PubMed Health⌕ Search

PubMed · 15215451

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.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

William J Salerno, Samuel M Seaver, Brian R Armstrong, Ishwar Radhakrishnan. 2004-07-01. MONSTER: inferring non-covalent interactions in macromolecular structures from atomic coordinate data.. https://doi.org/10.1093/nar%2Fgkh434

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Malaria-GENOMAP: a web-based tool for exploring genomic variation of malaria parasites.

MOTIVATION: Malaria, caused by Plasmodium parasites, imposes a significant public health burden. While Plasmodium falciparum remains the primary target of elimination strategies due to its high mortality rate, lesser-known species such as P. malariae, P. vivax, and P. knowlesi continue to contribute to substantial human morbidity. Genomic approaches, including whole-genome sequencing, offer powerful tools for understanding the biology, transmission, and emerging drug resistance of these neglected Plasmodium species. However, there is an urgent need for informatic tools to summarize and visualize the high-dimensional and complex genomic data generated. RESULTS: We developed Malaria-GENOMAP, a user-friendly web-based tool, which integrates genomic variant data, such as allele frequencies, with geographical maps and chromosome-wide to gene views for in-depth exploration. The tool includes variation from P. knowlesi (n = 139), P. malariae (n = 158), P. ovale curtisi (n = 36), P. ovale wallikeri (n = 47), P. simium (n = 38), and P. vivax (n = 1359). It enables the investigation of population structure, geographic associations of mutations, and putative drug resistance markers, offering valuable insights for malaria control efforts. AVAILABILITY AND IMPLEMENTATION: Malaria-GENOMAP is available online at https://genomics.lshtm.ac.uk/malaria-genomaps.

Internet↗

Oncoradiology.

This is the seventh in a series of short reviews of internet-based radiological educational resources and will focus on oncological radiology. What follows is a list of carefully selected websites to save you time searching them out for yourself. Most of the sites cater for trainee or non-specialist radiologists but may also be of interest to oncology trainees and specialists for use in teaching. Hyperlinks are available in the electronic version of this article.

Internet↗