Predicting Secondary Structures, Contact Numbers, and Residue-wise Contact Orders of Native Protein Structure from Amino Acid Sequence by Critical Random Networks

Prediction of one-dimensional protein structures such as secondary structures and contact numbers is useful for the three-dimensional structure prediction and important for the understanding of sequence-structure relationship. Here we present a new machine-learning method, critical random networks (CRNs), for predicting one-dimensional structures, and apply it, with position-specific scoring matrices, to the prediction of secondary structures (SS), contact numbers (CN), and residue-wise contact orders (RWCO). The present method achieves, on average, $Q_3$ accuracy of 77.8% for SS, correlation coefficients of 0.726 and 0.601 for CN and RWCO, respectively. The accuracy of the SS prediction is comparable to other state-of-the-art methods, and that of the CN prediction is a significant improvement over previous methods. We give a detailed formulation of critical random networks-based prediction scheme, and examine the context-dependence of prediction accuracies. In order to study the nonlinear and multi-body effects, we compare the CRNs-based method with a purely linear method based on position-specific scoring matrices. Although not superior to the CRNs-based method, the surprisingly good accuracy achieved by the linear method highlights the difficulty in extracting structural features of higher order from amino acid sequence beyond that provided by the position-specific scoring matrices.Comment: 20 pages, 1 figure, 5 tables; minor revision; accepted for publication in BIOPHYSIC

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs which represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.Comment: 34 pages, 7 figure

Unique Interplay between Sugar and Lipid in Determining the Antigenic Potency of Bacterial Antigens for NKT Cells

Structural and biophysical studies reveal the induced-fit mechanism underlying the stringent specificity of invariant natural killer T cells for unique glycolipid antigens from the pathogen Streptococcus pneumoniae

Release of magnesium from vermiculite by acid dissolution

The vermiculite from Paulistânia, State of Piauí, was used to study a release of magnesium by acid dissolution. The material was ground and sieved to separate two fractions: 0.50 to 0.15mm and < 0.10mm. Each fraction was divided into three parts, two of which were heated respectively to 550°C and 950°C in a muffle furnace for one hour. These vermiculites were treated with concentrated sulfuric acid and concentrated phosphoric acid in order to evaluate their efficiency in acid dissolution of vermiculite. A release of magnesium in relation to a quantity of sulfuric acid added and a amount of calcium carbonate necessary to neutralize a residual acidity of the product were also investigated. The sulfuric acid was just as effective as phosphoric acid in the dissolution of vermiculites and the release of magnesium. The particle-size and heat treatment of vermiculite had no influence on the amount of magnesium released by acid dissolution. The addiction of sulfuric acid to vermiculite in equal amount released more than 80% of magnesium. A quantity of calcium carbonate necessary to neutralize the residual acidity of the product was about one half the weight of the vermiculite.Estudou-se a liberação de magnésio estrutural da vermiculita procedente de Paulistânia, Estado de Piauí. O material foi triturado e peneirado para obter duas frações de 0,50 a l,15mm e de < 0,10mm. Cada fração de vermiculita foi dividida em três partes. As duas partes foram aquecidas num forno mufla às temperaturas de 550 e 950°C, respectivamente, durante uma hora. As vermiculitas, assim preparadas, foram tratadas com ácido sulfúrico conc. e ácido fosfórico conc. para avaliar a eficiência dos ácidos na liberação de magnésio. Em seguida, estudou-se a liberação de magnésio em função da quantidade de ácido sulfúrico e a necessidade de carbonato de cálcio para neutralizar a acidez residual do produto. Não houve diferença entre o ácido sulfúrico e o ácido fosfórico quanto a extração de magnésio da vermiculita. A granulometria e o aquecimento não influiram na liberação de magnésio pelos ácidos. A adição de ácido sulfúrico à vermiculita em quantidades iguais liberou mais que 80% de magnésio. A quantidade de carbonato de cálcio necessária para neutralizar a acidez residual do produto foi aproximadamente a metade do peso da vermiculita

Predicting residue-wise contact orders in proteins by support vector regression

BACKGROUND: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. RESULTS: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. CONCLUSION: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences

Induction of Expandable Tissue-Specific Progenitor Cells from Human Pancreatic Tissue through Transient Expression of Defined Factors

We recently demonstrated the generation of mouse induced tissue-specific stem (iTS) cells through transient overexpression of reprogramming factors combined with tissue-specific selection. Here we induced expandable tissue-specific progenitor (iTP) cells from human pancreatic tissue through transient expression of genes encoding the reprogramming factors OCT4 (octamer-binding transcription factor 4), p53 small hairpin RNA (shRNA), SOX2 (sex-determining region Y-box 2), KLF4 (Kruppel-like factor 4), L-MYC, and LIN28. Transfection of episomal plasmid vectors into human pancreatic tissue efficiently generated iTP cells expressing genetic markers of endoderm and pancreatic progenitors. The iTP cells differentiated into insulin-producing cells more efficiently than human induced pluripotent stem cells (iPSCs). iTP cells continued to proliferate faster than pancreatic tissue cells until days 100–120 (passages 15–20). iTP cells subcutaneously inoculated into immunodeficient mice did not form teratomas. Genomic bisulfite nucleotide sequence analysis demonstrated that the OCT4 and NANOG promoters remained partially methylated in iTP cells. We compared the global gene expression profiles of iPSCs, iTP cells, and pancreatic cells (islets >80%). Microarray analyses revealed that the gene expression profiles of iTP cells were similar, but not identical, to those of iPSCs but different from those of pancreatic cells. The generation of human iTP cells may have important implications for the clinical application of stem/progenitor cells

Amino acid "little Big Bang": Representing amino acid substitution matrices as dot products of Euclidian vectors

<p>Abstract</p> <p>Background</p> <p>Sequence comparisons make use of a one-letter representation for amino acids, the necessary quantitative information being supplied by the substitution matrices. This paper deals with the problem of finding a representation that provides a comprehensive description of amino acid intrinsic properties consistent with the substitution matrices.</p> <p>Results</p> <p>We present a Euclidian vector representation of the amino acids, obtained by the singular value decomposition of the substitution matrices. The substitution matrix entries correspond to the dot product of amino acid vectors. We apply this vector encoding to the study of the relative importance of various amino acid physicochemical properties upon the substitution matrices. We also characterize and compare the PAM and BLOSUM series substitution matrices.</p> <p>Conclusions</p> <p>This vector encoding introduces a Euclidian metric in the amino acid space, consistent with substitution matrices. Such a numerical description of the amino acid is useful when intrinsic properties of amino acids are necessary, for instance, building sequence profiles or finding consensus sequences, using machine learning algorithms such as Support Vector Machine and Neural Networks algorithms.</p