EBDIMS server: Protein transition pathways with ensemble analysis in 2D-motion spaces
Bioinformatics, ISSN: 1460-2059, Vol: 35, Issue: 18, Page: 3505-3507
2019
- 18Citations
- 22Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations18
- Citation Indexes18
- CrossRef18
- 15
- Captures22
- Readers22
- 22
Article Description
Understanding how proteins transition between different conformers, and how conformers relate to each other in terms of structure and function, is not trivial. Here, we present an online tool for transition pathway generation between two protein conformations using Elastic Network Driven Brownian Dynamics Importance Sampling, a coarse-grained simulation algorithm, which spontaneously predicts transition intermediates trapped experimentally. In addition to path-generation, the server provides an interactive 2D-motion landscape graphical representation of the transitions or any additional conformers to explore their structural relationships. Availability and implementation: eBDIMS is available online: http://ebdims.biophysics.se/ or as standalone software: https://github.com/laura-orellana/eBDIMS, https://github.com/cabergh/eBDIMS. Supplementary information: Supplementary data are available at Bioinformatics online.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85065714382&origin=inward; http://dx.doi.org/10.1093/bioinformatics/btz104; http://www.ncbi.nlm.nih.gov/pubmed/30838394; https://academic.oup.com/bioinformatics/article/35/18/3505/5341428; https://dx.doi.org/10.1093/bioinformatics/btz104
Oxford University Press (OUP)
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