HorA web server to infer homology between proteins using sequence and structural similarity

By Bong-Hyun Kim, Hua Cheng, Nick V. Grishin. 2009

14 citations recorded by the source corpus.

The biological properties of proteins are often gleaned through comparative analysis of evolutionary relatives. Although protein structure similarity search methods detect more distant homologs than purely sequence-based methods, structural resemblance can result from either homology (common ancestry) or analogy (similarity without common ancestry). While many existing web servers detect structural neighbors, they do not explicitly address the question of homology versus analogy. Here, we present a web server named HorA (Homology or Analogy) that identifies likely homologs for a query protein structure. Unlike other servers, HorA combines sequence information from state-of-the-art profile methods with structure information from spatial similarity measures using an advanced computational technique. HorA aims to identify biologically meaningful connections rather than purely 3D-geometric similarities. The HorA method finds ∼90% of remote homologs defined in the manually curated database SCOP. HorA will be especially useful for finding remote homologs that might be overlooked by other sequence or structural similarity search servers. The HorA server is available at http://prodata.swmed.edu/horaserver.

Subfields: Molecular Biology

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Citations in the corpus

ECOD: An Evolutionary Classification of Protein Domains (2014, methodology): “In order to extend distant evolutionary relationships beyond the SCOP superfamily level in ECOD, we apply state of the art homology-inference algorithms both developed in our group [19,20] as well as by others [21,22], manually analyze and verify the suggested homologous links, and incorporate findi”
CASP9 Target Classification (2011, methodology): “…structures with similar folds and evaluated the potential evolutionary relationships of identified folds using data provided by the HorA server,14 which uses a Support Vector Machine to discriminate homology (structure similarity due to a common ancestor) from analogy (convergent structure…”