Method for sequencing nucleic acid molecules

DOEpatents

Korlach, Jonas; Webb, Watt W.; Levene, Michael; Turner, Stephen; Craighead, Harold G.; Foquet, Mathieu

2006-06-06

The present invention is directed to a method of sequencing a target nucleic acid molecule having a plurality of bases. In its principle, the temporal order of base additions during the polymerization reaction is measured on a molecule of nucleic acid, i.e. the activity of a nucleic acid polymerizing enzyme on the template nucleic acid molecule to be sequenced is followed in real time. The sequence is deduced by identifying which base is being incorporated into the growing complementary strand of the target nucleic acid by the catalytic activity of the nucleic acid polymerizing enzyme at each step in the sequence of base additions. A polymerase on the target nucleic acid molecule complex is provided in a position suitable to move along the target nucleic acid molecule and extend the oligonucleotide primer at an active site. A plurality of labelled types of nucleotide analogs are provided proximate to the active site, with each distinguishable type of nucleotide analog being complementary to a different nucleotide in the target nucleic acid sequence. The growing nucleic acid strand is extended by using the polymerase to add a nucleotide analog to the nucleic acid strand at the active site, where the nucleotide analog being added is complementary to the nucleotide of the target nucleic acid at the active site. The nucleotide analog added to the oligonucleotide primer as a result of the polymerizing step is identified. The steps of providing labelled nucleotide analogs, polymerizing the growing nucleic acid strand, and identifying the added nucleotide analog are repeated so that the nucleic acid strand is further extended and the sequence of the target nucleic acid is determined.

Method for sequencing nucleic acid molecules

DOEpatents

Korlach, Jonas; Webb, Watt W.; Levene, Michael; Turner, Stephen; Craighead, Harold G.; Foquet, Mathieu

2006-05-30

The present invention is directed to a method of sequencing a target nucleic acid molecule having a plurality of bases. In its principle, the temporal order of base additions during the polymerization reaction is measured on a molecule of nucleic acid, i.e. the activity of a nucleic acid polymerizing enzyme on the template nucleic acid molecule to be sequenced is followed in real time. The sequence is deduced by identifying which base is being incorporated into the growing complementary strand of the target nucleic acid by the catalytic activity of the nucleic acid polymerizing enzyme at each step in the sequence of base additions. A polymerase on the target nucleic acid molecule complex is provided in a position suitable to move along the target nucleic acid molecule and extend the oligonucleotide primer at an active site. A plurality of labelled types of nucleotide analogs are provided proximate to the active site, with each distinguishable type of nucleotide analog being complementary to a different nucleotide in the target nucleic acid sequence. The growing nucleic acid strand is extended by using the polymerase to add a nucleotide analog to the nucleic acid strand at the active site, where the nucleotide analog being added is complementary to the nucleotide of the target nucleic acid at the active site. The nucleotide analog added to the oligonucleotide primer as a result of the polymerizing step is identified. The steps of providing labelled nucleotide analogs, polymerizing the growing nucleic acid strand, and identifying the added nucleotide analog are repeated so that the nucleic acid strand is further extended and the sequence of the target nucleic acid is determined.

Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences

PubMed Central

Zhang, Long; Jia, Lianyin; Ren, Yazhou

2017-01-01

Protein-protein interactions (PPIs) play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs) and a novel local conjoint triad description (LCTD) feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae, DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC) as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study. PMID:29117139

Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences.

PubMed

Wang, Jun; Zhang, Long; Jia, Lianyin; Ren, Yazhou; Yu, Guoxian

2017-11-08

Protein-protein interactions (PPIs) play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs) and a novel local conjoint triad description (LCTD) feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae , DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC) as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study.

The complete amino acid sequence of human skeletal-muscle fructose-bisphosphate aldolase.

PubMed Central

Freemont, P S; Dunbar, B; Fothergill-Gilmore, L A

1988-01-01

The complete amino acid sequence of human skeletal-muscle fructose-bisphosphate aldolase, comprising 363 residues, was determined. The sequence was deduced by automated sequencing of CNBr-cleavage, o-iodosobenzoic acid-cleavage, trypsin-digest and staphylococcal-proteinase-digest fragments. Comparison of the sequence with other class I aldolase sequences shows that the mammalian muscle isoenzyme is one of the most highly conserved enzymes known, with only about 2% of the residues changing per 100 million years. Non-mammalian aldolases appear to be evolving at the same rate as other glycolytic enzymes, with about 4% of the residues changing per 100 million years. Secondary-structure predictions are analysed in an accompanying paper [Sawyer, Fothergill-Gilmore & Freemont (1988) Biochem. J. 249, 789-793]. PMID:3355497

SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

PubMed

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-05-01

Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

PubMed Central

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-01-01

Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of

Method for identifying and quantifying nucleic acid sequence aberrations

DOEpatents

Lucas, Joe N.; Straume, Tore; Bogen, Kenneth T.

1998-01-01

A method for detecting nucleic acid sequence aberrations by detecting nucleic acid sequences having both a first and a second nucleic acid sequence type, the presence of the first and second sequence type on the same nucleic acid sequence indicating the presence of a nucleic acid sequence aberration. The method uses a first hybridization probe which includes a nucleic acid sequence that is complementary to a first sequence type and a first complexing agent capable of attaching to a second complexing agent and a second hybridization probe which includes a nucleic acid sequence that selectively hybridizes to the second nucleic acid sequence type over the first sequence type and includes a detectable marker for detecting the second hybridization probe.

Method for identifying and quantifying nucleic acid sequence aberrations

DOEpatents

Lucas, J.N.; Straume, T.; Bogen, K.T.

1998-07-21

A method is disclosed for detecting nucleic acid sequence aberrations by detecting nucleic acid sequences having both a first and a second nucleic acid sequence type, the presence of the first and second sequence type on the same nucleic acid sequence indicating the presence of a nucleic acid sequence aberration. The method uses a first hybridization probe which includes a nucleic acid sequence that is complementary to a first sequence type and a first complexing agent capable of attaching to a second complexing agent and a second hybridization probe which includes a nucleic acid sequence that selectively hybridizes to the second nucleic acid sequence type over the first sequence type and includes a detectable marker for detecting the second hybridization probe. 11 figs.

Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

PubMed Central

Burger, Lukas; van Nimwegen, Erik

2008-01-01

Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

Solid phase sequencing of double-stranded nucleic acids

DOEpatents

Fu, Dong-Jing; Cantor, Charles R.; Koster, Hubert; Smith, Cassandra L.

2002-01-01

This invention relates to methods for detecting and sequencing of target double-stranded nucleic acid sequences, to nucleic acid probes and arrays of probes useful in these methods, and to kits and systems which contain these probes. Useful methods involve hybridizing the nucleic acids or nucleic acids which represent complementary or homologous sequences of the target to an array of nucleic acid probes. These probe comprise a single-stranded portion, an optional double-stranded portion and a variable sequence within the single-stranded portion. The molecular weights of the hybridized nucleic acids of the set can be determined by mass spectroscopy, and the sequence of the target determined from the molecular weights of the fragments. Nucleic acids whose sequences can be determined include nucleic acids in biological samples such as patient biopsies and environmental samples. Probes may be fixed to a solid support such as a hybridization chip to facilitate automated determination of molecular weights and identification of the target sequence.

The DynaMine webserver: predicting protein dynamics from sequence.

PubMed

Cilia, Elisa; Pancsa, Rita; Tompa, Peter; Lenaerts, Tom; Vranken, Wim F

2014-07-01

Protein dynamics are important for understanding protein function. Unfortunately, accurate protein dynamics information is difficult to obtain: here we present the DynaMine webserver, which provides predictions for the fast backbone movements of proteins directly from their amino-acid sequence. DynaMine rapidly produces a profile describing the statistical potential for such movements at residue-level resolution. The predicted values have meaning on an absolute scale and go beyond the traditional binary classification of residues as ordered or disordered, thus allowing for direct dynamics comparisons between protein regions. Through this webserver, we provide molecular biologists with an efficient and easy to use tool for predicting the dynamical characteristics of any protein of interest, even in the absence of experimental observations. The prediction results are visualized and can be directly downloaded. The DynaMine webserver, including instructive examples describing the meaning of the profiles, is available at http://dynamine.ibsquare.be. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

PubMed Central

2012-01-01

Background The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. Results We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest index score, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence

Improved therapy-success prediction with GSS estimated from clinical HIV-1 sequences.

PubMed

Pironti, Alejandro; Pfeifer, Nico; Kaiser, Rolf; Walter, Hauke; Lengauer, Thomas

2014-01-01

Rules-based HIV-1 drug-resistance interpretation (DRI) systems disregard many amino-acid positions of the drug's target protein. The aims of this study are (1) the development of a drug-resistance interpretation system that is based on HIV-1 sequences from clinical practice rather than hard-to-get phenotypes, and (2) the assessment of the benefit of taking all available amino-acid positions into account for DRI. A dataset containing 34,934 therapy-naïve and 30,520 drug-exposed HIV-1 pol sequences with treatment history was extracted from the EuResist database and the Los Alamos National Laboratory database. 2,550 therapy-change-episode baseline sequences (TCEB) were assigned to test set A. Test set B contains 1,084 TCEB from the HIVdb TCE repository. Sequences from patients absent in the test sets were used to train three linear support vector machines to produce scores that predict drug exposure pertaining to each of 20 antiretrovirals: the first one uses the full amino-acid sequences (DEfull), the second one only considers IAS drug-resistance positions (DEonlyIAS), and the third one disregards IAS drug-resistance positions (DEnoIAS). For performance comparison, test sets A and B were evaluated with DEfull, DEnoIAS, DEonlyIAS, geno2pheno[resistance], HIVdb, ANRS, HIV-GRADE, and REGA. Clinically-validated cut-offs were used to convert the continuous output of the first four methods into susceptible-intermediate-resistant (SIR) predictions. With each method, a genetic susceptibility score (GSS) was calculated for each therapy episode in each test set by converting the SIR prediction for its compounds to integer: S=2, I=1, and R=0. The GSS were used to predict therapy success as defined by the EuResist standard datum definition. Statistical significance was assessed using a Wilcoxon signed-rank test. A comparison of the therapy-success prediction performances among the different interpretation systems for test set A can be found in Table 1, while those for test set

Protein binding hot spots prediction from sequence only by a new ensemble learning method.

PubMed

Hu, Shan-Shan; Chen, Peng; Wang, Bing; Li, Jinyan

2017-10-01

Hot spots are interfacial core areas of binding proteins, which have been applied as targets in drug design. Experimental methods are costly in both time and expense to locate hot spot areas. Recently, in-silicon computational methods have been widely used for hot spot prediction through sequence or structure characterization. As the structural information of proteins is not always solved, and thus hot spot identification from amino acid sequences only is more useful for real-life applications. This work proposes a new sequence-based model that combines physicochemical features with the relative accessible surface area of amino acid sequences for hot spot prediction. The model consists of 83 classifiers involving the IBk (Instance-based k means) algorithm, where instances are encoded by important properties extracted from a total of 544 properties in the AAindex1 (Amino Acid Index) database. Then top-performance classifiers are selected to form an ensemble by a majority voting technique. The ensemble classifier outperforms the state-of-the-art computational methods, yielding an F1 score of 0.80 on the benchmark binding interface database (BID) test set. http://www2.ahu.edu.cn/pchen/web/HotspotEC.htm .

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

PubMed Central

Song, Jiangning; Tan, Hao; Wang, Mingjun; Webb, Geoffrey I.; Akutsu, Tatsuya

2012-01-01

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/. PMID:22319565

NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

PubMed

Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

2010-11-30

β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

Discrete sequence prediction and its applications

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

Learning from experience to predict sequences of discrete symbols is a fundamental problem in machine learning with many applications. We apply sequence prediction using a simple and practical sequence-prediction algorithm, called TDAG. The TDAG algorithm is first tested by comparing its performance with some common data compression algorithms. Then it is adapted to the detailed requirements of dynamic program optimization, with excellent results.

Amino acid sequence analysis of the annexin super-gene family of proteins.

PubMed

Barton, G J; Newman, R H; Freemont, P S; Crumpton, M J

1991-06-15

The annexins are a widespread family of calcium-dependent membrane-binding proteins. No common function has been identified for the family and, until recently, no crystallographic data existed for an annexin. In this paper we draw together 22 available annexin sequences consisting of 88 similar repeat units, and apply the techniques of multiple sequence alignment, pattern matching, secondary structure prediction and conservation analysis to the characterisation of the molecules. The analysis clearly shows that the repeats cluster into four distinct families and that greatest variation occurs within the repeat 3 units. Multiple alignment of the 88 repeats shows amino acids with conserved physicochemical properties at 22 positions, with only Gly at position 23 being absolutely conserved in all repeats. Secondary structure prediction techniques identify five conserved helices in each repeat unit and patterns of conserved hydrophobic amino acids are consistent with one face of a helix packing against the protein core in predicted helices a, c, d, e. Helix b is generally hydrophobic in all repeats, but contains a striking pattern of repeat-specific residue conservation at position 31, with Arg in repeats 4 and Glu in repeats 2, but unconserved amino acids in repeats 1 and 3. This suggests repeats 2 and 4 may interact via a buried saltbridge. The loop between predicted helices a and b of repeat 3 shows features distinct from the equivalent loop in repeats 1, 2 and 4, suggesting an important structural and/or functional role for this region. No compelling evidence emerges from this study for uteroglobin and the annexins sharing similar tertiary structures, or for uteroglobin representing a derivative of a primordial one-repeat structure that underwent duplication to give the present day annexins. The analyses performed in this paper are re-evaluated in the Appendix, in the light of the recently published X-ray structure for human annexin V. The structure confirms most of

[Complete genome sequencing of polymalic acid-producing strain Aureobasidium pullulans CCTCC M2012223].

PubMed

Wang, Yongkang; Song, Xiaodan; Li, Xiaorong; Yang, Sang-tian; Zou, Xiang

2017-01-04

To explore the genome sequence of Aureobasidium pullulans CCTCC M2012223, analyze the key genes related to the biosynthesis of important metabolites, and provide genetic background for metabolic engineering. Complete genome of A. pullulans CCTCC M2012223 was sequenced by Illumina HiSeq high throughput sequencing platform. Then, fragment assembly, gene prediction, functional annotation, and GO/COG cluster were analyzed in comparison with those of other five A. pullulans varieties. The complete genome sequence of A. pullulans CCTCC M2012223 was 30756831 bp with an average GC content of 47.49%, and 9452 genes were successfully predicted. Genome-wide analysis showed that A. pullulans CCTCC M2012223 had the biggest genome assembly size. Protein sequences involved in the pullulan and polymalic acid pathway were highly conservative in all of six A. pullulans varieties. Although both A. pullulans CCTCC M2012223 and A. pullulans var. melanogenum have a close affinity, some point mutation and inserts were occurred in protein sequences involved in melanin biosynthesis. Genome information of A. pullulans CCTCC M2012223 was annotated and genes involved in melanin, pullulan and polymalic acid pathway were compared, which would provide a theoretical basis for genetic modification of metabolic pathway in A. pullulans.

Predicting DNA hybridization kinetics from sequence

NASA Astrophysics Data System (ADS)

Zhang, Jinny X.; Fang, John Z.; Duan, Wei; Wu, Lucia R.; Zhang, Angela W.; Dalchau, Neil; Yordanov, Boyan; Petersen, Rasmus; Phillips, Andrew; Zhang, David Yu

2018-01-01

Hybridization is a key molecular process in biology and biotechnology, but so far there is no predictive model for accurately determining hybridization rate constants based on sequence information. Here, we report a weighted neighbour voting (WNV) prediction algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants. To construct this algorithm we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (36 nt sub-sequences of the CYCS and VEGF genes) at temperatures ranging from 28 to 55 °C. Automated feature selection and weighting optimization resulted in a final six-feature WNV model, which can predict hybridization rate constants of new sequences to within a factor of 3 with ∼91% accuracy, based on leave-one-out cross-validation. Accurate prediction of hybridization kinetics allows the design of efficient probe sequences for genomics research.

SeqRate: sequence-based protein folding type classification and rates prediction

PubMed Central

2010-01-01

Background Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines. Results We systematically studied the contributions of individual features to folding rate prediction. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference between predicted and experimental folding rates (sec-1) in the base-10 logarithmic scale are 0.81 and 0.79 for two-state protein folders, and 0.80 and 0.68 for three-state protein folders. SeqRate is the first sequence-based method for protein folding type classification and its accuracy of fold rate prediction is improved over previous sequence-based methods. Its performance can be further enhanced with additional information, such as structure-based geometric contacts, as inputs. Conclusions Both the web server and software of predicting folding rate are publicly available at http://casp.rnet.missouri.edu/fold_rate/index.html. PMID:20438647

Secondary Structure Predictions for Long RNA Sequences Based on Inversion Excursions and MapReduce.

PubMed

Yehdego, Daniel T; Zhang, Boyu; Kodimala, Vikram K R; Johnson, Kyle L; Taufer, Michela; Leung, Ming-Ying

2013-05-01

Secondary structures of ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Experimental observations and computing limitations suggest that we can approach the secondary structure prediction problem for long RNA sequences by segmenting them into shorter chunks, predicting the secondary structures of each chunk individually using existing prediction programs, and then assembling the results to give the structure of the original sequence. The selection of cutting points is a crucial component of the segmenting step. Noting that stem-loops and pseudoknots always contain an inversion, i.e., a stretch of nucleotides followed closely by its inverse complementary sequence, we developed two cutting methods for segmenting long RNA sequences based on inversion excursions: the centered and optimized method. Each step of searching for inversions, chunking, and predictions can be performed in parallel. In this paper we use a MapReduce framework, i.e., Hadoop, to extensively explore meaningful inversion stem lengths and gap sizes for the segmentation and identify correlations between chunking methods and prediction accuracy. We show that for a set of long RNA sequences in the RFAM database, whose secondary structures are known to contain pseudoknots, our approach predicts secondary structures more accurately than methods that do not segment the sequence, when the latter predictions are possible computationally. We also show that, as sequences exceed certain lengths, some programs cannot computationally predict pseudoknots while our chunking methods can. Overall, our predicted structures still retain the accuracy level of the original prediction programs when compared with known experimental secondary structure.

Coevolutionary modeling of protein sequences: Predicting structure, function, and mutational landscapes

NASA Astrophysics Data System (ADS)

Weigt, Martin

Over the last years, biological research has been revolutionized by experimental high-throughput techniques, in particular by next-generation sequencing technology. Unprecedented amounts of data are accumulating, and there is a growing request for computational methods unveiling the information hidden in raw data, thereby increasing our understanding of complex biological systems. Statistical-physics models based on the maximum-entropy principle have, in the last few years, played an important role in this context. To give a specific example, proteins and many non-coding RNA show a remarkable degree of structural and functional conservation in the course of evolution, despite a large variability in amino acid sequences. We have developed a statistical-mechanics inspired inference approach - called Direct-Coupling Analysis - to link this sequence variability (easy to observe in sequence alignments, which are available in public sequence databases) to bio-molecular structure and function. In my presentation I will show, how this methodology can be used (i) to infer contacts between residues and thus to guide tertiary and quaternary protein structure prediction and RNA structure prediction, (ii) to discriminate interacting from non-interacting protein families, and thus to infer conserved protein-protein interaction networks, and (iii) to reconstruct mutational landscapes and thus to predict the phenotypic effect of mutations. References [1] M. Figliuzzi, H. Jacquier, A. Schug, O. Tenaillon and M. Weigt ''Coevolutionary landscape inference and the context-dependence of mutations in beta-lactamase TEM-1'', Mol. Biol. Evol. (2015), doi: 10.1093/molbev/msv211 [2] E. De Leonardis, B. Lutz, S. Ratz, S. Cocco, R. Monasson, A. Schug, M. Weigt ''Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction'', Nucleic Acids Research (2015), doi: 10.1093/nar/gkv932 [3] F. Morcos, A. Pagnani, B. Lunt, A. Bertolino, D. Marks, C

Detection of nucleic acid sequences by invader-directed cleavage

DOEpatents

Brow, Mary Ann D.; Hall, Jeff Steven Grotelueschen; Lyamichev, Victor; Olive, David Michael; Prudent, James Robert

1999-01-01

The present invention relates to means for the detection and characterization of nucleic acid sequences, as well as variations in nucleic acid sequences. The present invention also relates to methods for forming a nucleic acid cleavage structure on a target sequence and cleaving the nucleic acid cleavage structure in a site-specific manner. The 5' nuclease activity of a variety of enzymes is used to cleave the target-dependent cleavage structure, thereby indicating the presence of specific nucleic acid sequences or specific variations thereof. The present invention further relates to methods and devices for the separation of nucleic acid molecules based by charge.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.

PubMed

Mizianty, Marcin J; Kurgan, Lukasz

2009-12-13

Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. The improved predictions stem from the novel features that express collocation

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

PubMed Central

2009-01-01

Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions The improved predictions stem from the novel

Can-Evo-Ens: Classifier stacking based evolutionary ensemble system for prediction of human breast cancer using amino acid sequences.

PubMed

Ali, Safdar; Majid, Abdul

2015-04-01

The diagnostic of human breast cancer is an intricate process and specific indicators may produce negative results. In order to avoid misleading results, accurate and reliable diagnostic system for breast cancer is indispensable. Recently, several interesting machine-learning (ML) approaches are proposed for prediction of breast cancer. To this end, we developed a novel classifier stacking based evolutionary ensemble system "Can-Evo-Ens" for predicting amino acid sequences associated with breast cancer. In this paper, first, we selected four diverse-type of ML algorithms of Naïve Bayes, K-Nearest Neighbor, Support Vector Machines, and Random Forest as base-level classifiers. These classifiers are trained individually in different feature spaces using physicochemical properties of amino acids. In order to exploit the decision spaces, the preliminary predictions of base-level classifiers are stacked. Genetic programming (GP) is then employed to develop a meta-classifier that optimal combine the predictions of the base classifiers. The most suitable threshold value of the best-evolved predictor is computed using Particle Swarm Optimization technique. Our experiments have demonstrated the robustness of Can-Evo-Ens system for independent validation dataset. The proposed system has achieved the highest value of Area Under Curve (AUC) of ROC Curve of 99.95% for cancer prediction. The comparative results revealed that proposed approach is better than individual ML approaches and conventional ensemble approaches of AdaBoostM1, Bagging, GentleBoost, and Random Subspace. It is expected that the proposed novel system would have a major impact on the fields of Biomedical, Genomics, Proteomics, Bioinformatics, and Drug Development. Copyright © 2015 Elsevier Inc. All rights reserved.

Methods and compositions for efficient nucleic acid sequencing

DOEpatents

Drmanac, Radoje

2006-07-04

Disclosed are novel methods and compositions for rapid and highly efficient nucleic acid sequencing based upon hybridization with two sets of small oligonucleotide probes of known sequences. Extremely large nucleic acid molecules, including chromosomes and non-amplified RNA, may be sequenced without prior cloning or subcloning steps. The methods of the invention also solve various current problems associated with sequencing technology such as, for example, high noise to signal ratios and difficult discrimination, attaching many nucleic acid fragments to a surface, preparing many, longer or more complex probes and labelling more species.

Methods and compositions for efficient nucleic acid sequencing

DOEpatents

Drmanac, Radoje

2002-01-01

Disclosed are novel methods and compositions for rapid and highly efficient nucleic acid sequencing based upon hybridization with two sets of small oligonucleotide probes of known sequences. Extremely large nucleic acid molecules, including chromosomes and non-amplified RNA, may be sequenced without prior cloning or subcloning steps. The methods of the invention also solve various current problems associated with sequencing technology such as, for example, high noise to signal ratios and difficult discrimination, attaching many nucleic acid fragments to a surface, preparing many, longer or more complex probes and labelling more species.

SeqAPASS: Sequence alignment to predict across-species ...

EPA Pesticide Factsheets

Efforts to shift the toxicity testing paradigm from whole organism studies to those focused on the initiation of toxicity and relevant pathways have led to increased utilization of in vitro and in silico methods. Hence the emergence of high through-put screening (HTS) programs, such as U.S. EPA ToxCast, and application of the adverse outcome pathway (AOP) framework for identifying and defining biological key events triggered upon perturbation of molecular initiating events and leading to adverse outcomes occuring at a level of organization relevant for risk assessment [1]. With these recent initiatives to harness the power of “the pathway” in describing and evaluating toxicity comes the need to extrapolate data beyond the model species. Sequence alignment to predict across-species susceptibilty (SeqAPASS) is a web-based tool that allows the user to begin to understand how broadly HTS data or AOP constructs may plausibly be extrapolated across species, while describing the relative intrinsic susceptibiltiy of different taxa to chemicals with known modes of action (e.g., pharmaceuticals and pesticides). The tool rapidly and strategically assesses available molecular target information to describe protein sequence similarity at the primary amino acid sequence, conserved domain, and individual amino acid residue levels. This in silico approach to species extrapolation was designed to automate and streamline the relatively complex and time-consuming process of co

NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

PubMed Central

Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

2010-01-01

β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC  = 0.50, Qtotal = 82.1%, sensitivity  = 75.6%, PPV  = 68.8% and AUC  = 0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17 – 0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. Conclusion The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences. PMID:21152409

Prediction of Spontaneous Protein Deamidation from Sequence-Derived Secondary Structure and Intrinsic Disorder.

PubMed

Lorenzo, J Ramiro; Alonso, Leonardo G; Sánchez, Ignacio E

2015-01-01

Asparagine residues in proteins undergo spontaneous deamidation, a post-translational modification that may act as a molecular clock for the regulation of protein function and turnover. Asparagine deamidation is modulated by protein local sequence, secondary structure and hydrogen bonding. We present NGOME, an algorithm able to predict non-enzymatic deamidation of internal asparagine residues in proteins in the absence of structural data, using sequence-based predictions of secondary structure and intrinsic disorder. Compared to previous algorithms, NGOME does not require three-dimensional structures yet yields better predictions than available sequence-only methods. Four case studies of specific proteins show how NGOME may help the user identify deamidation-prone asparagine residues, often related to protein gain of function, protein degradation or protein misfolding in pathological processes. A fifth case study applies NGOME at a proteomic scale and unveils a correlation between asparagine deamidation and protein degradation in yeast. NGOME is freely available as a webserver at the National EMBnet node Argentina, URL: http://www.embnet.qb.fcen.uba.ar/ in the subpage "Protein and nucleic acid structure and sequence analysis".

Two-level QSAR network (2L-QSAR) for peptide inhibitor design based on amino acid properties and sequence positions.

PubMed

Du, Q S; Ma, Y; Xie, N Z; Huang, R B

2014-01-01

In the design of peptide inhibitors the huge possible variety of the peptide sequences is of high concern. In collaboration with the fast accumulation of the peptide experimental data and database, a statistical method is suggested for peptide inhibitor design. In the two-level peptide prediction network (2L-QSAR) one level is the physicochemical properties of amino acids and the other level is the peptide sequence position. The activity contributions of amino acids are the functions of physicochemical properties and the sequence positions. In the prediction equation two weight coefficient sets {ak} and {bl} are assigned to the physicochemical properties and to the sequence positions, respectively. After the two coefficient sets are optimized based on the experimental data of known peptide inhibitors using the iterative double least square (IDLS) procedure, the coefficients are used to evaluate the bioactivities of new designed peptide inhibitors. The two-level prediction network can be applied to the peptide inhibitor design that may aim for different target proteins, or different positions of a protein. A notable advantage of the two-level statistical algorithm is that there is no need for host protein structural information. It may also provide useful insight into the amino acid properties and the roles of sequence positions.

77 FR 65537 - Requirements for Patent Applications Containing Nucleotide Sequence and/or Amino Acid Sequence...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-29

... DEPARTMENT OF COMMERCE Patent and Trademark Office Requirements for Patent Applications Containing Nucleotide Sequence and/or Amino Acid Sequence Disclosures ACTION: Proposed collection; comment request... Patent applications that contain nucleotide and/or amino acid sequence disclosures must include a copy of...

High speed nucleic acid sequencing

DOEpatents

Korlach, Jonas [Ithaca, NY; Webb, Watt W [Ithaca, NY; Levene, Michael [Ithaca, NY; Turner, Stephen [Ithaca, NY; Craighead, Harold G [Ithaca, NY; Foquet, Mathieu [Ithaca, NY

2011-05-17

The present invention is directed to a method of sequencing a target nucleic acid molecule having a plurality of bases. In its principle, the temporal order of base additions during the polymerization reaction is measured on a molecule of nucleic acid. Each type of labeled nucleotide comprises an acceptor fluorophore attached to a phosphate portion of the nucleotide such that the fluorophore is removed upon incorporation into a growing strand. Fluorescent signal is emitted via fluorescent resonance energy transfer between the donor fluorophore and the acceptor fluorophore as each nucleotide is incorporated into the growing strand. The sequence is deduced by identifying which base is being incorporated into the growing strand.

Sequence-based predictive modeling to identify cancerlectins

PubMed Central

Lai, Hong-Yan; Chen, Xin-Xin; Chen, Wei; Tang, Hua; Lin, Hao

2017-01-01

Lectins are a diverse type of glycoproteins or carbohydrate-binding proteins that have a wide distribution to various species. They can specially identify and exclusively bind to a certain kind of saccharide groups. Cancerlectins are a group of lectins that are closely related to cancer and play a major role in the initiation, survival, growth, metastasis and spread of tumor. Several computational methods have emerged to discriminate cancerlectins from non-cancerlectins, which promote the study on pathogenic mechanisms and clinical treatment of cancer. However, the predictive accuracies of most of these techniques are very limited. In this work, by constructing a benchmark dataset based on the CancerLectinDB database, a new amino acid sequence-based strategy for feature description was developed, and then the binomial distribution was applied to screen the optimal feature set. Ultimately, an SVM-based predictor was performed to distinguish cancerlectins from non-cancerlectins, and achieved an accuracy of 77.48% with AUC of 85.52% in jackknife cross-validation. The results revealed that our prediction model could perform better comparing with published predictive tools. PMID:28423655

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern.

PubMed

Zhang, Tong-Liang; Ding, Yong-Sheng; Chou, Kuo-Chen

2008-01-07

Compared with the conventional amino acid (AA) composition, the pseudo-amino acid (PseAA) composition as originally introduced for protein subcellular location prediction can incorporate much more information of a protein sequence, so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, based on the concept of PseAA composition, the approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the PseAA components. Also, the immune genetic algorithm (IGA) is applied to search the optimal weight factors in generating the PseAA composition. Thus, for a given protein sequence sample, a 27-D (dimensional) PseAA composition is generated as its descriptor. The fuzzy K nearest neighbors (FKNN) classifier is adopted as the prediction engine. The results thus obtained in predicting protein structural classification are quite encouraging, indicating that the current approach may also be used to improve the prediction quality of other protein attributes, or at least can play a complimentary role to the existing methods in the relevant areas. Our algorithm is written in Matlab that is available by contacting the corresponding author.

Optical Processing Techniques For Pseudorandom Sequence Prediction

NASA Astrophysics Data System (ADS)

Gustafson, Steven C.

1983-11-01

Pseudorandom sequences are series of apparently random numbers generated, for example, by linear or nonlinear feedback shift registers. An important application of these sequences is in spread spectrum communication systems, in which, for example, the transmitted carrier phase is digitally modulated rapidly and pseudorandomly and in which the information to be transmitted is incorporated as a slow modulation in the pseudorandom sequence. In this case the transmitted information can be extracted only by a receiver that uses for demodulation the same pseudorandom sequence used by the transmitter, and thus this type of communication system has a very high immunity to third-party interference. However, if a third party can predict in real time the probable future course of the transmitted pseudorandom sequence given past samples of this sequence, then interference immunity can be significantly reduced.. In this application effective pseudorandom sequence prediction techniques should be (1) applicable in real time to rapid (e.g., megahertz) sequence generation rates, (2) applicable to both linear and nonlinear pseudorandom sequence generation processes, and (3) applicable to error-prone past sequence samples of limited number and continuity. Certain optical processing techniques that may meet these requirements are discussed in this paper. In particular, techniques based on incoherent optical processors that perform general linear transforms or (more specifically) matrix-vector multiplications are considered. Computer simulation examples are presented which indicate that significant prediction accuracy can be obtained using these transforms for simple pseudorandom sequences. However, the useful prediction of more complex pseudorandom sequences will probably require the application of more sophisticated optical processing techniques.

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

PubMed Central

Roy, Sushmita; Martinez, Diego; Platero, Harriett; Lane, Terran; Werner-Washburne, Margaret

2009-01-01

Background Computational 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. Results AAC 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. Conclusion AAC 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. PMID:19936254

Draft genome sequence of the docosahexaenoic acid producing thraustochytrid Aurantiochytrium sp. T66.

PubMed

Liu, Bin; Ertesvåg, Helga; Aasen, Inga Marie; Vadstein, Olav; Brautaset, Trygve; Heggeset, Tonje Marita Bjerkan

2016-06-01

Thraustochytrids are unicellular, marine protists, and there is a growing industrial interest in these organisms, particularly because some species, including strains belonging to the genus Aurantiochytrium, accumulate high levels of docosahexaenoic acid (DHA). Here, we report the draft genome sequence of Aurantiochytrium sp. T66 (ATCC PRA-276), with a size of 43 Mbp, and 11,683 predicted protein-coding sequences. The data has been deposited at DDBJ/EMBL/Genbank under the accession LNGJ00000000. The genome sequence will contribute new insight into DHA biosynthesis and regulation, providing a basis for metabolic engineering of thraustochytrids.

BrEPS 2.0: Optimization of sequence pattern prediction for enzyme annotation.

PubMed

Dudek, Christian-Alexander; Dannheim, Henning; Schomburg, Dietmar

2017-01-01

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at

BrEPS 2.0: Optimization of sequence pattern prediction for enzyme annotation

PubMed Central

Schomburg, Dietmar

2017-01-01

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at

Synthetic oligonucleotide probes deduced from amino acid sequence data. Theoretical and practical considerations.

PubMed

Lathe, R

1985-05-05

Synthetic probes deduced from amino acid sequence data are widely used to detect cognate coding sequences in libraries of cloned DNA segments. The redundancy of the genetic code dictates that a choice must be made between (1) a mixture of probes reflecting all codon combinations, and (2) a single longer "optimal" probe. The second strategy is examined in detail. The frequency of sequences matching a given probe by chance alone can be determined and also the frequency of sequences closely resembling the probe and contributing to the hybridization background. Gene banks cannot be treated as random associations of the four nucleotides, and probe sequences deduced from amino acid sequence data occur more often than predicted by chance alone. Probe lengths must be increased to confer the necessary specificity. Examination of hybrids formed between unique homologous probes and their cognate targets reveals that short stretches of perfect homology occurring by chance make a significant contribution to the hybridization background. Statistical methods for improving homology are examined, taking human coding sequences as an example, and considerations of codon utilization and dinucleotide frequencies yield an overall homology of greater than 82%. Recommendations for probe design and hybridization are presented, and the choice between using multiple probes reflecting all codon possibilities and a unique optimal probe is discussed.

PreSSAPro: a software for the prediction of secondary structure by amino acid properties.

PubMed

Costantini, Susan; Colonna, Giovanni; Facchiano, Angelo M

2007-10-01

PreSSAPro is a software, available to the scientific community as a free web service designed to provide predictions of secondary structures starting from the amino acid sequence of a given protein. Predictions are based on our recently published work on the amino acid propensities for secondary structures in either large but not homogeneous protein data sets, as well as in smaller but homogeneous data sets corresponding to protein structural classes, i.e. all-alpha, all-beta, or alpha-beta proteins. Predictions result improved by the use of propensities evaluated for the right protein class. PreSSAPro predicts the secondary structure according to the right protein class, if known, or gives a multiple prediction with reference to the different structural classes. The comparison of these predictions represents a novel tool to evaluate what sequence regions can assume different secondary structures depending on the structural class assignment, in the perspective of identifying proteins able to fold in different conformations. The service is available at the URL http://bioinformatica.isa.cnr.it/PRESSAPRO/.

Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning.

PubMed

Jain, Tushar; Boland, Todd; Lilov, Asparouh; Burnina, Irina; Brown, Michael; Xu, Yingda; Vásquez, Maximiliano

2017-12-01

The hydrophobicity of a monoclonal antibody is an important biophysical property relevant for its developability into a therapeutic. In addition to characterizing heterogeneity, Hydrophobic Interaction Chromatography (HIC) is an assay that is often used to quantify the hydrophobicity of an antibody to assess downstream risks. Earlier studies have shown that retention times in this assay can be correlated to amino-acid or atomic propensities weighted by the surface areas obtained from protein 3-dimensional structures. The goal of this study is to develop models to enable prediction of delayed HIC retention times directly from sequence. We utilize the randomforest machine learning approach to estimate the surface exposure of amino-acid side-chains in the variable region directly from the antibody sequence. We obtain mean-absolute errors of 4.6% for the prediction of surface exposure. Using experimental HIC data along with the estimated surface areas, we derive an amino-acid propensity scale that enables prediction of antibodies likely to have delayed retention times in the assay. We achieve a cross-validation Area Under Curve of 0.85 for the Receiver Operating Characteristic curve of our model. The low computational expense and high accuracy of this approach enables real-time assessment of hydrophobic character to enable prioritization of antibodies during the discovery process and rational engineering to reduce hydrophobic liabilities. Structure data, aligned sequences, experimental data and prediction scores for test-cases, and R scripts used in this work are provided as part of the Supplementary Material. tushar.jain@adimab.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Kit for detecting nucleic acid sequences using competitive hybridization probes

DOEpatents

Lucas, Joe N.; Straume, Tore; Bogen, Kenneth T.

2001-01-01

A kit is provided for detecting a target nucleic acid sequence in a sample, the kit comprising: a first hybridization probe which includes a nucleic acid sequence that is sufficiently complementary to selectively hybridize to a first portion of the target sequence, the first hybridization probe including a first complexing agent for forming a binding pair with a second complexing agent; and a second hybridization probe which includes a nucleic acid sequence that is sufficiently complementary to selectively hybridize to a second portion of the target sequence to which the first hybridization probe does not selectively hybridize, the second hybridization probe including a detectable marker; a third hybridization probe which includes a nucleic acid sequence that is sufficiently complementary to selectively hybridize to a first portion of the target sequence, the third hybridization probe including the same detectable marker as the second hybridization probe; and a fourth hybridization probe which includes a nucleic acid sequence that is sufficiently complementary to selectively hybridize to a second portion of the target sequence to which the third hybridization probe does not selectively hybridize, the fourth hybridization probe including the first complexing agent for forming a binding pair with the second complexing agent; wherein the first and second hybridization probes are capable of simultaneously hybridizing to the target sequence and the third and fourth hybridization probes are capable of simultaneously hybridizing to the target sequence, the detectable marker is not present on the first or fourth hybridization probes and the first, second, third, and fourth hybridization probes each include a competitive nucleic acid sequence which is sufficiently complementary to a third portion of the target sequence that the competitive sequences of the first, second, third, and fourth hybridization probes compete with each other to hybridize to the third portion of the

Discriminative prediction of mammalian enhancers from DNA sequence

PubMed Central

Lee, Dongwon; Karchin, Rachel; Beer, Michael A.

2011-01-01

Accurately predicting regulatory sequences and enhancers in entire genomes is an important but difficult problem, especially in large vertebrate genomes. With the advent of ChIP-seq technology, experimental detection of genome-wide EP300/CREBBP bound regions provides a powerful platform to develop predictive tools for regulatory sequences and to study their sequence properties. Here, we develop a support vector machine (SVM) framework which can accurately identify EP300-bound enhancers using only genomic sequence and an unbiased set of general sequence features. Moreover, we find that the predictive sequence features identified by the SVM classifier reveal biologically relevant sequence elements enriched in the enhancers, but we also identify other features that are significantly depleted in enhancers. The predictive sequence features are evolutionarily conserved and spatially clustered, providing further support of their functional significance. Although our SVM is trained on experimental data, we also predict novel enhancers and show that these putative enhancers are significantly enriched in both ChIP-seq signal and DNase I hypersensitivity signal in the mouse brain and are located near relevant genes. Finally, we present results of comparisons between other EP300/CREBBP data sets using our SVM and uncover sequence elements enriched and/or depleted in the different classes of enhancers. Many of these sequence features play a role in specifying tissue-specific or developmental-stage-specific enhancer activity, but our results indicate that some features operate in a general or tissue-independent manner. In addition to providing a high confidence list of enhancer targets for subsequent experimental investigation, these results contribute to our understanding of the general sequence structure of vertebrate enhancers. PMID:21875935

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes.

PubMed

Jespersen, Martin Closter; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo

2017-07-03

Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

A machine-learning approach for predicting palmitoylation sites from integrated sequence-based features.

PubMed

Li, Liqi; Luo, Qifa; Xiao, Weidong; Li, Jinhui; Zhou, Shiwen; Li, Yongsheng; Zheng, Xiaoqi; Yang, Hua

2017-02-01

Palmitoylation is the covalent attachment of lipids to amino acid residues in proteins. As an important form of protein posttranslational modification, it increases the hydrophobicity of proteins, which contributes to the protein transportation, organelle localization, and functions, therefore plays an important role in a variety of cell biological processes. Identification of palmitoylation sites is necessary for understanding protein-protein interaction, protein stability, and activity. Since conventional experimental techniques to determine palmitoylation sites in proteins are both labor intensive and costly, a fast and accurate computational approach to predict palmitoylation sites from protein sequences is in urgent need. In this study, a support vector machine (SVM)-based method was proposed through integrating PSI-BLAST profile, physicochemical properties, [Formula: see text]-mer amino acid compositions (AACs), and [Formula: see text]-mer pseudo AACs into the principal feature vector. A recursive feature selection scheme was subsequently implemented to single out the most discriminative features. Finally, an SVM method was implemented to predict palmitoylation sites in proteins based on the optimal features. The proposed method achieved an accuracy of 99.41% and Matthews Correlation Coefficient of 0.9773 for a benchmark dataset. The result indicates the efficiency and accuracy of our method in prediction of palmitoylation sites based on protein sequences.

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.

PubMed

Song, Jiangning; Yuan, Zheng; Tan, Hao; Huber, Thomas; Burrage, Kevin

2007-12-01

Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide

alpha-Amylase gene of Streptomyces limosus: nucleotide sequence, expression motifs, and amino acid sequence homology to mammalian and invertebrate alpha-amylases.

PubMed Central

Long, C M; Virolle, M J; Chang, S Y; Chang, S; Bibb, M J

1987-01-01

The nucleotide sequence of the coding and regulatory regions of the alpha-amylase gene (aml) of Streptomyces limosus was determined. High-resolution S1 mapping was used to locate the 5' end of the transcript and demonstrated that the gene is transcribed from a unique promoter. The predicted amino acid sequence has considerable identity to mammalian and invertebrate alpha-amylases, but not to those of plant, fungal, or eubacterial origin. Consistent with this is the susceptibility of the enzyme to an inhibitor of mammalian alpha-amylases. The amino-terminal sequence of the extracellular enzyme was determined, revealing the presence of a typical signal peptide preceding the mature form of the alpha-amylase. Images PMID:3500166

Hybridization and sequencing of nucleic acids using base pair mismatches

DOEpatents

Fodor, Stephen P. A.; Lipshutz, Robert J.; Huang, Xiaohua

2001-01-01

Devices and techniques for hybridization of nucleic acids and for determining the sequence of nucleic acids. Arrays of nucleic acids are formed by techniques, preferably high resolution, light-directed techniques. Positions of hybridization of a target nucleic acid are determined by, e.g., epifluorescence microscopy. Devices and techniques are proposed to determine the sequence of a target nucleic acid more efficiently and more quickly through such synthesis and detection techniques.

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

PubMed

Plewczynski, Dariusz; Basu, Subhadip; Saha, Indrajit

2012-08-01

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The

Mouse Vk gene classification by nucleic acid sequence similarity.

PubMed

Strohal, R; Helmberg, A; Kroemer, G; Kofler, R

1989-01-01

Analyses of immunoglobulin (Ig) variable (V) region gene usage in the immune response, estimates of V gene germline complexity, and other nucleic acid hybridization-based studies depend on the extent to which such genes are related (i.e., sequence similarity) and their organization in gene families. While mouse Igh heavy chain V region (VH) gene families are relatively well-established, a corresponding systematic classification of Igk light chain V region (Vk) genes has not been reported. The present analysis, in the course of which we reviewed the known extent of the Vk germline gene repertoire and Vk gene usage in a variety of responses to foreign and self antigens, provides a classification of mouse Vk genes in gene families composed of members with greater than 80% overall nucleic acid sequence similarity. This classification differed in several aspects from that of VH genes: only some Vk gene families were as clearly separated (by greater than 25% sequence dissimilarity) as typical VH gene families; most Vk gene families were closely related and, in several instances, members from different families were very similar (greater than 80%) over large sequence portions; frequently, classification by nucleic acid sequence similarity diverged from existing classifications based on amino-terminal protein sequence similarity. Our data have implications for Vk gene analyses by nucleic acid hybridization and describe potentially important differences in sequence organization between VH and Vk genes.

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.

PubMed

Jones, David T; Kandathil, Shaun M

2018-04-26

In addition to substitution frequency data from protein sequence alignments, many state-of-the-art methods for contact prediction rely on additional sources of information, or features, of protein sequences in order to predict residue-residue contacts, such as solvent accessibility, predicted secondary structure, and scores from other contact prediction methods. It is unclear how much of this information is needed to achieve state-of-the-art results. Here, we show that using deep neural network models, simple alignment statistics contain sufficient information to achieve state-of-the-art precision. Our prediction method, DeepCov, uses fully convolutional neural networks operating on amino-acid pair frequency or covariance data derived directly from sequence alignments, without using global statistical methods such as sparse inverse covariance or pseudolikelihood estimation. Comparisons against CCMpred and MetaPSICOV2 show that using pairwise covariance data calculated from raw alignments as input allows us to match or exceed the performance of both of these methods. Almost all of the achieved precision is obtained when considering relatively local windows (around 15 residues) around any member of a given residue pairing; larger window sizes have comparable performance. Assessment on a set of shallow sequence alignments (fewer than 160 effective sequences) indicates that the new method is substantially more precise than CCMpred and MetaPSICOV2 in this regime, suggesting that improved precision is attainable on smaller sequence families. Overall, the performance of DeepCov is competitive with the state of the art, and our results demonstrate that global models, which employ features from all parts of the input alignment when predicting individual contacts, are not strictly needed in order to attain precise contact predictions. DeepCov is freely available at https://github.com/psipred/DeepCov. d.t.jones@ucl.ac.uk.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

PubMed

Srinivasulu, Yerukala Sathipati; Wang, Jyun-Rong; Hsu, Kai-Ti; Tsai, Ming-Ju; Charoenkwan, Phasit; Huang, Wen-Lin; Huang, Hui-Ling; Ho, Shinn-Ying

2015-01-01

Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization

Gene and translation initiation site prediction in metagenomic sequences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hyatt, Philip Douglas; LoCascio, Philip F; Hauser, Loren John

2012-01-01

Gene prediction in metagenomic sequences remains a difficult problem. Current sequencing technologies do not achieve sufficient coverage to assemble the individual genomes in a typical sample; consequently, sequencing runs produce a large number of short sequences whose exact origin is unknown. Since these sequences are usually smaller than the average length of a gene, algorithms must make predictions based on very little data. We present MetaProdigal, a metagenomic version of the gene prediction program Prodigal, that can identify genes in short, anonymous coding sequences with a high degree of accuracy. The novel value of the method consists of enhanced translationmore » initiation site identification, ability to identify sequences that use alternate genetic codes and confidence values for each gene call. We compare the results of MetaProdigal with other methods and conclude with a discussion of future improvements.« less

SEAN: SNP prediction and display program utilizing EST sequence clusters.

PubMed

Huntley, Derek; Baldo, Angela; Johri, Saurabh; Sergot, Marek

2006-02-15

SEAN is an application that predicts single nucleotide polymorphisms (SNPs) using multiple sequence alignments produced from expressed sequence tag (EST) clusters. The algorithm uses rules of sequence identity and SNP abundance to determine the quality of the prediction. A Java viewer is provided to display the EST alignments and predicted SNPs.

Genome-Wide Prediction and Analysis of 3D-Domain Swapped Proteins in the Human Genome from Sequence Information.

PubMed

Upadhyay, Atul Kumar; Sowdhamini, Ramanathan

2016-01-01

3D-domain swapping is one of the mechanisms of protein oligomerization and the proteins exhibiting this phenomenon have many biological functions. These proteins, which undergo domain swapping, have acquired much attention owing to their involvement in human diseases, such as conformational diseases, amyloidosis, serpinopathies, proteionopathies etc. Early realisation of proteins in the whole human genome that retain tendency to domain swap will enable many aspects of disease control management. Predictive models were developed by using machine learning approaches with an average accuracy of 78% (85.6% of sensitivity, 87.5% of specificity and an MCC value of 0.72) to predict putative domain swapping in protein sequences. These models were applied to many complete genomes with special emphasis on the human genome. Nearly 44% of the protein sequences in the human genome were predicted positive for domain swapping. Enrichment analysis was performed on the positively predicted sequences from human genome for their domain distribution, disease association and functional importance based on Gene Ontology (GO). Enrichment analysis was also performed to infer a better understanding of the functional importance of these sequences. Finally, we developed hinge region prediction, in the given putative domain swapped sequence, by using important physicochemical properties of amino acids.

First draft genome sequencing of indole acetic acid producing and plant growth promoting fungus Preussia sp. BSL10.

PubMed

Khan, Abdul Latif; Asaf, Sajjad; Khan, Abdur Rahim; Al-Harrasi, Ahmed; Al-Rawahi, Ahmed; Lee, In-Jung

2016-05-10

Preussia sp. BSL10, family Sporormiaceae, was actively producing phytohormone (indole-3-acetic acid) and extra-cellular enzymes (phosphatases and glucosidases). The fungus was also promoting the growth of arid-land tree-Boswellia sacra. Looking at such prospects of this fungus, we sequenced its draft genome for the first time. The Illumina based sequence analysis reveals an approximate genome size of 31.4Mbp for Preussia sp. BSL10. Based on ab initio gene prediction, total 32,312 coding sequences were annotated consisting of 11,967 coding genes, pseudogenes, and 221 tRNA genes. Furthermore, 321 carbohydrate-active enzymes were predicted and classified into many functional families. Copyright © 2016 Elsevier B.V. All rights reserved.

The amino acid sequence of Staphylococcus aureus penicillinase.

PubMed Central

Ambler, R P

1975-01-01

The amino acid sequence of the penicillinase (penicillin amido-beta-lactamhydrolase, EC 3.5.2.6) from Staphylococcus aureus strain PC1 was determined. The protein consists of a single polypeptide chain of 257 residues, and the sequence was determined by characterization of tryptic, chymotryptic, peptic and CNBr peptides, with some additional evidence from thermolysin and S. aureus proteinase peptides. A mistake in the preliminary report of the sequence is corrected; residues 113-116 are now thought to be -Lys-Lys-Val-Lys- rather than -Lys-Val-Lys-Lys-. Detailed evidence for the amino acid sequence has been deposited as Supplementary Publication SUP 50056 (91 pages) at the British Library (Lending Division), Boston Spa, Wetherby, West Yorkshire LS23 7BQ, U.K., from whom copies may be obtained on the terms given in Biochem. J. (1975) 145, 5. PMID:1218078

Sequence-Based Prediction of RNA-Binding Residues in Proteins.

PubMed

Walia, Rasna R; El-Manzalawy, Yasser; Honavar, Vasant G; Dobbs, Drena

2017-01-01

Identifying individual residues in the interfaces of protein-RNA complexes is important for understanding the molecular determinants of protein-RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein-RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein-RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner.

Sequence-Based Prediction of RNA-Binding Residues in Proteins

PubMed Central

Walia, Rasna R.; EL-Manzalawy, Yasser; Honavar, Vasant G.; Dobbs, Drena

2017-01-01

Identifying individual residues in the interfaces of protein–RNA complexes is important for understanding the molecular determinants of protein–RNA recognition and has many potential applications. Recent technical advances have led to several high-throughput experimental methods for identifying partners in protein–RNA complexes, but determining RNA-binding residues in proteins is still expensive and time-consuming. This chapter focuses on available computational methods for identifying which amino acids in an RNA-binding protein participate directly in contacting RNA. Step-by-step protocols for using three different web-based servers to predict RNA-binding residues are described. In addition, currently available web servers and software tools for predicting RNA-binding sites, as well as databases that contain valuable information about known protein–RNA complexes, RNA-binding motifs in proteins, and protein-binding recognition sites in RNA are provided. We emphasize sequence-based methods that can reliably identify interfacial residues without the requirement for structural information regarding either the RNA-binding protein or its RNA partner. PMID:27787829

Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods

PubMed Central

Wang, Ping; Hu, Lele; Liu, Guiyou; Jiang, Nan; Chen, Xiaoyun; Xu, Jianyong; Zheng, Wen; Li, Li; Tan, Ming; Chen, Zugen; Song, Hui; Cai, Yu-Dong; Chou, Kuo-Chen

2011-01-01

Antimicrobial peptides (AMPs) represent a class of natural peptides that form a part of the innate immune system, and this kind of ‘nature's antibiotics’ is quite promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop an effective computational method for accurately predicting novel AMPs because it can provide us with more candidates and useful insights for drug design. In this study, a new method for predicting AMPs was implemented by integrating the sequence alignment method and the feature selection method. It was observed that, the overall jackknife success rate by the new predictor on a newly constructed benchmark dataset was over 80.23%, and the Mathews correlation coefficient is 0.73, indicating a good prediction. Moreover, it is indicated by an in-depth feature analysis that the results are quite consistent with the previously known knowledge that some amino acids are preferential in AMPs and that these amino acids do play an important role for the antimicrobial activity. For the convenience of most experimental scientists who want to use the prediction method without the interest to follow the mathematical details, a user-friendly web-server is provided at http://amp.biosino.org/. PMID:21533231

Partial amino acid sequence of the branched chain amino acid aminotransferase (TmB) of E. coli JA199 pDU11

DOE Office of Scientific and Technical Information (OSTI.GOV)

Feild, M.J.; Armstrong, F.B.

1987-05-01

E. coli JA199 pDU11 harbors a multicopy plasmid containing the ilv GEDAY gene cluster of S. typhimurium. TmB, gene product of ilv E, was purified, crystallized, and subjected to Edman degradation using a gas phase sequencer. The intact protein yielded an amino terminal 31 residue sequence. Both carboxymethylated apoenzyme and (/sup 3/H)-NaBH-reduced holoenzyme were then subjected to digestion by trypsin. The digests were fractionated using reversed phase HPLC, and the peptides isolated were sequenced. The borohydride-treated holoenzyme was used to isolate the cofactor-binding peptide. The peptide is 27 residues long and a comparison with known sequences of other aminotransferases revealedmore » limited homology. Peptides accounting for 211 of 288 predicted residues have been sequenced, including 9 residues of the carboxyl terminus. Comparison of peptides with the inferred amino acid sequence of the E. coli K-12 enzyme has helped determine the sequence of the amino terminal 59 residues; only two differences between the sequences are noted in this region.« less

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

PubMed Central

2015-01-01

Background Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. Results This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. Conclusions The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein

Detection and isolation of nucleic acid sequences using competitive hybridization probes

DOEpatents

Lucas, Joe N.; Straume, Tore; Bogen, Kenneth T.

1997-01-01

A method for detecting a target nucleic acid sequence in a sample is provided using hybridization probes which competitively hybridize to a target nucleic acid. According to the method, a target nucleic acid sequence is hybridized to first and second hybridization probes which are complementary to overlapping portions of the target nucleic acid sequence, the first hybridization probe including a first complexing agent capable of forming a binding pair with a second complexing agent and the second hybridization probe including a detectable marker. The first complexing agent attached to the first hybridization probe is contacted with a second complexing agent, the second complexing agent being attached to a solid support such that when the first and second complexing agents are attached, target nucleic acid sequences hybridized to the first hybridization probe become immobilized on to the solid support. The immobilized target nucleic acids are then separated and detected by detecting the detectable marker attached to the second hybridization probe. A kit for performing the method is also provided.

Detection and isolation of nucleic acid sequences using competitive hybridization probes

DOEpatents

Lucas, J.N.; Straume, T.; Bogen, K.T.

1997-04-01

A method for detecting a target nucleic acid sequence in a sample is provided using hybridization probes which competitively hybridize to a target nucleic acid. According to the method, a target nucleic acid sequence is hybridized to first and second hybridization probes which are complementary to overlapping portions of the target nucleic acid sequence, the first hybridization probe including a first complexing agent capable of forming a binding pair with a second complexing agent and the second hybridization probe including a detectable marker. The first complexing agent attached to the first hybridization probe is contacted with a second complexing agent, the second complexing agent being attached to a solid support such that when the first and second complexing agents are attached, target nucleic acid sequences hybridized to the first hybridization probe become immobilized on to the solid support. The immobilized target nucleic acids are then separated and detected by detecting the detectable marker attached to the second hybridization probe. A kit for performing the method is also provided. 7 figs.

Learning predictive statistics from temporal sequences: Dynamics and strategies

PubMed Central

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E.; Kourtzi, Zoe

2017-01-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments. PMID:28973111

Learning predictive statistics from temporal sequences: Dynamics and strategies.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Amino acid sequence of the human fibronectin receptor

PubMed Central

1987-01-01

The amino acid sequence deduced from cDNA of the human placental fibronectin receptor is reported. The receptor is composed of two subunits: an alpha subunit of 1,008 amino acids which is processed into two polypeptides disulfide bonded to one another, and a beta subunit of 778 amino acids. Each subunit has near its COOH terminus a hydrophobic segment. This and other sequence features suggest a structure for the receptor in which the hydrophobic segments serve as transmembrane domains anchoring each subunit to the membrane and dividing each into a large ectodomain and a short cytoplasmic domain. The alpha subunit ectodomain has five sequence elements homologous to consensus Ca2+- binding sites of several calcium-binding proteins, and the beta subunit contains a fourfold repeat strikingly rich in cysteine. The alpha subunit sequence is 46% homologous to the alpha subunit of the vitronectin receptor. The beta subunit is 44% homologous to the human platelet adhesion receptor subunit IIIa and 47% homologous to a leukocyte adhesion receptor beta subunit. The high degree of homology (85%) of the beta subunit with one of the polypeptides of a chicken adhesion receptor complex referred to as integrin complex strongly suggests that the latter polypeptide is the chicken homologue of the fibronectin receptor beta subunit. These receptor subunit homologies define a superfamily of adhesion receptors. The availability of the entire protein sequence for the fibronectin receptor will facilitate studies on the functions of these receptors. PMID:2958481

Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection.

PubMed

Ma, Xin; Guo, Jing; Sun, Xiao

2015-01-01

The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR) method, followed by incremental feature selection (IFS). We incorporated features of conjoint triad features and three novel features: binding propensity (BP), nonbinding propensity (NBP), and evolutionary information combined with physicochemical properties (EIPP). The results showed that these novel features have important roles in improving the performance of the predictor. Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient). High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.

PubMed

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2015-01-01

Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

Phenolic acid esterases, coding sequences and methods

DOEpatents

Blum, David L.; Kataeva, Irina; Li, Xin-Liang; Ljungdahl, Lars G.

2002-01-01

Described herein are four phenolic acid esterases, three of which correspond to domains of previously unknown function within bacterial xylanases, from XynY and XynZ of Clostridium thermocellum and from a xylanase of Ruminococcus. The fourth specifically exemplified xylanase is a protein encoded within the genome of Orpinomyces PC-2. The amino acids of these polypeptides and nucleotide sequences encoding them are provided. Recombinant host cells, expression vectors and methods for the recombinant production of phenolic acid esterases are also provided.

Identification of random nucleic acid sequence aberrations using dual capture probes which hybridize to different chromosome regions

DOEpatents

Lucas, Joe N.; Straume, Tore; Bogen, Kenneth T.

1998-01-01

A method is provided for detecting nucleic acid sequence aberrations using two immobilization steps. According to the method, a nucleic acid sequence aberration is detected by detecting nucleic acid sequences having both a first nucleic acid sequence type (e.g., from a first chromosome) and a second nucleic acid sequence type (e.g., from a second chromosome), the presence of the first and the second nucleic acid sequence type on the same nucleic acid sequence indicating the presence of a nucleic acid sequence aberration. In the method, immobilization of a first hybridization probe is used to isolate a first set of nucleic acids in the sample which contain the first nucleic acid sequence type. Immobilization of a second hybridization probe is then used to isolate a second set of nucleic acids from within the first set of nucleic acids which contain the second nucleic acid sequence type. The second set of nucleic acids are then detected, their presence indicating the presence of a nucleic acid sequence aberration.

SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

PubMed

Suresh, V; Parthasarathy, S

2014-01-01

We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.

PubMed

Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin

2011-08-21

Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

Identification of random nucleic acid sequence aberrations using dual capture probes which hybridize to different chromosome regions

DOEpatents

Lucas, J.N.; Straume, T.; Bogen, K.T.

1998-03-24

A method is provided for detecting nucleic acid sequence aberrations using two immobilization steps. According to the method, a nucleic acid sequence aberration is detected by detecting nucleic acid sequences having both a first nucleic acid sequence type (e.g., from a first chromosome) and a second nucleic acid sequence type (e.g., from a second chromosome), the presence of the first and the second nucleic acid sequence type on the same nucleic acid sequence indicating the presence of a nucleic acid sequence aberration. In the method, immobilization of a first hybridization probe is used to isolate a first set of nucleic acids in the sample which contain the first nucleic acid sequence type. Immobilization of a second hybridization probe is then used to isolate a second set of nucleic acids from within the first set of nucleic acids which contain the second nucleic acid sequence type. The second set of nucleic acids are then detected, their presence indicating the presence of a nucleic acid sequence aberration. 14 figs.

Nucleic and Amino Acid Sequences Support Structure-Based Viral Classification.

PubMed

Sinclair, Robert M; Ravantti, Janne J; Bamford, Dennis H

2017-04-15

Viral capsids ensure viral genome integrity by protecting the enclosed nucleic acids. Interactions between the genome and capsid and between individual capsid proteins (i.e., capsid architecture) are intimate and are expected to be characterized by strong evolutionary conservation. For this reason, a capsid structure-based viral classification has been proposed as a way to bring order to the viral universe. The seeming lack of sufficient sequence similarity to reproduce this classification has made it difficult to reject structural convergence as the basis for the classification. We reinvestigate whether the structure-based classification for viral coat proteins making icosahedral virus capsids is in fact supported by previously undetected sequence similarity. Since codon choices can influence nascent protein folding cotranslationally, we searched for both amino acid and nucleotide sequence similarity. To demonstrate the sensitivity of the approach, we identify a candidate gene for the pandoravirus capsid protein. We show that the structure-based classification is strongly supported by amino acid and also nucleotide sequence similarities, suggesting that the similarities are due to common descent. The correspondence between structure-based and sequence-based analyses of the same proteins shown here allow them to be used in future analyses of the relationship between linear sequence information and macromolecular function, as well as between linear sequence and protein folds. IMPORTANCE Viral capsids protect nucleic acid genomes, which in turn encode capsid proteins. This tight coupling of protein shell and nucleic acids, together with strong functional constraints on capsid protein folding and architecture, leads to the hypothesis that capsid protein-coding nucleotide sequences may retain signatures of ancient viral evolution. We have been able to show that this is indeed the case, using the major capsid proteins of viruses forming icosahedral capsids. Importantly

Nucleic and Amino Acid Sequences Support Structure-Based Viral Classification

PubMed Central

Sinclair, Robert M.; Ravantti, Janne J.

2017-01-01

ABSTRACT Viral capsids ensure viral genome integrity by protecting the enclosed nucleic acids. Interactions between the genome and capsid and between individual capsid proteins (i.e., capsid architecture) are intimate and are expected to be characterized by strong evolutionary conservation. For this reason, a capsid structure-based viral classification has been proposed as a way to bring order to the viral universe. The seeming lack of sufficient sequence similarity to reproduce this classification has made it difficult to reject structural convergence as the basis for the classification. We reinvestigate whether the structure-based classification for viral coat proteins making icosahedral virus capsids is in fact supported by previously undetected sequence similarity. Since codon choices can influence nascent protein folding cotranslationally, we searched for both amino acid and nucleotide sequence similarity. To demonstrate the sensitivity of the approach, we identify a candidate gene for the pandoravirus capsid protein. We show that the structure-based classification is strongly supported by amino acid and also nucleotide sequence similarities, suggesting that the similarities are due to common descent. The correspondence between structure-based and sequence-based analyses of the same proteins shown here allow them to be used in future analyses of the relationship between linear sequence information and macromolecular function, as well as between linear sequence and protein folds. IMPORTANCE Viral capsids protect nucleic acid genomes, which in turn encode capsid proteins. This tight coupling of protein shell and nucleic acids, together with strong functional constraints on capsid protein folding and architecture, leads to the hypothesis that capsid protein-coding nucleotide sequences may retain signatures of ancient viral evolution. We have been able to show that this is indeed the case, using the major capsid proteins of viruses forming icosahedral capsids

Aggregating and Predicting Sequence Labels from Crowd Annotations

PubMed Central

Nguyen, An T.; Wallace, Byron C.; Li, Junyi Jessy; Nenkova, Ani; Lease, Matthew

2017-01-01

Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant. To predict sequences in unannotated text, we propose a neural approach using Long Short Term Memory. We evaluate a suite of methods across two different applications and text genres: Named-Entity Recognition in news articles and Information Extraction from biomedical abstracts. Results show improvement over strong baselines. Our source code and data are available online1. PMID:29093611

Towards predicting the encoding capability of MR fingerprinting sequences.

PubMed

Sommer, K; Amthor, T; Doneva, M; Koken, P; Meineke, J; Börnert, P

2017-09-01

Sequence optimization and appropriate sequence selection is still an unmet need in magnetic resonance fingerprinting (MRF). The main challenge in MRF sequence design is the lack of an appropriate measure of the sequence's encoding capability. To find such a measure, three different candidates for judging the encoding capability have been investigated: local and global dot-product-based measures judging dictionary entry similarity as well as a Monte Carlo method that evaluates the noise propagation properties of an MRF sequence. Consistency of these measures for different sequence lengths as well as the capability to predict actual sequence performance in both phantom and in vivo measurements was analyzed. While the dot-product-based measures yielded inconsistent results for different sequence lengths, the Monte Carlo method was in a good agreement with phantom experiments. In particular, the Monte Carlo method could accurately predict the performance of different flip angle patterns in actual measurements. The proposed Monte Carlo method provides an appropriate measure of MRF sequence encoding capability and may be used for sequence optimization. Copyright © 2017 Elsevier Inc. All rights reserved.

Statistical potential-based amino acid similarity matrices for aligning distantly related protein sequences.

PubMed

Tan, Yen Hock; Huang, He; Kihara, Daisuke

2006-08-15

Aligning distantly related protein sequences is a long-standing problem in bioinformatics, and a key for successful protein structure prediction. Its importance is increasing recently in the context of structural genomics projects because more and more experimentally solved structures are available as templates for protein structure modeling. Toward this end, recent structure prediction methods employ profile-profile alignments, and various ways of aligning two profiles have been developed. More fundamentally, a better amino acid similarity matrix can improve a profile itself; thereby resulting in more accurate profile-profile alignments. Here we have developed novel amino acid similarity matrices from knowledge-based amino acid contact potentials. Contact potentials are used because the contact propensity to the other amino acids would be one of the most conserved features of each position of a protein structure. The derived amino acid similarity matrices are tested on benchmark alignments at three different levels, namely, the family, the superfamily, and the fold level. Compared to BLOSUM45 and the other existing matrices, the contact potential-based matrices perform comparably in the family level alignments, but clearly outperform in the fold level alignments. The contact potential-based matrices perform even better when suboptimal alignments are considered. Comparing the matrices themselves with each other revealed that the contact potential-based matrices are very different from BLOSUM45 and the other matrices, indicating that they are located in a different basin in the amino acid similarity matrix space.

Prediction of protein secondary structure content for the twilight zone sequences.

PubMed

Homaeian, Leila; Kurgan, Lukasz A; Ruan, Jishou; Cios, Krzysztof J; Chen, Ke

2007-11-15

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it

DEMO: Sequence Alignment to Predict Across Species Susceptibility

EPA Science Inventory

The US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility tool (SeqAPASS; https://seqapass.epa.gov/seqapass/) was developed to comparatively evaluate protein sequence and structural similarity across species as a means to extrapolate toxic...

Complete amino acid sequence of bovine colostrum low-Mr cysteine proteinase inhibitor.

PubMed

Hirado, M; Tsunasawa, S; Sakiyama, F; Niinobe, M; Fujii, S

1985-07-01

The complete amino acid sequence of bovine colostrum cysteine proteinase inhibitor was determined by sequencing native inhibitor and peptides obtained by cyanogen bromide degradation, Achromobacter lysylendopeptidase digestion and partial acid hydrolysis of reduced and S-carboxymethylated protein. Achromobacter peptidase digestion was successfully used to isolate two disulfide-containing peptides. The inhibitor consists of 112 amino acids with an Mr of 12787. Two disulfide bonds were established between Cys 66 and Cys 77 and between Cys 90 and Cys 110. A high degree of homology in the sequence was found between the colostrum inhibitor and human gamma-trace, human salivary acidic protein and chicken egg-white cystatin.

Hybridization properties of long nucleic acid probes for detection of variable target sequences, and development of a hybridization prediction algorithm

PubMed Central

Öhrmalm, Christina; Jobs, Magnus; Eriksson, Ronnie; Golbob, Sultan; Elfaitouri, Amal; Benachenhou, Farid; Strømme, Maria; Blomberg, Jonas

2010-01-01

One of the main problems in nucleic acid-based techniques for detection of infectious agents, such as influenza viruses, is that of nucleic acid sequence variation. DNA probes, 70-nt long, some including the nucleotide analog deoxyribose-Inosine (dInosine), were analyzed for hybridization tolerance to different amounts and distributions of mismatching bases, e.g. synonymous mutations, in target DNA. Microsphere-linked 70-mer probes were hybridized in 3M TMAC buffer to biotinylated single-stranded (ss) DNA for subsequent analysis in a Luminex® system. When mismatches interrupted contiguous matching stretches of 6 nt or longer, it had a strong impact on hybridization. Contiguous matching stretches are more important than the same number of matching nucleotides separated by mismatches into several regions. dInosine, but not 5-nitroindole, substitutions at mismatching positions stabilized hybridization remarkably well, comparable to N (4-fold) wobbles in the same positions. In contrast to shorter probes, 70-nt probes with judiciously placed dInosine substitutions and/or wobble positions were remarkably mismatch tolerant, with preserved specificity. An algorithm, NucZip, was constructed to model the nucleation and zipping phases of hybridization, integrating both local and distant binding contributions. It predicted hybridization more exactly than previous algorithms, and has the potential to guide the design of variation-tolerant yet specific probes. PMID:20864443

Nucleic acid sequence detection using multiplexed oligonucleotide PCR

DOEpatents

Nolan, John P [Santa Fe, NM; White, P Scott [Los Alamos, NM

2006-12-26

Methods for rapidly detecting single or multiple sequence alleles in a sample nucleic acid are described. Provided are all of the oligonucleotide pairs capable of annealing specifically to a target allele and discriminating among possible sequences thereof, and ligating to each other to form an oligonucleotide complex when a particular sequence feature is present (or, alternatively, absent) in the sample nucleic acid. The design of each oligonucleotide pair permits the subsequent high-level PCR amplification of a specific amplicon when the oligonucleotide complex is formed, but not when the oligonucleotide complex is not formed. The presence or absence of the specific amplicon is used to detect the allele. Detection of the specific amplicon may be achieved using a variety of methods well known in the art, including without limitation, oligonucleotide capture onto DNA chips or microarrays, oligonucleotide capture onto beads or microspheres, electrophoresis, and mass spectrometry. Various labels and address-capture tags may be employed in the amplicon detection step of multiplexed assays, as further described herein.

Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion

PubMed Central

Thomsen, Martin Christen Frølund; Nielsen, Morten

2012-01-01

Seq2Logo is a web-based sequence logo generator. Sequence logos are a graphical representation of the information content stored in a multiple sequence alignment (MSA) and provide a compact and highly intuitive representation of the position-specific amino acid composition of binding motifs, active sites, etc. in biological sequences. Accurate generation of sequence logos is often compromised by sequence redundancy and low number of observations. Moreover, most methods available for sequence logo generation focus on displaying the position-specific enrichment of amino acids, discarding the equally valuable information related to amino acid depletion. Seq2logo aims at resolving these issues allowing the user to include sequence weighting to correct for data redundancy, pseudo counts to correct for low number of observations and different logotype representations each capturing different aspects related to amino acid enrichment and depletion. Besides allowing input in the format of peptides and MSA, Seq2Logo accepts input as Blast sequence profiles, providing easy access for non-expert end-users to characterize and identify functionally conserved/variable amino acids in any given protein of interest. The output from the server is a sequence logo and a PSSM. Seq2Logo is available at http://www.cbs.dtu.dk/biotools/Seq2Logo (14 May 2012, date last accessed). PMID:22638583

BEST: Improved Prediction of B-Cell Epitopes from Antigen Sequences

PubMed Central

Gao, Jianzhao; Faraggi, Eshel; Zhou, Yaoqi; Ruan, Jishou; Kurgan, Lukasz

2012-01-01

Accurate identification of immunogenic regions in a given antigen chain is a difficult and actively pursued problem. Although accurate predictors for T-cell epitopes are already in place, the prediction of the B-cell epitopes requires further research. We overview the available approaches for the prediction of B-cell epitopes and propose a novel and accurate sequence-based solution. Our BEST (B-cell Epitope prediction using Support vector machine Tool) method predicts epitopes from antigen sequences, in contrast to some method that predict only from short sequence fragments, using a new architecture based on averaging selected scores generated from sliding 20-mers by a Support Vector Machine (SVM). The SVM predictor utilizes a comprehensive and custom designed set of inputs generated by combining information derived from the chain, sequence conservation, similarity to known (training) epitopes, and predicted secondary structure and relative solvent accessibility. Empirical evaluation on benchmark datasets demonstrates that BEST outperforms several modern sequence-based B-cell epitope predictors including ABCPred, method by Chen et al. (2007), BCPred, COBEpro, BayesB, and CBTOPE, when considering the predictions from antigen chains and from the chain fragments. Our method obtains a cross-validated area under the receiver operating characteristic curve (AUC) for the fragment-based prediction at 0.81 and 0.85, depending on the dataset. The AUCs of BEST on the benchmark sets of full antigen chains equal 0.57 and 0.6, which is significantly and slightly better than the next best method we tested. We also present case studies to contrast the propensity profiles generated by BEST and several other methods. PMID:22761950

Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.

PubMed

Chou, Kuo-Chen

2005-01-01

With protein sequences entering into databanks at an explosive pace, the early determination of the family or subfamily class for a newly found enzyme molecule becomes important because this is directly related to the detailed information about which specific target it acts on, as well as to its catalytic process and biological function. Unfortunately, it is both time-consuming and costly to do so by experiments alone. In a previous study, the covariant-discriminant algorithm was introduced to identify the 16 subfamily classes of oxidoreductases. Although the results were quite encouraging, the entire prediction process was based on the amino acid composition alone without including any sequence-order information. Therefore, it is worthy of further investigation. To incorporate the sequence-order effects into the predictor, the 'amphiphilic pseudo amino acid composition' is introduced to represent the statistical sample of a protein. The novel representation contains 20 + 2lambda discrete numbers: the first 20 numbers are the components of the conventional amino acid composition; the next 2lambda numbers are a set of correlation factors that reflect different hydrophobicity and hydrophilicity distribution patterns along a protein chain. Based on such a concept and formulation scheme, a new predictor is developed. It is shown by the self-consistency test, jackknife test and independent dataset tests that the success rates obtained by the new predictor are all significantly higher than those by the previous predictors. The significant enhancement in success rates also implies that the distribution of hydrophobicity and hydrophilicity of the amino acid residues along a protein chain plays a very important role to its structure and function.

Promoter Sequences Prediction Using Relational Association Rule Mining

PubMed Central

Czibula, Gabriela; Bocicor, Maria-Iuliana; Czibula, Istvan Gergely

2012-01-01

In this paper we are approaching, from a computational perspective, the problem of promoter sequences prediction, an important problem within the field of bioinformatics. As the conditions for a DNA sequence to function as a promoter are not known, machine learning based classification models are still developed to approach the problem of promoter identification in the DNA. We are proposing a classification model based on relational association rules mining. Relational association rules are a particular type of association rules and describe numerical orderings between attributes that commonly occur over a data set. Our classifier is based on the discovery of relational association rules for predicting if a DNA sequence contains or not a promoter region. An experimental evaluation of the proposed model and comparison with similar existing approaches is provided. The obtained results show that our classifier overperforms the existing techniques for identifying promoter sequences, confirming the potential of our proposal. PMID:22563233

Improved nucleic acid descriptors for siRNA efficacy prediction.

PubMed

Sciabola, Simone; Cao, Qing; Orozco, Modesto; Faustino, Ignacio; Stanton, Robert V

2013-02-01

Although considerable progress has been made recently in understanding how gene silencing is mediated by the RNAi pathway, the rational design of effective sequences is still a challenging task. In this article, we demonstrate that including three-dimensional descriptors improved the discrimination between active and inactive small interfering RNAs (siRNAs) in a statistical model. Five descriptor types were used: (i) nucleotide position along the siRNA sequence, (ii) nucleotide composition in terms of presence/absence of specific combinations of di- and trinucleotides, (iii) nucleotide interactions by means of a modified auto- and cross-covariance function, (iv) nucleotide thermodynamic stability derived by the nearest neighbor model representation and (v) nucleic acid structure flexibility. The duplex flexibility descriptors are derived from extended molecular dynamics simulations, which are able to describe the sequence-dependent elastic properties of RNA duplexes, even for non-standard oligonucleotides. The matrix of descriptors was analysed using three statistical packages in R (partial least squares, random forest, and support vector machine), and the most predictive model was implemented in a modeling tool we have made publicly available through SourceForge. Our implementation of new RNA descriptors coupled with appropriate statistical algorithms resulted in improved model performance for the selection of siRNA candidates when compared with publicly available siRNA prediction tools and previously published test sets. Additional validation studies based on in-house RNA interference projects confirmed the robustness of the scoring procedure in prospective studies.

NEP: web server for epitope prediction based on antibody neutralization of viral strains with diverse sequences.

PubMed

Chuang, Gwo-Yu; Liou, David; Kwong, Peter D; Georgiev, Ivelin S

2014-07-01

Delineation of the antigenic site, or epitope, recognized by an antibody can provide clues about functional vulnerabilities and resistance mechanisms, and can therefore guide antibody optimization and epitope-based vaccine design. Previously, we developed an algorithm for antibody-epitope prediction based on antibody neutralization of viral strains with diverse sequences and validated the algorithm on a set of broadly neutralizing HIV-1 antibodies. Here we describe the implementation of this algorithm, NEP (Neutralization-based Epitope Prediction), as a web-based server. The users must supply as input: (i) an alignment of antigen sequences of diverse viral strains; (ii) neutralization data for the antibody of interest against the same set of antigen sequences; and (iii) (optional) a structure of the unbound antigen, for enhanced prediction accuracy. The prediction results can be downloaded or viewed interactively on the antigen structure (if supplied) from the web browser using a JSmol applet. Since neutralization experiments are typically performed as one of the first steps in the characterization of an antibody to determine its breadth and potency, the NEP server can be used to predict antibody-epitope information at no additional experimental costs. NEP can be accessed on the internet at http://exon.niaid.nih.gov/nep. Published by Oxford University Press on behalf of Nucleic Acids Research 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle.

PubMed

van Binsbergen, Rianne; Calus, Mario P L; Bink, Marco C A M; van Eeuwijk, Fred A; Schrooten, Chris; Veerkamp, Roel F

2015-09-17

In contrast to currently used single nucleotide polymorphism (SNP) panels, the use of whole-genome sequence data is expected to enable the direct estimation of the effects of causal mutations on a given trait. This could lead to higher reliabilities of genomic predictions compared to those based on SNP genotypes. Also, at each generation of selection, recombination events between a SNP and a mutation can cause decay in reliability of genomic predictions based on markers rather than on the causal variants. Our objective was to investigate the use of imputed whole-genome sequence genotypes versus high-density SNP genotypes on (the persistency of) the reliability of genomic predictions using real cattle data. Highly accurate phenotypes based on daughter performance and Illumina BovineHD Beadchip genotypes were available for 5503 Holstein Friesian bulls. The BovineHD genotypes (631,428 SNPs) of each bull were used to impute whole-genome sequence genotypes (12,590,056 SNPs) using the Beagle software. Imputation was done using a multi-breed reference panel of 429 sequenced individuals. Genomic estimated breeding values for three traits were predicted using a Bayesian stochastic search variable selection (BSSVS) model and a genome-enabled best linear unbiased prediction model (GBLUP). Reliabilities of predictions were based on 2087 validation bulls, while the other 3416 bulls were used for training. Prediction reliabilities ranged from 0.37 to 0.52. BSSVS performed better than GBLUP in all cases. Reliabilities of genomic predictions were slightly lower with imputed sequence data than with BovineHD chip data. Also, the reliabilities tended to be lower for both sequence data and BovineHD chip data when relationships between training animals were low. No increase in persistency of prediction reliability using imputed sequence data was observed. Compared to BovineHD genotype data, using imputed sequence data for genomic prediction produced no advantage. To investigate the

Immunoreactivity of polyclonal antibodies generated against the carboxy terminus of the predicted amino acid sequence of the Huntington disease gene

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alkatib, G.; Graham, R.; Pelmear-Telenius, A.

1994-09-01

A cDNA fragment spanning the 3{prime}-end of the Huntington disease gene (from 8052 to 9252) was cloned into a prokaryotic expression vector containing the E. Coli lac promoter and a portion of the coding sequence for {beta}-galactosidase. The truncated {beta}-galactosidase gene was cleaved with BamHl and fused in frame to the BamHl fragment of the Huntington disease gene 3{prime}-end. Expression analysis of proteins made in E. Coli revealed that 20-30% of the total cellular proteins was represented by the {beta}-galactosidase-huntingtin fusion protein. The identity of the Huntington disease protein amino acid sequences was confirmed by protein sequence analysis. Affinity chromatographymore » was used to purify large quantities of the fusion protein from bacterial cell lysates. Affinity-purified proteins were used to immunize New Zealand white rabbits for antibody production. The generated polyclonal antibodies were used to immunoprecipitate the Huntington disease gene product expressed in a neuroblastoma cell line. In this cell line the antibodies precipitated two protein bands of apparent gel migrations of 200 and 150 kd which together, correspond to the calculated molecular weight of the Huntington disease gene product (350 kd). Immunoblotting experiments revealed the presence of a large precursor protein in the range of 350-750 kd which is in agreement with the predicted molecular weight of the protein without post-translational modifications. These results indicate that the huntingtin protein is cleaved into two subunits in this neuroblastoma cell line and implicate that cleavage of a large precursor protein may contribute to its biological activity. Experiments are ongoing to determine the precursor-product relationship and to examine the synthesis of the huntingtin protein in freshly isolated rat brains, and to determine cellular and subcellular distribution of the gene product.« less

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

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

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. 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

WEB-server for search of a periodicity in amino acid and nucleotide sequences

NASA Astrophysics Data System (ADS)

E Frenkel, F.; Skryabin, K. G.; Korotkov, E. V.

2017-12-01

A new web server (http://victoria.biengi.ac.ru/splinter/login.php) was designed and developed to search for periodicity in nucleotide and amino acid sequences. The web server operation is based upon a new mathematical method of searching for multiple alignments, which is founded on the position weight matrices optimization, as well as on implementation of the two-dimensional dynamic programming. This approach allows the construction of multiple alignments of the indistinctly similar amino acid and nucleotide sequences that accumulated more than 1.5 substitutions per a single amino acid or a nucleotide without performing the sequences paired comparisons. The article examines the principles of the web server operation and two examples of studying amino acid and nucleotide sequences, as well as information that could be obtained using the web server.

Detection and isolation of nucleic acid sequences using a bifunctional hybridization probe

DOEpatents

Lucas, Joe N.; Straume, Tore; Bogen, Kenneth T.

2000-01-01

A method for detecting and isolating a target sequence in a sample of nucleic acids is provided using a bifunctional hybridization probe capable of hybridizing to the target sequence that includes a detectable marker and a first complexing agent capable of forming a binding pair with a second complexing agent. A kit is also provided for detecting a target sequence in a sample of nucleic acids using a bifunctional hybridization probe according to this method.

Structural details (kinks and non-α conformations) in transmembrane helices are intrahelically determined and can be predicted by sequence pattern descriptors

PubMed Central

Rigoutsos, Isidore; Riek, Peter; Graham, Robert M.; Novotny, Jiri

2003-01-01

One of the promising methods of protein structure prediction involves the use of amino acid sequence-derived patterns. Here we report on the creation of non-degenerate motif descriptors derived through data mining of training sets of residues taken from the transmembrane-spanning segments of polytopic proteins. These residues correspond to short regions in which there is a deviation from the regular α-helical character (i.e. π-helices, 310-helices and kinks). A ‘search engine’ derived from these motif descriptors correctly identifies, and discriminates amongst instances of the above ‘non-canonical’ helical motifs contained in the SwissProt/TrEMBL database of protein primary structures. Our results suggest that deviations from α-helicity are encoded locally in sequence patterns only about 7–9 residues long and can be determined in silico directly from the amino acid sequence. Delineation of such variations in helical habit is critical to understanding the complex structure–function relationships of polytopic proteins and for drug discovery. The success of our current methodology foretells development of similar prediction tools capable of identifying other structural motifs from sequence alone. The method described here has been implemented and is available on the World Wide Web at http://cbcsrv.watson.ibm.com/Ttkw.html. PMID:12888523

Structural details (kinks and non-alpha conformations) in transmembrane helices are intrahelically determined and can be predicted by sequence pattern descriptors.

PubMed

Rigoutsos, Isidore; Riek, Peter; Graham, Robert M; Novotny, Jiri

2003-08-01

One of the promising methods of protein structure prediction involves the use of amino acid sequence-derived patterns. Here we report on the creation of non-degenerate motif descriptors derived through data mining of training sets of residues taken from the transmembrane-spanning segments of polytopic proteins. These residues correspond to short regions in which there is a deviation from the regular alpha-helical character (i.e. pi-helices, 3(10)-helices and kinks). A 'search engine' derived from these motif descriptors correctly identifies, and discriminates amongst instances of the above 'non-canonical' helical motifs contained in the SwissProt/TrEMBL database of protein primary structures. Our results suggest that deviations from alpha-helicity are encoded locally in sequence patterns only about 7-9 residues long and can be determined in silico directly from the amino acid sequence. Delineation of such variations in helical habit is critical to understanding the complex structure-function relationships of polytopic proteins and for drug discovery. The success of our current methodology foretells development of similar prediction tools capable of identifying other structural motifs from sequence alone. The method described here has been implemented and is available on the World Wide Web at http://cbcsrv.watson.ibm.com/Ttkw.html.

Soil amino acid composition across a boreal forest successional sequence

Treesearch

Nancy R. Werdin-Pfisterer; Knut Kielland; Richard D. Boone

2009-01-01

Soil amino acids are important sources of organic nitrogen for plant nutrition, yet few studies have examined which amino acids are most prevalent in the soil. In this study, we examined the composition, concentration, and seasonal patterns of soil amino acids across a primary successional sequence encompassing a natural gradient of plant productivity and soil...

Development of a sugar-binding residue prediction system from protein sequences using support vector machine.

PubMed

Banno, Masaki; Komiyama, Yusuke; Cao, Wei; Oku, Yuya; Ueki, Kokoro; Sumikoshi, Kazuya; Nakamura, Shugo; Terada, Tohru; Shimizu, Kentaro

2017-02-01

Several methods have been proposed for protein-sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor. We also developed a combination predictor which combines the results of the two predictors. We showed that when a sugar is known to be an acidic sugar, the acidic sugar binding predictor achieves the best performance, and showed that when a sugar is known to be a nonacidic sugar or is not known to be either of the two classes, the combination predictor achieves the best performance. Our method uses only amino acid sequences for prediction. Support vector machine was used as a machine learning algorithm and the position-specific scoring matrix created by the position-specific iterative basic local alignment search tool was used as the feature vector. We evaluated the performance of the predictors using five-fold cross-validation. We have launched our system, as an open source freeware tool on the GitHub repository (https://doi.org/10.5281/zenodo.61513). Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Selecting sequence variants to improve genomic predictions for dairy cattle

USDA-ARS?s Scientific Manuscript database

Millions of genetic variants have been identified by population-scale sequencing projects, but subsets are needed for routine genomic predictions or to include on genotyping arrays. Methods of selecting sequence variants were compared using both simulated sequence genotypes and actual data from run ...

Preferential amino acid sequences in alumina-catalyzed peptide bond formation.

PubMed

Bujdák, J; Rode, B M

2002-05-21

The catalytic effect of activated alumina on amino acid condensation was investigated. The readiness of amino acids to form peptide sequences was estimated on the basis of the yield of dipeptides and was found to decrease in the order glycine (Gly), alanine (Ala), leucine (Leu), valine (Val), proline (Pro). For example, approximately 15% Gly was converted to the dipeptide (Gly(2)), 5% to cyclic anhydride (cyc(Gly(2))) and small amounts of tri- (Gly(3)) and tetrapeptide (Gly(4)) were formed after 28 days. On the other hand, only trace amounts of Pro(2) were formed from proline under the same conditions. Preferential formation of certain sequences was observed in the mixed reaction systems containing two amino acids. For example, almost ten times more Gly-Val than Val-Gly was formed in the Gly+Val reaction system. The preferred sequences can be explained on the basis of an inductive effect that side groups have on the nucleophilicity and electrophilicity, respectively, of the amino and carboxyl groups. A comparison with published data of amino acid reactions in other reaction systems revealed that the main trends of preferential sequence formation were the same as those described for the salt-induced peptide formation (SIPF) reaction. The results of this work and other previously published papers show that alumina and related mineral surfaces might have played a crucial role in the prebiotic formation of the first peptides on the primitive earth.

Predicting protein-protein interactions by combing various sequence- derived features into the general form of Chou's Pseudo amino acid composition.

PubMed

Zhao, Xiao-Wei; Ma, Zhi-Qiang; Yin, Ming-Hao

2012-05-01

Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences § 1.821 Nucleotide and/or amino acid sequence disclosures in patent applications. (a) Nucleotide and...

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 37 Patents, Trademarks, and Copyrights 1 2011-07-01 2011-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences § 1.821 Nucleotide and/or amino acid sequence disclosures in patent applications. (a) Nucleotide and...

cgDNA: a software package for the prediction of sequence-dependent coarse-grain free energies of B-form DNA.

PubMed

Petkevičiūtė, D; Pasi, M; Gonzalez, O; Maddocks, J H

2014-11-10

cgDNA is a package for the prediction of sequence-dependent configuration-space free energies for B-form DNA at the coarse-grain level of rigid bases. For a fragment of any given length and sequence, cgDNA calculates the configuration of the associated free energy minimizer, i.e. the relative positions and orientations of each base, along with a stiffness matrix, which together govern differences in free energies. The model predicts non-local (i.e. beyond base-pair step) sequence dependence of the free energy minimizer. Configurations can be input or output in either the Curves+ definition of the usual helical DNA structural variables, or as a PDB file of coordinates of base atoms. We illustrate the cgDNA package by comparing predictions of free energy minimizers from (a) the cgDNA model, (b) time-averaged atomistic molecular dynamics (or MD) simulations, and (c) NMR or X-ray experimental observation, for (i) the Dickerson-Drew dodecamer and (ii) three oligomers containing A-tracts. The cgDNA predictions are rather close to those of the MD simulations, but many orders of magnitude faster to compute. Both the cgDNA and MD predictions are in reasonable agreement with the available experimental data. Our conclusion is that cgDNA can serve as a highly efficient tool for studying structural variations in B-form DNA over a wide range of sequences. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

OPAL: prediction of MoRF regions in intrinsically disordered protein sequences.

PubMed

Sharma, Ronesh; Raicar, Gaurav; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok

2018-06-01

Intrinsically disordered proteins lack stable 3-dimensional structure and play a crucial role in performing various biological functions. Key to their biological function are the molecular recognition features (MoRFs) located within long disordered regions. Computationally identifying these MoRFs from disordered protein sequences is a challenging task. In this study, we present a new MoRF predictor, OPAL, to identify MoRFs in disordered protein sequences. OPAL utilizes two independent sources of information computed using different component predictors. The scores are processed and combined using common averaging method. The first score is computed using a component MoRF predictor which utilizes composition and sequence similarity of MoRF and non-MoRF regions to detect MoRFs. The second score is calculated using half-sphere exposure (HSE), solvent accessible surface area (ASA) and backbone angle information of the disordered protein sequence, using information from the amino acid properties of flanks surrounding the MoRFs to distinguish MoRF and non-MoRF residues. OPAL is evaluated using test sets that were previously used to evaluate MoRF predictors, MoRFpred, MoRFchibi and MoRFchibi-web. The results demonstrate that OPAL outperforms all the available MoRF predictors and is the most accurate predictor available for MoRF prediction. It is available at http://www.alok-ai-lab.com/tools/opal/. ashwini@hgc.jp or alok.sharma@griffith.edu.au. Supplementary data are available at Bioinformatics online.

Human somatostatin I: sequence of the cDNA.

PubMed Central

Shen, L P; Pictet, R L; Rutter, W J

1982-01-01

RNA has been isolated from a human pancreatic somatostatinoma and used to prepare a cDNA library. After prescreening, clones containing somatostatin I sequences were identified by hybridization with an anglerfish somatostatin I-cloned cDNA probe. From the nucleotide sequence of two of these clones, we have deduced an essentially full-length mRNA sequence, including the preprosomatostatin coding region, 105 nucleotides from the 5' untranslated region and the complete 150-nucleotide 3' untranslated region. The coding region predicts a 116-amino acid precursor protein (Mr, 12.727) that contains somatostatin-14 and -28 at its COOH terminus. The predicted amino acid sequence of human somatostatin-28 is identical to that of somatostatin-28 isolated from the porcine and ovine species. A comparison of the amino acid sequences of human and anglerfish preprosomatostatin I indicated that the COOH-terminal region encoding somatostatin-14 and the adjacent 6 amino acids are highly conserved, whereas the remainder of the molecule, including the signal peptide region, is more divergent. However, many of the amino acid differences found in the pro region of the human and anglerfish proteins are conservative changes. This suggests that the propeptides have a similar secondary structure, which in turn may imply a biological function for this region of the molecule. Images PMID:6126875

αIIbβ3 variants defined by next-generation sequencing: Predicting variants likely to cause Glanzmann thrombasthenia

PubMed Central

Buitrago, Lorena; Rendon, Augusto; Liang, Yupu; Simeoni, Ilenia; Negri, Ana; Filizola, Marta; Ouwehand, Willem H.; Coller, Barry S.; Alessi, Marie-Christine; Ballmaier, Matthias; Bariana, Tadbir; Bellissimo, Daniel; Bertoli, Marta; Bray, Paul; Bury, Loredana; Carrell, Robin; Cattaneo, Marco; Collins, Peter; French, Deborah; Favier, Remi; Freson, Kathleen; Furie, Bruce; Germeshausen, Manuela; Ghevaert, Cedric; Gomez, Keith; Goodeve, Anne; Gresele, Paolo; Guerrero, Jose; Hampshire, Dan J.; Hadinnapola, Charaka; Heemskerk, Johan; Henskens, Yvonne; Hill, Marian; Hogg, Nancy; Johnsen, Jill; Kahr, Walter; Kerr, Ron; Kunishima, Shinji; Laffan, Michael; Natwani, Amit; Neerman-Arbez, Marguerite; Nurden, Paquita; Nurden, Alan; Ormiston, Mark; Othman, Maha; Ouwehand, Willem; Perry, David; Vilk, Shoshana Ravel; Reitsma, Pieter; Rondina, Matthew; Simeoni, Ilenia; Smethurst, Peter; Stephens, Jonathan; Stevenson, William; Szkotak, Artur; Turro, Ernest; Van Geet, Christel; Vries, Minka; Ward, June; Waye, John; Westbury, Sarah; Whiteheart, Sidney; Wilcox, David; Zhang, Bi

2015-01-01

Next-generation sequencing is transforming our understanding of human genetic variation but assessing the functional impact of novel variants presents challenges. We analyzed missense variants in the integrin αIIbβ3 receptor subunit genes ITGA2B and ITGB3 identified by whole-exome or -genome sequencing in the ThromboGenomics project, comprising ∼32,000 alleles from 16,108 individuals. We analyzed the results in comparison with 111 missense variants in these genes previously reported as being associated with Glanzmann thrombasthenia (GT), 20 associated with alloimmune thrombocytopenia, and 5 associated with aniso/macrothrombocytopenia. We identified 114 novel missense variants in ITGA2B (affecting ∼11% of the amino acids) and 68 novel missense variants in ITGB3 (affecting ∼9% of the amino acids). Of the variants, 96% had minor allele frequencies (MAF) < 0.1%, indicating their rarity. Based on sequence conservation, MAF, and location on a complete model of αIIbβ3, we selected three novel variants that affect amino acids previously associated with GT for expression in HEK293 cells. αIIb P176H and β3 C547G severely reduced αIIbβ3 expression, whereas αIIb P943A partially reduced αIIbβ3 expression and had no effect on fibrinogen binding. We used receiver operating characteristic curves of combined annotation-dependent depletion, Polyphen 2-HDIV, and sorting intolerant from tolerant to estimate the percentage of novel variants likely to be deleterious. At optimal cut-off values, which had 69–98% sensitivity in detecting GT mutations, between 27% and 71% of the novel αIIb or β3 missense variants were predicted to be deleterious. Our data have implications for understanding the evolutionary pressure on αIIbβ3 and highlight the challenges in predicting the clinical significance of novel missense variants. PMID:25827233

Sequences Of Amino Acids For Human Serum Albumin

NASA Technical Reports Server (NTRS)

Carter, Daniel C.

1992-01-01

Sequences of amino acids defined for use in making polypeptides one-third to one-sixth as large as parent human serum albumin molecule. Smaller, chemically stable peptides have diverse applications including service as artificial human serum and as active components of biosensors and chromatographic matrices. In applications involving production of artificial sera from new sequences, little or no concern about viral contaminants. Smaller genetically engineered polypeptides more easily expressed and produced in large quantities, making commercial isolation and production more feasible and profitable.

Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis

PubMed Central

Zheng, Lu-Lu; Niu, Shen; Hao, Pei; Feng, KaiYan; Cai, Yu-Dong; Li, Yixue

2011-01-01

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations. PMID:22174779

Prediction of Human Activity by Discovering Temporal Sequence Patterns.

PubMed

Li, Kang; Fu, Yun

2014-08-01

Early prediction of ongoing human activity has become more valuable in a large variety of time-critical applications. To build an effective representation for prediction, human activities can be characterized by a complex temporal composition of constituent simple actions and interacting objects. Different from early detection on short-duration simple actions, we propose a novel framework for long -duration complex activity prediction by discovering three key aspects of activity: Causality, Context-cue, and Predictability. The major contributions of our work include: (1) a general framework is proposed to systematically address the problem of complex activity prediction by mining temporal sequence patterns; (2) probabilistic suffix tree (PST) is introduced to model causal relationships between constituent actions, where both large and small order Markov dependencies between action units are captured; (3) the context-cue, especially interactive objects information, is modeled through sequential pattern mining (SPM), where a series of action and object co-occurrence are encoded as a complex symbolic sequence; (4) we also present a predictive accumulative function (PAF) to depict the predictability of each kind of activity. The effectiveness of our approach is evaluated on two experimental scenarios with two data sets for each: action-only prediction and context-aware prediction. Our method achieves superior performance for predicting global activity classes and local action units.

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis

PubMed Central

2013-01-01

Background Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs. Results We present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance. Conclusions When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent

Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis.

PubMed

You, Zhu-Hong; Lei, Ying-Ke; Zhu, Lin; Xia, Junfeng; Wang, Bing

2013-01-01

Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs. We present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance. When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time.

A novel method based on new adaptive LVQ neural network for predicting protein-protein interactions from protein sequences.

PubMed

Yousef, Abdulaziz; Moghadam Charkari, Nasrollah

2013-11-07

Protein-Protein interaction (PPI) is one of the most important data in understanding the cellular processes. Many interesting methods have been proposed in order to predict PPIs. However, the methods which are based on the sequence of proteins as a prior knowledge are more universal. In this paper, a sequence-based, fast, and adaptive PPI prediction method is introduced to assign two proteins to an interaction class (yes, no). First, in order to improve the presentation of the sequences, twelve physicochemical properties of amino acid have been used by different representation methods to transform the sequence of protein pairs into different feature vectors. Then, for speeding up the learning process and reducing the effect of noise PPI data, principal component analysis (PCA) is carried out as a proper feature extraction algorithm. Finally, a new and adaptive Learning Vector Quantization (LVQ) predictor is designed to deal with different models of datasets that are classified into balanced and imbalanced datasets. The accuracy of 93.88%, 90.03%, and 89.72% has been found on S. cerevisiae, H. pylori, and independent datasets, respectively. The results of various experiments indicate the efficiency and validity of the method. © 2013 Published by Elsevier Ltd.

RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis.

PubMed

Afzal, Muhammad; Shahid, Ahmad Ali; Shehzadi, Abida; Nadeem, Shahid; Husnain, Tayyab

2012-01-01

RDNAnalyzer is an innovative computer based tool designed for DNA secondary structure prediction and sequence analysis. It can randomly generate the DNA sequence or user can upload the sequences of their own interest in RAW format. It uses and extends the Nussinov dynamic programming algorithm and has various application for the sequence analysis. It predicts the DNA secondary structure and base pairings. It also provides the tools for routinely performed sequence analysis by the biological scientists such as DNA replication, reverse compliment generation, transcription, translation, sequence specific information as total number of nucleotide bases, ATGC base contents along with their respective percentages and sequence cleaner. RDNAnalyzer is a unique tool developed in Microsoft Visual Studio 2008 using Microsoft Visual C# and Windows Presentation Foundation and provides user friendly environment for sequence analysis. It is freely available. http://www.cemb.edu.pk/sw.html RDNAnalyzer - Random DNA Analyser, GUI - Graphical user interface, XAML - Extensible Application Markup Language.

RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis

PubMed Central

Afzal, Muhammad; Shahid, Ahmad Ali; Shehzadi, Abida; Nadeem, Shahid; Husnain, Tayyab

2012-01-01

RDNAnalyzer is an innovative computer based tool designed for DNA secondary structure prediction and sequence analysis. It can randomly generate the DNA sequence or user can upload the sequences of their own interest in RAW format. It uses and extends the Nussinov dynamic programming algorithm and has various application for the sequence analysis. It predicts the DNA secondary structure and base pairings. It also provides the tools for routinely performed sequence analysis by the biological scientists such as DNA replication, reverse compliment generation, transcription, translation, sequence specific information as total number of nucleotide bases, ATGC base contents along with their respective percentages and sequence cleaner. RDNAnalyzer is a unique tool developed in Microsoft Visual Studio 2008 using Microsoft Visual C# and Windows Presentation Foundation and provides user friendly environment for sequence analysis. It is freely available. Availability http://www.cemb.edu.pk/sw.html Abbreviations RDNAnalyzer - Random DNA Analyser, GUI - Graphical user interface, XAML - Extensible Application Markup Language. PMID:23055611

Comparative characterization of random-sequence proteins consisting of 5, 12, and 20 kinds of amino acids

PubMed Central

Tanaka, Junko; Doi, Nobuhide; Takashima, Hideaki; Yanagawa, Hiroshi

2010-01-01

Screening of functional proteins from a random-sequence library has been used to evolve novel proteins in the field of evolutionary protein engineering. However, random-sequence proteins consisting of the 20 natural amino acids tend to aggregate, and the occurrence rate of functional proteins in a random-sequence library is low. From the viewpoint of the origin of life, it has been proposed that primordial proteins consisted of a limited set of amino acids that could have been abundantly formed early during chemical evolution. We have previously found that members of a random-sequence protein library constructed with five primitive amino acids show high solubility (Doi et al., Protein Eng Des Sel 2005;18:279–284). Although such a library is expected to be appropriate for finding functional proteins, the functionality may be limited, because they have no positively charged amino acid. Here, we constructed three libraries of 120-amino acid, random-sequence proteins using alphabets of 5, 12, and 20 amino acids by preselection using mRNA display (to eliminate sequences containing stop codons and frameshifts) and characterized and compared the structural properties of random-sequence proteins arbitrarily chosen from these libraries. We found that random-sequence proteins constructed with the 12-member alphabet (including five primitive amino acids and positively charged amino acids) have higher solubility than those constructed with the 20-member alphabet, though other biophysical properties are very similar in the two libraries. Thus, a library of moderate complexity constructed from 12 amino acids may be a more appropriate resource for functional screening than one constructed from 20 amino acids. PMID:20162614

37 CFR 1.822 - Symbols and format to be used for nucleotide and/or amino acid sequence data.

Code of Federal Regulations, 2011 CFR

2011-07-01

... for nucleotide and/or amino acid sequence data. 1.822 Section 1.822 Patents, Trademarks, and... Amino Acid Sequences § 1.822 Symbols and format to be used for nucleotide and/or amino acid sequence data. (a) The symbols and format to be used for nucleotide and/or amino acid sequence data shall...

BPP: a sequence-based algorithm for branch point prediction.

PubMed

Zhang, Qing; Fan, Xiaodan; Wang, Yejun; Sun, Ming-An; Shao, Jianlin; Guo, Dianjing

2017-10-15

Although high-throughput sequencing methods have been proposed to identify splicing branch points in the human genome, these methods can only detect a small fraction of the branch points subject to the sequencing depth, experimental cost and the expression level of the mRNA. An accurate computational model for branch point prediction is therefore an ongoing objective in human genome research. We here propose a novel branch point prediction algorithm that utilizes information on the branch point sequence and the polypyrimidine tract. Using experimentally validated data, we demonstrate that our proposed method outperforms existing methods. Availability and implementation: https://github.com/zhqingit/BPP. djguo@cuhk.edu.hk. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Yeast Prions and Human Prion-like Proteins: Sequence Features and Prediction Methods

PubMed Central

Cascarina, Sean; Ross, Eric D.

2014-01-01

Prions are self-propagating infectious protein isoforms. A growing number of prions have been identified in yeast, each resulting from the conversion of soluble proteins into an insoluble amyloid form. These yeast prions have served as a powerful model system for studying the causes and consequences of prion aggregation. Remarkably, a number of human proteins containing prion-like domains, defined as domains with compositional similarity to yeast prion domains, have recently been linked to various human degenerative diseases, including amyotrophic lateral sclerosis (ALS). This suggests that the lessons learned from yeast prions may help in understanding these human diseases. In this review, we examine what has been learned about the amino acid sequence basis for prion aggregation in yeast, and how this information has been used to develop methods to predict aggregation propensity. We then discuss how this information is being applied to understand human disease, and the challenges involved in applying yeast prediction methods to higher organisms. PMID:24390581

Yeast prions and human prion-like proteins: sequence features and prediction methods.

PubMed

Cascarina, Sean M; Ross, Eric D

2014-06-01

Prions are self-propagating infectious protein isoforms. A growing number of prions have been identified in yeast, each resulting from the conversion of soluble proteins into an insoluble amyloid form. These yeast prions have served as a powerful model system for studying the causes and consequences of prion aggregation. Remarkably, a number of human proteins containing prion-like domains, defined as domains with compositional similarity to yeast prion domains, have recently been linked to various human degenerative diseases, including amyotrophic lateral sclerosis. This suggests that the lessons learned from yeast prions may help in understanding these human diseases. In this review, we examine what has been learned about the amino acid sequence basis for prion aggregation in yeast, and how this information has been used to develop methods to predict aggregation propensity. We then discuss how this information is being applied to understand human disease, and the challenges involved in applying yeast prediction methods to higher organisms.

Amino acid sequence of a trypsin inhibitor from a Spirometra (Spirometra erinaceieuropaei).

PubMed

Sanda, A; Uchida, A; Itagaki, T; Kobayashi, H; Inokuchi, N; Koyama, T; Iwama, M; Ohgi, K; Irie, M

2001-12-01

A trypsin inhibitor that is highly homologous with bovine pancreatic trypsin inhibitor (BPTI) was co-purified along with RNase from Spirometra (Spirometra erinaceieuropaei). The amino acid sequence of this inhibitor (SETI) and the nucleotide sequence of the cDNA encoding this protein were determined by protein chemistry and gene technology. SETI contains 68 amino acid residues and has a molecular mass of 7,798 Da. SETI has 31 amino acid residues that are identical with BPTI's sequence, including 6 half-cystine and 5 aromatic amino acid residues. The active site Lys residue in BPTI is replaced by an Arg residue in SETI. SETI is an effective inhibitor of trypsin and moderately inhibits a-chymotrypsin, but less inhibits elastase or subtilisin. SETI was expressed by E. coli containing a PelB vector carrying the SETI encoding cDNA; an expression yield of 0.68 mg/l was obtained. The phylogenetic relationship of SETI and the other BPTI-like trypsin inhibitors was analyzed using most likelihood inference methods.

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2011 CFR

2011-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences § 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by § 1.52(e)(5). The presentation of the...

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2013 CFR

2013-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences § 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by § 1.52(e)(5). The presentation of the...

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2012 CFR

2012-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences § 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by § 1.52(e)(5). The presentation of the...

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2010 CFR

2010-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences § 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by § 1.52(e)(5). The presentation of the...

37 CFR 1.823 - Requirements for nucleotide and/or amino acid sequences as part of the application.

Code of Federal Regulations, 2014 CFR

2014-07-01

... and/or amino acid sequences as part of the application. 1.823 Section 1.823 Patents, Trademarks, and... Amino Acid Sequences § 1.823 Requirements for nucleotide and/or amino acid sequences as part of the... incorporation-by-reference of the Sequence Listing as required by § 1.52(e)(5). The presentation of the...

Nucleotide sequence analysis of the gene encoding the Deinococcus radiodurans surface protein, derived amino acid sequence, and complementary protein chemical studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, J.; Peters, M.; Lottspeich, F.

1987-11-01

The complete nucleotide sequence of the gene encoding the surface (hexagonally packed intermediate (HPI))-layer polypeptide of Deinococcus radiodurans Sark was determined and found to encode a polypeptide of 1036 amino acids. Amino acid sequence analysis of about 30% of the residues revealed that the mature polypeptide consists of at least 978 amino acids. The N terminus was blocked to Edman degradation. The results of proteolytic modification of the HPI layer in situ and M/sub r/ estimations of the HPI polypeptide expressed in Escherichia coli indicated that there is a leader sequence. The N-terminal region contained a very high percentage (29%)more » of threonine and serine, including a cluster of nine consecutive serine or threonine residues, whereas a stretch near the C terminus was extremely rich in aromatic amino acids (29%). The protein contained at least two disulfide bridges, as well as tightly bound reducing sugars and fatty acids.« less

Mfold web server for nucleic acid folding and hybridization prediction

PubMed Central

Zuker, Michael

2003-01-01

The abbreviated name, ‘mfold web server’, describes a number of closely related software applications available on the World Wide Web (WWW) for the prediction of the secondary structure of single stranded nucleic acids. The objective of this web server is to provide easy access to RNA and DNA folding and hybridization software to the scientific community at large. By making use of universally available web GUIs (Graphical User Interfaces), the server circumvents the problem of portability of this software. Detailed output, in the form of structure plots with or without reliability information, single strand frequency plots and ‘energy dot plots’, are available for the folding of single sequences. A variety of ‘bulk’ servers give less information, but in a shorter time and for up to hundreds of sequences at once. The portal for the mfold web server is http://www.bioinfo.rpi.edu/applications/mfold. This URL will be referred to as ‘MFOLDROOT’. PMID:12824337

Mfold web server for nucleic acid folding and hybridization prediction.

PubMed

Zuker, Michael

2003-07-01

The abbreviated name, 'mfold web server', describes a number of closely related software applications available on the World Wide Web (WWW) for the prediction of the secondary structure of single stranded nucleic acids. The objective of this web server is to provide easy access to RNA and DNA folding and hybridization software to the scientific community at large. By making use of universally available web GUIs (Graphical User Interfaces), the server circumvents the problem of portability of this software. Detailed output, in the form of structure plots with or without reliability information, single strand frequency plots and 'energy dot plots', are available for the folding of single sequences. A variety of 'bulk' servers give less information, but in a shorter time and for up to hundreds of sequences at once. The portal for the mfold web server is http://www.bioinfo.rpi.edu/applications/mfold. This URL will be referred to as 'MFOLDROOT'.

Modeling and prediction of peptide drift times in ion mobility spectrometry using sequence-based and structure-based approaches.

PubMed

Zhang, Yiming; Jin, Quan; Wang, Shuting; Ren, Ren

2011-05-01

The mobile behavior of 1481 peptides in ion mobility spectrometry (IMS), which are generated by protease digestion of the Drosophila melanogaster proteome, is modeled and predicted based on two different types of characterization methods, i.e. sequence-based approach and structure-based approach. In this procedure, the sequence-based approach considers both the amino acid composition of a peptide and the local environment profile of each amino acid in the peptide; the structure-based approach is performed with the CODESSA protocol, which regards a peptide as a common organic compound and generates more than 200 statistically significant variables to characterize the whole structure profile of a peptide molecule. Subsequently, the nonlinear support vector machine (SVM) and Gaussian process (GP) as well as linear partial least squares (PLS) regression is employed to correlate the structural parameters of the characterizations with the IMS drift times of these peptides. The obtained quantitative structure-spectrum relationship (QSSR) models are evaluated rigorously and investigated systematically via both one-deep and two-deep cross-validations as well as the rigorous Monte Carlo cross-validation (MCCV). We also give a comprehensive comparison on the resulting statistics arising from the different combinations of variable types with modeling methods and find that the sequence-based approach can give the QSSR models with better fitting ability and predictive power but worse interpretability than the structure-based approach. In addition, though the QSSR modeling using sequence-based approach is not needed for the preparation of the minimization structures of peptides before the modeling, it would be considerably efficient as compared to that using structure-based approach. Copyright © 2011 Elsevier Ltd. All rights reserved.

Amino-terminal sequence of glycoprotein D of herpes simplex virus types 1 and 2

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eisenberg, R.J.; Long, D.; Hogue-Angeletti, R.

1984-01-01

Glycoprotein D (gD) of herpes simplex virus is a structural component of the virion envelope which stimulates production of high titers of herpes simplex virus type-common neutralizing antibody. The authors caried out automated N-terminal amino acid sequencing studies on radiolabeled preparations of gD-1 (gD of herpes simplex virus type 1) and gD-2 (gD of herpes simplex virus type 2). Although some differences were noted, particularly in the methionine and alanine profiles for gD-1 and gD-2, the amino acid sequence of a number of the first 30 residues of the amino terminus of gD-1 and gD-2 appears to be quite similar.more » For both proteins, the first residue is a lysine. When we compared out sequence data for gD-1 with those predicted by nucleic acid sequencing, the two sequences could be aligned (with one exception) starting at residue 26 (lysine) of the predicted sequence. Thus, the first 25 amino acids of the predicted sequence are absent from the polypeptides isolated from infected cells.« less

Amino acid sequence of tyrosinase from Neurospora crassa.

PubMed Central

Lerch, K

1978-01-01

The amino-acid sequence of tyrosinase from Neurospora crassa (monophenol,dihydroxyphenylalanine:oxygen oxidoreductase, EC 1.14.18.1) is reported. This copper-containing oxidase consists of a single polypeptide chain of 407 amino acids. The primary structure was determined by automated and manual sequence analysis on fragments produced by cleavage with cyanogen bromide and on peptides obtained by digestion with trypsin, pepsin, thermolysin, or chymotrypsin. The amino terminus of the protein is acetylated and the single cysteinyl residue 96 is covalently linked via a thioether bridge to histidyl residue 94. The formation and the possible role of this unusual structure in Neurospora tyrosinase is discussed. Dye-sensitized photooxidation of apotyrosinase and active-site-directed inactivation of the native enzyme indicate the possible involvement of histidyl residues 188, 192, 289, and 305 or 306 as ligands to the active-site copper as well as in the catalytic mechanism of this monooxygenase. PMID:151279

A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.

PubMed

Huang, Yu-An; You, Zhu-Hong; Chen, Xing

2018-01-01

Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

HBC-Evo: predicting human breast cancer by exploiting amino acid sequence-based feature spaces and evolutionary ensemble system.

PubMed

Majid, Abdul; Ali, Safdar

2015-01-01

We developed genetic programming (GP)-based evolutionary ensemble system for the early diagnosis, prognosis and prediction of human breast cancer. This system has effectively exploited the diversity in feature and decision spaces. First, individual learners are trained in different feature spaces using physicochemical properties of protein amino acids. Their predictions are then stacked to develop the best solution during GP evolution process. Finally, results for HBC-Evo system are obtained with optimal threshold, which is computed using particle swarm optimization. Our novel approach has demonstrated promising results compared to state of the art approaches.

Nanopores and nucleic acids: prospects for ultrarapid sequencing

NASA Technical Reports Server (NTRS)

Deamer, D. W.; Akeson, M.

2000-01-01

DNA and RNA molecules can be detected as they are driven through a nanopore by an applied electric field at rates ranging from several hundred microseconds to a few milliseconds per molecule. The nanopore can rapidly discriminate between pyrimidine and purine segments along a single-stranded nucleic acid molecule. Nanopore detection and characterization of single molecules represents a new method for directly reading information encoded in linear polymers. If single-nucleotide resolution can be achieved, it is possible that nucleic acid sequences can be determined at rates exceeding a thousand bases per second.

PubDNA Finder: a web database linking full-text articles to sequences of nucleic acids.

PubMed

García-Remesal, Miguel; Cuevas, Alejandro; Pérez-Rey, David; Martín, Luis; Anguita, Alberto; de la Iglesia, Diana; de la Calle, Guillermo; Crespo, José; Maojo, Víctor

2010-11-01

PubDNA Finder is an online repository that we have created to link PubMed Central manuscripts to the sequences of nucleic acids appearing in them. It extends the search capabilities provided by PubMed Central by enabling researchers to perform advanced searches involving sequences of nucleic acids. This includes, among other features (i) searching for papers mentioning one or more specific sequences of nucleic acids and (ii) retrieving the genetic sequences appearing in different articles. These additional query capabilities are provided by a searchable index that we created by using the full text of the 176 672 papers available at PubMed Central at the time of writing and the sequences of nucleic acids appearing in them. To automatically extract the genetic sequences occurring in each paper, we used an original method we have developed. The database is updated monthly by automatically connecting to the PubMed Central FTP site to retrieve and index new manuscripts. Users can query the database via the web interface provided. PubDNA Finder can be freely accessed at http://servet.dia.fi.upm.es:8080/pubdnafinder

Nucleic Acid Amplification Testing and Sequencing Combined with Acid-Fast Staining in Needle Biopsy Lung Tissues for the Diagnosis of Smear-Negative Pulmonary Tuberculosis.

PubMed

Jiang, Faming; Huang, Weiwei; Wang, Ye; Tian, Panwen; Chen, Xuerong; Liang, Zongan

2016-01-01

Smear-negative pulmonary tuberculosis (PTB) is common and difficult to diagnose. In this study, we investigated the diagnostic value of nucleic acid amplification testing and sequencing combined with acid-fast bacteria (AFB) staining of needle biopsy lung tissues for patients with suspected smear-negative PTB. Patients with suspected smear-negative PTB who underwent percutaneous transthoracic needle biopsy between May 1, 2012, and June 30, 2015, were enrolled in this retrospective study. Patients with AFB in sputum smears were excluded. All lung biopsy specimens were fixed in formalin, embedded in paraffin, and subjected to acid-fast staining and tuberculous polymerase chain reaction (TB-PCR). For patients with positive AFB and negative TB-PCR results in lung tissues, probe assays and 16S rRNA sequencing were used for identification of nontuberculous mycobacteria (NTM). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of PCR and AFB staining were calculated separately and in combination. Among the 220 eligible patients, 133 were diagnosed with TB (men/women: 76/57; age range: 17-80 years, confirmed TB: 9, probable TB: 124). Forty-eight patients who were diagnosed with other specific diseases were assigned as negative controls, and 39 patients with indeterminate final diagnosis were excluded from statistical analysis. The sensitivity, specificity, PPV, NPV, and accuracy of histological AFB (HAFB) for the diagnosis of smear-negative were 61.7% (82/133), 100% (48/48), 100% (82/82), 48.5% (48/181), and 71.8% (130/181), respectively. The sensitivity, specificity, PPV, and NPV of histological PCR were 89.5% (119/133), 95.8% (46/48), 98.3% (119/121), and 76.7% (46/60), respectively, demonstrating that histological PCR had significantly higher accuracy (91.2% [165/181]) than histological acid-fast staining (71.8% [130/181]), P < 0.001. Parallel testing of histological AFB staining and PCR showed the

Prediction of the translocon-mediated membrane insertion free energies of protein sequences.

PubMed

Park, Yungki; Helms, Volkhard

2008-05-15

Helical membrane proteins (HMPs) play crucial roles in a variety of cellular processes. Unlike water-soluble proteins, HMPs need not only to fold but also get inserted into the membrane to be fully functional. This process of membrane insertion is mediated by the translocon complex. Thus, it is of great interest to develop computational methods for predicting the translocon-mediated membrane insertion free energies of protein sequences. We have developed Membrane Insertion (MINS), a novel sequence-based computational method for predicting the membrane insertion free energies of protein sequences. A benchmark test gives a correlation coefficient of 0.74 between predicted and observed free energies for 357 known cases, which corresponds to a mean unsigned error of 0.41 kcal/mol. These results are significantly better than those obtained by traditional hydropathy analysis. Moreover, the ability of MINS to reasonably predict membrane insertion free energies of protein sequences allows for effective identification of transmembrane (TM) segments. Subsequently, MINS was applied to predict the membrane insertion free energies of 316 TM segments found in known structures. An in-depth analysis of the predicted free energies reveals a number of interesting findings about the biogenesis and structural stability of HMPs. A web server for MINS is available at http://service.bioinformatik.uni-saarland.de/mins

Complete complementary DNA-derived amino acid sequence of canine cardiac phospholamban.

PubMed Central

Fujii, J; Ueno, A; Kitano, K; Tanaka, S; Kadoma, M; Tada, M

1987-01-01

Complementary DNA (cDNA) clones specific for phospholamban of sarcoplasmic reticulum membranes have been isolated from a canine cardiac cDNA library. The amino acid sequence deduced from the cDNA sequence indicates that phospholamban consists of 52 amino acid residues and lacks an amino-terminal signal sequence. The protein has an inferred mol wt 6,080 that is in agreement with its apparent monomeric mol wt 6,000, estimated previously by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Phospholamban contains two distinct domains, a hydrophilic region at the amino terminus (domain I) and a hydrophobic region at the carboxy terminus (domain II). We propose that domain I is localized at the cytoplasmic surface and offers phosphorylatable sites whereas domain II is anchored into the sarcoplasmic reticulum membrane. PMID:3793929

Improved Prediction of Non-methylated Islands in Vertebrates Highlights Different Characteristic Sequence Patterns

PubMed Central

Vingron, Martin

2016-01-01

Non-methylated islands (NMIs) of DNA are genomic regions that are important for gene regulation and development. A recent study of genome-wide non-methylation data in vertebrates by Long et al. (eLife 2013;2:e00348) has shown that many experimentally identified non-methylated regions do not overlap with classically defined CpG islands which are computationally predicted using simple DNA sequence features. This is especially true in cold-blooded vertebrates such as Danio rerio (zebrafish). In order to investigate how predictive DNA sequence is of a region’s methylation status, we applied a supervised learning approach using a spectrum kernel support vector machine, to see if a more complex model and supervised learning can be used to improve non-methylated island prediction and to understand the sequence properties of these regions. We demonstrate that DNA sequence is highly predictive of methylation status, and that in contrast to existing CpG island prediction methods our method is able to provide more useful predictions of NMIs genome-wide in all vertebrate organisms that were studied. Our results also show that in cold-blooded vertebrates (Anolis carolinensis, Xenopus tropicalis and Danio rerio) where genome-wide classical CpG island predictions consist primarily of false positives, longer primarily AT-rich DNA sequence features are able to identify these regions much more accurately. PMID:27984582

Using the concept of Chou's pseudo amino acid composition to predict apoptosis proteins subcellular location: an approach by approximate entropy.

PubMed

Jiang, Xiaoying; Wei, Rong; Zhang, Tongliang; Gu, Quan

2008-01-01

The function of protein is closely correlated with it subcellular location. Prediction of subcellular location of apoptosis proteins is an important research area in post-genetic era because the knowledge of apoptosis proteins is useful to understand the mechanism of programmed cell death. Compared with the conventional amino acid composition (AAC), the Pseudo Amino Acid composition (PseAA) as originally introduced by Chou can incorporate much more information of a protein sequence so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, a novel approach is presented to predict apoptosis protein solely from sequence based on the concept of Chou's PseAA composition. The concept of approximate entropy (ApEn), which is a parameter denoting complexity of time series, is used to construct PseAA composition as additional features. Fuzzy K-nearest neighbor (FKNN) classifier is selected as prediction engine. Particle swarm optimization (PSO) algorithm is adopted for optimizing the weight factors which are important in PseAA composition. Two datasets are used to validate the performance of the proposed approach, which incorporate six subcellular location and four subcellular locations, respectively. The results obtained by jackknife test are quite encouraging. It indicates that the ApEn of protein sequence could represent effectively the information of apoptosis proteins subcellular locations. It can at least play a complimentary role to many of the existing methods, and might become potentially useful tool for protein function prediction. The software in Matlab is available freely by contacting the corresponding author.

The complete amino acid sequence of human erythrocyte diphosphoglycerate mutase.

PubMed Central

Haggarty, N W; Dunbar, B; Fothergill, L A

1983-01-01

The complete amino acid sequence of human erythrocyte diphosphoglycerate mutase, comprising 239 residues, was determined. The sequence was deduced from the four cyanogen bromide fragments, and from the peptides derived from these fragments after digestion with a number of proteolytic enzymes. Comparison of this sequence with that of the yeast glycolytic enzyme, phosphoglycerate mutase, shows that these enzymes are 47% identical. Most, but not all, of the residues implicated as being important for the activity of the glycolytic mutase are conserved in the erythrocyte diphosphoglycerate mutase. PMID:6313356

Determination of protein folding kinetic types using sequence and predicted secondary structure and solvent accessibility.

PubMed

Zhang, Hua; Zhang, Tuo; Gao, Jianzhao; Ruan, Jishou; Shen, Shiyi; Kurgan, Lukasz

2012-01-01

Proteins fold through a two-state (TS), with no visible intermediates, or a multi-state (MS), via at least one intermediate, process. We analyze sequence-derived factors that determine folding types by introducing a novel sequence-based folding type predictor called FOKIT. This method implements a logistic regression model with six input features which hybridize information concerning amino acid composition and predicted secondary structure and solvent accessibility. FOKIT provides predictions with average Matthews correlation coefficient (MCC) between 0.58 and 0.91 measured using out-of-sample tests on four benchmark datasets. These results are shown to be competitive or better than results of four modern predictors. We also show that FOKIT outperforms these methods when predicting chains that share low similarity with the chains used to build the model, which is an important advantage given the limited number of annotated chains. We demonstrate that inclusion of solvent accessibility helps in discrimination of the folding kinetic types and that three of the features constitute statistically significant markers that differentiate TS and MS folders. We found that the increased content of exposed Trp and buried Leu are indicative of the MS folding, which implies that the exposure/burial of certain hydrophobic residues may play important role in the formation of the folding intermediates. Our conclusions are supported by two case studies.

KM+, a mannose-binding lectin from Artocarpus integrifolia: amino acid sequence, predicted tertiary structure, carbohydrate recognition, and analysis of the beta-prism fold.

PubMed Central

Rosa, J. C.; De Oliveira, P. S.; Garratt, R.; Beltramini, L.; Resing, K.; Roque-Barreira, M. C.; Greene, L. J.

1999-01-01

The complete amino acid sequence of the lectin KM+ from Artocarpus integrifolia (jackfruit), which contains 149 residues/mol, is reported and compared to those of other members of the Moraceae family, particularly that of jacalin, also from jackfruit, with which it shares 52% sequence identity. KM+ presents an acetyl-blocked N-terminus and is not posttranslationally modified by proteolytic cleavage as is the case for jacalin. Rather, it possesses a short, glycine-rich linker that unites the regions homologous to the alpha- and beta-chains of jacalin. The results of homology modeling implicate the linker sequence in sterically impeding rotation of the side chain of Asp141 within the binding site pocket. As a consequence, the aspartic acid is locked into a conformation adequate only for the recognition of equatorial hydroxyl groups on the C4 epimeric center (alpha-D-mannose, alpha-D-glucose, and their derivatives). In contrast, the internal cleavage of the jacalin chain permits free rotation of the homologous aspartic acid, rendering it capable of accepting hydrogen bonds from both possible hydroxyl configurations on C4. We suggest that, together with direct recognition of epimeric hydroxyls and the steric exclusion of disfavored ligands, conformational restriction of the lectin should be considered to be a new mechanism by which selectivity may be built into carbohydrate binding sites. Jacalin and KM+ adopt the beta-prism fold already observed in two unrelated protein families. Despite presenting little or no sequence similarity, an analysis of the beta-prism reveals a canonical feature repeatedly present in all such structures, which is based on six largely hydrophobic residues within a beta-hairpin containing two classic-type beta-bulges. We suggest the term beta-prism motif to describe this feature. PMID:10210179

Quantum-Sequencing: Biophysics of quantum tunneling through nucleic acids

NASA Astrophysics Data System (ADS)

Casamada Ribot, Josep; Chatterjee, Anushree; Nagpal, Prashant

2014-03-01

Tunneling microscopy and spectroscopy has extensively been used in physical surface sciences to study quantum tunneling to measure electronic local density of states of nanomaterials and to characterize adsorbed species. Quantum-Sequencing (Q-Seq) is a new method based on tunneling microscopy for electronic sequencing of single molecule of nucleic acids. A major goal of third-generation sequencing technologies is to develop a fast, reliable, enzyme-free single-molecule sequencing method. Here, we present the unique ``electronic fingerprints'' for all nucleotides on DNA and RNA using Q-Seq along their intrinsic biophysical parameters. We have analyzed tunneling spectra for the nucleotides at different pH conditions and analyzed the HOMO, LUMO and energy gap for all of them. In addition we show a number of biophysical parameters to further characterize all nucleobases (electron and hole transition voltage and energy barriers). These results highlight the robustness of Q-Seq as a technique for next-generation sequencing.

Comparative analysis of grapevine whole-genome gene predictions, functional annotation, categorization and integration of the predicted gene sequences

PubMed Central

2012-01-01

Background The first draft assembly and gene prediction of the grapevine genome (8X base coverage) was made available to the scientific community in 2007, and functional annotation was developed on this gene prediction. Since then additional Sanger sequences were added to the 8X sequences pool and a new version of the genomic sequence with superior base coverage (12X) was produced. Results In order to more efficiently annotate the function of the genes predicted in the new assembly, it is important to build on as much of the previous work as possible, by transferring 8X annotation of the genome to the 12X version. The 8X and 12X assemblies and gene predictions of the grapevine genome were compared to answer the question, “Can we uniquely map 8X predicted genes to 12X predicted genes?” The results show that while the assemblies and gene structure predictions are too different to make a complete mapping between them, most genes (18,725) showed a one-to-one relationship between 8X predicted genes and the last version of 12X predicted genes. In addition, reshuffled genomic sequence structures appeared. These highlight regions of the genome where the gene predictions need to be taken with caution. Based on the new grapevine gene functional annotation and in-depth functional categorization, twenty eight new molecular networks have been created for VitisNet while the existing networks were updated. Conclusions The outcomes of this study provide a functional annotation of the 12X genes, an update of VitisNet, the system of the grapevine molecular networks, and a new functional categorization of genes. Data are available at the VitisNet website (http://www.sdstate.edu/ps/research/vitis/pathways.cfm). PMID:22554261

Amino acid sequence of the Amur tiger prion protein.

PubMed

Wu, Changde; Pang, Wanyong; Zhao, Deming

2006-10-01

Prion diseases are fatal neurodegenerative disorders in human and animal associated with conformational conversion of a cellular prion protein (PrP(C)) into the pathologic isoform (PrP(Sc)). Various data indicate that the polymorphisms within the open reading frame (ORF) of PrP are associated with the susceptibility and control the species barrier in prion diseases. In the present study, partial Prnp from 25 Amur tigers (tPrnp) were cloned and screened for polymorphisms. Four single nucleotide polymorphisms (T423C, A501G, C511A, A610G) were found; the C511A and A610G nucleotide substitutions resulted in the amino acid changes Lysine171Glutamine and Alanine204Threoine, respectively. The tPrnp amino acid sequence is similar to house cat (Felis catus ) and sheep, but differs significantly from other two cat Prnp sequences that were previously deposited in GenBank.

GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank.

PubMed

You, Ronghui; Zhang, Zihan; Xiong, Yi; Sun, Fengzhu; Mamitsuka, Hiroshi; Zhu, Shanfeng

2018-03-07

Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only <1% of more than 70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multilabel classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input). Furthermore homology-based SAFP tools are competitive in AFP competitions, while they do not necessarily work well for so-called difficult proteins, which have <60% sequence identity to proteins with annotations already. Thus the vital and challenging problem now is how to develop a method for SAFP, particularly for difficult proteins. The key of this method is to extract not only homology information but also diverse, deep- rooted information/evidence from sequence inputs and integrate them into a predictor in a both effective and efficient manner. We propose GOLabeler, which integrates five component classifiers, trained from different features, including GO term frequency, sequence alignment, amino acid trigram, domains and motifs, and biophysical properties, etc., in the framework of learning to rank (LTR), a paradigm of machine learning, especially powerful for multilabel classification. The empirical results obtained by examining GOLabeler extensively and thoroughly by using large-scale datasets revealed numerous favorable aspects of GOLabeler, including significant performance advantage over state-of-the-art AFP methods. http://datamining-iip.fudan.edu.cn/golabeler. zhusf@fudan.edu.cn. Supplementary data are available at Bioinformatics online.

Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

PubMed

Adhikari, Badri; Hou, Jie; Cheng, Jianlin

2018-03-01

In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on contact prediction. The first method (MULTICOM-NOVEL) uses only traditional features (sequence profile, secondary structure, and solvent accessibility) with deep learning to predict contacts and serves as a baseline. The second method (MULTICOM-CONSTRUCT) uses our new alignment algorithm to generate deep multiple sequence alignment to derive coevolution-based features, which are integrated by a neural network method to predict contacts. The third method (MULTICOM-CLUSTER) is a consensus combination of the predictions of the first two methods. We evaluated our methods on 94 CASP12 domains. On a subset of 38 free-modeling domains, our methods achieved an average precision of up to 41.7% for top L/5 long-range contact predictions. The comparison of the three methods shows that the quality and effective depth of multiple sequence alignments, coevolution-based features, and machine learning integration of coevolution-based features and traditional features drive the quality of predicted protein contacts. On the full CASP12 dataset, the coevolution-based features alone can improve the average precision from 28.4% to 41.6%, and the machine learning integration of all the features further raises the precision to 56.3%, when top L/5 predicted long-range contacts are evaluated. And the correlation between the precision of contact prediction and the logarithm of the number of effective sequences in alignments is 0.66. © 2017 Wiley Periodicals, Inc.

HomPPI: a class of sequence homology based protein-protein interface prediction methods

PubMed Central

2011-01-01

Background Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate. Results We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the

Method for high-volume sequencing of nucleic acids: random and directed priming with libraries of oligonucleotides

DOEpatents

Studier, F. William

1995-04-18

Random and directed priming methods for determining nucleotide sequences by enzymatic sequencing techniques, using libraries of primers of lengths 8, 9 or 10 bases, are disclosed. These methods permit direct sequencing of nucleic acids as large as 45,000 base pairs or larger without the necessity for subcloning. Individual primers are used repeatedly to prime sequence reactions in many different nucleic acid molecules. Libraries containing as few as 10,000 octamers, 14,200 nonamers, or 44,000 decamers would have the capacity to determine the sequence of almost any cosmid DNA. Random priming with a fixed set of primers from a smaller library can also be used to initiate the sequencing of individual nucleic acid molecules, with the sequence being completed by directed priming with primers from the library. In contrast to random cloning techniques, a combined random and directed priming strategy is far more efficient.

Method for high-volume sequencing of nucleic acids: random and directed priming with libraries of oligonucleotides

DOEpatents

Studier, F.W.

1995-04-18

Random and directed priming methods for determining nucleotide sequences by enzymatic sequencing techniques, using libraries of primers of lengths 8, 9 or 10 bases, are disclosed. These methods permit direct sequencing of nucleic acids as large as 45,000 base pairs or larger without the necessity for subcloning. Individual primers are used repeatedly to prime sequence reactions in many different nucleic acid molecules. Libraries containing as few as 10,000 octamers, 14,200 nonamers, or 44,000 decamers would have the capacity to determine the sequence of almost any cosmid DNA. Random priming with a fixed set of primers from a smaller library can also be used to initiate the sequencing of individual nucleic acid molecules, with the sequence being completed by directed priming with primers from the library. In contrast to random cloning techniques, a combined random and directed priming strategy is far more efficient. 2 figs.

The influence of visual training on predicting complex action sequences.

PubMed

Cross, Emily S; Stadler, Waltraud; Parkinson, Jim; Schütz-Bosbach, Simone; Prinz, Wolfgang

2013-02-01

Linking observed and executable actions appears to be achieved by an action observation network (AON), comprising parietal, premotor, and occipitotemporal cortical regions of the human brain. AON engagement during action observation is thought to aid in effortless, efficient prediction of ongoing movements to support action understanding. Here, we investigate how the AON responds when observing and predicting actions we cannot readily reproduce before and after visual training. During pre- and posttraining neuroimaging sessions, participants watched gymnasts and wind-up toys moving behind an occluder and pressed a button when they expected each agent to reappear. Between scanning sessions, participants visually trained to predict when a subset of stimuli would reappear. Posttraining scanning revealed activation of inferior parietal, superior temporal, and cerebellar cortices when predicting occluded actions compared to perceiving them. Greater activity emerged when predicting untrained compared to trained sequences in occipitotemporal cortices and to a lesser degree, premotor cortices. The occipitotemporal responses when predicting untrained agents showed further specialization, with greater responses within body-processing regions when predicting gymnasts' movements and in object-selective cortex when predicting toys' movements. The results suggest that (1) select portions of the AON are recruited to predict the complex movements not easily mapped onto the observer's body and (2) greater recruitment of these AON regions supports prediction of less familiar sequences. We suggest that the findings inform both the premotor model of action prediction and the predictive coding account of AON function. Copyright © 2011 Wiley Periodicals, Inc.

Predicting Protein-Protein Interactions by Combing Various Sequence-Derived.

PubMed

Zhao, Xiao-Wei; Ma, Zhi-Qiang; Yin, Ming-Hao

2011-09-20

Knowledge of protein-protein interactions (PPIs) plays an important role in constructing protein interaction networks and understanding the general machineries of biological systems. In this study, a new method is proposed to predict PPIs using a comprehensive set of 930 features based only on sequence information, these features measure the interactions between residues a certain distant apart in the protein sequences from different aspects. To achieve better performance, the principal component analysis (PCA) is first employed to obtain an optimized feature subset. Then, the resulting 67-dimensional feature vectors are fed to Support Vector Machine (SVM). Experimental results on Drosophila melanogaster and Helicobater pylori datasets show that our method is very promising to predict PPIs and may at least be a useful supplement tool to existing methods.

A Model of BGA Thermal Fatigue Life Prediction Considering Load Sequence Effects

PubMed Central

Hu, Weiwei; Li, Yaqiu; Sun, Yufeng; Mosleh, Ali

2016-01-01

Accurate testing history data is necessary for all fatigue life prediction approaches, but such data is always deficient especially for the microelectronic devices. Additionally, the sequence of the individual load cycle plays an important role in physical fatigue damage. However, most of the existing models based on the linear damage accumulation rule ignore the sequence effects. This paper proposes a thermal fatigue life prediction model for ball grid array (BGA) packages to take into consideration the load sequence effects. For the purpose of improving the availability and accessibility of testing data, a new failure criterion is discussed and verified by simulation and experimentation. The consequences for the fatigue underlying sequence load conditions are shown. PMID:28773980

The LINE-1 DNA sequences in four mammalian orders predict proteins that conserve homologies to retrovirus proteins.

PubMed Central

Fanning, T; Singer, M

1987-01-01

Recent work suggests that one or more members of the highly repeated LINE-1 (L1) DNA family found in all mammals may encode one or more proteins. Here we report the sequence of a portion of an L1 cloned from the domestic cat (Felis catus). These data permit comparison of the L1 sequences in four mammalian orders (Carnivore, Lagomorph, Rodent and Primate) and the comparison supports the suggested coding potential. In two separate, noncontiguous regions in the carboxy terminal half of the proteins predicted from the DNA sequences, there are several strongly conserved segments. In one region, these share homology with known or suspected reverse transcriptases, as described by others in rodents and primates. In the second region, closer to the carboxy terminus, the strongly conserved segments are over 90% homologous among the four orders. One of the latter segments is cysteine rich and resembles the putative metal binding domains of nucleic acid binding proteins, including those of TFIIIA and retroviruses. PMID:3562227

Analyses of mitochondrial amino acid sequence datasets support the proposal that specimens of Hypodontus macropi from three species of macropodid hosts represent distinct species

PubMed Central

2013-01-01

Background Hypodontus macropi is a common intestinal nematode of a range of kangaroos and wallabies (macropodid marsupials). Based on previous multilocus enzyme electrophoresis (MEE) and nuclear ribosomal DNA sequence data sets, H. macropi has been proposed to be complex of species. To test this proposal using independent molecular data, we sequenced the whole mitochondrial (mt) genomes of individuals of H. macropi from three different species of hosts (Macropus robustus robustus, Thylogale billardierii and Macropus [Wallabia] bicolor) as well as that of Macropicola ocydromi (a related nematode), and undertook a comparative analysis of the amino acid sequence datasets derived from these genomes. Results The mt genomes sequenced by next-generation (454) technology from H. macropi from the three host species varied from 13,634 bp to 13,699 bp in size. Pairwise comparisons of the amino acid sequences predicted from these three mt genomes revealed differences of 5.8% to 18%. Phylogenetic analysis of the amino acid sequence data sets using Bayesian Inference (BI) showed that H. macropi from the three different host species formed distinct, well-supported clades. In addition, sliding window analysis of the mt genomes defined variable regions for future population genetic studies of H. macropi in different macropodid hosts and geographical regions around Australia. Conclusions The present analyses of inferred mt protein sequence datasets clearly supported the hypothesis that H. macropi from M. robustus robustus, M. bicolor and T. billardierii represent distinct species. PMID:24261823

MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins

PubMed Central

Disfani, Fatemeh Miri; Hsu, Wei-Lun; Mizianty, Marcin J.; Oldfield, Christopher J.; Xue, Bin; Dunker, A. Keith; Uversky, Vladimir N.; Kurgan, Lukasz

2012-01-01

Motivation: Molecular recognition features (MoRFs) are short binding regions located within longer intrinsically disordered regions that bind to protein partners via disorder-to-order transitions. MoRFs are implicated in important processes including signaling and regulation. However, only a limited number of experimentally validated MoRFs is known, which motivates development of computational methods that predict MoRFs from protein chains. Results: We introduce a new MoRF predictor, MoRFpred, which identifies all MoRF types (α, β, coil and complex). We develop a comprehensive dataset of annotated MoRFs to build and empirically compare our method. MoRFpred utilizes a novel design in which annotations generated by sequence alignment are fused with predictions generated by a Support Vector Machine (SVM), which uses a custom designed set of sequence-derived features. The features provide information about evolutionary profiles, selected physiochemical properties of amino acids, and predicted disorder, solvent accessibility and B-factors. Empirical evaluation on several datasets shows that MoRFpred outperforms related methods: α-MoRF-Pred that predicts α-MoRFs and ANCHOR which finds disordered regions that become ordered when bound to a globular partner. We show that our predicted (new) MoRF regions have non-random sequence similarity with native MoRFs. We use this observation along with the fact that predictions with higher probability are more accurate to identify putative MoRF regions. We also identify a few sequence-derived hallmarks of MoRFs. They are characterized by dips in the disorder predictions and higher hydrophobicity and stability when compared to adjacent (in the chain) residues. Availability: http://biomine.ece.ualberta.ca/MoRFpred/; http://biomine.ece.ualberta.ca/MoRFpred/Supplement.pdf Contact: lkurgan@ece.ualberta.ca Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22689782

Conservation of Shannon's redundancy for proteins. [information theory applied to amino acid sequences

NASA Technical Reports Server (NTRS)

Gatlin, L. L.

1974-01-01

Concepts of information theory are applied to examine various proteins in terms of their redundancy in natural originators such as animals and plants. The Monte Carlo method is used to derive information parameters for random protein sequences. Real protein sequence parameters are compared with the standard parameters of protein sequences having a specific length. The tendency of a chain to contain some amino acids more frequently than others and the tendency of a chain to contain certain amino acid pairs more frequently than other pairs are used as randomness measures of individual protein sequences. Non-periodic proteins are generally found to have random Shannon redundancies except in cases of constraints due to short chain length and genetic codes. Redundant characteristics of highly periodic proteins are discussed. A degree of periodicity parameter is derived.

Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks

PubMed Central

Avsec, Žiga; Cheng, Jun; Gagneur, Julien

2018-01-01

Abstract Motivation Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genomic landmarks in deep neural networks has not been addressed. Results Here we developed spline transformation, a neural network module based on splines to flexibly and robustly model distances. Modeling distances to various genomic landmarks with spline transformations significantly increased state-of-the-art prediction accuracy of in vivo RNA-binding protein binding sites for 120 out of 123 proteins. We also developed a deep neural network for human splice branchpoint based on spline transformations that outperformed the current best, already distance-based, machine learning model. Compared to piecewise linear transformation, as obtained by composition of rectified linear units, spline transformation yields higher prediction accuracy as well as faster and more robust training. As spline transformation can be applied to further quantities beyond distances, such as methylation or conservation, we foresee it as a versatile component in the genomics deep learning toolbox. Availability and implementation Spline transformation is implemented as a Keras layer in the CONCISE python package: https://github.com/gagneurlab/concise. Analysis code is available at https://github.com/gagneurlab/Manuscript_Avsec_Bioinformatics_2017. Contact avsec@in.tum.de or gagneur@in.tum.de Supplementary information Supplementary data are available at Bioinformatics online. PMID:29155928

SGP-1: Prediction and Validation of Homologous Genes Based on Sequence Alignments

PubMed Central

Wiehe, Thomas; Gebauer-Jung, Steffi; Mitchell-Olds, Thomas; Guigó, Roderic

2001-01-01

Conventional methods of gene prediction rely on the recognition of DNA-sequence signals, the coding potential or the comparison of a genomic sequence with a cDNA, EST, or protein database. Reasons for limited accuracy in many circumstances are species-specific training and the incompleteness of reference databases. Lately, comparative genome analysis has attracted increasing attention. Several analysis tools that are based on human/mouse comparisons are already available. Here, we present a program for the prediction of protein-coding genes, termed SGP-1 (Syntenic Gene Prediction), which is based on the similarity of homologous genomic sequences. In contrast to most existing tools, the accuracy of SGP-1 depends little on species-specific properties such as codon usage or the nucleotide distribution. SGP-1 may therefore be applied to nonstandard model organisms in vertebrates as well as in plants, without the need for extensive parameter training. In addition to predicting genes in large-scale genomic sequences, the program may be useful to validate gene structure annotations from databases. To this end, SGP-1 output also contains comparisons between predicted and annotated gene structures in HTML format. The program can be accessed via a Web server at http://soft.ice.mpg.de/sgp-1. The source code, written in ANSI C, is available on request from the authors. PMID:11544202

Sequence diversity within the reovirus S2 gene: reovirus genes reassort in nature, and their termini are predicted to form a panhandle motif.

PubMed Central

Chapell, J D; Goral, M I; Rodgers, S E; dePamphilis, C W; Dermody, T S

1994-01-01

To better understand genetic diversity within mammalian reoviruses, we determined S2 nucleotide and deduced sigma 2 amino acid sequences of nine reovirus strains and compared these sequences with those of prototype strains of the three reovirus serotypes. The S2 gene and sigma 2 protein are highly conserved among the four type 1, one type 2, and seven type 3 strains studied. Phylogenetic analyses based on S2 nucleotide sequences of the 12 reovirus strains indicate that diversity within the S2 gene is independent of viral serotype. Additionally, we found marked topological differences between phylogenetic trees generated from S1 and S2 gene nucleotide sequences of the seven type 3 strains. These results demonstrate that reovirus S1 and S2 genes have distinct evolutionary histories, thus providing phylogenetic evidence for lateral transfer of reovirus genes in nature. When variability among the 12 sigma 2-encoding S2 nucleotide sequences was analyzed at synonymous positions, we found that approximately 60 nucleotides at the 5' terminus and 30 nucleotides at the 3' terminus were markedly conserved in comparison with other sigma 2-encoding regions of S2. Predictions of RNA secondary structures indicate that the more conserved S2 sequences participate in the formation of an extended region of duplex RNA interrupted by a pair of stem-loops. Among the 12 deduced sigma 2 amino acid sequences examined, substitutions were observed at only 11% of amino acid positions. This finding suggests that constraints on the structure or function of sigma 2, perhaps in part because of its location in the virion core, have limited sequence diversity within this protein. PMID:8289378

Full genome sequence of Rocio virus reveal substantial variations from the prototype Rocio virus SPH 34675 sequence.

PubMed

Setoh, Yin Xiang; Amarilla, Alberto A; Peng, Nias Y; Slonchak, Andrii; Periasamy, Parthiban; Figueiredo, Luiz T M; Aquino, Victor H; Khromykh, Alexander A

2018-01-01

Rocio virus (ROCV) is an arbovirus belonging to the genus Flavivirus, family Flaviviridae. We present an updated sequence of ROCV strain SPH 34675 (GenBank: AY632542.4), the only available full genome sequence prior to this study. Using next-generation sequencing of the entire genome, we reveal substantial sequence variation from the prototype sequence, with 30 nucleotide differences amounting to 14 amino acid changes, as well as significant changes to predicted 3'UTR RNA structures. Our results present an updated and corrected sequence of a potential emerging human-virulent flavivirus uniquely indigenous to Brazil (GenBank: MF461639).

Complete Amino Acid Sequence of a Copper/Zinc-Superoxide Dismutase from Ginger Rhizome.

PubMed

Nishiyama, Yuki; Fukamizo, Tamo; Yoneda, Kazunari; Araki, Tomohiro

2017-04-01

Superoxide dismutase (SOD) is an antioxidant enzyme protecting cells from oxidative stress. Ginger (Zingiber officinale) is known for its antioxidant properties, however, there are no data on SODs from ginger rhizomes. In this study, we purified SOD from the rhizome of Z. officinale (Zo-SOD) and determined its complete amino acid sequence using N terminal sequencing, amino acid analysis, and de novo sequencing by tandem mass spectrometry. Zo-SOD consists of 151 amino acids with two signature Cu/Zn-SOD motifs and has high similarity to other plant Cu/Zn-SODs. Multiple sequence alignment showed that Cu/Zn-binding residues and cysteines forming a disulfide bond, which are highly conserved in Cu/Zn-SODs, are also present in Zo-SOD. Phylogenetic analysis revealed that plant Cu/Zn-SODs clustered into distinct chloroplastic, cytoplasmic, and intermediate groups. Among them, only chloroplastic enzymes carried amino acid substitutions in the region functionally important for enzymatic activity, suggesting that chloroplastic SODs may have a function distinct from those of SODs localized in other subcellular compartments. The nucleotide sequence of the Zo-SOD coding region was obtained by reverse-translation, and the gene was synthesized, cloned, and expressed. The recombinant Zo-SOD demonstrated pH stability in the range of 5-10, which is similar to other reported Cu/Zn-SODs, and thermal stability in the range of 10-60 °C, which is higher than that for most plant Cu/Zn-SODs but lower compared to the enzyme from a Z. officinale relative Curcuma aromatica.

Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences

PubMed Central

2018-01-01

Prediction of taxonomy for marker gene sequences such as 16S ribosomal RNA (rRNA) is a fundamental task in microbiology. Most experimentally observed sequences are diverged from reference sequences of authoritatively named organisms, creating a challenge for prediction methods. I assessed the accuracy of several algorithms using cross-validation by identity, a new benchmark strategy which explicitly models the variation in distances between query sequences and the closest entry in a reference database. When the accuracy of genus predictions was averaged over a representative range of identities with the reference database (100%, 99%, 97%, 95% and 90%), all tested methods had ≤50% accuracy on the currently-popular V4 region of 16S rRNA. Accuracy was found to fall rapidly with identity; for example, better methods were found to have V4 genus prediction accuracy of ∼100% at 100% identity but ∼50% at 97% identity. The relationship between identity and taxonomy was quantified as the probability that a rank is the lowest shared by a pair of sequences with a given pair-wise identity. With the V4 region, 95% identity was found to be a twilight zone where taxonomy is highly ambiguous because the probabilities that the lowest shared rank between pairs of sequences is genus, family, order or class are approximately equal. PMID:29682424

EST-PAC a web package for EST annotation and protein sequence prediction

PubMed Central

Strahm, Yvan; Powell, David; Lefèvre, Christophe

2006-01-01

With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST) from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST) annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1) searching local or remote biological databases for sequence similarities using Blast services, 2) predicting protein coding sequence from EST data and, 3) annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics. PMID:17147782

Structure and Sequence Search on Aptamer-Protein Docking

NASA Astrophysics Data System (ADS)

Xiao, Jiajie; Bonin, Keith; Guthold, Martin; Salsbury, Freddie

2015-03-01

Interactions between proteins and deoxyribonucleic acid (DNA) play a significant role in the living systems, especially through gene regulation. However, short nucleic acids sequences (aptamers) with specific binding affinity to specific proteins exhibit clinical potential as therapeutics. Our capillary and gel electrophoresis selection experiments show that specific sequences of aptamers can be selected that bind specific proteins. Computationally, given the experimentally-determined structure and sequence of a thrombin-binding aptamer, we can successfully dock the aptamer onto thrombin in agreement with experimental structures of the complex. In order to further study the conformational flexibility of this thrombin-binding aptamer and to potentially develop a predictive computational model of aptamer-binding, we use GPU-enabled molecular dynamics simulations to both examine the conformational flexibility of the aptamer in the absence of binding to thrombin, and to determine our ability to fold an aptamer. This study should help further de-novo predictions of aptamer sequences by enabling the study of structural and sequence-dependent effects on aptamer-protein docking specificity.

Hysteretic energy prediction method for mainshock-aftershock sequences

NASA Astrophysics Data System (ADS)

Zhai, Changhai; Ji, Duofa; Wen, Weiping; Li, Cuihua; Lei, Weidong; Xie, Lili

2018-04-01

Structures located in seismically active regions may be subjected to mainshock-aftershock (MSAS) sequences. Strong aftershocks significantly affect the hysteretic energy demand of structures. The hysteretic energy, E H,seq, is normalized by mass m and expressed in terms of the equivalent velocity, V D,seq, to quantitatively investigate aftershock effects on the hysteretic energy of structures. The equivalent velocity, V D,seq, is computed by analyzing the response time-history of an inelastic single-degree-of-freedom (SDOF) system with a varying vibration period subjected to 309 MSAS sequences. The present study selected two kinds of MSAS sequences, with one aftershock and two aftershocks, respectively. The aftershocks are scaled to maintain different relative intensities. The variation of the equivalent velocity, V D,seq, is studied for consideration of the ductility values, site conditions, relative intensities, number of aftershocks, hysteretic models, and damping ratios. The MSAS sequence with one aftershock exhibited a 10% to 30% hysteretic energy increase, whereas the MSAS sequence with two aftershocks presented a 20% to 40% hysteretic energy increase. Finally, a hysteretic energy prediction equation is proposed as a function of the vibration period, ductility value, and damping ratio to estimate hysteretic energy for mainshock-aftershock sequences.

Human retroviruses and AIDS 1996. A compilation and analysis of nucleic acid and amino acid sequences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Myers, G.; Foley, B.; Korber, B.

1997-04-01

This compendium and the accompanying floppy diskettes are the result of an effort to compile and rapidly publish all relevant molecular data concerning the human immunodeficiency viruses (HIV) and related retroviruses. The scope of the compendium and database is best summarized by the five parts that it comprises: (1) Nuclear Acid Alignments and Sequences; (2) Amino Acid Alignments; (3) Analysis; (4) Related Sequences; and (5) Database Communications. Information within all the parts is updated throughout the year on the Web site, http://hiv-web.lanl.gov. While this publication could take the form of a review or sequence monograph, it is not so conceived.more » Instead, the literature from which the database is derived has simply been summarized and some elementary computational analyses have been performed upon the data. Interpretation and commentary have been avoided insofar as possible so that the reader can form his or her own judgments concerning the complex information. In addition to the general descriptions of the parts of the compendium, the user should read the individual introductions for each part.« less

Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Version 3.0 User Guide

EPA Science Inventory

User Guide to describe the complete functionality of the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) Version 3.0 online tool. The US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility tool (SeqAPASS; https://seqa...

Streptococcal phosphoenolpyruvate-sugar phosphotransferase system: amino acid sequence and site of ATP-dependent phosphorylation of HPr

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deutscher, J.; Pevec, B.; Beyreuther, K.

1986-10-21

The amino acid sequence of histidine-containing protein (HPr) from Streptococcus faecalis has been determined by direct Edman degradation of intact HPr and by amino acid sequence analysis of tryptic peptides, V8 proteolyptic peptides, thermolytic peptides, and cyanogen bromide cleavage products. HPr from S. faecalis was found to contain 89 amino acid residues, corresponding to a molecular weight of 9438. The amino acid sequence of HPr from S. faecalis shows extended homology to the primary structure of HPr proteins from other bacteria. Besides the phosphoenolpyruvate-dependent phosphorylation of a histidyl residue in HPr, catalyzed by enzyme I of the bacterial phosphotransferase system,more » HPr was also found to be phosphorylated at a seryl residue in an ATP-dependent protein kinase catalyzed reaction. The site of ATP-dependent phosphorylation in HPr of S faecalis has now been determined. (/sup 32/P)P-Ser-HPr was digested with three different proteases, and in each case, a single labeled peptide was isolated. Following digestion with subtilisin, they obtained a peptide with the sequence -(P)Ser-Ile-Met-. Using chymotrypsin, they isolated a peptide with the sequence -Ser-Val-Asn-Leu-Lys-(P)Ser-Ile-Met-Gly-Val-Met-. The longest labeled peptide was obtained with V8 staphylococcal protease. According to amino acid analysis, this peptide contained 36 out of the 89 amino acid residues of HPr. The following sequence of 12 amino acid residues of the V8 peptide was determined: -Tyr-Lys-Gly-Lys-Ser-Val-Asn-Leu-Lys-(P)Ser-Ile-Met-. Thus, the site of ATP-dependent phosphorylation was determined to be Ser-46 within the primary structure of HPr.« less

Diagnostics based on nucleic acid sequence variant profiling: PCR, hybridization, and NGS approaches.

PubMed

Khodakov, Dmitriy; Wang, Chunyan; Zhang, David Yu

2016-10-01

Nucleic acid sequence variations have been implicated in many diseases, and reliable detection and quantitation of DNA/RNA biomarkers can inform effective therapeutic action, enabling precision medicine. Nucleic acid analysis technologies being translated into the clinic can broadly be classified into hybridization, PCR, and sequencing, as well as their combinations. Here we review the molecular mechanisms of popular commercial assays, and their progress in translation into in vitro diagnostics. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Representation of DNA sequences in genetic codon context with applications in exon and intron prediction.

PubMed

Yin, Changchuan

2015-04-01

To apply digital signal processing (DSP) methods to analyze DNA sequences, the sequences first must be specially mapped into numerical sequences. Thus, effective numerical mappings of DNA sequences play key roles in the effectiveness of DSP-based methods such as exon prediction. Despite numerous mappings of symbolic DNA sequences to numerical series, the existing mapping methods do not include the genetic coding features of DNA sequences. We present a novel numerical representation of DNA sequences using genetic codon context (GCC) in which the numerical values are optimized by simulation annealing to maximize the 3-periodicity signal to noise ratio (SNR). The optimized GCC representation is then applied in exon and intron prediction by Short-Time Fourier Transform (STFT) approach. The results show the GCC method enhances the SNR values of exon sequences and thus increases the accuracy of predicting protein coding regions in genomes compared with the commonly used 4D binary representation. In addition, this study offers a novel way to reveal specific features of DNA sequences by optimizing numerical mappings of symbolic DNA sequences.

"De-novo" amino acid sequence elucidation of protein G'e by combined "top-down" and "bottom-up" mass spectrometry.

PubMed

Yefremova, Yelena; Al-Majdoub, Mahmoud; Opuni, Kwabena F M; Koy, Cornelia; Cui, Weidong; Yan, Yuetian; Gross, Michael L; Glocker, Michael O

2015-03-01

Mass spectrometric de-novo sequencing was applied to review the amino acid sequence of a commercially available recombinant protein G´ with great scientific and economic importance. Substantial deviations to the published amino acid sequence (Uniprot Q54181) were found by the presence of 46 additional amino acids at the N-terminus, including a so-called "His-tag" as well as an N-terminal partial α-N-gluconoylation and α-N-phosphogluconoylation, respectively. The unexpected amino acid sequence of the commercial protein G' comprised 241 amino acids and resulted in a molecular mass of 25,998.9 ± 0.2 Da for the unmodified protein. Due to the higher mass that is caused by its extended amino acid sequence compared with the original protein G' (185 amino acids), we named this protein "protein G'e." By means of mass spectrometric peptide mapping, the suggested amino acid sequence, as well as the N-terminal partial α-N-gluconoylations, was confirmed with 100% sequence coverage. After the protein G'e sequence was determined, we were able to determine the expression vector pET-28b from Novagen with the Xho I restriction enzyme cleavage site as the best option that was used for cloning and expressing the recombinant protein G'e in E. coli. A dissociation constant (K(d)) value of 9.4 nM for protein G'e was determined thermophoretically, showing that the N-terminal flanking sequence extension did not cause significant changes in the binding affinity to immunoglobulins.

Sequencing proteins with transverse ionic transport in nanochannels.

PubMed

Boynton, Paul; Di Ventra, Massimiliano

2016-05-03

De novo protein sequencing is essential for understanding cellular processes that govern the function of living organisms and all sequence modifications that occur after a protein has been constructed from its corresponding DNA code. By obtaining the order of the amino acids that compose a given protein one can then determine both its secondary and tertiary structures through structure prediction, which is used to create models for protein aggregation diseases such as Alzheimer's Disease. Here, we propose a new technique for de novo protein sequencing that involves translocating a polypeptide through a synthetic nanochannel and measuring the ionic current of each amino acid through an intersecting perpendicular nanochannel. We find that the distribution of ionic currents for each of the 20 proteinogenic amino acids encoded by eukaryotic genes is statistically distinct, showing this technique's potential for de novo protein sequencing.

Correlation between fibroin amino acid sequence and physical silk properties.

PubMed

Fedic, Robert; Zurovec, Michal; Sehnal, Frantisek

2003-09-12

The fiber properties of lepidopteran silk depend on the amino acid repeats that interact during H-fibroin polymerization. The aim of our research was to relate repeat composition to insect biology and fiber strength. Representative regions of the H-fibroin genes were sequenced and analyzed in three pyralid species: wax moth (Galleria mellonella), European flour moth (Ephestia kuehniella), and Indian meal moth (Plodia interpunctella). The amino acid repeats are species-specific, evidently a diversification of an ancestral region of 43 residues, and include three types of regularly dispersed motifs: modifications of GSSAASAA sequence, stretches of tripeptides GXZ where X and Z represent bulky residues, and sequences similar to PVIVIEE. No concatenations of GX dipeptide or alanine, which are typical for Bombyx silkworms and Antheraea silk moths, respectively, were found. Despite different repeat structure, the silks of G. mellonella and E. kuehniella exhibit similar tensile strength as the Bombyx and Antheraea silks. We suggest that in these latter two species, variations in the repeat length obstruct repeat alignment, but sufficiently long stretches of iterated residues get superposed to interact. In the pyralid H-fibroins, interactions of the widely separated and diverse motifs depend on the precision of repeat matching; silk is strong in G. mellonella and E. kuehniella, with 2-3 types of long homogeneous repeats, and nearly 10 times weaker in P. interpunctella, with seven types of shorter erratic repeats. The high proportion of large amino acids in the H-fibroin of pyralids has probably evolved in connection with the spinning habit of caterpillars that live in protective silk tubes and spin continuously, enlarging the tubes on one end and partly devouring the other one. The silk serves as a depot of energetically rich and essential amino acids that may be scarce in the diet.

All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences.

PubMed

Hayat, Sikander; Sander, Chris; Marks, Debora S; Elofsson, Arne

2015-04-28

Transmembrane β-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting β-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent β-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand-strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.

Many amino acid substitution variants identified in DNA repair genes during human population screenings are predicted to impact protein function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xi, T; Jones, I M; Mohrenweiser, H W

2003-11-03

Over 520 different amino acid substitution variants have been previously identified in the systematic screening of 91 human DNA repair genes for sequence variation. Two algorithms were employed to predict the impact of these amino acid substitutions on protein activity. Sorting Intolerant From Tolerant (SIFT) classified 226 of 508 variants (44%) as ''Intolerant''. Polymorphism Phenotyping (PolyPhen) classed 165 of 489 amino acid substitutions (34%) as ''Probably or Possibly Damaging''. Another 9-15% of the variants were classed as ''Potentially Intolerant or Damaging''. The results from the two algorithms are highly associated, with concordance in predicted impact observed for {approx}62% of themore » variants. Twenty one to thirty one percent of the variant proteins are predicted to exhibit reduced activity by both algorithms. These variants occur at slightly lower individual allele frequency than do the variants classified as ''Tolerant'' or ''Benign''. Both algorithms correctly predicted the impact of 26 functionally characterized amino acid substitutions in the APE1 protein on biochemical activity, with one exception. It is concluded that a substantial fraction of the missense variants observed in the general human population are functionally relevant. These variants are expected to be the molecular genetic and biochemical basis for the associations of reduced DNA repair capacity phenotypes with elevated cancer risk.« less

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data.

PubMed

Hawkins, Troy; Chitale, Meghana; Luban, Stanislav; Kihara, Daisuke

2009-02-15

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http

Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction.

PubMed

Huang, Ying; Chen, Shi-Yi; Deng, Feilong

2016-01-01

In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools.

Structural protein descriptors in 1-dimension and their sequence-based predictions.

PubMed

Kurgan, Lukasz; Disfani, Fatemeh Miri

2011-09-01

The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.

Sequence-Specific Model for Peptide Retention Time Prediction in Strong Cation Exchange Chromatography.

PubMed

Gussakovsky, Daniel; Neustaeter, Haley; Spicer, Victor; Krokhin, Oleg V

2017-11-07

The development of a peptide retention prediction model for strong cation exchange (SCX) separation on a Polysulfoethyl A column is reported. Off-line 2D LC-MS/MS analysis (SCX-RPLC) of S. cerevisiae whole cell lysate was used to generate a retention dataset of ∼30 000 peptides, sufficient for identifying the major sequence-specific features of peptide retention mechanisms in SCX. In contrast to RPLC/hydrophilic interaction liquid chromatography (HILIC) separation modes, where retention is driven by hydrophobic/hydrophilic contributions of all individual residues, SCX interactions depend mainly on peptide charge (number of basic residues at acidic pH) and size. An additive model (incorporating the contributions of all 20 residues into the peptide retention) combined with a peptide length correction produces a 0.976 R 2 value prediction accuracy, significantly higher than the additive models for either HILIC or RPLC. Position-dependent effects on peptide retention for different residues were driven by the spatial orientation of tryptic peptides upon interaction with the negatively charged surface functional groups. The positively charged N-termini serve as a primary point of interaction. For example, basic residues (Arg, His, Lys) increase peptide retention when located closer to the N-terminus. We also found that hydrophobic interactions, which could lead to a mixed-mode separation mechanism, are largely suppressed at 20-30% of acetonitrile in the eluent. The accuracy of the final Sequence-Specific Retention Calculator (SSRCalc) SCX model (∼0.99 R 2 value) exceeds all previously reported predictors for peptide LC separations. This also provides a solid platform for method development in 2D LC-MS protocols in proteomics and peptide retention prediction filtering of false positive identifications.

Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition.

PubMed

Chen, Yen-Kuang; Li, Kuo-Bin

2013-02-07

The type information of un-annotated membrane proteins provides an important hint for their biological functions. The experimental determination of membrane protein types, despite being more accurate and reliable, is not always feasible due to the costly laboratory procedures, thereby creating a need for the development of bioinformatics methods. This article describes a novel computational classifier for the prediction of membrane protein types using proteins' sequences. The classifier, comprising a collection of one-versus-one support vector machines, makes use of the following sequence attributes: (1) the cationic patch sizes, the orientation, and the topology of transmembrane segments; (2) the amino acid physicochemical properties; (3) the presence of signal peptides or anchors; and (4) the specific protein motifs. A new voting scheme was implemented to cope with the multi-class prediction. Both the training and the testing sequences were collected from SwissProt. Homologous proteins were removed such that there is no pair of sequences left in the datasets with a sequence identity higher than 40%. The performance of the classifier was evaluated by a Jackknife cross-validation and an independent testing experiments. Results show that the proposed classifier outperforms earlier predictors in prediction accuracy in seven of the eight membrane protein types. The overall accuracy was increased from 78.3% to 88.2%. Unlike earlier approaches which largely depend on position-specific substitution matrices and amino acid compositions, most of the sequence attributes implemented in the proposed classifier have supported literature evidences. The classifier has been deployed as a web server and can be accessed at http://bsaltools.ym.edu.tw/predmpt. Copyright © 2012 Elsevier Ltd. All rights reserved.

Complete cDNA sequence and amino acid analysis of a bovine ribonuclease K6 gene.

PubMed

Pietrowski, D; Förster, M

2000-01-01

The complete cDNA sequence of a ribonuclease k6 gene of Bos Taurus has been determined. It codes for a protein with 154 amino acids and contains the invariant cysteine, histidine and lysine residues as well as the characteristic motifs specific to ribonuclease active sites. The deduced protein sequence is 27 residues longer than other known ribonucleases k6 and shows amino acids exchanges which could reflect a strain specificity or polymorphism within the bovine genome. Based on sequence similarity we have termed the identified gene bovine ribonuclease k6 b (brk6b).

Negative Ion In-Source Decay Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry for Sequencing Acidic Peptides

NASA Astrophysics Data System (ADS)

McMillen, Chelsea L.; Wright, Patience M.; Cassady, Carolyn J.

2016-05-01

Matrix-assisted laser desorption/ionization (MALDI) in-source decay was studied in the negative ion mode on deprotonated peptides to determine its usefulness for obtaining extensive sequence information for acidic peptides. Eight biological acidic peptides, ranging in size from 11 to 33 residues, were studied by negative ion mode ISD (nISD). The matrices 2,5-dihydroxybenzoic acid, 2-aminobenzoic acid, 2-aminobenzamide, 1,5-diaminonaphthalene, 5-amino-1-naphthol, 3-aminoquinoline, and 9-aminoacridine were used with each peptide. Optimal fragmentation was produced with 1,5-diaminonphthalene (DAN), and extensive sequence informative fragmentation was observed for every peptide except hirudin(54-65). Cleavage at the N-Cα bond of the peptide backbone, producing c' and z' ions, was dominant for all peptides. Cleavage of the N-Cα bond N-terminal to proline residues was not observed. The formation of c and z ions is also found in electron transfer dissociation (ETD), electron capture dissociation (ECD), and positive ion mode ISD, which are considered to be radical-driven techniques. Oxidized insulin chain A, which has four highly acidic oxidized cysteine residues, had less extensive fragmentation. This peptide also exhibited the only charged localized fragmentation, with more pronounced product ion formation adjacent to the highly acidic residues. In addition, spectra were obtained by positive ion mode ISD for each protonated peptide; more sequence informative fragmentation was observed via nISD for all peptides. Three of the peptides studied had no product ion formation in ISD, but extensive sequence informative fragmentation was found in their nISD spectra. The results of this study indicate that nISD can be used to readily obtain sequence information for acidic peptides.

Negative Ion In-Source Decay Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry for Sequencing Acidic Peptides.

PubMed

McMillen, Chelsea L; Wright, Patience M; Cassady, Carolyn J

2016-05-01

Matrix-assisted laser desorption/ionization (MALDI) in-source decay was studied in the negative ion mode on deprotonated peptides to determine its usefulness for obtaining extensive sequence information for acidic peptides. Eight biological acidic peptides, ranging in size from 11 to 33 residues, were studied by negative ion mode ISD (nISD). The matrices 2,5-dihydroxybenzoic acid, 2-aminobenzoic acid, 2-aminobenzamide, 1,5-diaminonaphthalene, 5-amino-1-naphthol, 3-aminoquinoline, and 9-aminoacridine were used with each peptide. Optimal fragmentation was produced with 1,5-diaminonphthalene (DAN), and extensive sequence informative fragmentation was observed for every peptide except hirudin(54-65). Cleavage at the N-Cα bond of the peptide backbone, producing c' and z' ions, was dominant for all peptides. Cleavage of the N-Cα bond N-terminal to proline residues was not observed. The formation of c and z ions is also found in electron transfer dissociation (ETD), electron capture dissociation (ECD), and positive ion mode ISD, which are considered to be radical-driven techniques. Oxidized insulin chain A, which has four highly acidic oxidized cysteine residues, had less extensive fragmentation. This peptide also exhibited the only charged localized fragmentation, with more pronounced product ion formation adjacent to the highly acidic residues. In addition, spectra were obtained by positive ion mode ISD for each protonated peptide; more sequence informative fragmentation was observed via nISD for all peptides. Three of the peptides studied had no product ion formation in ISD, but extensive sequence informative fragmentation was found in their nISD spectra. The results of this study indicate that nISD can be used to readily obtain sequence information for acidic peptides.

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2014 CFR

2014-07-01

...” means those amino acids other than “Xaa” and those nucleotide bases other than “n”defined in accordance... 37 Patents, Trademarks, and Copyrights 1 2014-07-01 2014-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences...

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2013 CFR

2013-07-01

...” means those amino acids other than “Xaa” and those nucleotide bases other than “n”defined in accordance... 37 Patents, Trademarks, and Copyrights 1 2013-07-01 2013-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences...

37 CFR 1.821 - Nucleotide and/or amino acid sequence disclosures in patent applications.

Code of Federal Regulations, 2012 CFR

2012-07-01

...” means those amino acids other than “Xaa” and those nucleotide bases other than “n”defined in accordance... 37 Patents, Trademarks, and Copyrights 1 2012-07-01 2012-07-01 false Nucleotide and/or amino acid... Biotechnology Invention Disclosures Application Disclosures Containing Nucleotide And/or Amino Acid Sequences...

Biosynthesis of Lipoic Acid in Arabidopsis: Cloning and Characterization of the cDNA for Lipoic Acid Synthase1

PubMed Central

Yasuno, Rie; Wada, Hajime

1998-01-01

Lipoic acid is a coenzyme that is essential for the activity of enzyme complexes such as those of pyruvate dehydrogenase and glycine decarboxylase. We report here the isolation and characterization of LIP1 cDNA for lipoic acid synthase of Arabidopsis. The Arabidopsis LIP1 cDNA was isolated using an expressed sequence tag homologous to the lipoic acid synthase of Escherichia coli. This cDNA was shown to code for Arabidopsis lipoic acid synthase by its ability to complement a lipA mutant of E. coli defective in lipoic acid synthase. DNA-sequence analysis of the LIP1 cDNA revealed an open reading frame predicting a protein of 374 amino acids. Comparisons of the deduced amino acid sequence with those of E. coli and yeast lipoic acid synthase homologs showed a high degree of sequence similarity and the presence of a leader sequence presumably required for import into the mitochondria. Southern-hybridization analysis suggested that LIP1 is a single-copy gene in Arabidopsis. Western analysis with an antibody against lipoic acid synthase demonstrated that this enzyme is located in the mitochondrial compartment in Arabidopsis cells as a 43-kD polypeptide. PMID:9808738

Full genome virus detection in fecal samples using sensitive nucleic acid preparation, deep sequencing, and a novel iterative sequence classification algorithm.

PubMed

Cotten, Matthew; Oude Munnink, Bas; Canuti, Marta; Deijs, Martin; Watson, Simon J; Kellam, Paul; van der Hoek, Lia

2014-01-01

We have developed a full genome virus detection process that combines sensitive nucleic acid preparation optimised for virus identification in fecal material with Illumina MiSeq sequencing and a novel post-sequencing virus identification algorithm. Enriched viral nucleic acid was converted to double-stranded DNA and subjected to Illumina MiSeq sequencing. The resulting short reads were processed with a novel iterative Python algorithm SLIM for the identification of sequences with homology to known viruses. De novo assembly was then used to generate full viral genomes. The sensitivity of this process was demonstrated with a set of fecal samples from HIV-1 infected patients. A quantitative assessment of the mammalian, plant, and bacterial virus content of this compartment was generated and the deep sequencing data were sufficient to assembly 12 complete viral genomes from 6 virus families. The method detected high levels of enteropathic viruses that are normally controlled in healthy adults, but may be involved in the pathogenesis of HIV-1 infection and will provide a powerful tool for virus detection and for analyzing changes in the fecal virome associated with HIV-1 progression and pathogenesis.

Full Genome Virus Detection in Fecal Samples Using Sensitive Nucleic Acid Preparation, Deep Sequencing, and a Novel Iterative Sequence Classification Algorithm

PubMed Central

Cotten, Matthew; Oude Munnink, Bas; Canuti, Marta; Deijs, Martin; Watson, Simon J.; Kellam, Paul; van der Hoek, Lia

2014-01-01

We have developed a full genome virus detection process that combines sensitive nucleic acid preparation optimised for virus identification in fecal material with Illumina MiSeq sequencing and a novel post-sequencing virus identification algorithm. Enriched viral nucleic acid was converted to double-stranded DNA and subjected to Illumina MiSeq sequencing. The resulting short reads were processed with a novel iterative Python algorithm SLIM for the identification of sequences with homology to known viruses. De novo assembly was then used to generate full viral genomes. The sensitivity of this process was demonstrated with a set of fecal samples from HIV-1 infected patients. A quantitative assessment of the mammalian, plant, and bacterial virus content of this compartment was generated and the deep sequencing data were sufficient to assembly 12 complete viral genomes from 6 virus families. The method detected high levels of enteropathic viruses that are normally controlled in healthy adults, but may be involved in the pathogenesis of HIV-1 infection and will provide a powerful tool for virus detection and for analyzing changes in the fecal virome associated with HIV-1 progression and pathogenesis. PMID:24695106

NullSeq: A Tool for Generating Random Coding Sequences with Desired Amino Acid and GC Contents.

PubMed

Liu, Sophia S; Hockenberry, Adam J; Lancichinetti, Andrea; Jewett, Michael C; Amaral, Luís A N

2016-11-01

The existence of over- and under-represented sequence motifs in genomes provides evidence of selective evolutionary pressures on biological mechanisms such as transcription, translation, ligand-substrate binding, and host immunity. In order to accurately identify motifs and other genome-scale patterns of interest, it is essential to be able to generate accurate null models that are appropriate for the sequences under study. While many tools have been developed to create random nucleotide sequences, protein coding sequences are subject to a unique set of constraints that complicates the process of generating appropriate null models. There are currently no tools available that allow users to create random coding sequences with specified amino acid composition and GC content for the purpose of hypothesis testing. Using the principle of maximum entropy, we developed a method that generates unbiased random sequences with pre-specified amino acid and GC content, which we have developed into a python package. Our method is the simplest way to obtain maximally unbiased random sequences that are subject to GC usage and primary amino acid sequence constraints. Furthermore, this approach can easily be expanded to create unbiased random sequences that incorporate more complicated constraints such as individual nucleotide usage or even di-nucleotide frequencies. The ability to generate correctly specified null models will allow researchers to accurately identify sequence motifs which will lead to a better understanding of biological processes as well as more effective engineering of biological systems.

Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction.

PubMed

Bossi, Flavia; Fan, Jue; Xiao, Jun; Chandra, Lilyana; Shen, Max; Dorone, Yanniv; Wagner, Doris; Rhee, Seung Y

2017-06-26

The molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. To identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation. We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.

Lossless Video Sequence Compression Using Adaptive Prediction

NASA Technical Reports Server (NTRS)

Li, Ying; Sayood, Khalid

2007-01-01

We present an adaptive lossless video compression algorithm based on predictive coding. The proposed algorithm exploits temporal, spatial, and spectral redundancies in a backward adaptive fashion with extremely low side information. The computational complexity is further reduced by using a caching strategy. We also study the relationship between the operational domain for the coder (wavelet or spatial) and the amount of temporal and spatial redundancy in the sequence being encoded. Experimental results show that the proposed scheme provides significant improvements in compression efficiencies.

Complete amino acid sequence of ananain and a comparison with stem bromelain and other plant cysteine proteases.

PubMed Central

Lee, K L; Albee, K L; Bernasconi, R J; Edmunds, T

1997-01-01

The amino acid sequences of ananain (EC3.4.22.31) and stem bromelain (3.4.22.32), two cysteine proteases from pineapple stem, are similar yet ananain and stem bromelain possess distinct specificities towards synthetic peptide substrates and different reactivities towards the cysteine protease inhibitors E-64 and chicken egg white cystatin. We present here the complete amino acid sequence of ananain and compare it with the reported sequences of pineapple stem bromelain, papain and chymopapain from papaya and actinidin from kiwifruit. Ananain is comprised of 216 residues with a theoretical mass of 23464 Da. This primary structure includes a sequence insert between residues 170 and 174 not present in stem bromelain or papain and a hydrophobic series of amino acids adjacent to His-157. It is possible that these sequence differences contribute to the different substrate and inhibitor specificities exhibited by ananain and stem bromelain. PMID:9355753

Predicted stem-loop structures and variation in nucleotide sequence of 3' noncoding regions among animal calicivirus genomes.

PubMed

Seal, B S; Neill, J D; Ridpath, J F

1994-07-01

Caliciviruses are nonenveloped with a polyadenylated genome of approximately 7.6 kb and a single capsid protein. The "RNA Fold" computer program was used to analyze 3'-terminal noncoding sequences of five feline calicivirus (FCV), rabbit hemorrhagic disease virus (RHDV), and two San Miguel sea lion virus (SMSV) isolates. The FCV 3'-terminal sequences are 40-46 nucleotides in length and 72-91% similar. The FCV sequences were predicted to contain two possible duplex structures and one stem-loop structure with free energies of -2.1 to -18.2 kcal/mole. The RHDV genomic 3'-terminal RNA sequences are 54 nucleotides in length and share 49% sequence similarity to homologous regions of the FCV genome. The RHDV sequence was predicted to form two duplex structures in the 3'-terminal noncoding region with a single stem-loop structure, resembling that of FCV. In contrast, the SMSV 1 and 4 genomic 3'-terminal noncoding sequences were 185 and 182 nucleotides in length, respectively. Ten possible duplex structures were predicted with an average structural free energy of -35 kcal/mole. Sequence similarity between the two SMSV isolates was 75%. Furthermore, extensive cloverleaflike structures are predicted in the 3' noncoding region of the SMSV genome, in contrast to the predicted single stem-loop structures of FCV or RHDV.

Prediction of multi-drug resistance transporters using a novel sequence analysis method [version 2; referees: 2 approved

DOE PAGES

McDermott, Jason E.; Bruillard, Paul; Overall, Christopher C.; ...

2015-03-09

There are many examples of groups of proteins that have similar function, but the determinants of functional specificity may be hidden by lack of sequencesimilarity, or by large groups of similar sequences with different functions. Transporters are one such protein group in that the general function, transport, can be easily inferred from the sequence, but the substrate specificity can be impossible to predict from sequence with current methods. In this paper we describe a linguistic-based approach to identify functional patterns from groups of unaligned protein sequences and its application to predict multi-drug resistance transporters (MDRs) from bacteria. We first showmore » that our method can recreate known patterns from PROSITE for several motifs from unaligned sequences. We then show that the method, MDRpred, can predict MDRs with greater accuracy and positive predictive value than a collection of currently available family-based models from the Pfam database. Finally, we apply MDRpred to a large collection of protein sequences from an environmental microbiome study to make novel predictions about drug resistance in a potential environmental reservoir.« less

Computational Prediction of miRNA Genes from Small RNA Sequencing Data

PubMed Central

Kang, Wenjing; Friedländer, Marc R.

2015-01-01

Next-generation sequencing now for the first time allows researchers to gage the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. microRNAs (miRNAs) are 22 nucleotide small RNAs (sRNAs) that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq), which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here, we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field. PMID:25674563

FrameD: A flexible program for quality check and gene prediction in prokaryotic genomes and noisy matured eukaryotic sequences.

PubMed

Schiex, Thomas; Gouzy, Jérôme; Moisan, Annick; de Oliveira, Yannick

2003-07-01

We describe FrameD, a program that predicts coding regions in prokaryotic and matured eukaryotic sequences. Initially targeted at gene prediction in bacterial GC rich genomes, the gene model used in FrameD also allows to predict genes in the presence of frameshifts and partially undetermined sequences which makes it also very suitable for gene prediction and frameshift correction in unfinished sequences such as EST and EST cluster sequences. Like recent eukaryotic gene prediction programs, FrameD also includes the ability to take into account protein similarity information both in its prediction and its graphical output. Its performances are evaluated on different bacterial genomes. The web site (http://genopole.toulouse.inra.fr/bioinfo/FrameD/FD) allows direct prediction, sequence correction and translation and the ability to learn new models for new organisms.

CDSbank: taxonomy-aware extraction, selection, renaming and formatting of protein-coding DNA or amino acid sequences.

PubMed

Hazes, Bart

2014-02-28

Protein-coding DNA sequences and their corresponding amino acid sequences are routinely used to study relationships between sequence, structure, function, and evolution. The rapidly growing size of sequence databases increases the power of such comparative analyses but it makes it more challenging to prepare high quality sequence data sets with control over redundancy, quality, completeness, formatting, and labeling. Software tools for some individual steps in this process exist but manual intervention remains a common and time consuming necessity. CDSbank is a database that stores both the protein-coding DNA sequence (CDS) and amino acid sequence for each protein annotated in Genbank. CDSbank also stores Genbank feature annotation, a flag to indicate incomplete 5' and 3' ends, full taxonomic data, and a heuristic to rank the scientific interest of each species. This rich information allows fully automated data set preparation with a level of sophistication that aims to meet or exceed manual processing. Defaults ensure ease of use for typical scenarios while allowing great flexibility when needed. Access is via a free web server at http://hazeslab.med.ualberta.ca/CDSbank/. CDSbank presents a user-friendly web server to download, filter, format, and name large sequence data sets. Common usage scenarios can be accessed via pre-programmed default choices, while optional sections give full control over the processing pipeline. Particular strengths are: extract protein-coding DNA sequences just as easily as amino acid sequences, full access to taxonomy for labeling and filtering, awareness of incomplete sequences, and the ability to take one protein sequence and extract all synonymous CDS or identical protein sequences in other species. Finally, CDSbank can also create labeled property files to, for instance, annotate or re-label phylogenetic trees.

Discriminative Prediction of A-To-I RNA Editing Events from DNA Sequence

PubMed Central

Sun, Jiangming; Singh, Pratibha; Bagge, Annika; Valtat, Bérengère; Vikman, Petter; Spégel, Peter; Mulder, Hindrik

2016-01-01

RNA editing is a post-transcriptional alteration of RNA sequences that, via insertions, deletions or base substitutions, can affect protein structure as well as RNA and protein expression. Recently, it has been suggested that RNA editing may be more frequent than previously thought. A great impediment, however, to a deeper understanding of this process is the paramount sequencing effort that needs to be undertaken to identify RNA editing events. Here, we describe an in silico approach, based on machine learning, that ameliorates this problem. Using 41 nucleotide long DNA sequences, we show that novel A-to-I RNA editing events can be predicted from known A-to-I RNA editing events intra- and interspecies. The validity of the proposed method was verified in an independent experimental dataset. Using our approach, 203 202 putative A-to-I RNA editing events were predicted in the whole human genome. Out of these, 9% were previously reported. The remaining sites require further validation, e.g., by targeted deep sequencing. In conclusion, the approach described here is a useful tool to identify potential A-to-I RNA editing events without the requirement of extensive RNA sequencing. PMID:27764195

Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles.

PubMed

Zou, Lingyun; Nan, Chonghan; Hu, Fuquan

2013-12-15

Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species; however, widely applicable prediction approaches are still unavailable In this work, four types of distinctive features, namely, amino acid composition, dipeptide composition, .position-specific scoring matrix composition and auto covariance transformation of position-specific scoring matrix, were calculated from primary sequences. A classifier, T4EffPred, was developed using the support vector machine with these features and their different combinations for effector prediction. Various theoretical tests were performed in a newly established dataset, and the results were measured with four indexes. We demonstrated that T4EffPred can discriminate IVA and IVB effectors in benchmark datasets with positive rates of 76.7% and 89.7%, respectively. The overall accuracy of 95.9% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences. A classifier ensemble was designed to synthesize all single classifiers. Notable performance improvement was observed using this ensemble system in benchmark tests. To demonstrate the model's application, a genome-scale prediction of effectors was performed in Bartonella henselae, an important zoonotic pathogen. A number of putative candidates were distinguished. A web server implementing the prediction method and the source code are both available at http://bioinfo.tmmu.edu.cn/T4EffPred.

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes

PubMed Central

Jespersen, Martin Closter; Peters, Bjoern

2017-01-01

Abstract Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community. PMID:28472356

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.

PubMed

Song, Jiangning; Burrage, Kevin; Yuan, Zheng; Huber, Thomas

2006-03-09

The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0

ANCAC: amino acid, nucleotide, and codon analysis of COGs--a tool for sequence bias analysis in microbial orthologs.

PubMed

Meiler, Arno; Klinger, Claudia; Kaufmann, Michael

2012-09-08

The COG database is the most popular collection of orthologous proteins from many different completely sequenced microbial genomes. Per definition, a cluster of orthologous groups (COG) within this database exclusively contains proteins that most likely achieve the same cellular function. Recently, the COG database was extended by assigning to every protein both the corresponding amino acid and its encoding nucleotide sequence resulting in the NUCOCOG database. This extended version of the COG database is a valuable resource connecting sequence features with the functionality of the respective proteins. Here we present ANCAC, a web tool and MySQL database for the analysis of amino acid, nucleotide, and codon frequencies in COGs on the basis of freely definable phylogenetic patterns. We demonstrate the usefulness of ANCAC by analyzing amino acid frequencies, codon usage, and GC-content in a species- or function-specific context. With respect to amino acids we, at least in part, confirm the cognate bias hypothesis by using ANCAC's NUCOCOG dataset as the largest one available for that purpose thus far. Using the NUCOCOG datasets, ANCAC connects taxonomic, amino acid, and nucleotide sequence information with the functional classification via COGs and provides a GUI for flexible mining for sequence-bias. Thereby, to our knowledge, it is the only tool for the analysis of sequence composition in the light of physiological roles and phylogenetic context without requirement of substantial programming-skills.

Identification and Analysis of Novel Amino-Acid Sequence Repeats in Bacillus anthracis str. Ames Proteome Using Computational Tools

PubMed Central

Hemalatha, G. R.; Rao, D. Satyanarayana; Guruprasad, L.

2007-01-01

We have identified four repeats and ten domains that are novel in proteins encoded by the Bacillus anthracis str. Ames proteome using automated in silico methods. A “repeat” corresponds to a region comprising less than 55-amino-acid residues that occur more than once in the protein sequence and sometimes present in tandem. A “domain” corresponds to a conserved region with greater than 55-amino-acid residues and may be present as single or multiple copies in the protein sequence. These correspond to (1) 57-amino-acid-residue PxV domain, (2) 122-amino-acid-residue FxF domain, (3) 111-amino-acid-residue YEFF domain, (4) 109-amino-acid-residue IMxxH domain, (5) 103-amino-acid-residue VxxT domain, (6) 84-amino-acid-residue ExW domain, (7) 104-amino-acid-residue NTGFIG domain, (8) 36-amino-acid-residue NxGK repeat, (9) 95-amino-acid-residue VYV domain, (10) 75-amino-acid-residue KEWE domain, (11) 59-amino-acid-residue AFL domain, (12) 53-amino-acid-residue RIDVK repeat, (13) (a) 41-amino-acid-residue AGQF repeat and (b) 42-amino-acid-residue GSAL repeat. A repeat or domain type is characterized by specific conserved sequence motifs. We discuss the presence of these repeats and domains in proteins from other genomes and their probable secondary structure. PMID:17538688

Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bossi, Flavia; Fan, Jue; Xiao, Jun

Here, the molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. As a result, to identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation.more » We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.« less

Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction

DOE PAGES

Bossi, Flavia; Fan, Jue; Xiao, Jun; ...

2017-06-26

Here, the molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. As a result, to identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation.more » We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.« less

Prediction and characterization of enzymatic activities guided by sequence similarity and genome neighborhood networks

DOE PAGES

Zhao, Suwen; Sakai, Ayano; Zhang, Xinshuai; ...

2014-06-30

Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters (genome neighborhoods) that provide important clues for assignment of both enzyme functions and metabolic pathways. We describe a bioinformatic approach (genome neighborhood network; GNN) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions (metabolic pathways) of uncharacterized enzymes in protein families. We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily (PRS; InterPro IPR008794). The predictions were verified by measuring in vitro activities for 51 proteins inmore » 12 families in the PRS that represent ~85% of the sequences; in vitro activities of pathway enzymes, carbon/nitrogen source phenotypes, and/or transcriptomic studies confirmed the predicted pathways. The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel, uncharacterized metabolic pathways in sequenced genomes.« less

SeqAPASS (Sequence Alignment to Predict Across Species Susceptibility) software and documentation

EPA Science Inventory

SeqAPASS is a software application facilitates rapid and streamlined, yet transparent, comparisons of the similarity of toxicologically-significant molecular targets across species. The present application facilitates analysis of primary amino acid sequence similarity (including ...

Evidence of Divergent Amino Acid Usage in Comparative Analyses of R5- and X4-Associated HIV-1 Vpr Sequences

PubMed Central

Antell, Gregory C.; Zhong, Wen; Kercher, Katherine; Passic, Shendra; Williams, Jean; Liu, Yucheng; James, Tony; Jacobson, Jeffrey M.; Szep, Zsofia

2017-01-01

Vpr is an HIV-1 accessory protein that plays numerous roles during viral replication, and some of which are cell type dependent. To test the hypothesis that HIV-1 tropism extends beyond the envelope into the vpr gene, studies were performed to identify the associations between coreceptor usage and Vpr variation in HIV-1-infected patients. Colinear HIV-1 Env-V3 and Vpr amino acid sequences were obtained from the LANL HIV-1 sequence database and from well-suppressed patients in the Drexel/Temple Medicine CNS AIDS Research and Eradication Study (CARES) Cohort. Genotypic classification of Env-V3 sequences as X4 (CXCR4-utilizing) or R5 (CCR5-utilizing) was used to group colinear Vpr sequences. To reveal the sequences associated with a specific coreceptor usage genotype, Vpr amino acid sequences were assessed for amino acid diversity and Jensen-Shannon divergence between the two groups. Five amino acid alphabets were used to comprehensively examine the impact of amino acid substitutions involving side chains with similar physiochemical properties. Positions 36, 37, 41, 89, and 96 of Vpr were characterized by statistically significant divergence across multiple alphabets when X4 and R5 sequence groups were compared. In addition, consensus amino acid switches were found at positions 37 and 41 in comparisons of the R5 and X4 sequence populations. These results suggest an evolutionary link between Vpr and gp120 in HIV-1-infected patients. PMID:28620613

Human Retroviruses and AIDS. A compilation and analysis of nucleic acid and amino acid sequences: I--II; III--V

DOE Office of Scientific and Technical Information (OSTI.GOV)

Myers, G.; Korber, B.; Wain-Hobson, S.

1993-12-31

This compendium and the accompanying floppy diskettes are the result of an effort to compile and rapidly publish all relevant molecular data concerning the human immunodeficiency viruses (HIV) and related retroviruses. The scope of the compendium and database is best summarized by the five parts that it comprises: (I) HIV and SIV Nucleotide Sequences; (II) Amino Acid Sequences; (III) Analyses; (IV) Related Sequences; and (V) Database Communications. Information within all the parts is updated at least twice in each year, which accounts for the modes of binding and pagination in the compendium.

In Vitro and In Vivo Activities of Antimicrobial Peptides Developed Using an Amino Acid-Based Activity Prediction Method

PubMed Central

Wu, Xiaozhe; Wang, Zhenling; Li, Xiaolu; Fan, Yingzi; He, Gu; Wan, Yang; Yu, Chaoheng; Tang, Jianying; Li, Meng; Zhang, Xian; Zhang, Hailong; Xiang, Rong; Pan, Ying; Liu, Yan; Lu, Lian

2014-01-01

To design and discover new antimicrobial peptides (AMPs) with high levels of antimicrobial activity, a number of machine-learning methods and prediction methods have been developed. Here, we present a new prediction method that can identify novel AMPs that are highly similar in sequence to known peptides but offer improved antimicrobial activity along with lower host cytotoxicity. Using previously generated AMP amino acid substitution data, we developed an amino acid activity contribution matrix that contained an activity contribution value for each amino acid in each position of the model peptide. A series of AMPs were designed with this method. After evaluating the antimicrobial activities of these novel AMPs against both Gram-positive and Gram-negative bacterial strains, DP7 was chosen for further analysis. Compared to the parent peptide HH2, this novel AMP showed broad-spectrum, improved antimicrobial activity, and in a cytotoxicity assay it showed lower toxicity against human cells. The in vivo antimicrobial activity of DP7 was tested in a Staphylococcus aureus infection murine model. When inoculated and treated via intraperitoneal injection, DP7 reduced the bacterial load in the peritoneal lavage solution. Electron microscope imaging and the results indicated disruption of the S. aureus outer membrane by DP7. Our new prediction method can therefore be employed to identify AMPs possessing minor amino acid differences with improved antimicrobial activities, potentially increasing the therapeutic agents available to combat multidrug-resistant infections. PMID:24982064

Axolotl hemoglobin: cDNA-derived amino acid sequences of two alpha globins and a beta globin from an adult Ambystoma mexicanum.

PubMed

Shishikura, Fumio; Takeuchi, Hiro-aki; Nagai, Takatoshi

2005-11-01

Erythrocytes of the adult axolotl, Ambystoma mexicanum, have multiple hemoglobins. We separated and purified two kinds of hemoglobin, termed major hemoglobin (Hb M) and minor hemoglobin (Hb m), from a five-year-old male by hydrophobic interaction column chromatography on Alkyl Superose. The hemoglobins have two distinct alpha type globin polypeptides (alphaM and alpham) and a common beta globin polypeptide, all of which were purified in FPLC on a reversed-phase column after S-pyridylethylation. The complete amino acid sequences of the three globin chains were determined separately using nucleotide sequencing with the assistance of protein sequencing. The mature globin molecules were composed of 141 amino acid residues for alphaM globin, 143 for alpham globin and 146 for beta globin. Comparing primary structures of the five kinds of axolotl globins, including two previously established alpha type globins from the same species, with other known globins of amphibians and representatives of other vertebrates, we constructed phylogenetic trees for amphibian hemoglobins and tetrapod hemoglobins. The molecular trees indicated that alphaM, alpham, beta and the previously known alpha major globin were adult types of globins and the other known alpha globin was a larval type. The existence of two to four more globins in the axolotl erythrocyte is predicted.

Genome sequence analysis of predicted polyprenol reductase gene from mangrove plant kandelia obovata

NASA Astrophysics Data System (ADS)

Basyuni, M.; Sagami, H.; Baba, S.; Oku, H.

2018-03-01

It has been previously reported that dolichols but not polyprenols were predominated in mangrove leaves and roots. Therefore, the occurrence of larger amounts of dolichol in leaves of mangrove plants implies that polyprenol reductase is responsible for the conversion of polyprenol to dolichol may be active in mangrove leaves. Here we report the early assessment of probably polyprenol reductase gene from genome sequence of mangrove plant Kandelia obovata. The functional assignment of the gene was based on a homology search of the sequences against the non-redundant (nr) peptide database of NCBI using Blastx. The degree of sequence identity between DNA sequence and known polyprenol reductase was confirmed using the Blastx probability E-value, total score, and identity. The genome sequence data resulted in three partial sequences, termed c23157 (700 bp), c23901 (960 bp), and c24171 (531 bp). The c23157 gene showed the highest similarity (61%) to predicted polyprenol reductase 2- like from Gossypium raimondii with E-value 2e-100. The second gene was c23901 to exhibit high similarity (78%) to the steroid 5-alpha-reductase Det2 from J. curcas with E-value 2e-140. Furthermore, the c24171 gene depicted highest similarity (79%) to the polyprenol reductase 2 isoform X1 from Jatropha curcas with E- value 7e-21.The present study suggested that the c23157, c23901, and c24171, genes may encode predicted polyprenol reductase. The c23157, c23901, c24171 are therefore the new type of predicted polyprenol reductase from K. obovata.

Prediction of protein tertiary structure from sequences using a very large back-propagation neural network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, X.; Wilcox, G.L.

1993-12-31

We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences.more » Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.« less

Molecular beacon sequence design algorithm.

PubMed

Monroe, W Todd; Haselton, Frederick R

2003-01-01

A method based on Web-based tools is presented to design optimally functioning molecular beacons. Molecular beacons, fluorogenic hybridization probes, are a powerful tool for the rapid and specific detection of a particular nucleic acid sequence. However, their synthesis costs can be considerable. Since molecular beacon performance is based on its sequence, it is imperative to rationally design an optimal sequence before synthesis. The algorithm presented here uses simple Microsoft Excel formulas and macros to rank candidate sequences. This analysis is carried out using mfold structural predictions along with other free Web-based tools. For smaller laboratories where molecular beacons are not the focus of research, the public domain algorithm described here may be usefully employed to aid in molecular beacon design.

UET: a database of evolutionarily-predicted functional determinants of protein sequences that cluster as functional sites in protein structures.

PubMed

Lua, Rhonald C; Wilson, Stephen J; Konecki, Daniel M; Wilkins, Angela D; Venner, Eric; Morgan, Daniel H; Lichtarge, Olivier

2016-01-04

The structure and function of proteins underlie most aspects of biology and their mutational perturbations often cause disease. To identify the molecular determinants of function as well as targets for drugs, it is central to characterize the important residues and how they cluster to form functional sites. The Evolutionary Trace (ET) achieves this by ranking the functional and structural importance of the protein sequence positions. ET uses evolutionary distances to estimate functional distances and correlates genotype variations with those in the fitness phenotype. Thus, ET ranks are worse for sequence positions that vary among evolutionarily closer homologs but better for positions that vary mostly among distant homologs. This approach identifies functional determinants, predicts function, guides the mutational redesign of functional and allosteric specificity, and interprets the action of coding sequence variations in proteins, people and populations. Now, the UET database offers pre-computed ET analyses for the protein structure databank, and on-the-fly analysis of any protein sequence. A web interface retrieves ET rankings of sequence positions and maps results to a structure to identify functionally important regions. This UET database integrates several ways of viewing the results on the protein sequence or structure and can be found at http://mammoth.bcm.tmc.edu/uet/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Identifying functionally informative evolutionary sequence profiles.

PubMed

Gil, Nelson; Fiser, Andras

2018-04-15

Multiple sequence alignments (MSAs) can provide essential input to many bioinformatics applications, including protein structure prediction and functional annotation. However, the optimal selection of sequences to obtain biologically informative MSAs for such purposes is poorly explored, and has traditionally been performed manually. We present Selection of Alignment by Maximal Mutual Information (SAMMI), an automated, sequence-based approach to objectively select an optimal MSA from a large set of alternatives sampled from a general sequence database search. The hypothesis of this approach is that the mutual information among MSA columns will be maximal for those MSAs that contain the most diverse set possible of the most structurally and functionally homogeneous protein sequences. SAMMI was tested to select MSAs for functional site residue prediction by analysis of conservation patterns on a set of 435 proteins obtained from protein-ligand (peptides, nucleic acids and small substrates) and protein-protein interaction databases. Availability and implementation: A freely accessible program, including source code, implementing SAMMI is available at https://github.com/nelsongil92/SAMMI.git. andras.fiser@einstein.yu.edu. Supplementary data are available at Bioinformatics online.

Knowledge-based computational intelligence development for predicting protein secondary structures from sequences.

PubMed

Shen, Hong-Bin; Yi, Dong-Liang; Yao, Li-Xiu; Yang, Jie; Chou, Kuo-Chen

2008-10-01

In the postgenomic age, with the avalanche of protein sequences generated and relatively slow progress in determining their structures by experiments, it is important to develop automated methods to predict the structure of a protein from its sequence. The membrane proteins are a special group in the protein family that accounts for approximately 30% of all proteins; however, solved membrane protein structures only represent less than 1% of known protein structures to date. Although a great success has been achieved for developing computational intelligence techniques to predict secondary structures in both globular and membrane proteins, there is still much challenging work in this regard. In this review article, we firstly summarize the recent progress of automation methodology development in predicting protein secondary structures, especially in membrane proteins; we will then give some future directions in this research field.

Local backbone structure prediction of proteins

PubMed Central

De Brevern, Alexandre G.; Benros, Cristina; Gautier, Romain; Valadié, Hélène; Hazout, Serge; Etchebest, Catherine

2004-01-01

Summary A statistical analysis of the PDB structures has led us to define a new set of small 3D structural prototypes called Protein Blocks (PBs). This structural alphabet includes 16 PBs, each one is defined by the (φ, Ψ) dihedral angles of 5 consecutive residues. The amino acid distributions observed in sequence windows encompassing these PBs are used to predict by a Bayesian approach the local 3D structure of proteins from the sole knowledge of their sequences. LocPred is a software which allows the users to submit a protein sequence and performs a prediction in terms of PBs. The prediction results are given both textually and graphically. PMID:15724288

Folding and Stabilization of Native-Sequence-Reversed Proteins

PubMed Central

Zhang, Yuanzhao; Weber, Jeffrey K; Zhou, Ruhong

2016-01-01

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols. PMID:27113844

Folding and Stabilization of Native-Sequence-Reversed Proteins

NASA Astrophysics Data System (ADS)

Zhang, Yuanzhao; Weber, Jeffrey K.; Zhou, Ruhong

2016-04-01

Though the problem of sequence-reversed protein folding is largely unexplored, one might speculate that reversed native protein sequences should be significantly more foldable than purely random heteropolymer sequences. In this article, we investigate how the reverse-sequences of native proteins might fold by examining a series of small proteins of increasing structural complexity (α-helix, β-hairpin, α-helix bundle, and α/β-protein). Employing a tandem protein structure prediction algorithmic and molecular dynamics simulation approach, we find that the ability of reverse sequences to adopt native-like folds is strongly influenced by protein size and the flexibility of the native hydrophobic core. For β-hairpins with reverse-sequences that fail to fold, we employ a simple mutational strategy for guiding stable hairpin formation that involves the insertion of amino acids into the β-turn region. This systematic look at reverse sequence duality sheds new light on the problem of protein sequence-structure mapping and may serve to inspire new protein design and protein structure prediction protocols.

Nucleotide sequence of the phosphoglycerate kinase gene from the extreme thermophile Thermus thermophilus. Comparison of the deduced amino acid sequence with that of the mesophilic yeast phosphoglycerate kinase.

PubMed Central

Bowen, D; Littlechild, J A; Fothergill, J E; Watson, H C; Hall, L

1988-01-01

Using oligonucleotide probes derived from amino acid sequencing information, the structural gene for phosphoglycerate kinase from the extreme thermophile, Thermus thermophilus, was cloned in Escherichia coli and its complete nucleotide sequence determined. The gene consists of an open reading frame corresponding to a protein of 390 amino acid residues (calculated Mr 41,791) with an extreme bias for G or C (93.1%) in the codon third base position. Comparison of the deduced amino acid sequence with that of the corresponding mesophilic yeast enzyme indicated a number of significant differences. These are discussed in terms of the unusual codon bias and their possible role in enhanced protein thermal stability. Images Fig. 1. PMID:3052437

Sequence features of viral and human Internal Ribosome Entry Sites predictive of their activity

PubMed Central

Elias-Kirma, Shani; Nir, Ronit; Segal, Eran

2017-01-01

Translation of mRNAs through Internal Ribosome Entry Sites (IRESs) has emerged as a prominent mechanism of cellular and viral initiation. It supports cap-independent translation of select cellular genes under normal conditions, and in conditions when cap-dependent translation is inhibited. IRES structure and sequence are believed to be involved in this process. However due to the small number of IRESs known, there have been no systematic investigations of the determinants of IRES activity. With the recent discovery of thousands of novel IRESs in human and viruses, the next challenge is to decipher the sequence determinants of IRES activity. We present the first in-depth computational analysis of a large body of IRESs, exploring RNA sequence features predictive of IRES activity. We identified predictive k-mer features resembling IRES trans-acting factor (ITAF) binding motifs across human and viral IRESs, and found that their effect on expression depends on their sequence, number and position. Our results also suggest that the architecture of retroviral IRESs differs from that of other viruses, presumably due to their exposure to the nuclear environment. Finally, we measured IRES activity of synthetically designed sequences to confirm our prediction of increasing activity as a function of the number of short IRES elements. PMID:28922394

Quantitative thermodynamic predication of interactions between nucleic acid and non-nucleic acid species using Microsoft excel.

PubMed

Zou, Jiaqi; Li, Na

2013-09-01

Proper design of nucleic acid sequences is crucial for many applications. We have previously established a thermodynamics-based quantitative model to help design aptamer-based nucleic acid probes by predicting equilibrium concentrations of all interacting species. To facilitate customization of this thermodynamic model for different applications, here we present a generic and easy-to-use platform to implement the algorithm of the model with Microsoft(®) Excel formulas and VBA (Visual Basic for Applications) macros. Two Excel spreadsheets have been developed: one for the applications involving only nucleic acid species, the other for the applications involving both nucleic acid and non-nucleic acid species. The spreadsheets take the nucleic acid sequences and the initial concentrations of all species as input, guide the user to retrieve the necessary thermodynamic constants, and finally calculate equilibrium concentrations for all species in various bound and unbound conformations. The validity of both spreadsheets has been verified by comparing the modeling results with the experimental results on nucleic acid sequences reported in the literature. This Excel-based platform described here will allow biomedical researchers to rationalize the sequence design of nucleic acid probes using the thermodynamics-based modeling even without relevant theoretical and computational skills. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Prediction and identification of sequences coding for orphan enzymes using genomic and metagenomic neighbours

PubMed Central

Yamada, Takuji; Waller, Alison S; Raes, Jeroen; Zelezniak, Aleksej; Perchat, Nadia; Perret, Alain; Salanoubat, Marcel; Patil, Kiran R; Weissenbach, Jean; Bork, Peer

2012-01-01

Despite the current wealth of sequencing data, one-third of all biochemically characterized metabolic enzymes lack a corresponding gene or protein sequence, and as such can be considered orphan enzymes. They represent a major gap between our molecular and biochemical knowledge, and consequently are not amenable to modern systemic analyses. As 555 of these orphan enzymes have metabolic pathway neighbours, we developed a global framework that utilizes the pathway and (meta)genomic neighbour information to assign candidate sequences to orphan enzymes. For 131 orphan enzymes (37% of those for which (meta)genomic neighbours are available), we associate sequences to them using scoring parameters with an estimated accuracy of 70%, implying functional annotation of 16 345 gene sequences in numerous (meta)genomes. As a case in point, two of these candidate sequences were experimentally validated to encode the predicted activity. In addition, we augmented the currently available genome-scale metabolic models with these new sequence–function associations and were able to expand the models by on average 8%, with a considerable change in the flux connectivity patterns and improved essentiality prediction. PMID:22569339

Testing the Predictive Power of Coulomb Stress on Aftershock Sequences

NASA Astrophysics Data System (ADS)

Woessner, J.; Lombardi, A.; Werner, M. J.; Marzocchi, W.

2009-12-01

Empirical and statistical models of clustered seismicity are usually strongly stochastic and perceived to be uninformative in their forecasts, since only marginal distributions are used, such as the Omori-Utsu and Gutenberg-Richter laws. In contrast, so-called physics-based aftershock models, based on seismic rate changes calculated from Coulomb stress changes and rate-and-state friction, make more specific predictions: anisotropic stress shadows and multiplicative rate changes. We test the predictive power of models based on Coulomb stress changes against statistical models, including the popular Short Term Earthquake Probabilities and Epidemic-Type Aftershock Sequences models: We score and compare retrospective forecasts on the aftershock sequences of the 1992 Landers, USA, the 1997 Colfiorito, Italy, and the 2008 Selfoss, Iceland, earthquakes. To quantify predictability, we use likelihood-based metrics that test the consistency of the forecasts with the data, including modified and existing tests used in prospective forecast experiments within the Collaboratory for the Study of Earthquake Predictability (CSEP). Our results indicate that a statistical model performs best. Moreover, two Coulomb model classes seem unable to compete: Models based on deterministic Coulomb stress changes calculated from a given fault-slip model, and those based on fixed receiver faults. One model of Coulomb stress changes does perform well and sometimes outperforms the statistical models, but its predictive information is diluted, because of uncertainties included in the fault-slip model. Our results suggest that models based on Coulomb stress changes need to incorporate stochastic features that represent model and data uncertainty.

Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED).

PubMed

Li, ZhiLiang; Wu, ShiRong; Chen, ZeCong; Ye, Nancy; Yang, ShengXi; Liao, ChunYang; Zhang, MengJun; Yang, Li; Mei, Hu; Yang, Yan; Zhao, Na; Zhou, Yuan; Zhou, Ping; Xiong, Qing; Xu, Hong; Liu, ShuShen; Ling, ZiHua; Chen, Gang; Li, GenRong

2007-10-01

Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various

The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity

PubMed Central

Wang, Quanli; Halvorsen, Matt; Han, Yujun; Weir, William H.; Allen, Andrew S.; Goldstein, David B.

2015-01-01

Noncoding sequence contains pathogenic mutations. Yet, compared with mutations in protein-coding sequence, pathogenic regulatory mutations are notoriously difficult to recognize. Most fundamentally, we are not yet adept at recognizing the sequence stretches in the human genome that are most important in regulating the expression of genes. For this reason, it is difficult to apply to the regulatory regions the same kinds of analytical paradigms that are being successfully applied to identify mutations among protein-coding regions that influence risk. To determine whether dosage sensitive genes have distinct patterns among their noncoding sequence, we present two primary approaches that focus solely on a gene’s proximal noncoding regulatory sequence. The first approach is a regulatory sequence analogue of the recently introduced residual variation intolerance score (RVIS), termed noncoding RVIS, or ncRVIS. The ncRVIS compares observed and predicted levels of standing variation in the regulatory sequence of human genes. The second approach, termed ncGERP, reflects the phylogenetic conservation of a gene’s regulatory sequence using GERP++. We assess how well these two approaches correlate with four gene lists that use different ways to identify genes known or likely to cause disease through changes in expression: 1) genes that are known to cause disease through haploinsufficiency, 2) genes curated as dosage sensitive in ClinGen’s Genome Dosage Map, 3) genes judged likely to be under purifying selection for mutations that change expression levels because they are statistically depleted of loss-of-function variants in the general population, and 4) genes judged unlikely to cause disease based on the presence of copy number variants in the general population. We find that both noncoding scores are highly predictive of dosage sensitivity using any of these criteria. In a similar way to ncGERP, we assess two ensemble-based predictors of regional noncoding importance

kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets.

PubMed

Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S; Beer, Michael A

2013-07-01

Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167-80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org.

kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets

PubMed Central

Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S.; Beer, Michael A.

2013-01-01

Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167–80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org. PMID:23771147

The full genome sequences of 8 equine herpesvirus type 4 isolates from horses in Japan.

PubMed

Izume, Satoko; Kirisawa, Rikio; Ohya, Kenji; Ohnuma, Aiko; Kimura, Takashi; Omatsu, Tsutomu; Katayama, Yukie; Mizutani, Tetsuya; Fukushi, Hideto

2017-01-24

Equine herpesvirus type 4 (EHV-4) is one of the most important pathogens in horses. To clarify the key genes of the EHV-4 genome that cause abortion in female horses, we determined the whole genome sequences of a laboratory strain and 7 Japanese EHV-4 isolates that were isolated from 2 aborted fetuses and nasal swabs of 5 horses with respiratory disease. The full genome sequences and predicted amino acid sequences of each gene of these isolates were compared with of the reference EHV-4 strain NS80567 and Australian isolates that were reported in 2015. The EHV-4 isolates clustered in 2 groups which did not reflect their pathogenicity. A comparison of the predicted amino acid sequences of the genes did not reveal any genes that were associated with EHV-4-induced abortion.

Cloning and sequence analysis of Hemonchus contortus HC58cDNA.

PubMed

Muleke, Charles I; Ruofeng, Yan; Lixin, Xu; Xinwen, Bo; Xiangrui, Li

2007-06-01

The complete coding sequence of Hemonchus contortus HC58cDNA was generated by rapid amplification of cDNA ends and polymerase chain reaction using primers based on the 5' and 3' ends of the parasite mRNA, accession no. AF305964. The HC58cDNA gene was 851 bp long, with open reading frame of 717 bp, precursors to 239 amino acids coding for approximately 27 kDa protein. Analysis of amino acid sequence revealed conserved residues of cysteine, histidine, asparagine, occluding loop pattern, hemoglobinase motif and glutamine of the oxyanion hole characteristic of cathepsin B like proteases (CBL). Comparison of the predicted amino acid sequences showed the protein shared 33.5-58.7% identity to cathepsin B homologues in the papain clan CA family (family C1). Phylogenetic analysis revealed close evolutionary proximity of the protein sequence to counterpart sequences in the CBL, suggesting that HC58cDNA was a member of the papain family.

Sequence-dependent modelling of local DNA bending phenomena: curvature prediction and vibrational analysis.

PubMed

Vlahovicek, K; Munteanu, M G; Pongor, S

1999-01-01

Bending is a local conformational micropolymorphism of DNA in which the original B-DNA structure is only distorted but not extensively modified. Bending can be predicted by simple static geometry models as well as by a recently developed elastic model that incorporate sequence dependent anisotropic bendability (SDAB). The SDAB model qualitatively explains phenomena including affinity of protein binding, kinking, as well as sequence-dependent vibrational properties of DNA. The vibrational properties of DNA segments can be studied by finite element analysis of a model subjected to an initial bending moment. The frequency spectrum is obtained by applying Fourier analysis to the displacement values in the time domain. This analysis shows that the spectrum of the bending vibrations quite sensitively depends on the sequence, for example the spectrum of a curved sequence is characteristically different from the spectrum of straight sequence motifs of identical basepair composition. Curvature distributions are genome-specific, and pronounced differences are found between protein-coding and regulatory regions, respectively, that is, sites of extreme curvature and/or bendability are less frequent in protein-coding regions. A WWW server is set up for the prediction of curvature and generation of 3D models from DNA sequences (http:@www.icgeb.trieste.it/dna).

Automatic prediction of protein domains from sequence information using a hybrid learning system.

PubMed

Nagarajan, Niranjan; Yona, Golan

2004-06-12

We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence and are combined into a single predictor using a neural network. The output is further smoothed and post-processed using a probabilistic model to predict the most likely transition positions between domains. The method was assessed using the domain definitions in SCOP and CATH for proteins of known structure and was compared with several other existing methods. Our method performs well both in terms of accuracy and sensitivity. It improves significantly over the best methods available, even some of the semi-manual ones, while being fully automatic. Our method can also be used to suggest and verify domain partitions based on structural data. A few examples of predicted domain definitions and alternative partitions, as suggested by our method, are also discussed. An online domain-prediction server is available at http://biozon.org/tools/domains/

BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.

PubMed

Yang, Bite; Liu, Feng; Ren, Chao; Ouyang, Zhangyi; Xie, Ziwei; Bo, Xiaochen; Shu, Wenjie

2017-07-01

Enhancer elements are noncoding stretches of DNA that play key roles in controlling gene expression programmes. Despite major efforts to develop accurate enhancer prediction methods, identifying enhancer sequences continues to be a challenge in the annotation of mammalian genomes. One of the major issues is the lack of large, sufficiently comprehensive and experimentally validated enhancers for humans or other species. Thus, the development of computational methods based on limited experimentally validated enhancers and deciphering the transcriptional regulatory code encoded in the enhancer sequences is urgent. We present a deep-learning-based hybrid architecture, BiRen, which predicts enhancers using the DNA sequence alone. Our results demonstrate that BiRen can learn common enhancer patterns directly from the DNA sequence and exhibits superior accuracy, robustness and generalizability in enhancer prediction relative to other state-of-the-art enhancer predictors based on sequence characteristics. Our BiRen will enable researchers to acquire a deeper understanding of the regulatory code of enhancer sequences. Our BiRen method can be freely accessed at https://github.com/wenjiegroup/BiRen . shuwj@bmi.ac.cn or boxc@bmi.ac.cn. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Draft versus finished sequence data for DNA and protein diagnostic signature development

PubMed Central

Gardner, Shea N.; Lam, Marisa W.; Smith, Jason R.; Torres, Clinton L.; Slezak, Tom R.

2005-01-01

Sequencing pathogen genomes is costly, demanding careful allocation of limited sequencing resources. We built a computational Sequencing Analysis Pipeline (SAP) to guide decisions regarding the amount of genomic sequencing necessary to develop high-quality diagnostic DNA and protein signatures. SAP uses simulations to estimate the number of target genomes and close phylogenetic relatives (near neighbors or NNs) to sequence. We use SAP to assess whether draft data are sufficient or finished sequencing is required using Marburg and variola virus sequences. Simulations indicate that intermediate to high-quality draft with error rates of 10−3–10−5 (∼8× coverage) of target organisms is suitable for DNA signature prediction. Low-quality draft with error rates of ∼1% (3× to 6× coverage) of target isolates is inadequate for DNA signature prediction, although low-quality draft of NNs is sufficient, as long as the target genomes are of high quality. For protein signature prediction, sequencing errors in target genomes substantially reduce the detection of amino acid sequence conservation, even if the draft is of high quality. In summary, high-quality draft of target and low-quality draft of NNs appears to be a cost-effective investment for DNA signature prediction, but may lead to underestimation of predicted protein signatures. PMID:16243783

Genome Sequence Analysis of the Naphthenic Acid Degrading and Metal Resistant Bacterium Cupriavidus gilardii CR3

PubMed Central

Xiao, Jingfa; Hao, Lirui; Crowley, David E.; Zhang, Zhewen; Yu, Jun; Huang, Ning; Huo, Mingxin; Wu, Jiayan

2015-01-01

Cupriavidus sp. are generally heavy metal tolerant bacteria with the ability to degrade a variety of aromatic hydrocarbon compounds, although the degradation pathways and substrate versatilities remain largely unknown. Here we studied the bacterium Cupriavidus gilardii strain CR3, which was isolated from a natural asphalt deposit, and which was shown to utilize naphthenic acids as a sole carbon source. Genome sequencing of C. gilardii CR3 was carried out to elucidate possible mechanisms for the naphthenic acid biodegradation. The genome of C. gilardii CR3 was composed of two circular chromosomes chr1 and chr2 of respectively 3,539,530 bp and 2,039,213 bp in size. The genome for strain CR3 encoded 4,502 putative protein-coding genes, 59 tRNA genes, and many other non-coding genes. Many genes were associated with xenobiotic biodegradation and metal resistance functions. Pathway prediction for degradation of cyclohexanecarboxylic acid, a representative naphthenic acid, suggested that naphthenic acid undergoes initial ring-cleavage, after which the ring fission products can be degraded via several plausible degradation pathways including a mechanism similar to that used for fatty acid oxidation. The final metabolic products of these pathways are unstable or volatile compounds that were not toxic to CR3. Strain CR3 was also shown to have tolerance to at least 10 heavy metals, which was mainly achieved by self-detoxification through ion efflux, metal-complexation and metal-reduction, and a powerful DNA self-repair mechanism. Our genomic analysis suggests that CR3 is well adapted to survive the harsh environment in natural asphalts containing naphthenic acids and high concentrations of heavy metals. PMID:26301592

Escherichia coli promoter sequences predict in vitro RNA polymerase selectivity.

PubMed

Mulligan, M E; Hawley, D K; Entriken, R; McClure, W R

1984-01-11

We describe a simple algorithm for computing a homology score for Escherichia coli promoters based on DNA sequence alone. The homology score was related to 31 values, measured in vitro, of RNA polymerase selectivity, which we define as the product KBk2, the apparent second order rate constant for open complex formation. We found that promoter strength could be predicted to within a factor of +/-4.1 in KBk2 over a range of 10(4) in the same parameter. The quantitative evaluation was linked to an automated (Apple II) procedure for searching and evaluating possible promoters in DNA sequence files.

Metamorphic Proteins: Emergence of Dual Protein Folds from One Primary Sequence.

PubMed

Lella, Muralikrishna; Mahalakshmi, Radhakrishnan

2017-06-20

Every amino acid exhibits a different propensity for distinct structural conformations. Hence, decoding how the primary amino acid sequence undergoes the transition to a defined secondary structure and its final three-dimensional fold is presently considered predictable with reasonable certainty. However, protein sequences that defy the first principles of secondary structure prediction (they attain two different folds) have recently been discovered. Such proteins, aptly named metamorphic proteins, decrease the conformational constraint by increasing flexibility in the secondary structure and thereby result in efficient functionality. In this review, we discuss the major factors driving the conformational switch related both to protein sequence and to structure using illustrative examples. We discuss the concept of an evolutionary transition in sequence and structure, the functional impact of the tertiary fold, and the pressure of intrinsic and external factors that give rise to metamorphic proteins. We mainly focus on the major components of protein architecture, namely, the α-helix and β-sheet segments, which are involved in conformational switching within the same or highly similar sequences. These chameleonic sequences are widespread in both cytosolic and membrane proteins, and these folds are equally important for protein structure and function. We discuss the implications of metamorphic proteins and chameleonic peptide sequences in de novo peptide design.

Biocomputational identification and validation of novel microRNAs predicted from bubaline whole genome shotgun sequences.

PubMed

Manku, H K; Dhanoa, J K; Kaur, S; Arora, J S; Mukhopadhyay, C S

2017-10-01

MicroRNAs (miRNAs) are small (19-25 base long), non-coding RNAs that regulate post-transcriptional gene expression by cleaving targeted mRNAs in several eukaryotes. The miRNAs play vital roles in multiple biological and metabolic processes, including developmental timing, signal transduction, cell maintenance and differentiation, diseases and cancers. Experimental identification of microRNAs is expensive and lab-intensive. Alternatively, computational approaches for predicting putative miRNAs from genomic or exomic sequences rely on features of miRNAs viz. secondary structures, sequence conservation, minimum free energy index (MFEI) etc. To date, not a single miRNA has been identified in bubaline (Bubalus bubalis), which is an economically important livestock. The present study aims at predicting the putative miRNAs of buffalo using comparative computational approach from buffalo whole genome shotgun sequencing data (INSDC: AWWX00000000.1). The sequences were blasted against the known mammalian miRNA. The obtained miRNAs were then passed through a series of filtration criteria to obtain the set of predicted (putative and novel) bubaline miRNA. Eight miRNAs were selected based on lowest E-value and validated by real time PCR (SYBR green chemistry) using RNU6 as endogenous control. The results from different trails of real time PCR shows that out of selected 8 miRNAs, only 2 (hsa-miR-1277-5p; bta-miR-2285b) are not expressed in bubaline PBMCs. The potential target genes based on their sequence complementarities were then predicted using miRanda. This work is the first report on prediction of bubaline miRNA from whole genome sequencing data followed by experimental validation. The finding could pave the way to future studies in economically important traits in buffalo. Copyright © 2017 Elsevier Ltd. All rights reserved.

37 CFR 1.822 - Symbols and format to be used for nucleotide and/or amino acid sequence data.

Code of Federal Regulations, 2013 CFR

2013-07-01

... in WIPO Standard ST.25 (1998), Appendix 2, Tables 1 and 3. This incorporation by reference was... ST.25 (1998), Appendix 2, Tables 1 and 3, shall be listed in a given sequence as “n” or “Xaa... acids. (1) The amino acids in a protein or peptide sequence shall be listed using the three-letter...

37 CFR 1.822 - Symbols and format to be used for nucleotide and/or amino acid sequence data.

Code of Federal Regulations, 2010 CFR

2010-07-01

... in WIPO Standard ST.25 (1998), Appendix 2, Tables 1 and 3. This incorporation by reference was... ST.25 (1998), Appendix 2, Tables 1 and 3, shall be listed in a given sequence as “n” or “Xaa... acids. (1) The amino acids in a protein or peptide sequence shall be listed using the three-letter...

37 CFR 1.822 - Symbols and format to be used for nucleotide and/or amino acid sequence data.

Code of Federal Regulations, 2012 CFR

2012-07-01

... in WIPO Standard ST.25 (1998), Appendix 2, Tables 1 and 3. This incorporation by reference was... ST.25 (1998), Appendix 2, Tables 1 and 3, shall be listed in a given sequence as “n” or “Xaa... acids. (1) The amino acids in a protein or peptide sequence shall be listed using the three-letter...

ANCAC: amino acid, nucleotide, and codon analysis of COGs – a tool for sequence bias analysis in microbial orthologs

PubMed Central

2012-01-01

Background The COG database is the most popular collection of orthologous proteins from many different completely sequenced microbial genomes. Per definition, a cluster of orthologous groups (COG) within this database exclusively contains proteins that most likely achieve the same cellular function. Recently, the COG database was extended by assigning to every protein both the corresponding amino acid and its encoding nucleotide sequence resulting in the NUCOCOG database. This extended version of the COG database is a valuable resource connecting sequence features with the functionality of the respective proteins. Results Here we present ANCAC, a web tool and MySQL database for the analysis of amino acid, nucleotide, and codon frequencies in COGs on the basis of freely definable phylogenetic patterns. We demonstrate the usefulness of ANCAC by analyzing amino acid frequencies, codon usage, and GC-content in a species- or function-specific context. With respect to amino acids we, at least in part, confirm the cognate bias hypothesis by using ANCAC’s NUCOCOG dataset as the largest one available for that purpose thus far. Conclusions Using the NUCOCOG datasets, ANCAC connects taxonomic, amino acid, and nucleotide sequence information with the functional classification via COGs and provides a GUI for flexible mining for sequence-bias. Thereby, to our knowledge, it is the only tool for the analysis of sequence composition in the light of physiological roles and phylogenetic context without requirement of substantial programming-skills. PMID:22958836

Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.

PubMed

Lyons, James; Dehzangi, Abdollah; Heffernan, Rhys; Sharma, Alok; Paliwal, Kuldip; Sattar, Abdul; Zhou, Yaoqi; Yang, Yuedong

2014-10-30

Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between C(αi-1)-C(αi)-C(αi+1) (θ) and a dihedral angle rotated about the C(αi)-C(αi+1) bond (τ). θ and τ angles, as the representative of structural properties of three to four amino-acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine-learning technique for sequence-based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ-τ plane close to that of native values. The average root-mean-square distance of 10-residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template-based as well as template-free structure prediction. The deep neural network learning technique is available as an on-line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks-lab.org. Copyright © 2014 Wiley Periodicals, Inc.

Sequence similarity is more relevant than species specificity in probabilistic backtranslation.

PubMed

Ferro, Alfredo; Giugno, Rosalba; Pigola, Giuseppe; Pulvirenti, Alfredo; Di Pietro, Cinzia; Purrello, Michele; Ragusa, Marco

2007-02-21

Backtranslation is the process of decoding a sequence of amino acids into the corresponding codons. All synthetic gene design systems include a backtranslation module. The degeneracy of the genetic code makes backtranslation potentially ambiguous since most amino acids are encoded by multiple codons. The common approach to overcome this difficulty is based on imitation of codon usage within the target species. This paper describes EasyBack, a new parameter-free, fully-automated software for backtranslation using Hidden Markov Models. EasyBack is not based on imitation of codon usage within the target species, but instead uses a sequence-similarity criterion. The model is trained with a set of proteins with known cDNA coding sequences, constructed from the input protein by querying the NCBI databases with BLAST. Unlike existing software, the proposed method allows the quality of prediction to be estimated. When tested on a group of proteins that show different degrees of sequence conservation, EasyBack outperforms other published methods in terms of precision. The prediction quality of a protein backtranslation methis markedly increased by replacing the criterion of most used codon in the same species with a Hidden Markov Model trained with a set of most similar sequences from all species. Moreover, the proposed method allows the quality of prediction to be estimated probabilistically.

Analysis and Prediction of Exon Skipping Events from RNA-Seq with Sequence Information Using Rotation Forest.

PubMed

Du, Xiuquan; Hu, Changlin; Yao, Yu; Sun, Shiwei; Zhang, Yanping

2017-12-12

In bioinformatics, exon skipping (ES) event prediction is an essential part of alternative splicing (AS) event analysis. Although many methods have been developed to predict ES events, a solution has yet to be found. In this study, given the limitations of machine learning algorithms with RNA-Seq data or genome sequences, a new feature, called RS (RNA-seq and sequence) features, was constructed. These features include RNA-Seq features derived from the RNA-Seq data and sequence features derived from genome sequences. We propose a novel Rotation Forest classifier to predict ES events with the RS features (RotaF-RSES). To validate the efficacy of RotaF-RSES, a dataset from two human tissues was used, and RotaF-RSES achieved an accuracy of 98.4%, a specificity of 99.2%, a sensitivity of 94.1%, and an area under the curve (AUC) of 98.6%. When compared to the other available methods, the results indicate that RotaF-RSES is efficient and can predict ES events with RS features.

GuiTope: an application for mapping random-sequence peptides to protein sequences.

PubMed

Halperin, Rebecca F; Stafford, Phillip; Emery, Jack S; Navalkar, Krupa Arun; Johnston, Stephen Albert

2012-01-03

Random-sequence peptide libraries are a commonly used tool to identify novel ligands for binding antibodies, other proteins, and small molecules. It is often of interest to compare the selected peptide sequences to the natural protein binding partners to infer the exact binding site or the importance of particular residues. The ability to search a set of sequences for similarity to a set of peptides may sometimes enable the prediction of an antibody epitope or a novel binding partner. We have developed a software application designed specifically for this task. GuiTope provides a graphical user interface for aligning peptide sequences to protein sequences. All alignment parameters are accessible to the user including the ability to specify the amino acid frequency in the peptide library; these frequencies often differ significantly from those assumed by popular alignment programs. It also includes a novel feature to align di-peptide inversions, which we have found improves the accuracy of antibody epitope prediction from peptide microarray data and shows utility in analyzing phage display datasets. Finally, GuiTope can randomly select peptides from a given library to estimate a null distribution of scores and calculate statistical significance. GuiTope provides a convenient method for comparing selected peptide sequences to protein sequences, including flexible alignment parameters, novel alignment features, ability to search a database, and statistical significance of results. The software is available as an executable (for PC) at http://www.immunosignature.com/software and ongoing updates and source code will be available at sourceforge.net.

Evolutionary conservation analysis increases the colocalization of predicted exonic splicing enhancers in the BRCA1 gene with missense sequence changes and in-frame deletions, but not polymorphisms

PubMed Central

Pettigrew, Christopher; Wayte, Nicola; Lovelock, Paul K; Tavtigian, Sean V; Chenevix-Trench, Georgia; Spurdle, Amanda B; Brown, Melissa A

2005-01-01

Introduction Aberrant pre-mRNA splicing can be more detrimental to the function of a gene than changes in the length or nature of the encoded amino acid sequence. Although predicting the effects of changes in consensus 5' and 3' splice sites near intron:exon boundaries is relatively straightforward, predicting the possible effects of changes in exonic splicing enhancers (ESEs) remains a challenge. Methods As an initial step toward determining which ESEs predicted by the web-based tool ESEfinder in the breast cancer susceptibility gene BRCA1 are likely to be functional, we have determined their evolutionary conservation and compared their location with known BRCA1 sequence variants. Results Using the default settings of ESEfinder, we initially detected 669 potential ESEs in the coding region of the BRCA1 gene. Increasing the threshold score reduced the total number to 464, while taking into consideration the proximity to splice donor and acceptor sites reduced the number to 211. Approximately 11% of these ESEs (23/211) either are identical at the nucleotide level in human, primates, mouse, cow, dog and opossum Brca1 (conserved) or are detectable by ESEfinder in the same position in the Brca1 sequence (shared). The frequency of conserved and shared predicted ESEs between human and mouse is higher in BRCA1 exons (2.8 per 100 nucleotides) than in introns (0.6 per 100 nucleotides). Of conserved or shared putative ESEs, 61% (14/23) were predicted to be affected by sequence variants reported in the Breast Cancer Information Core database. Applying the filters described above increased the colocalization of predicted ESEs with missense changes, in-frame deletions and unclassified variants predicted to be deleterious to protein function, whereas they decreased the colocalization with known polymorphisms or unclassified variants predicted to be neutral. Conclusion In this report we show that evolutionary conservation analysis may be used to improve the specificity of an ESE

Amino acid selective unlabeling for sequence specific resonance assignments in proteins

PubMed Central

Krishnarjuna, B.; Jaipuria, Garima; Thakur, Anushikha

2010-01-01

Sequence specific resonance assignment constitutes an important step towards high-resolution structure determination of proteins by NMR and is aided by selective identification and assignment of amino acid types. The traditional approach to selective labeling yields only the chemical shifts of the particular amino acid being selected and does not help in establishing a link between adjacent residues along the polypeptide chain, which is important for sequential assignments. An alternative approach is the method of amino acid selective ‘unlabeling’ or reverse labeling, which involves selective unlabeling of specific amino acid types against a uniformly 13C/15N labeled background. Based on this method, we present a novel approach for sequential assignments in proteins. The method involves a new NMR experiment named, {12COi–15Ni+1}-filtered HSQC, which aids in linking the 1HN/15N resonances of the selectively unlabeled residue, i, and its C-terminal neighbor, i + 1, in HN-detected double and triple resonance spectra. This leads to the assignment of a tri-peptide segment from the knowledge of the amino acid types of residues: i − 1, i and i + 1, thereby speeding up the sequential assignment process. The method has the advantage of being relatively inexpensive, applicable to 2H labeled protein and can be coupled with cell-free synthesis and/or automated assignment approaches. A detailed survey involving unlabeling of different amino acid types individually or in pairs reveals that the proposed approach is also robust to misincorporation of 14N at undesired sites. Taken together, this study represents the first application of selective unlabeling for sequence specific resonance assignments and opens up new avenues to using this methodology in protein structural studies. Electronic supplementary material The online version of this article (doi:10.1007/s10858-010-9459-z) contains supplementary material, which is available to authorized users. PMID:21153044

Comprehensive red blood cell and platelet antigen prediction from whole genome sequencing: proof of principle

PubMed Central

Westhoff, Connie M.; Uy, Jon Michael; Aguad, Maria; Smeland‐Wagman, Robin; Kaufman, Richard M.; Rehm, Heidi L.; Green, Robert C.; Silberstein, Leslie E.

2015-01-01

BACKGROUND There are 346 serologically defined red blood cell (RBC) antigens and 33 serologically defined platelet (PLT) antigens, most of which have known genetic changes in 45 RBC or six PLT genes that correlate with antigen expression. Polymorphic sites associated with antigen expression in the primary literature and reference databases are annotated according to nucleotide positions in cDNA. This makes antigen prediction from next‐generation sequencing data challenging, since it uses genomic coordinates. STUDY DESIGN AND METHODS The conventional cDNA reference sequences for all known RBC and PLT genes that correlate with antigen expression were aligned to the human reference genome. The alignments allowed conversion of conventional cDNA nucleotide positions to the corresponding genomic coordinates. RBC and PLT antigen prediction was then performed using the human reference genome and whole genome sequencing (WGS) data with serologic confirmation. RESULTS Some major differences and alignment issues were found when attempting to convert the conventional cDNA to human reference genome sequences for the following genes: ABO, A4GALT, RHD, RHCE, FUT3, ACKR1 (previously DARC), ACHE, FUT2, CR1, GCNT2, and RHAG. However, it was possible to create usable alignments, which facilitated the prediction of all RBC and PLT antigens with a known molecular basis from WGS data. Traditional serologic typing for 18 RBC antigens were in agreement with the WGS‐based antigen predictions, providing proof of principle for this approach. CONCLUSION Detailed mapping of conventional cDNA annotated RBC and PLT alleles can enable accurate prediction of RBC and PLT antigens from whole genomic sequencing data. PMID:26634332

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features.

PubMed

Sun, Ming-An; Zhang, Qing; Wang, Yejun; Ge, Wei; Guo, Dianjing

2016-08-24

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not

37 CFR 1.824 - Form and format for nucleotide and/or amino acid sequence submissions in computer readable form.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 37 Patents, Trademarks, and Copyrights 1 2010-07-01 2010-07-01 false Form and format for... And/or Amino Acid Sequences § 1.824 Form and format for nucleotide and/or amino acid sequence... Code for Information Interchange (ASCII) text. No other formats shall be allowed. (3) The computer...

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

PubMed

Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

Sequencing, bioinformatic characterization and expression pattern of a putative amino acid transporter from the parasitic cestode Echinococcus granulosus.

PubMed

Camicia, Federico; Paredes, Rodolfo; Chalar, Cora; Galanti, Norbel; Kamenetzky, Laura; Gutierrez, Ariana; Rosenzvit, Mara C

2008-03-31

We have sequenced and partially characterized an Echinococcus granulosus cDNA, termed egat1, from a protoscolex signal sequence trap (SST) cDNA library. The isolated 1627 bp long cDNA contains an ORF of 489 amino acids and shows an amino acid identity of 30% with neutral and excitatory amino acid transporters members of the Dicarboxylate/Amino Acid Na+ and/or H+ Cation Symporter family (DAACS) (TC 2.A.23). Additional bioinformatics analysis of EgAT1, confirmed the results obtained by similarity searches and showed the presence of 9 to 10 transmembrane domains, consensus sequences for N-glycosylation between the third and fourth transmembrane domain, a highly similar hydropathy profile with ASCT1 (a known member of DAACS family), high score with SDF (Sodium Dicarboxilate Family) and similar motifs with EDTRANSPORT, a fingerprint of excitatory amino acid transporters. The localization of the putative amino acid transporter was analyzed by in situ hybridization and immunofluorescence in protoscoleces and associated germinal layer. The in situ hybridization labelling indicates the distribution of egat1 mRNA throughout the tegument. EgAT1 protein, which showed in Western blots a molecular mass of approximately 60 kD, is localized in the subtegumental region of the metacestode, particularly around suckers and rostellum of protoscoleces and layers from brood capsules. The sequence and expression analyses of EgAT1 pave the way for functional analysis of amino acids transporters of E. granulosus and its evaluation as new drug targets against cystic echinococcosis.

Amino- and carboxyl-terminal amino acid sequences of proteins coded by gag gene of murine leukemia virus

PubMed Central

Oroszlan, Stephen; Henderson, Louis E.; Stephenson, John R.; Copeland, Terry D.; Long, Cedric W.; Ihle, James N.; Gilden, Raymond V.

1978-01-01

The amino- and carboxyl-terminal amino acid sequences of proteins (p10, p12, p15, and p30) coded by the gag gene of Rauscher and AKR murine leukemia viruses were determined. Among these proteins, p15 from both viruses appears to have a blocked amino end. Proline was found to be the common NH2 terminus of both p30s and both p12s, and alanine of both p10s. The amino-terminal sequences of p30s are identical, as are those of p10s, while the p12 sequences are clearly distinctive but also show substantial homology. The carboxyl-terminal amino acids of both viral p30s and p12s are leucine and phenylalanine, respectively. Rauscher leukemia virus p15 has tyrosine as the carboxyl terminus while AKR virus p15 has phenylalanine in this position. The compositional and sequence data provide definite chemical criteria for the identification of analogous gag gene products and for the comparison of viral proteins isolated in different laboratories. On the basis of amino acid sequences and the previously proposed H-p15-p12-p30-p10-COOH peptide sequence in the precursor polyprotein, a model for cleavage sites involved in the post-translational processing of the precursor coded for by the gag gene is proposed. PMID:206897

Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis.

PubMed

Li, Zhan-Chao; Zhou, Xi-Bin; Dai, Zong; Zou, Xiao-Yong

2009-07-01

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou's pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246-255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.

Implication of the cause of differences in 3D structures of proteins with high sequence identity based on analyses of amino acid sequences and 3D structures.

PubMed

Matsuoka, Masanari; Sugita, Masatake; Kikuchi, Takeshi

2014-09-18

Proteins that share a high sequence homology while exhibiting drastically different 3D structures are investigated in this study. Recently, artificial proteins related to the sequences of the GA and IgG binding GB domains of human serum albumin have been designed. These artificial proteins, referred to as GA and GB, share 98% amino acid sequence identity but exhibit different 3D structures, namely, a 3α bundle versus a 4β + α structure. Discriminating between their 3D structures based on their amino acid sequences is a very difficult problem. In the present work, in addition to using bioinformatics techniques, an analysis based on inter-residue average distance statistics is used to address this problem. It was hard to distinguish which structure a given sequence would take only with the results of ordinary analyses like BLAST and conservation analyses. However, in addition to these analyses, with the analysis based on the inter-residue average distance statistics and our sequence tendency analysis, we could infer which part would play an important role in its structural formation. The results suggest possible determinants of the different 3D structures for sequences with high sequence identity. The possibility of discriminating between the 3D structures based on the given sequences is also discussed.

Structured prediction models for RNN based sequence labeling in clinical text.

PubMed

Jagannatha, Abhyuday N; Yu, Hong

2016-11-01

Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling of pairwise potentials. We also propose an approximate version of skip-chain CRF inference with RNN potentials. We use these methodologies for structured prediction in order to improve the exact phrase detection of various medical entities.

Structured prediction models for RNN based sequence labeling in clinical text

PubMed Central

Jagannatha, Abhyuday N; Yu, Hong

2016-01-01

Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling of pairwise potentials. We also propose an approximate version of skip-chain CRF inference with RNN potentials. We use these methodologies1 for structured prediction in order to improve the exact phrase detection of various medical entities. PMID:28004040

BIOPEP database and other programs for processing bioactive peptide sequences.

PubMed

Minkiewicz, Piotr; Dziuba, Jerzy; Iwaniak, Anna; Dziuba, Marta; Darewicz, Małgorzata

2008-01-01

This review presents the potential for application of computational tools in peptide science based on a sample BIOPEP database and program as well as other programs and databases available via the World Wide Web. The BIOPEP application contains a database of biologically active peptide sequences and a program enabling construction of profiles of the potential biological activity of protein fragments, calculation of quantitative descriptors as measures of the value of proteins as potential precursors of bioactive peptides, and prediction of bonds susceptible to hydrolysis by endopeptidases in a protein chain. Other bioactive and allergenic peptide sequence databases are also presented. Programs enabling the construction of binary and multiple alignments between peptide sequences, the construction of sequence motifs attributed to a given type of bioactivity, searching for potential precursors of bioactive peptides, and the prediction of sites susceptible to proteolytic cleavage in protein chains are available via the Internet as are other approaches concerning secondary structure prediction and calculation of physicochemical features based on amino acid sequence. Programs for prediction of allergenic and toxic properties have also been developed. This review explores the possibilities of cooperation between various programs.

GeneSilico protein structure prediction meta-server.

PubMed

Kurowski, Michal A; Bujnicki, Janusz M

2003-07-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.

Acid-base accounting to predict post-mining drainage quality on surface mines.

PubMed

Skousen, J; Simmons, J; McDonald, L M; Ziemkiewicz, P

2002-01-01

Acid-base accounting (ABA) is an analytical procedure that provides values to help assess the acid-producing and acid-neutralizing potential of overburden rocks prior to coal mining and other large-scale excavations. This procedure was developed by West Virginia University scientists during the 1960s. After the passage of laws requiring an assessment of surface mining on water quality, ABA became a preferred method to predict post-mining water quality, and permitting decisions for surface mines are largely based on the values determined by ABA. To predict the post-mining water quality, the amount of acid-producing rock is compared with the amount of acid-neutralizing rock, and a prediction of the water quality at the site (whether acid or alkaline) is obtained. We gathered geologic and geographic data for 56 mined sites in West Virginia, which allowed us to estimate total overburden amounts, and values were determined for maximum potential acidity (MPA), neutralization potential (NP), net neutralization potential (NNP), and NP to MPA ratios for each site based on ABA. These values were correlated to post-mining water quality from springs or seeps on the mined property. Overburden mass was determined by three methods, with the method used by Pennsylvania researchers showing the most accurate results for overburden mass. A poor relationship existed between MPA and post-mining water quality, NP was intermediate, and NNP and the NP to MPA ratio showed the best prediction accuracy. In this study, NNP and the NP to MPA ratio gave identical water quality prediction results. Therefore, with NP to MPA ratios, values were separated into categories: <1 should produce acid drainage, between 1 and 2 can produce either acid or alkaline water conditions, and >2 should produce alkaline water. On our 56 surface mined sites, NP to MPA ratios varied from 0.1 to 31, and six sites (11%) did not fit the expected pattern using this category approach. Two sites with ratios <1 did not

Amino acid sequence of the smaller basic protein from rat brain myelin

PubMed Central

Dunkley, Peter R.; Carnegie, Patrick R.

1974-01-01

1. The complete amino acid sequence of the smaller basic protein from rat brain myelin was determined. This protein differs from myelin basic proteins of other species in having a deletion of a polypeptide of 40 amino acid residues from the centre of the molecule. 2. A detailed comparison is made of the constant and variable regions in a group of myelin basic proteins from six species. 3. An arginine residue in the rat protein was found to be partially methylated. The ratio of methylated to unmethylated arginine at this position differed from that found for the human basic protein. 4. Three tryptic peptides were isolated in more than one form. The differences between the two forms of each peptide are discussed in relation to the electrophoretic heterogeneity of myelin basic proteins, which is known to occur at alkaline pH values. 5. Detailed evidence for the amino acid sequence of the protein has been deposited as Supplementary Publication SUP 50029 at the British Library (Lending Division) (formerly the National Lending Library for Science and Technology), Boston Spa, Yorks. LS23 7BQ, U.K., from whom copies may be obtained on the terms given in Biochem. J. (1973) 131, 5. PMID:4141893

Homology analyses of the protein sequences of fatty acid synthases from chicken liver, rat mammary gland, and yeast

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Soo-Ik; Hammes, G.G.

1989-11-01

Homology analyses of the protein sequences of chicken liver and rat mammary gland fatty acid synthases were carried out. The amino acid sequences of the chicken and rat enzymes are 67% identical. If conservative substitutions are allowed, 78% of the amino acids are matched. A region of low homologies exists between the functional domains, in particular around amino acid residues 1059-1264 of the chicken enzyme. Homologies between the active sites of chicken and rat and of chicken and yeast enzymes have been analyzed by an alignment method. A high degree of homology exists between the active sites of the chickenmore » and rat enzymes. However, the chicken and yeast enzymes show a lower degree of homology. The DADPH-binding dinucleotide folds of the {beta}-ketoacyl reductase and the enoyl reductase sites were identified by comparison with a known consensus sequence for the DADP- and FAD-binding dinucleotide folds. The active sites of all of the enzymes are primarily in hydrophobic regions of the protein. This study suggests that the genes for the functional domains of fatty acid synthase were originally separated, and these genes were connected to each other by using different connecting nucleotide sequences in different species. An alternative explanation for the differences in rat and chicken is a common ancestry and mutations in the joining regions during evolution.« less

Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning.

PubMed

Fletcher, Timothy L; Popelier, Paul L A

2016-06-14

A machine learning method called kriging is applied to the set of all 20 naturally occurring amino acids. Kriging models are built that predict electrostatic multipole moments for all topological atoms in any amino acid based on molecular geometry only. These models then predict molecular electrostatic interaction energies. On the basis of 200 unseen test geometries for each amino acid, no amino acid shows a mean prediction error above 5.3 kJ mol(-1), while the lowest error observed is 2.8 kJ mol(-1). The mean error across the entire set is only 4.2 kJ mol(-1) (or 1 kcal mol(-1)). Charged systems are created by protonating or deprotonating selected amino acids, and these show no significant deviation in prediction error over their neutral counterparts. Similarly, the proposed methodology can also handle amino acids with aromatic side chains, without the need for modification. Thus, we present a generic method capable of accurately capturing multipolar polarizable electrostatics in amino acids.

Predicting human activities in sequences of actions in RGB-D videos

NASA Astrophysics Data System (ADS)

Jardim, David; Nunes, Luís.; Dias, Miguel

2017-03-01

In our daily activities we perform prediction or anticipation when interacting with other humans or with objects. Prediction of human activity made by computers has several potential applications: surveillance systems, human computer interfaces, sports video analysis, human-robot-collaboration, games and health-care. We propose a system capable of recognizing and predicting human actions using supervised classifiers trained with automatically labeled data evaluated in our human activity RGB-D dataset (recorded with a Kinect sensor) and using only the position of the main skeleton joints to extract features. Using conditional random fields (CRFs) to model the sequential nature of actions in a sequence has been used before, but where other approaches try to predict an outcome or anticipate ahead in time (seconds), we try to predict what will be the next action of a subject. Our results show an activity prediction accuracy of 89.9% using an automatically labeled dataset.

Solid phase sequencing of biopolymers

DOEpatents

Cantor, Charles; Koster, Hubert

2010-09-28

This invention relates to methods for detecting and sequencing target nucleic acid sequences, to mass modified nucleic acid probes and arrays of probes useful in these methods, and to kits and systems which contain these probes. Useful methods involve hybridizing the nucleic acids or nucleic acids which represent complementary or homologous sequences of the target to an array of nucleic acid probes. These probes comprise a single-stranded portion, an optional double-stranded portion and a variable sequence within the single-stranded portion. The molecular weights of the hybridized nucleic acids of the set can be determined by mass spectroscopy, and the sequence of the target determined from the molecular weights of the fragments. Nucleic acids whose sequences can be determined include DNA or RNA in biological samples such as patient biopsies and environmental samples. Probes may be fixed to a solid support such as a hybridization chip to facilitate automated molecular weight analysis and identification of the target sequence.

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

PubMed

Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

2018-02-15

A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

N-Terminal Amino Acid Sequence Determination of Proteins by N-Terminal Dimethyl Labeling: Pitfalls and Advantages When Compared with Edman Degradation Sequence Analysis.

PubMed

Chang, Elizabeth; Pourmal, Sergei; Zhou, Chun; Kumar, Rupesh; Teplova, Marianna; Pavletich, Nikola P; Marians, Kenneth J; Erdjument-Bromage, Hediye

2016-07-01

In recent history, alternative approaches to Edman sequencing have been investigated, and to this end, the Association of Biomolecular Resource Facilities (ABRF) Protein Sequencing Research Group (PSRG) initiated studies in 2014 and 2015, looking into bottom-up and top-down N-terminal (Nt) dimethyl derivatization of standard quantities of intact proteins with the aim to determine Nt sequence information. We have expanded this initiative and used low picomole amounts of myoglobin to determine the efficiency of Nt-dimethylation. Application of this approach on protein domains, generated by limited proteolysis of overexpressed proteins, confirms that it is a universal labeling technique and is very sensitive when compared with Edman sequencing. Finally, we compared Edman sequencing and Nt-dimethylation of the same polypeptide fragments; results confirm that there is agreement in the identity of the Nt amino acid sequence between these 2 methods.

Extension of the COG and arCOG databases by amino acid and nucleotide sequences

PubMed Central

Meereis, Florian; Kaufmann, Michael

2008-01-01

Background The current versions of the COG and arCOG databases, both excellent frameworks for studies in comparative and functional genomics, do not contain the nucleotide sequences corresponding to their protein or protein domain entries. Results Using sequence information obtained from GenBank flat files covering the completely sequenced genomes of the COG and arCOG databases, we constructed NUCOCOG (nucleotide sequences containing COG databases) as an extended version including all nucleotide sequences and in addition the amino acid sequences originally utilized to construct the current COG and arCOG databases. We make available three comprehensive single XML files containing the complete databases including all sequence information. In addition, we provide a web interface as a utility suitable to browse the NUCOCOG database for sequence retrieval. The database is accessible at . Conclusion NUCOCOG offers the possibility to analyze any sequence related property in the context of the COG and arCOG framework simply by using script languages such as PERL applied to a large but single XML document. PMID:19014535

Conversion of amino-acid sequence in proteins to classical music: search for auditory patterns

PubMed Central

2007-01-01

We have converted genome-encoded protein sequences into musical notes to reveal auditory patterns without compromising musicality. We derived a reduced range of 13 base notes by pairing similar amino acids and distinguishing them using variations of three-note chords and codon distribution to dictate rhythm. The conversion will help make genomic coding sequences more approachable for the general public, young children, and vision-impaired scientists. PMID:17477882

The amino acid motif L/IIxxFE defines a novel actin-binding sequence in PDZ-RhoGEF

PubMed Central

Banerjee, Jayashree; Fischer, Christopher C.; Wedegaertner, Philip B.

2009-01-01

PDZ-RhoGEF is a member of the regulator of G protein signaling (RGS) domain-containing RhoGEFs (RGS-RhoGEFs) that link activated heterotrimeric G protein α subunits of the G12 family to activation of the small GTPase RhoA. Unique among the RGS-RhoGEFs, PDZ-RhoGEF contains a short sequence that localizes the protein to the actin cytoskeleton. In this report, we demonstrate that the actin-binding domain, located between amino acids 561–585, directly binds to F-actin in vitro. Extensive mutagenesis identifies isoleucine 568, isoleucine 569, phenylalanine 572, and glutamic acid 573 as necessary for binding to actin and for co-localization with the actin cytoskeleton in cells. These results define a novel actin-binding sequence in PDZ-RhoGEF with a critical amino acid motif of IIxxFE. Moreover, sequence analysis identifies a similar actin-binding motif in the N-terminus of the RhoGEF frabin, and, as with PDZ-RhoGEF, mutagenesis and actin interaction experiments demonstrate a motif of LIxxFE, consisting of the key amino acids leucine 23, isoleucine 24, phenylalanine 27, and glutamic acid 28. Taken together, results with PDZ-RhoGEF and frabin identify a novel actin binding sequence. Lastly, inducible dimerization of the actin-binding region of PDZ-RhoGEF revealed a dimerization-dependent actin bundling activity in vitro. PDZ-RhoGEF exists in cells as a dimer, raising the possibility that PDZ-RhoGEF could influence actin structure independent of its ability to activate RhoA. PMID:19618964

Preparation of Nucleic Acid Libraries for Personalized Sequencing Systems Using an Integrated Microfluidic Hub Technology (Seventh Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting 2012)

ScienceCinema

Patel, Kamlesh D.

2018-01-22

Kamlesh (Ken) Patel from Sandia National Laboratories (Livermore, California) presents "Preparation of Nucleic Acid Libraries for Personalized Sequencing Systems Using an Integrated Microfluidic Hub Technology " at the 7th Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting held in June, 2012 in Santa Fe, NM.

Preparation of Nucleic Acid Libraries for Personalized Sequencing Systems Using an Integrated Microfluidic Hub Technology (Seventh Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting 2012)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Patel, Kamlesh D.

2012-06-01

Kamlesh (Ken) Patel from Sandia National Laboratories (Livermore, California) presents "Preparation of Nucleic Acid Libraries for Personalized Sequencing Systems Using an Integrated Microfluidic Hub Technology " at the 7th Annual Sequencing, Finishing, Analysis in the Future (SFAF) Meeting held in June, 2012 in Santa Fe, NM.

Predictions of diagenetic reactions in the presence of organic acids

NASA Astrophysics Data System (ADS)

Harrison, Wendy J.; Thyne, Geoffrey D.

1992-02-01

Stability constants have been estimated for cation complexes with anions of monofunctional and difunctional acids (combinations of Ca, Mg, Fe, Al, Sr, Mn, U, Th, Pb, Cu, Zn with formate, acetate, propionate, oxalate, malonate, succinate, and salicylate) between 0 and 200°C. Difunctional acid anions form much more stable complexes than monofunctional acid anions with aluminum; the importance of the aluminum-acetate complex is relatively minor in comparison to aluminum oxalate and malonate complexes. Divalent metal cations such as Mg, Ca, and Fe form more stable complexes with acetate than with difunctional acid anions. Aluminum-oxalate can dominate the species distribution of aluminum under acidic pH conditions, whereas the divalent cation-acetate and oxalate complexes rarely account for more than 60% of the total dissolved cation, and then only in more alkaline waters. Mineral thermodynamic affinities were calculated using the reaction path model EQ3/6 for waters having variable organic acid anion (OAA) contents under conditions representative of those found during normal burial diagenesis. The following scenarios are possible: 1) K-feldspar and albite are stable, anorthite dissolves 2) All feldpars are stable 3) Carbonates can be very unstable to slightly unstable, but never increase in stability. Organic acid anions are ineffective at neutral to alkaline pH in modifying stabilities of aluminosilicate minerals whereas the anions are variably effective under a wide range of pH in modifying carbonate mineral stabilities. Reaction path calculations demonstrate that the sequence of mineral reactions occurring in an arkosic sandstone-fluid system is only slightly modified by the presence of OAA. A spectrum of possible sandstone alteration mineralogies can be obtained depending on the selected boundary conditions: EQ3/6 predictions include quartz overgrowth, calcite replacement of plagioclase, albitization of plagioclase, and the formation of porosity-occluding calcite

Protein Tertiary Structure Prediction Based on Main Chain Angle Using a Hybrid Bees Colony Optimization Algorithm

NASA Astrophysics Data System (ADS)

Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen

Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

Predicting nucleic acid binding interfaces from structural models of proteins.

PubMed

Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

2012-02-01

The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

Predicting nucleic acid binding interfaces from structural models of proteins

PubMed Central

Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

2011-01-01

The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared to patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. PMID:22086767

The cDNA sequence of a neutral horseradish peroxidase.

PubMed

Bartonek-Roxå, E; Eriksson, H; Mattiasson, B

1991-02-16

A cDNA clone encoding a horseradish (Armoracia rusticana) peroxidase has been isolated and characterized. The cDNA contains 1378 nucleotides excluding the poly(A) tail and the deduced protein contains 327 amino acids which includes a 28 amino acid leader sequence. The predicted amino acid sequence is nine amino acids shorter than the major isoenzyme belonging to the horseradish peroxidase C group (HRP-C) and the sequence shows 53.7% identity with this isoenzyme. The described clone encodes nine cysteines of which eight correspond well with the cysteines found in HRP-C. Five potential N-glycosylation sites with the general sequence Asn-X-Thr/Ser are present in the deduced sequence. Compared to the earlier described HRP-C this is three glycosylation sites less. The shorter sequence and fewer N-glycosylation sites give the native isoenzyme a molecular weight of several thousands less than the horseradish peroxidase C isoenzymes. Comparison with the net charge value of HRP-C indicates that the described cDNA clone encodes a peroxidase which has either the same or a slightly less basic pI value, depending on whether the encoded protein is N-terminally blocked or not. This excludes the possibility that HRP-n could belong to either the HRP-A, -D or -E groups. The low sequence identity (53.7%) with HRP-C indicates that the described clone does not belong to the HRP-C isoenzyme group and comparison of the total amino acid composition with the HRP-B group does not place the described clone within this isoenzyme group. Our conclusion is that the described cDNA clone encodes a neutral horseradish peroxidase which belongs to a new, not earlier described, horseradish peroxidase group.

Structure-Templated Predictions of Novel Protein Interactions from Sequence Information

PubMed Central

Betel, Doron; Breitkreuz, Kevin E; Isserlin, Ruth; Dewar-Darch, Danielle; Tyers, Mike; Hogue, Christopher W. V

2007-01-01

The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information. PMID:17892321

A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.

2004-12-01

We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, andmore » its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.« less

37 CFR 1.822 - Symbols and format to be used for nucleotide and/or amino acid sequence data.

Code of Federal Regulations, 2014 CFR

2014-07-01

... base or modified or unusual amino acid may be presented in a given sequence as the corresponding unmodified base or amino acid if the modified base or modified or unusual amino acid is one of those listed... the Feature section. Otherwise, each occurrence of a base or amino acid not appearing in WIPO Standard...

GeneSilico protein structure prediction meta-server

PubMed Central

Kurowski, Michal A.; Bujnicki, Janusz M.

2003-01-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta. PMID:12824313

Self-sequencing of amino acids and origins of polyfunctional protocells

NASA Technical Reports Server (NTRS)

Fox, S. W.

1984-01-01

The role of proteins in the origin of living things is discussed. It has been experimentally established that amino acids can sequence themselves under simulated geological conditions with highly nonrandom products which accordingly contain diverse information. Multiple copies of each type of macromolecule are formed, resulting in greater power for any protoenzymic molecule than would accrue from a single copy of each type. Thermal proteins are readily incorporated into laboratory protocells. The experimental evidence for original polyfunctional protocells is discussed.

SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method.

PubMed

Vasylenko, Tamara; Liou, Yi-Fan; Chen, Hong-An; Charoenkwan, Phasit; Huang, Hui-Ling; Ho, Shinn-Ying

2015-01-01

Photosynthetic proteins (PSPs) greatly differ in their structure and function as they are involved in numerous subprocesses that take place inside an organelle called a chloroplast. Few studies predict PSPs from sequences due to their high variety of sequences and structues. This work aims to predict and characterize PSPs by establishing the datasets of PSP and non-PSP sequences and developing prediction methods. A novel bioinformatics method of predicting and characterizing PSPs based on scoring card method (SCMPSP) was used. First, a dataset consisting of 649 PSPs was established by using a Gene Ontology term GO:0015979 and 649 non-PSPs from the SwissProt database with sequence identity <= 25%.- Several prediction methods are presented based on support vector machine (SVM), decision tree J48, Bayes, BLAST, and SCM. The SVM method using dipeptide features-performed well and yielded - a test accuracy of 72.31%. The SCMPSP method uses the estimated propensity scores of 400 dipeptides - as PSPs and has a test accuracy of 71.54%, which is comparable to that of the SVM method. The derived propensity scores of 20 amino acids were further used to identify informative physicochemical properties for characterizing PSPs. The analytical results reveal the following four characteristics of PSPs: 1) PSPs favour hydrophobic side chain amino acids; 2) PSPs are composed of the amino acids prone to form helices in membrane environments; 3) PSPs have low interaction with water; and 4) PSPs prefer to be composed of the amino acids of electron-reactive side chains. The SCMPSP method not only estimates the propensity of a sequence to be PSPs, it also discovers characteristics that further improve understanding of PSPs. The SCMPSP source code and the datasets used in this study are available at http://iclab.life.nctu.edu.tw/SCMPSP/.

Microwave-assisted acid and base hydrolysis of intact proteins containing disulfide bonds for protein sequence analysis by mass spectrometry.

PubMed

Reiz, Bela; Li, Liang

2010-09-01

Controlled hydrolysis of proteins to generate peptide ladders combined with mass spectrometric analysis of the resultant peptides can be used for protein sequencing. In this paper, two methods of improving the microwave-assisted protein hydrolysis process are described to enable rapid sequencing of proteins containing disulfide bonds and increase sequence coverage, respectively. It was demonstrated that proteins containing disulfide bonds could be sequenced by MS analysis by first performing hydrolysis for less than 2 min, followed by 1 h of reduction to release the peptides originally linked by disulfide bonds. It was shown that a strong base could be used as a catalyst for microwave-assisted protein hydrolysis, producing complementary sequence information to that generated by microwave-assisted acid hydrolysis. However, using either acid or base hydrolysis, amide bond breakages in small regions of the polypeptide chains of the model proteins (e.g., cytochrome c and lysozyme) were not detected. Dynamic light scattering measurement of the proteins solubilized in an acid or base indicated that protein-protein interaction or aggregation was not the cause of the failure to hydrolyze certain amide bonds. It was speculated that there were some unknown local structures that might play a role in preventing an acid or base from reacting with the peptide bonds therein. 2010 American Society for Mass Spectrometry. Published by Elsevier Inc. All rights reserved.

An atypical topoisomerase II sequence from the slime mold Physarum polycephalum.

PubMed

Hugodot, Yannick; Dutertre, Murielle; Duguet, Michel

2004-01-21

We have determined the complete nucleotide sequence of the cDNA encoding DNA topoisomerase II from Physarum polycephalum. Using degenerate primers, based on the conserved amino acid sequences of other eukaryotic enzymes, a 250-bp fragment was polymerase chain reaction (PCR) amplified. This fragment was used as a probe to screen a Physarum cDNA library. A partial cDNA clone was isolated that was truncated at the 3' end. Rapid amplification of cDNA ends (RACE)-PCR was employed to isolate the remaining portion of the gene. The complete sequence of 4613 bp contains an open reading frame of 4494 bp that codes for 1498 amino acid residues with a theoretical molecular weight of 167 kDa. The predicted amino acid sequence shares similarity with those of other eukaryotes and shows the highest degree of identity with the enzyme of Dictyostelium discoideum. However, the enzyme of P. polycephalum contains an atypical amino-terminal domain very rich in serine and proline, whose function is unknown. Remarkably, both a mitochondrial targeting sequence and a nuclear localization signal were predicted respectively in the amino and carboxy-terminus of the protein, as in the case of human topoisomerase III alpha. At the Physarum genomic level, the topoisomerase II gene encompasses a region of about 16 kbp suggesting a large proportion of intronic sequences, an unusual situation for a gene of a lower eukaryote, often free of introns. Finally, expression of topoisomerase II mRNA does not appear significantly dependent on the plasmodium cycle stage, possibly due to the lack of G1 phase or (and) to a mitochondrial localization of the enzyme.

PredPPCrys: Accurate Prediction of Sequence Cloning, Protein Production, Purification and Crystallization Propensity from Protein Sequences Using Multi-Step Heterogeneous Feature Fusion and Selection

PubMed Central

Wang, Huilin; Wang, Mingjun; Tan, Hao; Li, Yuan; Zhang, Ziding; Song, Jiangning

2014-01-01

X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed ‘PredPPCrys’ using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of

PredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.

PubMed

Wang, Huilin; Wang, Mingjun; Tan, Hao; Li, Yuan; Zhang, Ziding; Song, Jiangning

2014-01-01

X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM). Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I). Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II), which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization targets of currently

Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.

PubMed

Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Zhou, Yong; An, Ji-Yong

2017-07-05

Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

Genotyping by sequencing for genomic prediction in a soybean breeding population.

PubMed

Jarquín, Diego; Kocak, Kyle; Posadas, Luis; Hyma, Katie; Jedlicka, Joseph; Graef, George; Lorenz, Aaron

2014-08-29

Advances in genotyping technology, such as genotyping by sequencing (GBS), are making genomic prediction more attractive to reduce breeding cycle times and costs associated with phenotyping. Genomic prediction and selection has been studied in several crop species, but no reports exist in soybean. The objectives of this study were (i) evaluate prospects for genomic selection using GBS in a typical soybean breeding program and (ii) evaluate the effect of GBS marker selection and imputation on genomic prediction accuracy. To achieve these objectives, a set of soybean lines sampled from the University of Nebraska Soybean Breeding Program were genotyped using GBS and evaluated for yield and other agronomic traits at multiple Nebraska locations. Genotyping by sequencing scored 16,502 single nucleotide polymorphisms (SNPs) with minor-allele frequency (MAF) > 0.05 and percentage of missing values ≤ 5% on 301 elite soybean breeding lines. When SNPs with up to 80% missing values were included, 52,349 SNPs were scored. Prediction accuracy for grain yield, assessed using cross validation, was estimated to be 0.64, indicating good potential for using genomic selection for grain yield in soybean. Filtering SNPs based on missing data percentage had little to no effect on prediction accuracy, especially when random forest imputation was used to impute missing values. The highest accuracies were observed when random forest imputation was used on all SNPs, but differences were not significant. A standard additive G-BLUP model was robust; modeling additive-by-additive epistasis did not provide any improvement in prediction accuracy. The effect of training population size on accuracy began to plateau around 100, but accuracy steadily climbed until the largest possible size was used in this analysis. Including only SNPs with MAF > 0.30 provided higher accuracies when training populations were smaller. Using GBS for genomic prediction in soybean holds good potential to

High serum uric acid concentration predicts poor survival in patients with breast cancer.

PubMed

Yue, Cai-Feng; Feng, Pin-Ning; Yao, Zhen-Rong; Yu, Xue-Gao; Lin, Wen-Bin; Qian, Yuan-Min; Guo, Yun-Miao; Li, Lai-Sheng; Liu, Min

2017-10-01

Uric acid is a product of purine metabolism. Recently, uric acid has gained much attraction in cancer. In this study, we aim to investigate the clinicopathological and prognostic significance of serum uric acid concentration in breast cancer patients. A total of 443 female patients with histopathologically diagnosed breast cancer were included. After a mean follow-up time of 56months, survival was analysed using the Kaplan-Meier method. To further evaluate the prognostic significance of uric acid concentrations, univariate and multivariate Cox regression analyses were applied. Of the clinicopathological parameters, uric acid concentration was associated with age, body mass index, ER status and PR status. Univariate analysis identified that patients with increased uric acid concentration had a significantly inferior overall survival (HR 2.13, 95% CI 1.15-3.94, p=0.016). In multivariate analysis, we found that high uric acid concentration is an independent prognostic factor predicting death, but insufficient to predict local relapse or distant metastasis. Kaplan-Meier analysis indicated that high uric acid concentration is related to the poor overall survival (p=0.013). High uric acid concentration predicts poor survival in patients with breast cancer, and might serve as a potential marker for appropriate management of breast cancer patients. Copyright © 2017 Elsevier B.V. All rights reserved.

Statistical distribution of amino acid sequences: a proof of Darwinian evolution.

PubMed

Eitner, Krystian; Koch, Uwe; Gaweda, Tomasz; Marciniak, Jedrzej

2010-12-01

The article presents results of the listing of the quantity of amino acids, dipeptides and tripeptides for all proteins available in the UNIPROT-TREMBL database and the listing for selected species and enzymes. UNIPROT-TREMBL contains protein sequences associated with computationally generated annotations and large-scale functional characterization. Due to the distinct metabolic pathways of amino acid syntheses and their physicochemical properties, the quantities of subpeptides in proteins vary. We have proved that the distribution of amino acids, dipeptides and tripeptides is statistical which confirms that the evolutionary biodiversity development model is subject to the theory of independent events. It seems interesting that certain short peptide combinations occur relatively rarely or even not at all. First, it confirms the Darwinian theory of evolution and second, it opens up opportunities for designing pharmaceuticals among rarely represented short peptide combinations. Furthermore, an innovative approach to the mass analysis of bioinformatic data is presented. eitner@amu.edu.pl Supplementary data are available at Bioinformatics online.

DNA sequence of the lymphotropic variant of minute virus of mice, MVM(i), and comparison with the DNA sequence of the fibrotropic prototype strain.

PubMed

Astell, C R; Gardiner, E M; Tattersall, P

1986-02-01

The sequence of molecular clones of the genome of MVM(i), a lymphotropic variant of minute virus of mice, was determined and compared with that of MVM(p), the fibrotropic prototype strain. At the nucleotide level there are 163 base changes: 129 transitions and 34 transversions. Most nucleotide changes are silent, with only 27 amino acids changes predicted, of which 22 are conservative. Notable differences between the MVM(i) and MVM(p) genomes which may account for the cell specificities of these viruses occur within the 3' nontranslated regions. The differences discussed include the absence of a 65-base-pair direct in MVM(i), the presence of only two polyadenylation sites in MVM(i) compared with four in MVM(p), and sequences that bear a resemblance to enhancer sequences. Also included in this paper is an important correction to the MVM(p) sequence (C.R. Astell, M. Thomson, M. Merchlinsky, and D. C. Ward, Nucleic Acids Res. 11:999-1018, 1983).

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

PubMed

Zhang, Hua; Kurgan, Lukasz

2014-12-01

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

Purification, characterization, gene cloning and nucleotide sequencing of D: -stereospecific amino acid amidase from soil bacterium: Delftia acidovorans.

PubMed

Hongpattarakere, Tipparat; Komeda, Hidenobu; Asano, Yasuhisa

2005-12-01

The D-amino acid amidase-producing bacterium was isolated from soil samples using an enrichment culture technique in medium broth containing D-phenylalanine amide as a sole source of nitrogen. The strain exhibiting the strongest activity was identified as Delftia acidovorans strain 16. This strain produced intracellular D-amino acid amidase constitutively. The enzyme was purified about 380-fold to homogeneity and its molecular mass was estimated to be about 50 kDa, on sodium dodecyl sulfate polyacrylamide gel electrophoresis. The enzyme was active preferentially toward D-amino acid amides rather than their L-counterparts. It exhibited strong amino acid amidase activity toward aromatic amino acid amides including D-phenylalanine amide, D-tryptophan amide and D-tyrosine amide, yet it was not specifically active toward low-molecular-weight D-amino acid amides such as D-alanine amide, L-alanine amide and L-serine amide. Moreover, it was not specifically active toward oligopeptides. The enzyme showed maximum activity at 40 degrees C and pH 8.5 and appeared to be very stable, with 92.5% remaining activity after the reaction was performed at 45 degrees C for 30 min. However, it was mostly inactivated in the presence of phenylmethanesulfonyl fluoride or Cd2+, Ag+, Zn2+, Hg2+ and As3+ . The NH2 terminal and internal amino acid sequences of the enzyme were determined; and the gene was cloned and sequenced. The enzyme gene damA encodes a 466-amino-acid protein (molecular mass 49,860.46 Da); and the deduced amino acid sequence exhibits homology to the D-amino acid amidase from Variovorax paradoxus (67.9% identity), the amidotransferase A subunit from Burkholderia fungorum (50% identity) and other enantioselective amidases.

A Novel Phytase with Sequence Similarity to Purple Acid Phosphatases Is Expressed in Cotyledons of Germinating Soybean Seedlings 1

PubMed Central

Hegeman, Carla E.; Grabau, Elizabeth A.

2001-01-01

Phytic acid (myo-inositol hexakisphosphate) is the major storage form of phosphorus in plant seeds. During germination, stored reserves are used as a source of nutrients by the plant seedling. Phytic acid is degraded by the activity of phytases to yield inositol and free phosphate. Due to the lack of phytases in the non-ruminant digestive tract, monogastric animals cannot utilize dietary phytic acid and it is excreted into manure. High phytic acid content in manure results in elevated phosphorus levels in soil and water and accompanying environmental concerns. The use of phytases to degrade seed phytic acid has potential for reducing the negative environmental impact of livestock production. A phytase was purified to electrophoretic homogeneity from cotyledons of germinated soybeans (Glycine max L. Merr.). Peptide sequence data generated from the purified enzyme facilitated the cloning of the phytase sequence (GmPhy) employing a polymerase chain reaction strategy. The introduction of GmPhy into soybean tissue culture resulted in increased phytase activity in transformed cells, which confirmed the identity of the phytase gene. It is surprising that the soybean phytase was unrelated to previously characterized microbial or maize (Zea mays) phytases, which were classified as histidine acid phosphatases. The soybean phytase sequence exhibited a high degree of similarity to purple acid phosphatases, a class of metallophosphoesterases. PMID:11500558

The isolation, purification and amino-acid sequence of insulin from the teleost fish Cottus scorpius (daddy sculpin).

PubMed

Cutfield, J F; Cutfield, S M; Carne, A; Emdin, S O; Falkmer, S

1986-07-01

Insulin from the principal islets of the teleost fish, Cottus scorpius (daddy sculpin), has been isolated and sequenced. Purification involved acid/alcohol extraction, gel filtration, and reverse-phase high-performance liquid chromatography to yield nearly 1 mg pure insulin/g wet weight islet tissue. Biological potency was estimated as 40% compared to porcine insulin. The sculpin insulin crystallised in the absence of zinc ions although zinc is known to be present in the islets in significant amounts. Two other hormones, glucagon and pancreatic polypeptide, were copurified with the insulin, and an N-terminal sequence for pancreatic polypeptide was determined. The primary structure of sculpin insulin shows a number of sequence changes unique so far amongst teleost fish. These changes occur at A14 (Arg), A15 (Val), and B2 (Asp). The B chain contains 29 amino acids and there is no N-terminal extension as seen with several other fish. Presumably as a result of the amino acid substitutions, sculpin insulin does not readily form crystals containing zinc-insulin hexamers, despite the presence of the coordinating B10 His.

Design of nucleic acid sequences for DNA computing based on a thermodynamic approach

PubMed Central

Tanaka, Fumiaki; Kameda, Atsushi; Yamamoto, Masahito; Ohuchi, Azuma

2005-01-01

We have developed an algorithm for designing multiple sequences of nucleic acids that have a uniform melting temperature between the sequence and its complement and that do not hybridize non-specifically with each other based on the minimum free energy (ΔGmin). Sequences that satisfy these constraints can be utilized in computations, various engineering applications such as microarrays, and nano-fabrications. Our algorithm is a random generate-and-test algorithm: it generates a candidate sequence randomly and tests whether the sequence satisfies the constraints. The novelty of our algorithm is that the filtering method uses a greedy search to calculate ΔGmin. This effectively excludes inappropriate sequences before ΔGmin is calculated, thereby reducing computation time drastically when compared with an algorithm without the filtering. Experimental results in silico showed the superiority of the greedy search over the traditional approach based on the hamming distance. In addition, experimental results in vitro demonstrated that the experimental free energy (ΔGexp) of 126 sequences correlated well with ΔGmin (|R| = 0.90) than with the hamming distance (|R| = 0.80). These results validate the rationality of a thermodynamic approach. We implemented our algorithm in a graphic user interface-based program written in Java. PMID:15701762

Using next generation transcriptome sequencing to predict an ectomycorrhizal metablome.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larsen, P. E.; Sreedasyam, A.; Trivedi, G

Mycorrhizae, symbiotic interactions between soil fungi and tree roots, are ubiquitous in terrestrial ecosystems. The fungi contribute phosphorous, nitrogen and mobilized nutrients from organic matter in the soil and in return the fungus receives photosynthetically-derived carbohydrates. This union of plant and fungal metabolisms is the mycorrhizal metabolome. Understanding this symbiotic relationship at a molecular level provides important contributions to the understanding of forest ecosystems and global carbon cycling. We generated next generation short-read transcriptomic sequencing data from fully-formed ectomycorrhizae between Laccaria bicolor and aspen (Populus tremuloides) roots. The transcriptomic data was used to identify statistically significantly expressed gene models usingmore » a bootstrap-style approach, and these expressed genes were mapped to specific metabolic pathways. Integration of expressed genes that code for metabolic enzymes and the set of expressed membrane transporters generates a predictive model of the ectomycorrhizal metabolome. The generated model of mycorrhizal metabolome predicts that the specific compounds glycine, glutamate, and allantoin are synthesized by L. bicolor and that these compounds or their metabolites may be used for the benefit of aspen in exchange for the photosynthetically-derived sugars fructose and glucose. The analysis illustrates an approach to generate testable biological hypotheses to investigate the complex molecular interactions that drive ectomycorrhizal symbiosis. These models are consistent with experimental environmental data and provide insight into the molecular exchange processes for organisms in this complex ecosystem. The method used here for predicting metabolomic models of mycorrhizal systems from deep RNA sequencing data can be generalized and is broadly applicable to transcriptomic data derived from complex systems.« less

General overview on structure prediction of twilight-zone proteins.

PubMed

Khor, Bee Yin; Tye, Gee Jun; Lim, Theam Soon; Choong, Yee Siew

2015-09-04

Protein structure prediction from amino acid sequence has been one of the most challenging aspects in computational structural biology despite significant progress in recent years showed by critical assessment of protein structure prediction (CASP) experiments. When experimentally determined structures are unavailable, the predictive structures may serve as starting points to study a protein. If the target protein consists of homologous region, high-resolution (typically <1.5 Å) model can be built via comparative modelling. However, when confronted with low sequence similarity of the target protein (also known as twilight-zone protein, sequence identity with available templates is less than 30%), the protein structure prediction has to be initiated from scratch. Traditionally, twilight-zone proteins can be predicted via threading or ab initio method. Based on the current trend, combination of different methods brings an improved success in the prediction of twilight-zone proteins. In this mini review, the methods, progresses and challenges for the prediction of twilight-zone proteins were discussed.

cis-β-Bromostyrene derivatives from cinnamic acids via a tandem substitutive bromination-decarboxylation sequence.

PubMed

Tang, Khanh G; Kent, Greggory T; Erden, Ihsan; Wu, Weiming

2017-10-04

cis -β-Bromostyrene derivatives were synthesized stereospecifically from cinnamic acids through β-lactone intermediates. The synthetic sequence did not require the purification of the β-lactone intermediates although they were found to be stable and readily purified in most cases.

NEP: web server for epitope prediction based on antibody neutralization of viral strains with diverse sequences

PubMed Central

Chuang, Gwo-Yu; Liou, David; Kwong, Peter D.; Georgiev, Ivelin S.

2014-01-01

Delineation of the antigenic site, or epitope, recognized by an antibody can provide clues about functional vulnerabilities and resistance mechanisms, and can therefore guide antibody optimization and epitope-based vaccine design. Previously, we developed an algorithm for antibody-epitope prediction based on antibody neutralization of viral strains with diverse sequences and validated the algorithm on a set of broadly neutralizing HIV-1 antibodies. Here we describe the implementation of this algorithm, NEP (Neutralization-based Epitope Prediction), as a web-based server. The users must supply as input: (i) an alignment of antigen sequences of diverse viral strains; (ii) neutralization data for the antibody of interest against the same set of antigen sequences; and (iii) (optional) a structure of the unbound antigen, for enhanced prediction accuracy. The prediction results can be downloaded or viewed interactively on the antigen structure (if supplied) from the web browser using a JSmol applet. Since neutralization experiments are typically performed as one of the first steps in the characterization of an antibody to determine its breadth and potency, the NEP server can be used to predict antibody-epitope information at no additional experimental costs. NEP can be accessed on the internet at http://exon.niaid.nih.gov/nep. PMID:24782517

Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

PubMed

Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

2015-02-01

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have

Repeat sequence chromosome specific nucleic acid probes and methods of preparing and using

DOEpatents

Weier, H.U.G.; Gray, J.W.

1995-06-27

A primer directed DNA amplification method to isolate efficiently chromosome-specific repeated DNA wherein degenerate oligonucleotide primers are used is disclosed. The probes produced are a heterogeneous mixture that can be used with blocking DNA as a chromosome-specific staining reagent, and/or the elements of the mixture can be screened for high specificity, size and/or high degree of repetition among other parameters. The degenerate primers are sets of primers that vary in sequence but are substantially complementary to highly repeated nucleic acid sequences, preferably clustered within the template DNA, for example, pericentromeric alpha satellite repeat sequences. The template DNA is preferably chromosome-specific. Exemplary primers and probes are disclosed. The probes of this invention can be used to determine the number of chromosomes of a specific type in metaphase spreads, in germ line and/or somatic cell interphase nuclei, micronuclei and/or in tissue sections. Also provided is a method to select arbitrarily repeat sequence probes that can be screened for chromosome-specificity. 18 figs.

Repeat sequence chromosome specific nucleic acid probes and methods of preparing and using

DOEpatents

Weier, Heinz-Ulrich G.; Gray, Joe W.

1995-01-01

A primer directed DNA amplification method to isolate efficiently chromosome-specific repeated DNA wherein degenerate oligonucleotide primers are used is disclosed. The probes produced are a heterogeneous mixture that can be used with blocking DNA as a chromosome-specific staining reagent, and/or the elements of the mixture can be screened for high specificity, size and/or high degree of repetition among other parameters. The degenerate primers are sets of primers that vary in sequence but are substantially complementary to highly repeated nucleic acid sequences, preferably clustered within the template DNA, for example, pericentromeric alpha satellite repeat sequences. The template DNA is preferably chromosome-specific. Exemplary primers ard probes are disclosed. The probes of this invention can be used to determine the number of chromosomes of a specific type in metaphase spreads, in germ line and/or somatic cell interphase nuclei, micronuclei and/or in tissue sections. Also provided is a method to select arbitrarily repeat sequence probes that can be screened for chromosome-specificity.

Acid oro-pharyngeal secretions can predict gastro-oesophageal reflux in preterm infants.

PubMed

James, M E; Ewer, A K

1999-05-01

Acid gastro-oesophageal reflux (GOR) is common in preterm infants but there is a lack of a non-invasive technique to establish the diagnosis. The aim of this study was to identify whether the presence of acid in oro-pharyngeal secretions (OPS) was a valid indicator of clinically significant acid GOR in preterm infants. A total of 23 infants with suspected GOR were studied with 24 h lower-oesophageal pH monitoring and during this period the OPS were tested for acid with litmus paper at 6 hourly intervals. Median (range) gestation was 28 weeks (24-31), birth weight 1023 g (480-1750) and age at study 34 days (11-76). Significant GOR was defined as a reflux index >5%. Of the investigated infants, 18 subjects (78%) had significant GOR. Of this group, 16 infants had acid in the OPS on at least one occasion. Five infants did not demonstrate significant GOR and in four of these acid was not detected in the OPS. Our data indicate that as a predictor for significant GOR, litmus-testing OPS for acid has a sensitivity of 89%, specificity of 80%, positive predictive value of 94% and a negative predictive value of 67%. The difference in the incidence of acid OPS between the GOR and the No GOR group was significant (P<0.03). The presence of acid in the oropharyngeal secretions may help in the prediction of acid gastro-oesophageal reflux in preterm infants. The method is simple, inexpensive cheap and involves minimal disturbance. We suggest that it could aid clinical diagnosis and indicate a need for further investigation of gastro-oesophageal reflux.

DNA Cloning of Plasmodium falciparum Circumsporozoite Gene: Amino Acid Sequence of Repetitive Epitope

NASA Astrophysics Data System (ADS)

Enea, Vincenzo; Ellis, Joan; Zavala, Fidel; Arnot, David E.; Asavanich, Achara; Masuda, Aoi; Quakyi, Isabella; Nussenzweig, Ruth S.

1984-08-01

A clone of complementary DNA encoding the circumsporozoite (CS) protein of the human malaria parasite Plasmodium falciparum has been isolated by screening an Escherichia coli complementary DNA library with a monoclonal antibody to the CS protein. The DNA sequence of the complementary DNA insert encodes a four-amino acid sequence: proline-asparagine-alanine-asparagine, tandemly repeated 23 times. The CS β -lactamase fusion protein specifically binds monoclonal antibodies to the CS protein and inhibits the binding of these antibodies to native Plasmodium falciparum CS protein. These findings provide a basis for the development of a vaccine against Plasmodium falciparum malaria.

Hydroquinone: O-glucosyltransferase from cultivated Rauvolfia cells: enrichment and partial amino acid sequences.

PubMed

Arend, J; Warzecha, H; Stöckigt, J

2000-01-01

Plant cell suspension cultures of Rauvolfia are able to produce a high amount of arbutin by glucosylation of exogenously added hydroquinone. A four step purification procedure using anion exchange, hydrophobic interaction, hydroxyapatite-chromatography and chromatofocusing delivered in a yield of 0.5%, an approximately 390 fold enrichment of the involved glucosyltransferase. SDS-PAGE showed a M(r) for the enzyme of 52 kDa. Proteolysis of the pure enzyme with endoproteinase LysC revealed six peptide fragments with 9-23 amino acids which were sequenced. Sequence alignment of the six peptides showed high homologies to glycosyltransferases from other higher plants.

Partial amino-acid sequence of the precursor of an immunoglobulin light chain containing NH2-terminal pyroglutamic acid.

PubMed Central

Burstein, Y; Kantour, F; Schechter, I

1976-01-01

Analyses of amino-acid sequences of the total cell-free products programmed by the mRNA of MOPC-104E gamma light (L)-chain show that over 95% of the products have sequences of a distinct protein that correspond to the L-chain precursor. In this precursor an extra piece is coupled to the NH2-terminus of the mature L-chain. Analyses of products labeled with [3H]alanine, [3H]leucine, and [3H]proline demonstrate that the extra piece is composed of at least 18 residues. Analyses of [35S]methione-labeled product indicate that the extra piece may contain an additional NH2-terminal methionine, which is detected in about 10% of the molecules. Partial recovery of the NJ2-terminal methionine (alanine, leucine, and proline are recovered in yields close to theoretical, greater than 95%) suggests that it is the initiator methionine, which is known to be short lived in eukaryotes due to rapid hydrolysis. Thus, the extra piece seems to be 19 residues in length, and it contains one methionine at the NH2-terminus, three alanines at positions 2, 12, and 17, and five leucines at positions 6, 8, 10, 11, and 13. The close gathering of leucine residues, as well as their abundance (26%), suggest that the extra piece would be quite hydrophobic. Hydrophobicity seems to be a general property of the extra piece, since similar clusters of leucine were found in the precursors of 3 KL-chains (Burstein, Y. & Schechter, I. (1976) Biochem. J. 157, 145-151). The NH2-terminus of the mature MOPC-104E gamma L-chain is blocked by pyroglutamic acid. The fact that in the precursor a peptide segment precedes this NH2-terminus establishes that pyroglutamic acid is not the initiator residue for synthesis of the L-chain. Apparently, the pyroglutamic acid is formed by cyclization of glutamic acid or glutamine during cleavage of the extra piece to yield the mature L-chain. Images PMID:822420

Evolution of sequence-defined highly functionalized nucleic acid polymers

NASA Astrophysics Data System (ADS)

Chen, Zhen; Lichtor, Phillip A.; Berliner, Adrian P.; Chen, Jonathan C.; Liu, David R.

2018-03-01

The evolution of sequence-defined synthetic polymers made of building blocks beyond those compatible with polymerase enzymes or the ribosome has the potential to generate new classes of receptors, catalysts and materials. Here we describe a ligase-mediated DNA-templated polymerization and in vitro selection system to evolve highly functionalized nucleic acid polymers (HFNAPs) made from 32 building blocks that contain eight chemically diverse side chains on a DNA backbone. Through iterated cycles of polymer translation, selection and reverse translation, we discovered HFNAPs that bind proprotein convertase subtilisin/kexin type 9 (PCSK9) and interleukin-6, two protein targets implicated in human diseases. Mutation and reselection of an active PCSK9-binding polymer yielded evolved polymers with high affinity (KD = 3 nM). This evolved polymer potently inhibited the binding between PCSK9 and the low-density lipoprotein receptor. Structure-activity relationship studies revealed that specific side chains at defined positions in the polymers are required for binding to their respective targets. Our findings expand the chemical space of evolvable polymers to include densely functionalized nucleic acids with diverse, researcher-defined chemical repertoires.

Characterization and prediction of residues determining protein functional specificity.

PubMed

Capra, John A; Singh, Mona

2008-07-01

Within a homologous protein family, proteins may be grouped into subtypes that share specific functions that are not common to the entire family. Often, the amino acids present in a small number of sequence positions determine each protein's particular functional specificity. Knowledge of these specificity determining positions (SDPs) aids in protein function prediction, drug design and experimental analysis. A number of sequence-based computational methods have been introduced for identifying SDPs; however, their further development and evaluation have been hindered by the limited number of known experimentally determined SDPs. We combine several bioinformatics resources to automate a process, typically undertaken manually, to build a dataset of SDPs. The resulting large dataset, which consists of SDPs in enzymes, enables us to characterize SDPs in terms of their physicochemical and evolutionary properties. It also facilitates the large-scale evaluation of sequence-based SDP prediction methods. We present a simple sequence-based SDP prediction method, GroupSim, and show that, surprisingly, it is competitive with a representative set of current methods. We also describe ConsWin, a heuristic that considers sequence conservation of neighboring amino acids, and demonstrate that it improves the performance of all methods tested on our large dataset of enzyme SDPs. Datasets and GroupSim code are available online at http://compbio.cs.princeton.edu/specificity/. Supplementary data are available at Bioinformatics online.

An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids

PubMed Central

Li, Yushuang; Yang, Jiasheng; Zhang, Yi

2016-01-01

In this paper, we have proposed a novel alignment-free method for comparing the similarity of protein sequences. We first encode a protein sequence into a 440 dimensional feature vector consisting of a 400 dimensional Pseudo-Markov transition probability vector among the 20 amino acids, a 20 dimensional content ratio vector, and a 20 dimensional position ratio vector of the amino acids in the sequence. By evaluating the Euclidean distances among the representing vectors, we compare the similarity of protein sequences. We then apply this method into the ND5 dataset consisting of the ND5 protein sequences of 9 species, and the F10 and G11 datasets representing two of the xylanases containing glycoside hydrolase families, i.e., families 10 and 11. As a result, our method achieves a correlation coefficient of 0.962 with the canonical protein sequence aligner ClustalW in the ND5 dataset, much higher than those of other 5 popular alignment-free methods. In addition, we successfully separate the xylanases sequences in the F10 family and the G11 family and illustrate that the F10 family is more heat stable than the G11 family, consistent with a few previous studies. Moreover, we prove mathematically an identity equation involving the Pseudo-Markov transition probability vector and the amino acids content ratio vector. PMID:27918587

Amino acid sequence of human cholinesterase. Annual report, 30 September 1984-30 September 1985

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lockridge, O.

1985-10-01

The active-site serine residue is located 198 amino acids from the N-terminal. The active-site peptide was isolated from three different genetic types of human serum cholinesterase: from usual, atypical, and atypical-silent genotypes. It was found that the amino acid sequence of the active-site peptide was identical in all three genotypes. Comparison of the complete sequences of cholinesterase from human serum and acetylcholinesterase from the electric organ of Torpedo californica shows an identity of 53%. Cholinesterase is of interest to the Department of Defense because cholinesterase protects against organophosphate poisons of the type used in chemical warfare. The structural results presentedmore » here will serve as the basis for cloning the gene for cholinesterase. The potential uses of large amounts of cholinesterase would be for cleaning up spills of organophosphates and possibly for detoxifying exposed personnel.« less

The sequence of sequencers: The history of sequencing DNA

PubMed Central

Heather, James M.; Chain, Benjamin

2016-01-01

Determining the order of nucleic acid residues in biological samples is an integral component of a wide variety of research applications. Over the last fifty years large numbers of researchers have applied themselves to the production of techniques and technologies to facilitate this feat, sequencing DNA and RNA molecules. This time-scale has witnessed tremendous changes, moving from sequencing short oligonucleotides to millions of bases, from struggling towards the deduction of the coding sequence of a single gene to rapid and widely available whole genome sequencing. This article traverses those years, iterating through the different generations of sequencing technology, highlighting some of the key discoveries, researchers, and sequences along the way. PMID:26554401

Pseudoracemic amino acid complexes: blind predictions for flexible two-component crystals.

PubMed

Görbitz, Carl Henrik; Dalhus, Bjørn; Day, Graeme M

2010-08-14

Ab initio prediction of the crystal packing in complexes between two flexible molecules is a particularly challenging computational chemistry problem. In this work we present results of single crystal structure determinations as well as theoretical predictions for three 1 ratio 1 complexes between hydrophobic l- and d-amino acids (pseudoracemates), known from previous crystallographic work to form structures with one of two alternative hydrogen bonding arrangements. These are accurately reproduced in the theoretical predictions together with a series of patterns that have never been observed experimentally. In this bewildering forest of potential polymorphs, hydrogen bonding arrangements and molecular conformations, the theoretical predictions succeeded, for all three complexes, in finding the correct hydrogen bonding pattern. For two of the complexes, the calculations also reproduce the exact space group and side chain orientations in the best ranked predicted structure. This includes one complex for which the observed crystal packing clearly contradicted previous experience based on experimental data for a substantial number of related amino acid complexes. The results highlight the significant recent advances that have been made in computational methods for crystal structure prediction.

Amino acid and nucleotide recurrence in aligned sequences: synonymous substitution patterns in association with global and local base compositions.

PubMed

Nishizawa, M; Nishizawa, K

2000-10-01

The tendency for repetitiveness of nucleotides in DNA sequences has been reported for a variety of organisms. We show that the tendency for repetitive use of amino acids is widespread and is observed even for segments conserved between human and Drosophila melanogaster at the level of >50% amino acid identity. This indicates that repetitiveness influences not only the weakly constrained segments but also those sequence segments conserved among phyla. Not only glutamine (Q) but also many of the 20 amino acids show a comparable level of repetitiveness. Repetitiveness in bases at codon position 3 is stronger for human than for D.melanogaster, whereas local repetitiveness in intron sequences is similar between the two organisms. While genes for immune system-specific proteins, but not ancient human genes (i.e. human homologs of Escherichia coli genes), have repetitiveness at codon bases 1 and 2, repetitiveness at codon base 3 for these groups is similar, suggesting that the human genome has at least two mechanisms generating local repetitiveness. Neither amino acid nor nucleotide repetitiveness is observed beyond the exon boundary, denying the possibility that such repetitiveness could mainly stem from natural selection on mRNA or protein sequences. Analyses of mammalian sequence alignments show that while the 'between gene' GC content heterogeneity, which is linked to 'isochores', is a principal factor associated with the bias in substitution patterns in human, 'within gene' heterogeneity in nucleotide composition is also associated with such bias on a more local scale. The relationship amongst the various types of repetitiveness is discussed.

Amino acid and nucleotide recurrence in aligned sequences: synonymous substitution patterns in association with global and local base compositions

PubMed Central

Nishizawa, Manami; Nishizawa, Kazuhisa

2000-01-01

The tendency for repetitiveness of nucleotides in DNA sequences has been reported for a variety of organisms. We show that the tendency for repetitive use of amino acids is widespread and is observed even for segments conserved between human and Drosophila melanogaster at the level of >50% amino acid identity. This indicates that repetitiveness influences not only the weakly constrained segments but also those sequence segments conserved among phyla. Not only glutamine (Q) but also many of the 20 amino acids show a comparable level of repetitiveness. Repetitiveness in bases at codon position 3 is stronger for human than for D.melanogaster, whereas local repetitiveness in intron sequences is similar between the two organisms. While genes for immune system-specific proteins, but not ancient human genes (i.e. human homologs of Escherichia coli genes), have repetitiveness at codon bases 1 and 2, repetitiveness at codon base 3 for these groups is similar, suggesting that the human genome has at least two mechanisms generating local repetitiveness. Neither amino acid nor nucleotide repetitiveness is observed beyond the exon boundary, denying the possibility that such repetitiveness could mainly stem from natural selection on mRNA or protein sequences. Analyses of mammalian sequence alignments show that while the ‘between gene’ GC content heterogeneity, which is linked to ‘isochores’, is a principal factor associated with the bias in substitution patterns in human, ‘within gene’ heterogeneity in nucleotide composition is also associated with such bias on a more local scale. The relationship amongst the various types of repetitiveness is discussed. PMID:11000273

NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

PubMed

Mao, Wusong; Cong, Peisheng; Wang, Zhiheng; Lu, Longjian; Zhu, Zhongliang; Li, Tonghua

2013-01-01

Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool

EPA Science Inventory

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to address needs for rapid, cost effective methods of species extrapolation of chemical susceptibility. Specifically, the SeqAPASS tool compares the primary sequence (Level 1), functiona...

CoSMoS: Conserved Sequence Motif Search in the proteome

PubMed Central

Liu, Xiao I; Korde, Neeraj; Jakob, Ursula; Leichert, Lars I

2006-01-01

Background With the ever-increasing number of gene sequences in the public databases, generating and analyzing multiple sequence alignments becomes increasingly time consuming. Nevertheless it is a task performed on a regular basis by researchers in many labs. Results We have now created a database called CoSMoS to find the occurrences and at the same time evaluate the significance of sequence motifs and amino acids encoded in the whole genome of the model organism Escherichia coli K12. We provide a precomputed set of multiple sequence alignments for each individual E. coli protein with all of its homologues in the RefSeq database. The alignments themselves, information about the occurrence of sequence motifs together with information on the conservation of each of the more than 1.3 million amino acids encoded in the E. coli genome can be accessed via the web interface of CoSMoS. Conclusion CoSMoS is a valuable tool to identify highly conserved sequence motifs, to find regions suitable for mutational studies in functional analyses and to predict important structural features in E. coli proteins. PMID:16433915

Comparison of targeted next-generation sequencing with conventional sequencing for predicting the responsiveness to epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) therapy in never-smokers with lung adenocarcinoma.

PubMed

Han, Ji-Youn; Kim, Sun Hye; Lee, Yeon-Su; Lee, Seung-Youn; Hwang, Jung-Ah; Kim, Jin Young; Yoon, Sung Jin; Lee, Geon Kook

2014-08-01

To investigate the clinical utility of targeted next-generation sequencing (NGS) for predicting the responsiveness to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy, we compared the efficacy with conventional sequencing in never-smokers with lung adenocarcinoma (NSLAs). We obtained DNA from 48 NSLAs who received gefitinib or erlotinib for their recurrent disease after surgery. Sanger sequencing and peptide nucleic acid clamp polymerase chain reaction (PCR) were used to analyze EGFR, KRAS, BRAF, and PIK3CA mutations. We analyzed ALK, RET, and ROS1 rearrangements by fluorescent in situ hybridization or reverse transcriptase-PCR and quantitative real-time PCR. After molecular screening, Ion Torrent NGS was performed in 31 cases harboring only EGFR exon 19 deletions (19DEL), an L858R mutation, or none of the above mutations. The 31 samples were divided into four groups: (1) responders to EGFR-TKIs with only 19DEL or L858R (n=15); (2) primary resistance to EGFR-TKI with only 19DEL or L858R (n=4); (3) primary resistance to EGFR-TKI without any mutations (n=8); (4) responders to EGFR-TKI without any mutations (n=4). With NGS, all conventionally detected mutations were confirmed except for one L858R in group 2. Additional uncovered predictive mutations with NGS included one PIK3CA E542K in group 2, two KRAS (G12V and G12D), one PIK3CA E542K, one concomitant PIK3CA and EGFR L858R in group 3, and one EGFR 19DEL in group 4. Targeted NGS provided a more accurate and clinically useful molecular classification of NSLAs. It may improve the efficacy of EGFR-TKI therapy in lung cancer. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Amino acid sequence of bovine muzzle epithelial desmocollin derived from cloned cDNA: a novel subtype of desmosomal cadherins.

PubMed

Koch, P J; Goldschmidt, M D; Walsh, M J; Zimbelmann, R; Schmelz, M; Franke, W W

1991-05-01

Desmosomes are cell-type-specific intercellular junctions found in epithelium, myocardium and certain other tissues. They consist of assemblies of molecules involved in the adhesion of specific cell types and in the anchorage of cell-type-specific cytoskeletal elements, the intermediate-size filaments, to the plasma membrane. To explore the individual desmosomal components and their functions we have isolated DNA clones encoding the desmosomal glycoprotein, desmocollin, using antibodies and a cDNA expression library from bovine muzzle epithelium. The cDNA-deduced amino-acid sequence of desmocollin (presently we cannot decide to which of the two desmocollins, DC I or DC II, this clone relates) defines a polypeptide with a calculated molecular weight of 85,000, with a single candidate sequence of 24 amino acids sufficiently long for a transmembrane arrangement, and an extracellular aminoterminal portion of 561 amino acid residues, compared to a cytoplasmic part of only 176 amino acids. Amino acid sequence comparisons have revealed that desmocollin is highly homologous to members of the cadherin family of cell adhesion molecules, including the previously sequenced desmoglein, another desmosome-specific cadherin. Using riboprobes derived from cDNAs for Northern-blot analyses, we have identified an mRNA of approximately 6 kb in stratified epithelia such as muzzle epithelium and tongue mucosa but not in two epithelial cell culture lines containing desmosomes and desmoplakins. The difference may indicate drastic differences in mRNA concentration or the existence of cell-type-specific desmocollin subforms. The molecular topology of desmocollin(s) is discussed in relation to possible functions of the individual molecular domains.

The amino acid sequence around the active-site cysteine and histidine residues of stem bromelain

PubMed Central

Husain, S. S.; Lowe, G.

1970-01-01

Stem bromelain that had been irreversibly inhibited with 1,3-dibromo[2-14C]-acetone was reduced with sodium borohydride and carboxymethylated with iodoacetic acid. After digestion with trypsin and α-chymotrypsin three radioactive peptides were isolated chromatographically. The amino acid sequences around the cross-linked cysteine and histidine residues were determined and showed a high degree of homology with those around the active-site cysteine and histidine residues of papain and ficin. PMID:5420046

Computational Prediction of the Immunomodulatory Potential of RNA Sequences.

PubMed

Nagpal, Gandharva; Chaudhary, Kumardeep; Dhanda, Sandeep Kumar; Raghava, Gajendra Pal Singh

2017-01-01

Advances in the knowledge of various roles played by non-coding RNAs have stimulated the application of RNA molecules as therapeutics. Among these molecules, miRNA, siRNA, and CRISPR-Cas9 associated gRNA have been identified as the most potent RNA molecule classes with diverse therapeutic applications. One of the major limitations of RNA-based therapeutics is immunotoxicity of RNA molecules as it may induce the innate immune system. In contrast, RNA molecules that are potent immunostimulators are strong candidates for use in vaccine adjuvants. Thus, it is important to understand the immunotoxic or immunostimulatory potential of these RNA molecules. The experimental techniques for determining immunostimulatory potential of siRNAs are time- and resource-consuming. To overcome this limitation, recently our group has developed a web-based server "imRNA" for predicting the immunomodulatory potential of RNA sequences. This server integrates a number of modules that allow users to perform various tasks including (1) generation of RNA analogs with reduced immunotoxicity, (2) identification of highly immunostimulatory regions in RNA sequence, and (3) virtual screening. This server may also assist users in the identification of minimum mutations required in a given RNA sequence to minimize its immunomodulatory potential that is required for designing RNA-based therapeutics. Besides, the server can be used for designing RNA-based vaccine adjuvants as it may assist users in the identification of mutations required for increasing immunomodulatory potential of a given RNA sequence. In summary, this chapter describes major applications of the "imRNA" server in designing RNA-based therapeutics and vaccine adjuvants (http://www.imtech.res.in/raghava/imrna/).

A Large-Scale Assessment of Nucleic Acids Binding Site Prediction Programs

PubMed Central

Miao, Zhichao; Westhof, Eric

2015-01-01

Computational prediction of nucleic acid binding sites in proteins are necessary to disentangle functional mechanisms in most biological processes and to explore the binding mechanisms. Several strategies have been proposed, but the state-of-the-art approaches display a great diversity in i) the definition of nucleic acid binding sites; ii) the training and test datasets; iii) the algorithmic methods for the prediction strategies; iv) the performance measures and v) the distribution and availability of the prediction programs. Here we report a large-scale assessment of 19 web servers and 3 stand-alone programs on 41 datasets including more than 5000 proteins derived from 3D structures of protein-nucleic acid complexes. Well-defined binary assessment criteria (specificity, sensitivity, precision, accuracy…) are applied. We found that i) the tools have been greatly improved over the years; ii) some of the approaches suffer from theoretical defects and there is still room for sorting out the essential mechanisms of binding; iii) RNA binding and DNA binding appear to follow similar driving forces and iv) dataset bias may exist in some methods. PMID:26681179

The sequence of sequencers: The history of sequencing DNA.

PubMed

Heather, James M; Chain, Benjamin

2016-01-01

Determining the order of nucleic acid residues in biological samples is an integral component of a wide variety of research applications. Over the last fifty years large numbers of researchers have applied themselves to the production of techniques and technologies to facilitate this feat, sequencing DNA and RNA molecules. This time-scale has witnessed tremendous changes, moving from sequencing short oligonucleotides to millions of bases, from struggling towards the deduction of the coding sequence of a single gene to rapid and widely available whole genome sequencing. This article traverses those years, iterating through the different generations of sequencing technology, highlighting some of the key discoveries, researchers, and sequences along the way. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

PubMed

Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

2014-09-07

Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Uric Acid Excretion Predicts Increased Blood Pressure Among American Adolescents of African Descent.

PubMed

Mrug, Sylvie; Mrug, Michal; Morris, Anjana Madan; Reynolds, Nina; Patel, Anita; Hill, Danielle C; Feig, Daniel I

2017-04-01

Hyperuricemia predicts the incidence of hypertension in adults and its treatment has blood pressure (BP)-lowering effects in adolescents. To date, no studies have examined the predictive usage of hyperuricemia or urinary uric acid excretion on BP changes in adolescents. Mechanistic models suggest that uric acid impairs both endothelial function and vascular compliance, which would potentially exacerbate a myriad of hypertensive mechanisms, yet little is known about interaction of uric acid and other hypertension risk factors. The primary study was aimed at the effects of stress on BP in adolescents. A community sample of 84 low-income, urban adolescents (50% male, 95% African American, mean age = 13.36 ± 1 years) was recruited from public schools. Youth completed a 12-hour (overnight) urine collection at home and their BP was measured during rest and in response to acute psychosocial stress. Seventy-six of the adolescents participated in a follow-up visit at 1.5 years when their resting BP was reassessed. In this substudy, we assessed the relationship of renal urate excretion and BP reactivity. After adjusting for resting BP levels at baseline and other covariates, higher levels of uric acid excretion predicted greater BP reactivity to acute psychosocial stress and higher resting BP at 18 months. Urinary excretion of uric acid can serve as an alternative, noninvasive measure of serum uric acid levels that are predictive of BP changes. As hyperuricemia-associated hypertension is treatable, urban adolescents may benefit from routine screening for hyperuricemia or high uric acid excretion. Copyright © 2017 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.

Quantitative analysis and prediction of G-quadruplex forming sequences in double-stranded DNA