Backbone Statistical Potential from Local Sequence-Structure Interactions in Protein Loops

2010 ◽  
Vol 114 (5) ◽  
pp. 1859-1869 ◽  
Author(s):  
Ionel A. Rata ◽  
Yaohang Li ◽  
Eric Jakobsson
Keyword(s):  
Sequence Structure ◽  
Statistical Potential ◽  
Local Sequence ◽  
Protein Loops

LOCAL SEQUENCE-STRUCTURE MOTIFS IN RNA

2004 ◽  
Vol 02 (04) ◽  
pp. 681-698 ◽  
Author(s):  
ROLF BACKOFEN ◽  
SEBASTIAN WILL
Keyword(s):  
Information Structure ◽  
Structure Alignment ◽  
General Definition ◽  
Local Alignment ◽  
Sequence Information ◽  
Sequence Structure ◽  
Worst Case ◽  
Rna Molecules ◽  
Alignment Algorithms ◽  
Local Sequence

Ribonuclic acid (RNA) enjoys increasing interest in molecular biology; despite this interest fundamental algorithms are lacking, e.g. for identifying local motifs. As proteins, RNA molecules have a distinctive structure. Therefore, in addition to sequence information, structure plays an important part in assessing the similarity of RNAs. Furthermore, common sequence-structure features in two or several RNA molecules are often only spatially local, where possibly large parts of the molecules are dissimilar. Consequently, we address the problem of comparing RNA molecules by computing an optimal local alignment with respect to sequence and structure information. While local alignment is superior to global alignment for identifying local similarities, no general local sequence-structure alignment algorithms are currently known. We suggest a new general definition of locality for sequence-structure alignments that is biologically motivated and efficiently tractable. To show the former, we discuss locality of RNA and prove that the defined locality means connectivity by atomic and non-atomic bonds. To show the latter, we present an efficient algorithm for the newly defined pairwise local sequence-structure alignment (lssa) problem for RNA. For molecules of lengthes n and m, the algorithm has worst-case time complexity of O(n2·m2· max (n,m)) and a space complexity of only O(n·m). An implementation of our algorithm is available at . Its runtime is competitive with global sequence-structure alignment.

scholarly journals An Optimization Synchronization Algorithm for TDDM Signal

2016 ◽  
Vol 2016 ◽  
pp. 1-7
Author(s):  
Fang Liu ◽  
Yongxin Feng
Keyword(s):  
Spread Spectrum ◽  
Sequence Structure ◽  
Estimation Formula ◽  
Synchronization Algorithm ◽  
Local Sequence ◽  
Threshold Mechanism ◽  
Global Navigation Satellite ◽  
Time Division ◽  
Synchronization Accuracy ◽  
Better Than

The time division data modulation (TDDM) mechanism is recommended to improve the communications quality and enhance the antijamming capability of the spread spectrum communication system, which will be used in the next generation global navigation satellite (GNSS) systems. According to the principle and the characteristics of TDDM signal, an optimization synchronization algorithm is proposed. In the new algorithm, the synchronization accuracy and environmental adaptability have been improved with the special local sequence structure, the multicorrelation processing, and the proportion threshold mechanism. Thus, the inversion estimation formula was established. The simulation results demonstrate that the new algorithm can eliminate the illegibility threat in the synchronization process and can adapt to a lower SNR. In addition, this algorithm is better than the traditional algorithms in terms of synchronization accuracy and adaptability.

Local sequence-structure correlations in proteins

1996 ◽  
Vol 7 (4) ◽  
pp. 417-421 ◽  
Author(s):  
Christopher Bystroff ◽  
Kim T Simons ◽  
Karen F Han ◽  
David Baker
Keyword(s):  
Sequence Structure ◽  
Structure Correlations ◽  
Local Sequence

PREDICTING LOCAL QUALITY OF A SEQUENCE–STRUCTURE ALIGNMENT

2009 ◽  
Vol 07 (05) ◽  
pp. 789-810 ◽  
Author(s):  
XIN GAO ◽  
JINBO XU ◽  
SHUAI CHENG LI ◽  
MING LI
Keyword(s):  
Structure Prediction ◽  
Solvent Accessibility ◽  
Structure Alignment ◽  
Evolutionary Information ◽  
Support Vector ◽  
Sequence Structure ◽  
Protein Model ◽  
Local Sequence ◽  
Local Quality

Although protein structure prediction has made great progress in recent years, a protein model derived from automated prediction methods is subject to various errors. As methods for structure prediction develop, a continuing problem is how to evaluate the quality of a protein model, especially to identify some well-predicted regions of the model, so that the structural biology community can benefit from the automated structure prediction. It is also important to identify badly-predicted regions in a model so that some refinement measurements can be applied to it. We present two complementary techniques, FragQA and PosQA, to accurately predict local quality of a sequence–structure (i.e. sequence–template) alignment generated by comparative modeling (i.e. homology modeling and threading). FragQA and PosQA predict local quality from two different perspectives. Different from existing methods, FragQA directly predicts cRMSD between a continuously aligned fragment determined by an alignment and the corresponding fragment in the native structure, while PosQA predicts the quality of an individual aligned position. Both FragQA and PosQA use an SVM (Support Vector Machine) regression method to perform prediction using similar information extracted from a single given alignment. Experimental results demonstrate that FragQA performs well on predicting local fragment quality, and PosQA outperforms two top-notch methods, ProQres and ProQprof. Our results indicate that (1) local quality can be predicted well; (2) local sequence evolutionary information (i.e. sequence similarity) is the major factor in predicting local quality; and (3) structural information such as solvent accessibility and secondary structure helps to improve the prediction performance.

scholarly journals Remote homolog detection using local sequence-structure correlations

2004 ◽  
Vol 57 (3) ◽  
pp. 518-530 ◽  
Author(s):  
Yuna Hou ◽  
Wynne Hsu ◽  
Mong Li Lee ◽  
Christopher Bystroff
Keyword(s):  
Sequence Structure ◽  
Remote Homolog ◽  
Structure Correlations ◽  
Local Sequence
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