Physicochemical properties of proteinases from selected psychrotrophic bacteria.

The high-throughput technologies have led to vast amounts of protein-protein interaction (PPI) data, and a number of approaches based on PPI networks have been proposed for protein function prediction. However, these approaches do not work well if there is not enough PPI information. To address this issue, we propose a novel collective classification based approach that combines protein sequence information and PPI information to improve the prediction performance. We first reconstruct a PPI network by adding a number of computed edges based on protein sequence similarity, and then apply a collective classification algorithm to predict protein function based on the new PPI network. Experiments over two real datasets demonstrate that our approach outperforms most of existing approaches across a series of label situations, especially in sparsely-labeled networks where the existing approaches fail because of PPI information inadequacy. Experimental results also validate the robustness of our approach to the number of labeled proteins in PPI networks.

A new method for similarity analysis of protein sequences is presented in this paper. On the basis of positions, proportion difference and various physicochemical properties of 20 kinds of amino acid in different protein sequences, representative information was extracted from protein sequence and converted into a numeric vector, thus further similarities of protein sequences were analyzed by studying the similarities between vectors. To facilitate the comparison between protein sequences of different length, every protein sequence is first mapped to a fixed-length vector, of which the vector information is relative position of amino acids. Then percentage of 20 kinds of amino acids in the sequence and 3 physicochemical properties are combined to constitute physicochemical information vector. Finally, a one-dimensiona l feature vector with 80 elements(feat ure vector) representing a protein sequence is synthesized. The shortest distance method was applied for cluster analysis on feature vectors so as to analyze similarities in protein sequences. In the numerical experiment part of the article, similarity analysis was conducted for 9 different species of the mitochondrial NADH dehydrogenase. The result of numerical experiment is consistent with the biological fact, which validates the effectiveness of model to a certain extent.

Bauhinia pentandra (Bong) Vog. ex. Steua seeds were investigated with respect to phenologic aspects (size, mass, hilum and length) and with respect to their chemical composition. The total nitrogen content of the seed flour was determined, and the flour was extracted in different pH values. A lectin was isolated from the seeds by Sepharose-4B affinity chromatography. The homogeneity of the lectin was demonstrated by SDS-PAGE in the presence of beta-mercaptoethanol. Only one protein band with an apparent molecular mass of 30 kDa was found. The B. pentandra lectin showed a carbohydrate specificity for D-galactose, a requirement for divalent metal cations (Ca2+ and Mn2+) for full activity and amino acid composition with a high content of aspartic acid, glutamic acid and alanine and low levels of methionine, cysteine and tryptophan. The lectin agglutinated rabbit and human A group erythrocytes and was relatively stable to heat treatment, retaining half of its original activity after 60 min at 70 ºC.

Percolation of annotation errors through hierarchically structured protein sequence databases

Comparative protein analysis of the chitin metabolic pathway in extant organisms: A complex network approach

BackgroundComputational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.ResultsAAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins.ConclusionAAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains.

The kinetic models of metabolic pathways represent a system of biochemical reactions in terms of metabolic fluxes and enzyme kinetics. Therefore, the apparent differences of metabolic fluxes might reflect distinctive kinetic characteristics, as well as sequence-dependent properties of the employed enzymes. This study aims to examine possible linkages between kinetic constants and the amino acid (AA) composition (AAC) for enzymes from the yeast Saccharomyces cerevisiae glycolytic pathway. The values of Michaelis-Menten constant (KM), turnover number (kcat), and specificity constant (ksp = kcat/KM) were taken from BRENDA (15, 17, and 16 values, respectively) and protein sequences of nine enzymes (HXK, GADH, PGK, PGM, ENO, PK, PDC, TIM, and PYC) from UniProtKB. The AAC and sequence properties were computed by ExPASy/ProtParam tool and data processed by conventional methods of multivariate statistics. Multiple linear regressions were found between the log-values of kcat (3 models, 85.74% < Radj.2 <94.11%, p < 0.00001), KM (1 model, Radj.2 = 96.70%, p < 0.00001), ksp (3 models, 96.15% < Radj.2 < 96.50%, p < 0.00001), and the sets of AA frequencies (four to six for each model) selected from enzyme sequences while assessing the potential multicollinearity between variables. It was also found that the selection of independent variables in multiple regression models may reflect certain advantages for definite AA physicochemical and structural propensities, which could affect the properties of sequences. The results support the view on the actual interdependence of catalytic, binding, and structural residues to ensure the efficiency of biocatalysts, since the kinetic constants of the yeast enzymes appear as closely related to the overall AAC of sequences.

The Wingless Signaling Pathway Is Directly Involved inDrosophilaHeart Development

Development of an accurate and reliable intelligent decision-making method for the construction of cancer diagnosis system is one of the fast growing research areas of health sciences. Such decision-making system can provide adequate information for cancer diagnosis and drug discovery. Descriptors derived from physicochemical properties of protein sequences are very useful for classifying cancerous proteins. Recently, several interesting research studies have been reported on breast cancer classification. To this end, we propose the exploitation of the physicochemical properties of amino acids in protein primary sequences such as hydrophobicity (Hd) and hydrophilicity (Hb) for breast cancer classification. Hd and Hb properties of amino acids, in recent literature, are reported to be quite effective in characterizing the constituent amino acids and are used to study protein foldings, interactions, structures, and sequence-order effects. Especially, using these physicochemical properties, we observed that proline, serine, tyrosine, cysteine, arginine, and asparagine amino acids offer high discrimination between cancerous and healthy proteins. In addition, unlike traditional ensemble classification approaches, the proposed 'IDM-PhyChm-Ens' method was developed by combining the decision spaces of a specific classifier trained on different feature spaces. The different feature spaces used were amino acid composition, split amino acid composition, and pseudo amino acid composition. Consequently, we have exploited different feature spaces using Hd and Hb properties of amino acids to develop an accurate method for classification of cancerous protein sequences. We developed ensemble classifiers using diverse learning algorithms such as random forest (RF), support vector machines (SVM), and K-nearest neighbor (KNN) trained on different feature spaces. We observed that ensemble-RF, in case of cancer classification, performed better than ensemble-SVM and ensemble-KNN. Our analysis demonstrates that ensemble-RF, ensemble-SVM and ensemble-KNN are more effective than their individual counterparts. The proposed 'IDM-PhyChm-Ens' method has shown improved performance compared to existing techniques.

Dicer and Dicer like (DCLs) proteins are essential part of small RNA biogenesis pathway, is a type of RNase III digesting long dsRNA (pre-miRNA) to small RNA segments (miRNA). A total of 20 full length of Dicer like proteins (DCL1, DCL2, DCL3 and DCL4) from different organisms available in NCBI were evaluated by bioinformatics tools to investigate properties, structure of DCLs, domain analysis, multiple sequence alignment and phylogenetics tree construction. All DCLs protein sequences have Ribonuclease III protein family that contains RNaseIII domain including Helicase ATP-binding type-1, Helicase C-terminal, Dicer double-stranded RNA-binding fold, PAZ, Ribonuclease III, Double stranded RNA-binding domain (dsRB). Physicochemical analysis offers data such as pI, EC, Al, GRAVY and instability index about these enzymes. Putative phosphorylation sites were also identified which are found to be conserved in plant species and the results showed that the most abundant phosphorylation site is Serine residues in DCLs proteins. Patterns and profile analysis were performed using Prosite and conserved protein motifs subjected to MEME to obtain the best possible matches. The phylogenetics tree represented three major clusters and similar DCLs protein sequences of different plant species clustered together. The obtained results could be used for further in silico analysis and homology modeling studies. Keywords: Dicer, DCLs, miRNA, RNase, In silico analysis. *Corresponding Author: Ertugrul Filiz (e-mail: ertugrulfiliz@gmail.com). (Received: 25.05.2012 Accepted: 24.01.2013)

The Impact of Gene Expression Regulation on Evolution of Extracellular Signaling Pathways

BackgroundNuclear receptors (NRs) form a large family of ligand-inducible transcription factors that regulate gene expressions involved in numerous physiological phenomena, such as embryogenesis, homeostasis, cell growth and death. These nuclear receptors-related pathways are important targets of marketed drugs. Therefore, the design of a reliable computational model for predicting NRs from amino acid sequence has now been a significant biomedical problem.ResultsConjoint triad feature (CTF) mainly considers neighbor relationships in protein sequences by encoding each protein sequence using the triad (continuous three amino acids) frequency distribution extracted from a 7-letter reduced alphabet. In addition, chaos game representation (CGR) can investigate the patterns hidden in protein sequences and visually reveal previously unknown structure. In this paper, three methods, CTF, CGR, amino acid composition (AAC), are applied to formulate the protein samples. By considering different combinations of three methods, we study seven groups of features, and each group is evaluated by the 10-fold cross-validation test. Meanwhile, a new non-redundant dataset containing 474 NR sequences and 500 non-NR sequences is built based on the latest NucleaRDB database. Comparing the results of numerical experiments, the group of combined features with CTF and AAC gets the best result with the accuracy of 96.30 % for identifying NRs from non-NRs. Moreover, if it is classified as a NR, it will be further put into the second level, which will classify a NR into one of the eight main subfamilies. At the second level, the group of combined features with CTF and AAC also gets the best accuracy of 94.73 %. Subsequently, the proposed predictor is compared with two existing methods, and the comparisons show that the accuracies of two levels significantly increase to 98.79 % (NR-2L: 92.56 %; iNR-PhysChem: 98.18 %; the first level) and 93.71 % (NR-2L: 88.68 %; iNR-PhysChem: 92.45 %; the second level) with the introduction of our CTF-based method. Finally, each component of CTF features is analyzed via the statistical significant test, and a simplified model only with the resulting top-50 significant features achieves accuracy of 95.28 %.ConclusionsThe experimental results demonstrate that our CTF-based method is an effective way for predicting nuclear receptor proteins. Furthermore, the top-50 significant features obtained from the statistical significant test are considered as the “intrinsic features” in predicting NRs based on the analysis of relative importance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0828-1) contains supplementary material, which is available to authorized users.

Soybean seed storage protein is a very important protein source for human and livestock, and its amino acid composition is well-balanced except for the low level of sulfur amino acids, methionine, and cysteine. β-Conglycinin (7S globulin) and glycinin (11S globulin) are the major storage proteins in soybean seeds, and β-conglycinin consists mainly of α′, α, and β-subunits and glycinin consists of acidic and basic subunits. It is generally recognized that the sulfur amino acid content is higher in the following sequence: acidic subunit >; basic subunit> α′-subunit >; α-subunit >; β-subunit (Wilson 1987).

Amino acid composition of genomes, lifestyles of organisms, and evolutionary trends: a global picture with correspondence analysis

The prediction of protein function as well as the reconstruction of evolutionary genesis employing sequence comparison at large is still the most powerful tool in sequence analysis. Due to the exponential growth of the number of known protein sequences and the subsequent quadratic growth of the similarity matrix, the computation of the Similarity Matrix of Proteins (SIMAP) becomes a computational intensive task. The SIMAP database provides a comprehensive and up-to-date pre-calculation of the protein sequence similarity matrix, sequence-based features and sequence clusters. As of September 2009, SIMAP covers 48 million proteins and more than 23 million non-redundant sequences. Novel features of SIMAP include the expansion of the sequence space by including databases such as ENSEMBL as well as the integration of metagenomes based on their consistent processing and annotation. Furthermore, protein function predictions by Blast2GO are pre-calculated for all sequences in SIMAP and the data access and query functions have been improved. SIMAP assists biologists to query the up-to-date sequence space systematically and facilitates large-scale downstream projects in computational biology. Access to SIMAP is freely provided through the web portal for individuals (http://mips.gsf.de/simap/) and for programmatic access through DAS (http://webclu.bio.wzw.tum.de/das/) and Web-Service (http://mips.gsf.de/webservices/services/SimapService2.0?wsdl).