Discovery of structural motifs using protein structural alphabets and 1D motif-finding methods.

Although the increasing number of available 3D proteins structures has made a wide variety of computational protein structure research possible, yet the success is still hindered by the high 3D computational complexity. Based on 3D information, several 1D protein structural alphabets have been developed, which can not only describe the global folding structure of a protein as a 1D sequence, but can also characterize local structures in proteins. Instead of applying computationally intensive 3D structure alignment tools, we introduce an approach that combines standard 1D motif detection methods with structural alphabets to discover locally conserved protein motifs.

The power of detecting enriched patterns: an HMM approach.

The identification of binding sites of transcription factors (TF) and other regulatory regions, referred to as motifs, located in a set of molecular sequences is of fundamental importance in genomic research. Many computational and experimental approaches have been developed to locate motifs. The set of sequences of interest can be concatenated to form a long sequence of length n.

Protein structure search and local structure characterization.

Background: Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence.