design, ligands are built on a fragment-by-fragment basis. A novel scheme of computational combinatorial ligand design developed for the design of inhibitors that interfere with protein-protein interaction is described in detail. Current applications and limitations of this methodology, as well as its future prospects, are discussed.     

Knowledge acquisition and development of accurate rules for predicting protein stability changes

Knowing the mechanisms by which protein stability change is one of the most important and valuable tasks in molecular biology. The conventional methods of predicting protein stability changes mainly focus on improving prediction accuracy. However, it is desirable to extract domain knowledge from large databases that is beneficial to accurate prediction of the protein stability change. This paper presents an interpretable prediction tree method (named iPTREE) that produces explanatory rules to explore hidden knowledge accompanied with high prediction accuracy and consequently analyzes the factors influencing the protein stability changes. To evaluate iPTREE and the knowledge upon protein stability changes, a thermodynamic dataset consisting of 1615 mutants led by single point mutation from ProTherm is adopted. Being as a predictor for protein stability changes, the rule-based approach can achieve a prediction accuracy of 87%, which is better than other methods based on artificial neural networks (ANN) and support vector machines (SVM). Besides, these methods lack the ability in biological knowledge discovery. The human-interpretable rules produced by iPTREE reveal that temperature is a factor of concern in predicting protein stability changes. For example, one of interpretable rules with high support is as follows: if the introduced residue type is Alanine and temperature is between 4 °C and 40 °C, then the stability change will be negative (destabilizing). The present study demonstrates that iPTREE can easily be used in the application of protein stability changes where one requires more understandable knowledge.     

Mass spectrometry compatibility of two-dimensional gel protein stains

As proteomic technology evolves, protein staining sensitivity is constantly being improved, enabling researchers to better visualize the proteome of their system. The current challenge is to balance the limits of detection of protein visualization with those of the mass spectrometric methods. In this report, mass spectra generated from human serum or rat liver proteins stained with either colloidal Coomassie blue, Daiichi silver, SYPRO Orange, SYPRO Red, SYPRO Ruby, or SYPRO Tangerine are compared. It has been concluded that the newest generation of fluorescent protein stains, compared with traditional staining methods, are more compatible to matrix-assisted laser desorption/ionization (MALDI) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods. The number of database matches obtained using each mass spectrometry method and the percent sequence coverage obtained from trypsin digested proteins stained using these six methods is provided.     

Possibilities and Limitations of Different Separation Techniques for the Analysis of the Protein Corona

One of the biggest challenges in the field of nanomedicine is the adsorption of biomolecules on the nanomaterial upon contact with a biological medium. The interactions of the resulting protein corona are essential for their behavior in a biological system. Thus, it is now commonly accepted that understanding the formation and consequently understanding the influence of the protein corona on the biological response is crucial. However, the outcome of the protein corona characterization cannot easily be compared between different studies and techniques, since many different sample preparation procedures exist that are suitable for different materials or methods. Depending on the applied procedure, the nanomaterial–protein system will be altered in a certain way, so that it is necessary to consider the individual influence on the protein corona. Accordingly, the aim of this Minireview is to give an overview of the applied sample preparation methods for the analysis of the protein corona and to evaluate their influence on the outcome of the results especially with regard to the introduced terms “soft” and “hard protein corona”. Special focus will be placed on the comparison of the most commonly used techniques such as centrifugation, magnetic, and chromatographic separation.     

Performance comparison of generalized born and Poisson methods in the calculation of electrostatic solvation energies for protein structures

This study compares generalized Born (GB) and Poisson (PB) methods for calculating electrostatic solvation energies of proteins. A large set of GB and PB implementations from our own laboratories as well as others is applied to a series of protein structure test sets for evaluating the performance of these methods. The test sets cover a significant range of native protein structures of varying size, fold topology, and amino acid composition as well as nonnative extended and misfolded structures that may be found during structure prediction and folding/unfolding studies. We find that the methods tested here span a wide range from highly accurate and computationally demanding PB-based methods to somewhat less accurate but more affordable GB-based approaches and a few fast, approximate PB solvers. Compared with PB solvation energies, the latest, most accurate GB implementations were found to achieve errors of 1% for relative solvation energies between different proteins and 0.4% between different conformations of the same protein. This compares to accurate PB solvers that produce results with deviations of less than 0.25% between each other for both native and nonnative structures. The performance of the best GB methods is discussed in more detail for the application for force field-based minimizations or molecular dynamics simulations.     

Weighted edge based clustering to identify protein complexes in protein–protein interaction networks incorporating gene expression profile

Protein complex detection from protein–protein interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on topological information. While the methods based on identifying dense sub-graphs are more effective in identifying protein complexes, not all protein complexes have high density. Moreover, existing methods focus more on static PPI networks and usually overlook the dynamic nature of protein complexes. Here, we propose a new method, Weighted Edge based Clustering (WEC), to identify protein complexes based on the weight of the edge between two interacting proteins, where the weight is defined by the edge clustering coefficient and the gene expression correlation between the interacting proteins. Our WEC method is capable of detecting highly inter-connected and co-expressed protein complexes. The experimental results of WEC on three real life data shows that our method can detect protein complexes effectively in comparison with other highly cited existing methods.Availability: The WEC tool is available at http://agnigarh.tezu.ernet.in/~rosy8/shared.html.     

Toward chromatographic analysis of interacting protein networks

Protein complexes, collectively referred to as the cellular interactome, appear to play a major role in cellular regulation. At present it is thought that the interactome could be composed of hundreds of protein assemblies. The objective of the work described here was to examine the prospect that chromatographic methods widely used in the preparative isolation of native proteins could be incorporated into global proteomics methods in such a way that the primary structure of protein complexes of sufficient stability to survive chromatography could be recognized along with their participation in protein complexes. Because wide differences in sizes are a unique feature of protein complexes, size-exclusion chromatography (SEC) was incorporated into all the fractionation strategies examined. Anion-exchange chromatography (AEC) and hydrophobic-interaction chromatography (HIC) were also examined because of the broad utility that these methods have shown in the preparation of proteins with native structure. Slightly more than a third of all proteins identified in yeast lysates were found to elute from SEC, AEC, and HIC columns with an apparent molecular weight much higher than that predicted from their parent gene. These results were interpreted to mean that these proteins were migrating through columns as components of protein complexes. Based on studies with multidimensional SEC-->RPLC (reversed-phase liquid chromatography), AEC-->SEC, and HIC-->SEC systems, it was concluded that recognition of proteins in complexes could be easily incorporated into multidimensional chromatographic methods for global proteomics when at least one of the fractionation dimensions included SEC of native proteins.     

CAMWI: Detecting protein complexes using weighted clustering coefficient and weighted density

Detection of protein complexes is very important to understand the principles of cellular organization and function. Recently, large protein–protein interactions (PPIs) networks have become available using high-throughput experimental techniques. These networks make it possible to develop computational methods for protein complex detection. Most of the current methods rely on the assumption that protein complex as a module has dense structure. However complexes have core-attachment structure and proteins in a complex core share a high degree of functional similarity, so it expects that a core has high weighted density. In this paper we present a Core-Attachment based method for protein complex detection from Weighted PPI Interactions using clustering coefficient and weighted density. Experimental results show that the proposed method, CAMWI improves the accuracy of protein complex detection.     

Measurement of total protein in urine: Comparison of two dye-binding procedures with a gel filtration/modified biuret method

Two dye-binding methods for the determination of urinary total protein concentration have been compared to a biuret-based procedure requiring a preliminary gel filtration step. The biuret-based procedure is claimed to be both specific and sensitive for the measurement of protein in urine. With those urines having relatively low protein concentrations, the dye-binding methods provide values about one-half of those given by the modified biuret procedure. This difference disappears when urines containing high levels of protein are measured. The lower results obtained at normal protein levels are most likely caused by the inability of dye-binding methods to measure proteins of high carbohydrate content (such as uromucoid) accurately. In spite of this limitation, both dye-binding methods appear suitable for routine use. In contrast to the biuret-based procedure, we found the dye-binding methods to be free from interferences by compounds known to appear in urine in high concentrations.     

Using Nearest Feature Line and Tunable Nearest Neighbor methods for prediction of protein subcellular locations

The subcellular location of a protein is closely correlated with it biological function. In this paper, two new pattern classification methods termed as Nearest Feature Line (NFL) and Tunable Nearest Neighbor (TNN) have been introduced to predict the subcellular location of proteins based on their amino acid composition alone. The simulation experiments were performed with the jackknife test on a previously constructed data set, which consists of 2,427 eukaryotic and 997 prokaryotic proteins. All protein sequences in the data set fall into four eukaryotic subcellular locations and three prokaryotic subcellular locations. The NFL classifier reached the total prediction accuracies of 82.5% for the eukaryotic proteins and 91.0% for the prokaryotic proteins. The TNN classifier reached the total prediction accuracies of 83.6 and 92.2%, respectively. It is clear that high prediction accuracies have been achieved. Compared with Support Vector Machine (SVM) and Nearest Neighbor methods, these two methods display similar or even higher prediction accuracies. Hence, we conclude that NFL and TNN can be used as complementary methods for prediction of protein subcellular locations.     

Two-dimensional electrophoresis analysis of proteomics based on image feature and mathematical morphology

Protein analysis techniques, including 2-D electro- phoresis, image analysis, biological mass spectrometry, database search, etc., are fundamental technologies of proteomics, a front area in biochemistry and life sci- ences[1―3]. Among them image analysi…     

Mass spectrometry assisted assignment of NMR resonances in 15N labeled proteins

Application of nuclear magnetic resonance (NMR) methods for the structural characterization to larger and more complex protein systems can be facilitated through the development of new methods for resonance assignment. Here, a novel approach that relies on integration of NMR and mass spectrometry (MS) methods is explored. The approach relies on the fact that both NMR and MS are able to monitor rates of exchange of amide protons for water deuterons. Correlating the rates can connect cross-peak positions from NMR data with fragment masses from MS data to support sequential assignment. The example provided is for a small model protein, ubiquitin, but the potential for application to large, more difficult to express proteins is clear.     

Biochemical localization of a protein involved in synthesis of Gluconacetobacter hansenii cellulose

Using subcellular fractionation and Western blot methods, we have shown that AcsD, one of the proteins encoded by the Acetobacter cellulose synthase (acs) operon, is localized in the periplasmic region of the cell. AcsD protein was heterologously expressed in Escherichia coli and purified using histidine tag affinity methods. The purified protein was used to obtain rabbit polyclonal antibodies. The purity of the subcellular fractions was assessed by marker enzyme assays.     

A quantum chemical method for rapid optimization of protein structures

A quantum chemical method for rapid optimization of protein structures is proposed. In this method, a protein structure is treated as an assembly of amino acid units, and the geometry optimization of each unit is performed with taking the effect of its surrounding environment into account. The optimized geometry of a whole protein is obtained by repeated application of such a local optimization procedure over the entire part of the protein. Here, we implemented this method in the MOPAC program and performed geometry optimization for three different sizes of proteins. Consequently, these results demonstrate that the total energies of the proteins are much efficiently minimized compared with the use of conventional optimization methods, including the MOZYME algorithm (a representative linear-scaling method) with the BFGS routine. The proposed method is superior to the conventional methods in both CPU time and memory requirements.