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SimCAL: a flexible tool to compute biochemical reaction similarity.
Sivakumar, Tadi Venkata; Bhaduri, Anirban; Duvvuru Muni, Rajasekhara Reddy; Park, Jin Hwan; Kim, Tae Yong.
Afiliação
  • Sivakumar TV; Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore, 560037, India.
  • Bhaduri A; Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore, 560037, India.
  • Duvvuru Muni RR; Bioinformatics Lab, Samsung Advanced Institute of Technology, Bangalore, 560037, India.
  • Park JH; Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-do, 443803, South Korea.
  • Kim TY; Biomaterials Lab, Materials Center, Samsung Advanced Institute of Technology, Gyeonggi-do, 443803, South Korea. ty76.kim@samsung.com.
BMC Bioinformatics ; 19(1): 254, 2018 07 03.
Article em En | MEDLINE | ID: mdl-29969981
ABSTRACT

BACKGROUND:

Computation of reaction similarity is a pre-requisite for several bioinformatics applications including enzyme identification for specific biochemical reactions, enzyme classification and mining for specific inhibitors. Reaction similarity is often assessed at either two levels (i) comparison across all the constituent substrates and products of a reaction, reaction level similarity, (ii) comparison at the transformation center with various degrees of neighborhood, transformation level similarity. Existing reaction similarity computation tools are designed for specific applications and use different features and similarity measures. A single system integrating these diverse features enables comparison of the impact of different molecular properties on similarity score computation.

RESULTS:

To address these requirements, we present SimCAL, an integrated system to calculate reaction similarity with novel features and capability to perform comparative assessment. SimCAL provides reaction similarity computation at both whole reaction level and transformation level. Novel physicochemical features such as stereochemistry, mass, volume and charge are included in computing reaction fingerprint. Users can choose from four different fingerprint types and nine molecular similarity measures. Further, a comparative assessment of these features is also enabled. The performance of SimCAL is assessed on 3,688,122 reaction pairs with Enzyme Commission (EC) number from MetaCyc and achieved an area under the curve (AUC) of > 0.9. In addition, SimCAL results showed strong correlation with state-of-the-art EC-BLAST and molecular signature based reaction similarity methods.

CONCLUSIONS:

SimCAL is developed in java and is available as a standalone tool, with intuitive, user-friendly graphical interface and also as a console application. With its customizable feature selection and similarity calculations, it is expected to cater a wide audience interested in studying and analyzing biochemical reactions and metabolic networks.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Fenômenos Bioquímicos / Biologia Computacional Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Índia

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Fenômenos Bioquímicos / Biologia Computacional Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Índia