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This method will provide a versatile tool for protein structure prediction and protein domain recognition complementary to existing tools that rely on sequence homology.
Current domain prediction methods often rely on sequence similarity to conserved domains and as such are poorly suited to detect domain structure in poorly conserved or orphan proteins.
Our approach uses single protein structures, and does not rely on sequence conservation, comparison to other similar structures or any prior knowledge.
They rely on sequence or tridimensional similarities exploiting a typical concept in biology, which considers similarity in structure as a possible indication of similarity in role.
Computational predictions rely on sequence specificities that are typically taken from a database [4] or obtained as an output from a motif discovery method [6].
The technique does not rely on sequence conservation, so that rapid interface identification is possible even for proteins for which homologs are unknown or lack conserved residue patterns in interface region.
Similar(11)
High throughput sequencing approaches generally rely on sequencing of short DNA fragments (30 300 base pairs) derived from a genome under investigation.
Microarray technologies rely on sequence-specific probe hybridization, and fluorescence detection to measure gene expression levels.
This introduces a major issue for methods that rely on sequence-based data for CNV detection (e.g. [ 11, 12]).
Morphologic species identification was difficult, and we had to rely on sequence-based identification, which prompted this investigation of the role of E. quadrilineata as a causative agent of invasive aspergillosis.
Our approach to identify residue conservation among proteins differs from most of the currently used methods that rely on sequence-based comparisons only (eg, calculated multiple sequence alignments using PSI-BLAST, ClustalW, Muscle, or other sequence alignment programs).
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