Detection of long-range concerted motions in protein by a distance covariance
Open accessAbstract
We asses the ability of a distance correlation coefficient (DiCC), calculated from distance covariance, for detecting long-range concerted motion in proteins. We establish a set of criteria for ideal correlation coefficient values based on the coefficient of determination in multidimension, R 2 . We compare in detail DiCC and conventional correlation coefficients against these criteria. We demonstrate that, in contrast to conventional correlation coefficients, which capture long-distance correlation adequately only with certain restrictions in multidimension, DiCC reflects appropriate correlation in both one-dimension and multidimensions. Finally, we demonstrate the usefulness of DiCC for assessing long-distance correlated fluctuation in protein dynamics.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Distance correlation has theoretical advantages over Pearson's coefficient and has been recently used in biological applications.
“A more sophisticated measure of association between variables is the distance correlation method [57,58], which has theoretical advantages over Pearson's coefficient and has been recently used in biological applications [59,60].”
From Reverse engineering and identification in systems biology: strategies, perspectives and challenges (2014) · cites this paper as [59]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A note on a general definition of the coefficient of determination
NAGELKERKE, N. J. D. (1991) · Biometrika · doi:10.1093/biomet/78.3.691 · Cited by 5,927 · see citing works
Kraskov, A., Stögbauer, H., & Grassberger, P. (2004) · Physical Review E · doi:10.1103/physreve.69.066138 · Cited by 4,157 · see citing works
Detecting Novel Associations in Large Data Sets
Reshef, D. N., Reshef, Y., Finucane, H. K., Grossman, S. R., McVean, G., Turnbaugh, P. J., Lander, E. S., Mitzenmacher, M., & Sabeti, P. C. (2011) · Science · doi:10.1126/science.1205438 · Cited by 3,345 · see citing works
Measuring and testing dependence by correlation of distances
Székely, G. J., Rizzo, M. L., & Bakirov, N. K. (2007) · The Annals of Statistics · doi:10.1214/009053607000000505 · Cited by 2,684 · see citing works
Ichiye, T., & Karplus, M. (1991) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340110305 · Cited by 1,037 · see citing works
Székely, G. J., & Rizzo, M. L. (2009) · The Annals of Applied Statistics · doi:10.1214/09-aoas312 · Cited by 842 · see citing works
Canonical analysis of several sets of variables
Kettenring, J. R. (1971) · Biometrika · doi:10.1093/biomet/58.3.433 · Cited by 798 · see citing works
The most predictable criterion.
Hotelling, H. (1935) · Journal of Educational Psychology · doi:10.1037/h0058165 · Cited by 591 · see citing works
Generalized correlation for biomolecular dynamics
Lange, O., & Grubmüller, H. (2005) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.20784 · Cited by 453 · see citing works
Collective protein dynamics in relation to function
Berendsen, H. J. C. (2000) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(00)00061-0 · Cited by 425 · see citing works
Relative Entropy Measures of Multivariate Dependence
Joe, H. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478751 · Cited by 310 · see citing works
Quest for Spatially Correlated Fluctuations in the FMO Light-Harvesting Complex
Olbrich, C., Strümpfer, J., Schulten, K. J., & Kleinekathöfer, U. (2010) · The Journal of Physical Chemistry B · doi:10.1021/jp1099514 · Cited by 138 · see citing works
Extracting the Causality of Correlated Motions from Molecular Dynamics Simulations
Kamberaj, H., & van der Vaart, A. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2009.07.019 · Cited by 100 · see citing works
Davidson, J. P., Lubman, O., Rose, T., Waksman, G., & Martin, S. (2002) · Journal of the American Chemical Society · doi:10.1021/ja011746f · Cited by 80 · see citing works
Ward, J. M., Gorenstein, N., Tian, J., Martin, S. F., & Post, C. B. (2010) · Journal of the American Chemical Society · doi:10.1021/ja910535j · Cited by 43 · see citing works
Self-citationRoy, A., & Post, C. B. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1119174109 · Cited by 36 · see citing works
Self-citationCorrelated Motions and Interactions at the Onset of the DNA-Induced Partial Unfolding of Ets-1
Kamberaj, H., & van der Vaart, A. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2008.11.019 · Cited by 35 · see citing works
Dynamics in the Active Site of β-Secretase: A Network Analysis of Atomistic Simulations
Mishra, S., & Caflisch, A. (2011) · Biochemistry · doi:10.1021/bi2011948 · Cited by 20 · see citing works
Tan, Y. S., Fuentes, G., & Verma, C. S. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22994 · Cited by 3 · see citing works
Simulations of the p97 complex suggest novel conformational states of hydrolysis intermediates
Wereszczynski, J., & McCammon, J. A. (2012) · Protein Science · doi:10.1002/pro.2024 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [14]Rao CR Linear statistical inference and its application Wiley & Sons New York 1965 220
Cited by
Sorted by citations
Reverse engineering and identification in systems biology: strategies, perspectives and challenges
Villaverde, A. F., & Banga, J. R. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.0505 · Cited by 229 · see citing works
CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed
Hwang, W., Austin, S. L., Blondel, A., Boittier, E. D., Boresch, S., Buck, M., Buckner, J., Caflisch, A., Chang, H.-T., Cheng, X., et al. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c04100 · Cited by 136 · see citing works
Self-citation
- Authors
- Roy, A., & Post, C. B.
- Affiliations
- Purdue University West Lafayette (US)
- Corresponding author
- Roy, Amitava
- Venue
- Journal of Chemical Theory and Computation
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-08-30
- Volume
- 8
- Issue
- 9
- Pages
- 3009-3014
- ISSN
- 1549-9618 (ISSN-L)
- 1549-9626
- PMC
- PMC3630994
- OpenAlex
- W2319880230
- Cited by
- 21
- References
- 20
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 1 · 2021: 1 · 2020: 4 · 2019: 1 · 2018: 2 · 2015: 3 · 2014: 2 · 2013: 3
- FWCI
- 1.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Chemical Theory and Computation (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Purdue University
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases (R01AI039639)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- distance covariance, protein dynamics, protein fluctuations, correlation coefficient, R-squared
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review