CAVER: a new tool to explore routes from protein clefts, pockets and cavities View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2006-12

AUTHORS

Martin Petřek, Michal Otyepka, Pavel Banáš, Pavlína Košinová, Jaroslav Koča, Jiří Damborský

ABSTRACT

BACKGROUND: The main aim of this study was to develop and implement an algorithm for the rapid, accurate and automated identification of paths leading from buried protein clefts, pockets and cavities in dynamic and static protein structures to the outside solvent. RESULTS: The algorithm to perform a skeleton search was based on a reciprocal distance function grid that was developed and implemented for the CAVER program. The program identifies and visualizes routes from the interior of the protein to the bulk solvent. CAVER was primarily developed for proteins, but the algorithm is sufficiently robust to allow the analysis of any molecular system, including nucleic acids or inorganic material. Calculations can be performed using discrete structures from crystallographic analysis and NMR experiments as well as with trajectories from molecular dynamics simulations. The fully functional program is available as a stand-alone version and as plug-in for the molecular modeling program PyMol. Additionally, selected functions are accessible in an online version. CONCLUSION: The algorithm developed automatically finds the path from a starting point located within the interior of a protein. The algorithm is sufficiently rapid and robust to enable routine analysis of molecular dynamics trajectories containing thousands of snapshots. The algorithm is based on reciprocal metrics and provides an easy method to find a centerline, i.e. the spine, of complicated objects such as a protein tunnel. It can also be applied to many other molecules. CAVER is freely available from the web site http://loschmidt.chemi.muni.cz/caver/. More... »

PAGES

316

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-7-316

DOI

http://dx.doi.org/10.1186/1471-2105-7-316

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044949819

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16792811


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