Fold recognition and accurate sequence-structure alignment of sequences directing β-sheet proteins

2006 ◽  
Vol 63 (4) ◽  
pp. 976-985 ◽  
Author(s):  
Andrew V. McDonnell ◽  
Matthew Menke ◽  
Nathan Palmer ◽  
Jonathan King ◽  
Lenore Cowen ◽  
...  
Keyword(s):  
Fold Recognition ◽  
Structure Alignment ◽  
Sequence Structure ◽  
Β Sheet

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 Variable gap penalty for protein sequence–structure alignment

2006 ◽  
Vol 19 (3) ◽  
pp. 129-133 ◽  
Author(s):  
M.S. Madhusudhan ◽  
Marc A. Marti-Renom ◽  
Roberto Sanchez ◽  
Andrej Sali
Keyword(s):  
Protein Sequence ◽  
Structure Alignment ◽  
Sequence Structure

scholarly journals Alignnment of RNA with Structures of Unlimited Complexity

10.29007/f883 ◽  
2018 ◽  
Author(s):  
Alessandro Dal Palù ◽  
Mathias Möhl ◽  
Sebastian Will
Keyword(s):  
Dynamic Programming ◽  
Efficient Solutions ◽  
Structure Alignment ◽  
Sequence Structure ◽  
Crossing Structures ◽  
Input Structure ◽  
Alignment Problem ◽  
Nested Structure ◽  
Complex Structural ◽  
Constraint Approach

Sequence-structure alignment of RNAs with arbitrary secondary structures is Max-SNP-hard. Therefore, the problem of RNA alignment is commonly restricted to nested structure, where dynamic programming yields efficient solutions. However, nested structure cannot model pseudoknots or even more complex structural dependencies. Nevertheless those dependencies are essential and conserved features of many RNAs. Only few existing approaches deal with crossings of limited complexity or arbitrary crossing structures. Here, we present a constraint approach for alignment of structures in the even more general class of structures with unlimited complexity. Our central contribution is a new RNA alignment propagator. It is based on an efficient O(n<sup>2</sup>) relaxation of the RNA alignment problem. Specifically, our approach Carna solves the alignment problem for sequences with given input structure of unlimited complexity. Carna is implemented using Gecode.

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