MONSTER: Inferring non-covalent interactions in macromolecular structures from atomic coordinate data

William J. Salerno; Samuel M. Seaver; Brian R. Armstrong; Ishwar Radhakrishnan

doi:10.1093/nar/gkh434

MONSTER: Inferring non-covalent interactions in macromolecular structures from atomic coordinate data

William J. Salerno, Samuel M. Seaver, Brian R. Armstrong, Ishwar Radhakrishnan^*

^*Corresponding author for this work

Robert H. Lurie Comprehensive Cancer Center

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

Abstract

A web application for inferring potentially stabilizing non-bonding interactions in macromolecular structures from input atomic coordinate data is described. The core software, called Monster, comprises a PERL wrapper that takes advantage of scripts developed in- house as well as established software in the public domain to validate atomic coordinate files, identify interacting residues and assign the nature of these interactions. The results are assembled and presented in an intuitive and interactive graphical format. Potential applications of Monster range from mining and validating experimentally determined structures to guiding functional analysis. Non-commercial users can perform Monster analysis free of charge at http://monster.northwestern.edu.

Original language	English (US)
Pages (from-to)	W566-W568
Journal	Nucleic acids research
Volume	32
Issue number	WEB SERVER ISS.
DOIs	https://doi.org/10.1093/nar/gkh434
State	Published - Jul 1 2004

Funding

Jin Suntivich is thanked for developing the MONSTER Help utility. This work was supported by a career development award to I.R. from a SPORE grant in Prostate Cancer from NCI (CA90386). W.J.S. was supported by an undergraduate research grant from Northwestern University.

ASJC Scopus subject areas

Genetics

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10.1093/nar/gkh434

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title = "MONSTER: Inferring non-covalent interactions in macromolecular structures from atomic coordinate data",

abstract = "A web application for inferring potentially stabilizing non-bonding interactions in macromolecular structures from input atomic coordinate data is described. The core software, called Monster, comprises a PERL wrapper that takes advantage of scripts developed in- house as well as established software in the public domain to validate atomic coordinate files, identify interacting residues and assign the nature of these interactions. The results are assembled and presented in an intuitive and interactive graphical format. Potential applications of Monster range from mining and validating experimentally determined structures to guiding functional analysis. Non-commercial users can perform Monster analysis free of charge at http://monster.northwestern.edu.",

author = "Salerno, {William J.} and Seaver, {Samuel M.} and Armstrong, {Brian R.} and Ishwar Radhakrishnan",

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doi = "10.1093/nar/gkh434",

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AU - Salerno, William J.

AU - Seaver, Samuel M.

AU - Armstrong, Brian R.

AU - Radhakrishnan, Ishwar

N1 - Funding Information: Jin Suntivich is thanked for developing the MONSTER Help utility. This work was supported by a career development award to I.R. from a SPORE grant in Prostate Cancer from NCI (CA90386). W.J.S. was supported by an undergraduate research grant from Northwestern University.

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N2 - A web application for inferring potentially stabilizing non-bonding interactions in macromolecular structures from input atomic coordinate data is described. The core software, called Monster, comprises a PERL wrapper that takes advantage of scripts developed in- house as well as established software in the public domain to validate atomic coordinate files, identify interacting residues and assign the nature of these interactions. The results are assembled and presented in an intuitive and interactive graphical format. Potential applications of Monster range from mining and validating experimentally determined structures to guiding functional analysis. Non-commercial users can perform Monster analysis free of charge at http://monster.northwestern.edu.

AB - A web application for inferring potentially stabilizing non-bonding interactions in macromolecular structures from input atomic coordinate data is described. The core software, called Monster, comprises a PERL wrapper that takes advantage of scripts developed in- house as well as established software in the public domain to validate atomic coordinate files, identify interacting residues and assign the nature of these interactions. The results are assembled and presented in an intuitive and interactive graphical format. Potential applications of Monster range from mining and validating experimentally determined structures to guiding functional analysis. Non-commercial users can perform Monster analysis free of charge at http://monster.northwestern.edu.

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MONSTER: Inferring non-covalent interactions in macromolecular structures from atomic coordinate data

Abstract

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ASJC Scopus subject areas

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