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Required metrological tools, such as higher order reference measurements procedures, pure substance and matrix certified reference materials, are established for small well defined molecules. Difficulties still remain in the provision of SI traceable standards in the area of larger biomolecules such as peptides/proteins. The provision of Primary Calibration Reference Services has been identified as a core technical competency for National Measurement Institutes (NMIs). A concept has been elaborated by the Focus Group I on peptide/protein purity for the strategic planning of ongoing Key Comparisons of the Protein Analysis Working Group (PAWG) within the Comité Consultatif pour la Quantité de Matière (CCQM). The assignment of the mass fraction content of high purity peptide/protein materials will be the subject of the CCQM-K115/P55.2 comparison series to directly support NMI services and certified reference materials currently being provided by NMIs. [ABSTRACT FROM AUTHOR]

A simple method is described for the patterned deposition of Teflon on paper to create an integrated platform for parallel organic synthesis and cell-based assays. Solvent-repelling barriers made of Teflon-impregnated paper confine organic solvents to specific zones of the patterned array and allow for 96 parallel flow-through syntheses on paper. The confinement and flow-through mixing significantly improves the peptide yield and simplifies the automation of this synthesis. The synthesis of 100 peptides ranging from 7 to 14 amino acids in length gave over 60 % purity for the majority of the peptides (>95 % yield per coupling/deprotection cycle). The resulting peptide arrays were used in cell-based screening to identify 14 potent bioactive peptides that support the adhesion or proliferation of breast cancer cells in a 3D environment. In the future, this technology could be used for the screening of more complex phenotypic responses, such as cell migration or differentiation. [ABSTRACT FROM AUTHOR]

Two approaches based on intact cell matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (IC-MALDI-ToF MS) have been evaluated in order to discriminate and identify nine former Burkholderia cepaciacomplex (Bcc) species, Burkholderia contaminansbelonging to the novel Taxon K, Burkholderia gladioli, and the most relevant non-fermentative (NF) Gram-negative rods recovered from cystic fibrosis (CF) sputum cultures. In total, 146 clinical isolates and 26 reference strains were analysed. IC mass spectra were obtained with high reproducibility applying a recently developed inactivation protocol which is based on the extraction of microbial proteins by trifluoroacetic acid (TFA). In a first approach, spectral analysis was carried out by means of a gel-view representation of mass spectra, which turned out to be useful to recognize specific identifying biomarker proteins (SIBPs). A series of prominent mass peaks, mainly assigned to constitutively expressed proteins, were selected as SIBPs for identifications at the genus and species level. Two distinctive mass peaks present in B. contaminansspectra (7501 and 7900 Da) were proposed as SIBPs for the identification of this novel species. A second approach of spectral analysis based on data reduction, feature selection and subsequent hierarchical cluster analysis was used to obtain an objective discrimination of all species analysed. Both complementary modalities of analyzing complex IC-MALDI-ToF MS data open the path towards a rapid, accurate and objective means of routine clinical microbiology diagnosis of pathogens from sputum samples of CF patients. [ABSTRACT FROM AUTHOR]

As biological macromolecules, proteins are involved in important cellular functions ranging from DNA replication and biosynthesis to metabolic signalling and environmental sensing. Protein sequencing can help understand the relationship between protein function and structure, and provide key information for disease diagnosis and new drug design. Nanopore sensors are a novel technology to achieve the goal of label‐free and high‐throughput protein sequencing. In recent years, nanopore‐based biosensors have been widely used in the detection and analysis of biomolecules such as DNA, RNA, and proteins. At the same time, computer simulations can describe the transport of proteins through nanopores at the atomic level. This paper reviews the applications of nanopore sensors in protein sequencing over the past decade and the solutions to key problems from a computer simulation perspective, with the aim of pointing the way to the future of nanopore protein sequencing. [ABSTRACT FROM AUTHOR]

A new model to study proteinic systems including a many-body polarization and a hydrogen bond energy contribution is presented. This model represents an extension of an earlier water many-body model [M. Masella and J.-P. Flament, J. Chem. Phys. 107 9105 (1997)]. As in this earlier model, the new model is developed to reproduce quantum computations on small molecular aggregates, and, in this first paper, we focus our efforts in developing an accurate potential to describe interactions among all nonbonded atoms occurring in proteins, and among those atoms and six cations of biological interest: Li[SUP+], Na[SUP+], K[SUP+], Mg[SUP2+], Ca[SUP2+], and Zn[SUP2+]. Intramolecular degrees of freedom are described as in classical two-body force fields. In the present paper, the new model is applied to investigate the properties of small ion-neutral [M,L[SUBn][SUPm+] complexes and of small hydrogen-bonded systems. The results showed that this model is able to reproduce most of the theoretical quantum predictions and experimental data published until now regarding those systems. [ABSTRACT FROM AUTHOR]

A method for constructing protein semantic networks using MEDLINE abstracts is proposed. The publications retrieved by the context search for protein names (relevant) and the related publications were used. The proposed method is based on estimation of semantic connectivity between proteins. The connectivity score was calculated as a function of the number of relevant or related papers found for a pair of proteins. This score was used to construct a semantic network for 150 human proteins belonging to five different metabolic pathways. Analysis of the network demonstrated that the proteins involved in associated molecular processes formed the subgraphs with a high edge density. [ABSTRACT FROM AUTHOR]

Abstract: Commercially available mini-gels for sodium dodecyl sulphate (SDS)-PAGE and Western blotting are limited both by the number of lanes that can be loaded per gel and the minimum amount of protein per lane that must be loaded. Here we describe a method for loading protein samples onto existing commercially available mini-gels that allows loading of 50 or more lanes per gel. The enhanced sensitivity of the method allows Western blotting with sub-microgram quantities of protein. Samples are loaded onto filter paper strips mounted on a plastic backing sheet, and film-wrapped strips on a separate dummy loader interdigitate with the sample strips, creating a physical barrier to lateral diffusion. The sample loader sandwich is placed on top of the stacking gel, and is compatible with all commercially available SDS-PAGE systems. Comparison of 15-lane mini-gels with 30-lane micro-loader strips reveals up to a 10-fold increase in sensitivity with the new method. Using 50- and 66-lane micro-loaders, sub-microgram quantities of protein produce reliable and quantifiable signal by Western blotting. Manipulation of the ionic conditions within dummy loader strips provides a mechanism for enhancing lateral resolution, allowing for the possibility of further miniaturization. [Copyright &y& Elsevier]

Reports that Rui Monteiro's paper "Spatio-temporal Activation of Smad1 and Smad5 in Vivo: Monitoring Transcriptional Activity of Smad Proteins" received the award for the Best Paper published in 2004 in the "Journal of Cell Science". Profile of Monteiro; Prize; Insights about his research article.

The article focuses on the reasons why the author has spent nine years pursuing a map of the 450-protein nuclear pore complex (NPC). His fascination by how things move in and about the cell's nucleus contribute with his determination to mapping huge complexes. The author, together with his colleagues, has generated proteomics, biochemical and mass spectrometry data for all NPC proteins. Fitting each protein's volume into its hotspot gives a physical map of the pore. It is considered that this serves as a powerful approach to solve large assemblies.

The article presents information on various research papers which will be published in the forthcoming issue of the "European Journal of Biochemistry". Some of the papers discusses are "Enzymatic Synthesis of Neolactotetraosylceramide by the N-Acctyllactosamine Synthase of Human Serum". "Rate of Transplantation and Kinetics of Processing of Newly Synthesized Molecules of Two Major Outer-Membrane Proteins, the OmpA and OmpF Proteins of Escherichia Coli," by the researchers I. Crowlesmith and K. Gamon.

Self-assembling processes are ubiquitous phenomena that drive the organization and the hierarchical formation of complex molecular systems. The investigation of assembling dynamics, emerging from the interactions among biomolecules like amino-acids and polypeptides, is fundamental to determine how a mixture of simple objects can yield a complex structure at the nano-scale level. In this paper we present HyperBeta, a novel open-source software that exploits an innovative algorithm based on hyper-graphs to efficiently identify and graphically represent the dynamics of β -sheets formation. Differently from the existing tools, HyperBeta directly manipulates data generated by means of coarse-grained molecular dynamics simulation tools (GROMACS), performed using the MARTINI force field. Coarse-grained molecular structures are visualized using HyperBeta 's proprietary real-time high-quality 3D engine, which provides a plethora of analysis tools and statistical information, controlled by means of an intuitive event-based graphical user interface. The high-quality renderer relies on a variety of visual cues to improve the readability and interpretability of distance and depth relationships between peptides. We show that HyperBeta is able to track the β -sheets formation in coarse-grained molecular dynamics simulations, and provides a completely new and efficient mean for the investigation of the kinetics of these nano-structures. HyperBeta will therefore facilitate biotechnological and medical research where these structural elements play a crucial role, such as the development of novel high-performance biomaterials in tissue engineering, or a better comprehension of the molecular mechanisms at the basis of complex pathologies like Alzheimer's disease. [ABSTRACT FROM AUTHOR]

A number of experiments and experimentally based simulations showed that β-proteins are mechanically more stable than α-proteins. However, the theory that might explain this evidence is still lacking. In this paper we have developed a simple elastic theory, which allows to estimate critical forces for stretching both kinds of proteins. It has been shown that unfolding of β-proteins does really require notably higher forces as compared to the stretching of α-proteins. [ABSTRACT FROM AUTHOR]

S-layer proteins have a wide range of application potential due to their characteristic features concerning self-assembling, assembling on various surfaces, and forming of isoporous structures with functional groups located on the surface in an identical position and orientation. Although considerable knowledge has been experimentally accumulated on the structure, biochemistry, assemble characteristics, and genetics of S-layer proteins, no structural model at atomic resolution has been available so far. Therefore, neither the overall folding of the S-layer proteins—their tertiary structure—nor the exact amino acid or domain allocations in the lattices are known. In this paper, we describe the tertiary structure prediction for the S-layer protein SbsB from Geobacillus stearothermophilus PV72/p2. This calculation was based on its amino acid sequence using the mean force method (MF method) achieved by performing molecular dynamic simulations. This method includes mainly the thermodynamic aspects of protein folding as well as steric constraints of the amino acids and is therefore independent of experimental structure analysis problems resulting from biochemical properties of the S-layer proteins. Molecular dynamic simulations were performed in vacuum using the simulation software NAMD. The obtained tertiary structure of SbsB was systematically analyzed by using the mean force method, whereas the verification of the structure is based on calculating the global free energy minimum of the whole system. This corresponds to the potential of mean force, which is the thermodynamically most favorable conformation of the protein. Finally, an S-layer lattice was modeled graphically using CINEMA4D and compared with scanning force microscopy data down to a resolution of 1 nm. The results show that this approach leads to a thermodynamically favorable atomic model of the tertiary structure of the protein, which could be verified by both the MF Method and the lattice model. [ABSTRACT FROM AUTHOR]

An initial guess is one of the most important factors in solving self-consistent field (SCF) molecular orbital calculations for large molecules. Recently, the authors reported that the quasicanonical localized orbital (QCLO) method was useful to prove the initial guess for all-electron calculations for proteins. This paper describes a new QCLO method that takes into account protein information derived from protein structures such as salt bridges and the secondary structure of the molecule. In several test calculations using typical models, the difference between the initial guess and final atomic charges was markedly decreased, and the number of SCF iterations was reduced. We suggest that the structure-based QCLO method improves the precision of the initial guess and achievement of automatic all-electron calculations for proteins. [ABSTRACT FROM AUTHOR]

In order to probe the fundamental principles that govern protein evolution, we use a minimalist model of proteins to provide a mapping from genotype to phenotype. The model is based on physically realistic forces of protein folding and includes an explicit definition of protein function. Thus, we can find the fitness of a sequence from its ability to fold to a stable structure and perform a function. We study the fitness landscapes of these functional model proteins, that is, the set of all sequences mapped on to their corresponding fitnesses and connected to their one mutant neighbors. Through population dynamics simulations we directly study the influence of the nature of the fitness landscape on evolution. Populations are observed to move to a steady state, the distribution of which can often be predicted prior to the population dynamics simulations from the nature of the fitness landscape and a quantity analogous to a partition function. In this paper, we develop a scheme for predicting the steady-state population on a fitness landscape, based on the nature of the fitness landscape, thereby obviating the need for explicit population dynamics simulations and providing some insight into the impact on molecular evolution of the nature of fitness landscapes. Poor predictions are indicative of fitness landscapes that consist of a series of weakly connected sublandscapes. [ABSTRACT FROM AUTHOR]

Protein refolding to its native state in vitro is a challenging problem in biotechnology, i.e., in the biomedical, pharmaceutical, and food industry. Protein aggregation and misfolding usually inhibit the recovery of proteins with their native states. These problems can be partially solved by adding a surfactant into a suitable solution environment. However, the process of this surfactant-assisted protein refolding is not well understood. In this paper, we wish to report on the first-ever simulations of surfactant-assisted protein refolding. For these studies, we defined a simple model for the protein and the surfactant and investigated how a surfactant affected the folding behavior of a two-dimensional lattice protein molecule. The model protein and model surfactant were chosen such that we could capture the important features of the folding process and the interaction between the protein and the surfactant, namely, the hydrophobic interaction. It was shown that, in the absence of surfactants, a protein in an “energy trap” conformation, i.e., a local energy minima, could not fold into the native form, which was characterized by a global energy minimum. The addition of surfactants created folding pathways via the formation of protein-surfactant complexes and thus enabled the conformations that fell into energy trap states to escape from these traps and to form the native proteins. The simulation results also showed that it was necessary to match the hydrophobicity of surfactant to the concentration of denaturant, which was added to control the folding or unfolding of a protein. The surfactants with different hydrophobicity had their own concentration range on assisting protein refolding. All of these simulations agreed well with experimental results reported elsewhere, indicating both the validity of the simulations presented here and the potential application of the simulations for the design of a surfactant on assisting protein refolding. [ABSTRACT FROM AUTHOR]

An efficient molecular dynamics (MD) algorithm is presented in this paper for biomolecular systems, which incorporates a novel variation on the fast multipole method (FMM) coupled to the reversible reference system propagator algorithm (r-RESPA). A top-down FMM is proposed which calculates multipoles recursively from the top of the box tree instead of from the bottom in Greengard’s original FMM, in an effort to be more efficient for noncubic or nonuniform systems. In addition, the use of noncubic box subdivisions of biomolecular systems is used and discussed. Reversible RESPA based on a Trotter factorization of the Liouville propagator in generating numerical integration schemes is coupled to the top-down FMM and applied to a MD study of proteins in vacuo, and is shown to be able to use a much larger time-step than the standard velocity Verlet method for a comparable level of accuracy. Furthermore, by using the FMM it becomes possible to perform MD simulations for very large biomolecules, since memory and CPU time requirements are now nearly of order of O(N) instead of O(N2). For a protein with 9513 atoms (the photosynthetic reaction center), the efficient MD algorithm leads to 20-fold reduction in CPU time for the Coulomb interaction and approximately 15-fold reduction in total CPU time over the standard velocity Verlet algorithm with a direct evaluation of Coulomb forces. © 1995 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5. [ABSTRACT FROM AUTHOR]

Background: Protein function is closely intertwined with protein structure. Discovery of meaningful structurefunction relationships is of utmost importance in protein biochemistry and has led to creation of high-quality, manually curated classification databases, such as the gold-standard SCOP (Structural Classification of Proteins) database. The SCOP database and its counterparts such as CATH provide a detailed and comprehensive description of the structural and evolutionary relationships of the proteins of known structure and are widely employed in structural and computational biology. Since manual classification is both subjective and highly laborious, automated classification of novel structures is increasingly an active area of research. The design of methods for automated structure classification has been rendered even more important since the recent past, due to the explosion in number of solved structures arising out of various structural biology initiatives. In this paper we propose an approach to the problem of structure classification based on creating and tessellating low dimensional maps of the protein structure space (MPSS). Given a set of protein structures, an MPSS is a low dimensional embedding of structural similarity-based distances between the molecules. In an MPSS, a group of proteins (such as all the proteins in the PDB or sub-samplings thereof) under consideration are represented as point clouds and structural relatedness maps to spatial adjacency of the points. In this paper we present methods and results that show that MPSS can be used to create tessellations of the protein space comparable to the clade systems within SCOP. Though we have used SCOP as the gold standard, the proposed approach is equally applicable for other structural classifications. Methods: In the proposed approach, we first construct MPSS using pairwise alignment distances obtained from four established structure alignment algorithms (CE, Dali, FATCAT and MATT). The low dimensional embeddings are next computed using an embedding technique called multidimensional scaling (MDS). Next, by using the remotely homologous Superfamily and Fold levels of the hierarchical SCOP database, a distance threshold is determined to relate adjacency in the low dimensional map to functional relationships. In our approach, the optimal threshold is determined as the value that maximizes the total true classification rate vis-a-vis the SCOP classification. We also show that determining such a threshold is often straightforward, once the structural relationships are represented using MPSS. Results and conclusion: We demonstrate that MPSS constitute highly accurate representations of protein fold space and enable automatic classification of SCOP Superfamily and Fold-level relationships. The results from our automatic classification approach are remarkably similar to those found in the distantly homologous Superfamily level and the quite remotely homologous Fold levels of SCOP. The significance of our results are underlined by theBackground: Protein function is closely intertwined with protein structure. Discovery of meaningful structurefunction relationships is of utmost importance in protein biochemistry and has led to creation of high-quality, manually curated classification databases, such as the gold-standard SCOP (Structural Classification of Proteins) database. The SCOP database and its counterparts such as CATH provide a detailed and comprehensive description of the structural and evolutionary relationships of the proteins of known structure and are widely employed in structural and computational biology. Since manual classification is both subjective and highly laborious, automated classification of novel structures is increasingly an active area of research. The design of methods for automated structure classification has been rendered even more important since the recent past, due to the explosion in number of solved structures arising out of various structural biology initiatives. In this paper we propose an approach to the problem of structure classification based on creating and tessellating low dimensional maps of the protein structure space (MPSS). Given a set of protein structures, an MPSS is a low dimensional embedding of structural similarity-based distances between the molecules. In an MPSS, a group of proteins (such as all the proteins in the PDB or sub-samplings thereof) under consideration are represented as point clouds and structural relatedness maps to spatial adjacency of the points. In this paper we present methods and results that show that MPSS can be used to create tessellations of the protein space comparable to the clade systems within SCOP. Though we have used SCOP as the gold standard, the proposed approach is equally applicable for other structural classifications. Methods: In the proposed approach, we first construct MPSS using pairwise alignment distances obtained from four established structure alignment algorithms (CE, Dali, FATCAT and MATT). The low dimensional embeddings are next computed using an embedding technique called multidimensional scaling (MDS). Next, by using the remotely homologous Superfamily and Fold levels of the hierarchical SCOP database, a distance threshold is determined to relate adjacency in the low dimensional map to functional relationships. In our approach, the optimal threshold is determined as the value that maximizes the total true classification rate vis-a-vis the SCOP classification. We also show that determining such a threshold is often straightforward, once the structural relationships are represented using MPSS. Results and conclusion: We demonstrate that MPSS constitute highly accurate representations of protein fold space and enable automatic classification of SCOP Superfamily and Fold-level relationships. The results from our automatic classification approach are remarkably similar to those found in the distantly homologous Superfamily level and the quite remotely homologous Fold levels of SCOP. The significance of our results are underlined by thefact that most automated methods developed thus far have only managed to match the closest-homology Family level of the SCOP hierarchy and tend to differ considerably at the Superfamily and Fold levels. Furthermore, our research demonstrates that projection into a low-dimensional space using MDS constitutes a superior noisereducing transformation of pairwise distances than do the variety of probability- and alignment-length-based transformations currently used by structure alignment algorithms. [ABSTRACT FROM AUTHOR]

Background: Recently, information derived by correlated mutations in proteins has regained relevance forpredicting protein contacts. This is due to new forms of mutual information analysis that have been proven to bemore suitable to highlight direct coupling between pairs of residues in protein structures and to the large numberof protein chains that are currently available for statistical validation. It was previously discussed that disulfide bondtopology in proteins is also constrained by correlated mutations. Results: In this paper we exploit information derived from a corrected mutual information analysis and from theinverse of the covariance matrix to address the problem of the prediction of the topology of disulfide bonds in Eukaryotes. Recently, we have shown that Support Vector Regression (SVR) can improve the prediction for the disulfide connectivity patterns. Here we show that the inclusion of the correlated mutation information increases of 5 percentage points the SVR performance (from 54% to 59%). When this approach is used in combination with a method previously developed by us and scoring at the state of art in predicting both location and topology of disulfide bonds in Eukaryotes (DisLocate), the per-protein accuracy is 38%, 2 percentage points higher than that previously obtained. Conclusions: In this paper we show that the inclusion of information derived from correlated mutations can improve the performance of the state of the art methods for predicting disulfide connectivity patterns in Eukaryotic proteins. Our analysis also provides support to the notion that improving methods to extract evolutionary information from multiple sequence alignments greatly contributes to the scoring performance of predictors suited to detect relevant features from protein chains. [ABSTRACT FROM AUTHOR]

It has been generally acknowledged that the module structure of protein interaction networks plays a crucial role with respect to the functional understanding of these networks. In this paper, we study evolutionary aspects of the module structure of protein interaction networks, which forms a mesoscopic level of description with respect to the architectural principles of networks. The purpose of this paper is to investigate limitations of well known gene duplication models by showing that these models are lacking crucial structural features present in protein interaction networks on a mesoscopic scale. This observation reveals our incomplete understanding of the structural evolution of protein networks on the module level. [ABSTRACT FROM AUTHOR]

A previous paper considered the problems that presently limit the hydrogen exchange - mass spectrometry (HX-MS) method for studying the biophysical and functional properties of proteins. Many of these problems can be overcome by obtaining and analyzing hundreds of sequentially overlapping peptide fragments that cover the protein many times over (Mayne et al. J. Am. Soc. Mass Spectrom. 2011: ). This paper describes a computer program called ExMS that furthers this advance by making it possible to efficiently process crowded mass spectra and definitively identify and characterize these many peptide fragments. ExMS automatically scans through high resolution MS data to find the individual isotopic peaks and isotopic envelopes of a list of peptides previously identified by MS/MS. It performs a number of tests to ensure correct identification in spite of peptide overlap in both chromatographic and mass spectrometric dimensions and possible multi-modal envelopes due to static or dynamic structural heterogeneity or HX EX1 behavior. The program can automatically process data from many sequential HX time points with no operator intervention at the rate of ~2 sec per peptide per HX time point using desktop computer equipment, but it also provides for rapid manual checking and decision when ambiguity exists. Additional subroutines can provide a step by step report of performance at each test along the way and parameter adjustment, deconvolute isotopic envelopes, and plot the time course of single and multi-modal H-D exchange. The program will be available on an open source basis at: [ABSTRACT FROM AUTHOR]

Measurement of the naturally occurring hydrogen exchange (HX) behavior of proteins can in principle provide highly resolved thermodynamic and kinetic information on protein structure, dynamics, and interactions. The HX fragment separation-mass spectrometry method (HX-MS) is able to measure hydrogen exchange in biologically important protein systems that are not accessible to NMR methods. In order to achieve high structural resolution in HX-MS experiments, it will be necessary to obtain many sequentially overlapping peptide fragments and be able to identify and analyze them efficiently and accurately by mass spectrometry. This paper describes operations which, when applied to four different proteins ranging in size from 140 to 908 residues, routinely provides hundreds of useful unique peptides, covering the entire protein length many times over. Coverage in terms of the average number of peptide fragments that span each amino acid exceeds 10. The ability to achieve these results required the integrated application of experimental methods that are described here and a computer analysis program, called ExMS, described in a following paper. [ABSTRACT FROM AUTHOR]

Background: This paper presents a novel approach to the problem of hedge detection, which involves identifying so-called hedge cues for labeling sentences as certain or uncertain. This is the classification problem for Task 1 of the CoNLL-2010 Shared Task, which focuses on hedging in the biomedical domain. We here propose to view hedge detection as a simple disambiguation problem, restricted to words that have previously been observed as hedge cues. As the feature space for the classifier is still very large, we also perform experiments with dimensionality reduction using the method of random indexing. Results: The SVM-based classifiers developed in this paper achieves the best published results so far for sentence-level uncertainty prediction on the CoNLL-2010 Shared Task test data. We also show that the technique of random indexing can be successfully applied for reducing the dimensionality of the original feature space by several orders of magnitude, without sacrificing classifier performance. Conclusions: This paper introduces a simplified approach to detecting speculation or uncertainty in text, focusing on the biomedical domain. Evaluated at the sentence-level, our SVM-based classifiers achieve the best published results so far. We also show that the feature space can be aggressively compressed using random indexing while still maintaining comparable classifier performance. [ABSTRACT FROM AUTHOR]

Background: Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far. Results: This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis. Conclusions: This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses. [ABSTRACT FROM AUTHOR]

Background: Analyzing interaction networks for functional characterization poses significant challenges arising from the noisy, incomplete, and generic nature of both the interaction data as well as functional annotation of molecules. Network-based methods focus on interacting molecules (pairs or sets) occurring in close proximity to infer functional associations. Results: In this paper we perform a formal comparative investigation of the relationship between functional coherence and topological proximity in networks. We investigate the problem of assessing the coherence of sets of biomolecules (or segments thereof) taking into account functional specificity as well as the distribution of functional attributes across entity groups. We also propose novel measures of topological proximity that are more robust to noisy and incomplete interaction data. Conclusion: We derive the following results in this paper: (i) there exists strong correlation between functional similarity and topological proximity in various network abstractions, with domain interaction networks (DDIs) demonstrating higher correlation than protein interaction networks (PPIs); (ii) measures that quantify coherence among entire sets of proteins are superior to aggregates of known pair-wise measures; and (iii) random-walk based measures of topological proximity are better suited to existing interaction data. We validate our methods on diverse data, including experimentally and computationally derived PPIs and DDIs, as well as on sets of known biologically related groups of molecules. [ABSTRACT FROM AUTHOR]

Background: Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in Escherichia coli (E. coli). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression. Results: In this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in E. coli. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production systems to improve the chance of high soluble protein production. Conclusion: In this paper, we show that a machine learning approach to the prediction of the efficacy of a vector for a target protein in a recombinant protein production system is promising and may compliment traditional knowledge-driven study of the efficacy. We will release our program to share with other labs in the public domain when this paper is published. [ABSTRACT FROM AUTHOR]

Background: Most of the existing in silico phosphorylation site prediction systems use machine learning approach that requires preparing a good set of classification data in order to build the classification knowledge. Furthermore, phosphorylation is catalyzed by kinase enzymes and hence the kinase information of the phosphorylated sites has been used as major classification data in most of the existing systems. Since the number of kinase annotations in protein sequences is far less than that of the proteins being sequenced to date, the prediction systems that use the information found from the small clique of kinase annotated proteins can not be considered as completely perfect for predicting outside the clique. Hence the systems are certainly not generalized. In this paper, a novel generalized prediction system, PPRED (Phosphorylation PREDictor) is proposed that ignores the kinase information and only uses the evolutionary information of proteins for classifying phosphorylation sites. Results: Experimental results based on cross validations and an independent benchmark reveal the significance of using the evolutionary information alone to classify phosphorylation sites from protein sequences. The prediction performance of the proposed system is better than those of the existing prediction systems that also do not incorporate kinase information. The system is also comparable to systems that incorporate kinase information in predicting such sites. Conclusions: The approach presented in this paper provides an efficient way to identify phosphorylation sites in a given protein primary sequence that would be a valuable information for the molecular biologists working on protein phosphorylation sites and for bioinformaticians developing generalized prediction systems for the post translational modifications like phosphorylation or glycosylation. PPRED is publicly available at the URL http://www.cse.univdhaka.edu/~ashis/ppred/index.php. [ABSTRACT FROM AUTHOR]

Background: Structural variations caused by a wide range of physico-chemical and biological sources directly influence the function of a protein. For enzymatic proteins, the structure and chemistry of the catalytic binding site residues can be loosely defined as a substructure of the protein. Comparative analysis of drug-receptor substructures across and within species has been used for lead evaluation. Substructure-level similarity between the binding sites of functionally similar proteins has also been used to identify instances of convergent evolution among proteins. In functionally homologous protein families, shared chemistry and geometry at catalytic sites provide a common, local point of comparison among proteins that may differ significantly at the sequence, fold, or domain topology levels. Results: This paper describes two key results that can be used separately or in combination for protein function analysis. The Family-wise Analysis of SubStructural Templates (FASST) method uses all-against-all substructure comparison to determine Substructural Clusters (SCs). SCs characterize the binding site substructural variation within a protein family. In this paper we focus on examples of automatically determined SCs that can be linked to phylogenetic distance between family members, segregation by conformation, and organization by homology among convergent protein lineages. The Motif Ensemble Statistical Hypothesis (MESH) framework constructs a representative motif for each protein cluster among the SCs determined by FASST to build motif ensembles that are shown through a series of function prediction experiments to improve the function prediction power of existing motifs. Conclusions: FASST contributes a critical feedback and assessment step to existing binding site substructure identification methods and can be used for the thorough investigation of structure-function relationships. The application of MESH allows for an automated, statistically rigorous procedure for incorporating structural variation data into protein function prediction pipelines. Our work provides an unbiased, automated assessment of the structural variability of identified binding site substructures among protein structure families and a technique for exploring the relation of substructural variation to protein function. As available proteomic data continues to expand, the techniques proposed will be indispensable for the large-scale analysis and interpretation of structural data. [ABSTRACT FROM AUTHOR]

Abstract: The present paper intends to overview a wide range of natural–origin polymers with special focus on proteins and polysaccharides (the systems more inspired on the extracellular matrix) that are being used in research, or might be potentially useful as carriers systems for active biomolecules or as cell carriers with application in the tissue engineering field targeting several biological tissues. The combination of both applications into a single material has proven to be very challenging though. The paper presents also some examples of commercially available natural–origin polymers with applications in research or in clinical use in several applications. As it is recognized, this class of polymers is being widely used due to their similarities with the extracellular matrix, high chemical versatility, typically good biological performance and inherent cellular interaction and, also very significant, the cell or enzyme-controlled degradability. These biocharacteristics classify the natural–origin polymers as one of the most attractive options to be used in the tissue engineering field and drug delivery applications. [Copyright &y& Elsevier]

The paper analyzes Information Geometry (IG) generated by the evolutionary Information Macrodynamics (IMD) which are memorized by IG renovated geometrical structures. The IG encloses a cellular structure which carries a genetic code for the IMD restoration. The paper focuses on the natural enfolding of the universal optimal genetic code into the IG, obtained from the preceding dynamics, with a forthcoming creation of initial genetic information by the IG cells. The field of information geometry consists of the cell's sets that enable the discrete production of a quantum of information at the cells' boundaries. A particular spatial–time dynamic trajectory, located on a curved information space, enfolds and encodes a specific triplet's code, accumulated by the IG cellular information structure. The paper explores a potential conversion of imaginary into real information, associated with the transformation of mind into matter, and evaluates the dissimilar directional "difficulty" of crossing the boundary between imaginary, virtual and physical real information. The attractive information forces, as attributes of the curved IG, assemble the elementary quantums of time-space virtual dynamics into the cooperative macrostructures and information networks. The results analytically prove for the first time that a macrostructure's genetic information is enfolded into the IG geometrical structures. This means that the morphology of any biological macrostructure, as well as its external surface and inner structures, encloses the code of the macrostructure's genetic information. The complexity of encoded macrostructure depends not just on the total number of its corresponding code's symbols, but essentially on the complexity of the coding structure presented by a hierarchy of the code's symbols. This information systemic property, applied to the encoding of biogenetic information, demonstrates that the same gene's number (for different bio-species) can encode proteins with a variety of distinctive complexities, characterizing the huge diversity of biosystems from plants to humans. [ABSTRACT FROM AUTHOR]

The article present abstracts of various papers that were presented during the Scandinavian Physiological Society meeting titled "Transport and Membrane Trafficking in Cells" that was held form May 7-9, 2004 at Goteborg University, Lundsbrunn Spa. One of the papers discussed is "Trafficking of Ion Transport Proteins: New Partners and Potential Therapies," by M.J. Caplan, A. Duffield, E. Kamsteeg, J. Gloeckner-Pagel and M.E. Egan. The physiologic function of an ion transport protein is determined, in part, by its subcellular localization and by the cellular mechanisms that modulate its activity. The authors are interested in the molecular signals and interactions that control the sorting and regulation of ion transport proteins in polarized cells. Another paper discussed is "Receptor Dynamics at Synapses: A View From Single Molecule Imaging," by A. Triller.

Abstract: This paper examines chemically selective displacement chromatography using affinity ranking plots, batch displacer screening experiments, column displacements, multi-component adsorption isotherms and spectroscopy. The affinity ranking plot indicated that the displacers, sucrose octasulfate (SOS) and tatrazine, should possess sufficient affinity to displace the proteins amyloglucosidase and apoferritin over a wide range of operating conditions. In addition, the plots indicated that the separation of these proteins by displacement chromatography would be extremely difficult. Further, the two proteins were shown to have very similar retention times under shallow linear gradient conditions. When batch displacement experiments were carried out, both tartrazine and SOS exhibited significant selectivity differences with respect to their ability to displace these two proteins, in contrast to the affinity ranking plot results. Column displacement experiments carried out with sucrose octasulfate agreed with the predictions of the affinity ranking plots, with both proteins being displaced but poorly resolved under several column displacement conditions. On the other hand, column displacement with tartrazine as the displacer resulted in the selective displacement and partial purification of apoferritin. Single- and multi-component isotherms of the proteins with or without the presence of displacers were determined and were used to help explain the selectivity reversals observed in the column and batch displacement experiments. In addition, fluorescence and CD spectra suggested that the displacers did not induce any structural changes to either of the proteins. The results in this paper indicate that multi-component adsorption behavior can be exploited for creating chemically selective displacement separations. [Copyright &y& Elsevier]

This paper employs a parallel batch screening technique for the identification of both selective and high-affinity displacers for a model binary mixture of proteins in a cation-exchange system. A variety of molecules were screened as possible displacers for the proteins ribonuclease A (RNAseA) and α-chymotrypsinogen A (α-chyA) on high performance Sepharose SP. The batch screening data for each protein was used to select leads for selective and high-affinity displacers and column experiments were carried out to evaluate the performance of the selected leads. The data from the batch displacements was also employed to generate quantitative structure–efficacy relationship (QSER) models based on a support vector machine regression approach. The resulting models had high correlation coefficients and were able to predict the behaviour of molecules not included in the training set. The descriptors selected in the QSER models for both proteins were examined to provide insights into factors influencing displacer selectivity in ion-exchange systems. The results presented in this paper demonstrate that this parallel batch screening–QSER approach can be employed for the identification of selective and high-affinity displacers for protein mixtures. [Copyright &y& Elsevier]

The human mitochondrial (mt) DNA is commonly described as a small, maternally inherited molecule that encodes 13 protein components of the oxidative phosphorylation system and 24 structural RNAs required for their translation. However, recent studies indicate that the human mtDNA has a larger functional repertoire than previously believed. This paper briefly summarizes these studies, which suggest to reconsider our way to describe the human mitochondrial DNA as it may code for more than 13 proteins. [ABSTRACT FROM AUTHOR]

This article provides a timely critique of a recent Nature paper by Pittis and Gabaldón that has suggested a late origin of mitochondria in eukaryote evolution. It shows that the inferred ancestry of many mitochondrial proteins has been incorrectly assigned by Pittis and Gabaldón to bacteria other than the aerobic proteobacteria from which the ancestor of mitochondria originates, thereby questioning the validity of their suggestion that mitochondrial acquisition may be a late event in eukaryote evolution. The analysis and approach presented here may guide future studies to resolve the true ancestry of mitochondria. [ABSTRACT FROM AUTHOR]

Biomolecules including DNA, protein, and enzymes are of prime importance in biomedical field. There are several reports on the technologies for the detection of these biomolecules on various diagnostic platforms. It is important to note that the performance of the biosensor is highly dependent on the substrate material used and its meticulous modification for particular applications. Therefore, it is critical to understand the principles of a biosensor to identify the correct substrate material and its surface modification chemistry. The imperative surface modification for the attachment of biomolecules without losing their bioactivity is a key to sensitive detection. Therefore, finding of a modification method which gives minimum damage to the surface as well as biomolecule is highly inevitable. Different surface modification technologies are invented according to the type of a substrate used. Surface modification techniques of the materials used as platforms in the fabrication of biosensors are reviewed in this paper. [ABSTRACT FROM AUTHOR]

Disordered regions of proteins are highly abundant in various biological processes, involving regulation and signaling and also in relation with cancer, cardiovascular, autoimmune diseases and neurodegenerative disorders. Hence, recognizing disordered regions in proteins is a critical task. In this paper, we presented a new feature encoding technique built from physicochemical properties of residues selected as per the chaotic structure of related protein sequence. Our feature vector has been tested with various classification algorithms on an up-to-date data set and also compared to other methods. The proposed method shows better classification performance than many methods in terms of accuracy, sensitivity and specificity. Our results suggest that the new method that links the residues and their physicochemical properties using Lyapunov exponents is highly effective in recognition of disordered regions. [ABSTRACT FROM AUTHOR]

We briefly review experimental and computational studies of room temperature ionic liquids (RTILs) interacting with important classes of biomolecules, including phospholipids, peptides and proteins, nucleic acids and carbohydrates. Most of these studies have been driven by the interest for RTILs applications as solvents. Thus, available experimental data cover primarily thermodynamic properties such as the reciprocal solubility of RTILs and bio-molecules, as well as phase boundaries. Less extensive data are also available on transport properties such as diffusion and viscosity of homogeneous binary (RTILs/biomolecules) and ternary (RTIL/biomolecules/water) solutions. Most of the structural information at the atomistic level, of interest especially for biochemical, pharmaceutical and nanotechnology applications, has been made available by molecular dynamics simulations. Major exceptions to this statement are represented by the results from NMR and circular dichroism spectroscopy, by selected neutron and X-ray scattering data, and by recent neutron reflectometry measurements on lipid bilayers on surfaces, hydrated by water-RTIL solutions. A final section of our paper summarizes new developments in the field of RTILs based on amino acids, that combine in themselves the two main aspects of our discussion, i.e. ionic liquids and bio-molecules. [ABSTRACT FROM PUBLISHER]

The nanostructures formed by inorganic nanoparticles together with organic molecules especially biomolecules have attracted increasing attention from both industries and researching fields due to their unique hybrid properties. In this paper, we systemically studied the interactions between amphiphilic polymer coated silver nanoparticles and bovine serum albumins by employing the fluorescence quenching approach in combination with the Stern-Volmer and Hill equations. The binding affinity was determined to 1.30 × 10 7 M −1 and the interaction was spontaneously driven by mainly the van der Waals force and hydrogen-bond mediated interactions, and negatively cooperative from the point of view of thermodynamics. With the non-uniform coating of amphiphilic polymer, the silver nanoparticles can form protein coronas which can become discrete protein–nanoparticle conjugates when controlling their molar ratios of mixing. The protein's conformational changes upon binding nanoparticles was also studied by using the three-dimensional fluorescence spectroscopy. [ABSTRACT FROM AUTHOR]

Cellulose has a long history dating back to ancient times in the evolution of humanity. It was a key material for basic needs, especially for the construction of shelters, paper making, which allowed our ancestors to perpetuate the valuable literary, philosophical or artistic works. In modern era, cellulose has acquired new dimensions of knowledge and scientific interest. This increased interest in cellulose is due to the need to exploit the still unknown resources that cellulose provides us, possibly due to the remarkable progress made lately in the field of fine characterization of the structure using sophisticated electron microscopes and other characterization techniques that have recently emerged. The growing demands of modern society in the direction of computerization and technology, have led the general interest to move from the classical writing paper to other types of "papers" that incorporates a high degree of ingenuity and intelligence, the so-called special papers, ranging from sensors, chips, motherboards, papers with a high degree of security, and many others. Among these, paper-based biosensors are of special interest, due to their high selectivity, simplicity, low price, and fast response. In this article we will review the new trends in the immobilization of biomolecules on various cellulose-based supports. In the first part, we will discuss the stages prior to the manufacture of a such support by specific chemical modification of the cellulosic substrate, followed by an overview of the most studied proteins, but also the most commonly used methods in monitoring protein adsorption on cellulosic substrates. • Cellulose modified surfaces for biomolecules attachment. • Adsorption of proteins on cellulose-based supports. • Different strategies to adsorb enzymes onto cellulosic supports. • Methods for the protein's adsorption monitoring. [ABSTRACT FROM AUTHOR]

This paper is a further study of ion binding to protein surfaces and builds on the studies of the binding of [Crk(CN)6]3- and [Fe(edta)(H2O)]- previously reported [Williams et al. (1982) FEBS Lett. 15, 293-299; Eley et al. (1982) Eur. J. Biochem. 124, 295-303]. In the present paper the binding of polyaminocarboxylate complexes of gadolinium have been studied. Eight ion-binding sites have been identified on the surface of cytochrome c. These exhibit different binding specificities which, in some cases, are not fully understood. However it is clear that simple outer-sphere interactions are not the sole determining factor for the association of metal ion complexes with proteins. The NMR paramagnetic difference spectrum method has been shown to be good at locating binding sites and revealing qualitative differences in their relative affinities for a range of complex types. However the use of relaxation probes is not a good method for the quantitative determination of binding constants; for this, isostructural shift probes must be sought. [ABSTRACT FROM AUTHOR]

Background: Despite sensitivity of MRI to diagnose multiple sclerosis (MS), prognostic biomarkers are still needed for optimized treatment. Objective: The objective of this paper is to identify cerebrospinal fluid (CSF) diagnostic biomarkers of MS using quantitative proteomics and to analyze their expression at different disease stages. Methods: We conducted differential analysis of the CSF proteome from control and relapsing–remitting MS (RRMS) patients followed by verification by ELISA of candidate biomarkers in CSF and serum in control, clinically isolated syndrome (CIS), RRMS and progressive MS (PMS) patients. Results: Twenty-two of the 527 quantified proteins exhibited different abundances in control and RRMS CSF. These include chitinase 3-like protein 1 (CHI3L1) and 2 (CHI3L2), which showed a strong expression in brain of MS patients, especially in astrocytes and microglial cells from white matter plaques. CSF and serum CHI3L1 levels increased with the disease stage and CIS patients with high CSF (>189 ng/ml) and serum (>33 ng/ml) CHI3L1 converted more rapidly to RRMS (log rank test, p < 0.05 and p < 0.001, respectively). In contrast, CSF CHI3L2 levels were lower in PMS than in RRMS patients. Accordingly, CSF CHI3L1/CHI3L2 ratio accurately discriminated PMS from RRMS. Conclusions: CSF CHI3L1 and CHI3L2 and serum CHI3L1 might help to define MS disease stage and have a prognostic value in CIS. [ABSTRACT FROM AUTHOR]

Background: Molecular activation pathways are inherently complex, and understanding relations across many biochemical reactions and reaction types is difficult. Visualizing and analyzing a pathway is a challenge due to the network size and the diversity of relations between proteins and molecules. Results: In this paper, we introduce PathwayMatrix, a visualization tool that presents the binary relations between proteins in the pathway via the use of an interactive adjacency matrix. We provide filtering, lensing, clustering, and brushing and linking capabilities in order to present relevant details about proteins within a pathway. Conclusions: We evaluated PathwayMatrix by conducting a series of in-depth interviews with domain experts who provided positive feedback, leading us to believe that our visualization technique could be helpful for the larger community of researchers utilizing pathway visualizations. PathwayMatrix is freely available at https://github.com/ CreativeCodingLab/PathwayMatrix. [ABSTRACT FROM AUTHOR]

Hsp70 proteins represent a family of chaperones that regulate cellular homeostasis and are required for cancer cell survival. However, their function and regulation in mitosis remain unknown. In this paper, we show that the major inducible cytoplasmic Hsp70 isoform, Hsp72, is required for assembly of a robust bipolar spindle capable of efficient chromosome congres-sion. Mechanistically, Hsp72 associates with the K-fiber--stabilizing proteins, ch-TOG and TACC3, and promotes their interaction with each other and recruitment to spindle microtubules (MTs). Targeting of Hsp72 to the mitotic spindle is dependent on phosphorylation at Thr-66 within its nucleotide-binding domain by the Nek6 kinase. Phos-phorylated Hsp72 concentrates on spindle poles and sites of MT-kinetochore attachment. A phosphomimetic Hsp72 mutant rescued defects in K-fiber assembly, ch-TOG/TACC3 recruitment and mitotic progression that also resulted from Nek6 depletion. We therefore propose that Nek6 facilitates association of Hsp72 with the mitotic spindle, where it promotes stable K-fiber assembly through recruitment of the ch-TOG-TACC3 complex. [ABSTRACT FROM AUTHOR]

An active area of research in bioinformatics is finding structural similarity of proteins by alignment. Among many methods, the popular one is to find the similarity based on statistical features. This method involves gathering information from the complex biomolecule structure and obtaining the best alignment by maximizing the number of matched features. In this paper, after reviewing statistical models for matching the structural biomolecule, it is shown that local alignment based on the Delaunay tetrahedralization (DT) can be used for Bayesian alignment of proteins. In this method, we use DT to add a priori structural information of protein in the Bayesian methodology. We demonstrate that this method shows advantages over competing methods in achieving a global alignment of proteins, accelerating the convergence rate and improving the parameter estimates. [ABSTRACT FROM AUTHOR]

Salmons' muscles are divided in myotomes assembled under form of letter W, delimited by myoseptes, which allow the muscular fibres to insert on bones. Orientation of muscular fibres in myotomes is complex and varies on all fish length function of the distance from vertebral column. To achieve the goals of the current paper were analysed lateral musculature, hypaxial and epaxial, for a number of 60 individuals of rainbow trout, 20 individuals for each age group (2nd summer, 3rd summer and 4th summer), determining the diameter of muscular fibres and also the density of muscular fibres per square unit. Trout from those six batches are from two salmon farms situated in Neamţ County, and during our research those ones were feed with granulated fodder with different granulation and with different protein level. As a result of cytometric and hystometric determinations on musculature gathered from rainbow trout individuals from those six batches were obtained values between 56.82 and 89.20 μ for muscular fibres diameter, respectively between 157.40 and 193.35 m. f./mm2 for density of muscular fibres. [ABSTRACT FROM AUTHOR]

The paper describes a newmethod of Information Structure Analysis (ISAN) for protein sequences. The method uses a new approximation for description of protein sequences, which allows us to detect hierarchically organized elements (Information Structure Elements (ISEL)) in them. It is shown that elements of the higher hierarchy level of information structure correspond to stable elements in the spatial structure of globular proteins. Based on the ISAN method, we propose an approach to design of the new sequences of recombinant proteins. The proposed approach was confirmed experimentally in the case of obtaining functionally active recombinant proteins. [ABSTRACT FROM AUTHOR]