| Title |
Adaptive conformational sampling based on replicas
|
|---|---|
| Published in |
Journal of Mathematical Biology, June 2011
|
| DOI | 10.1007/s00285-011-0432-6 |
| Pubmed ID | |
| Authors |
Jeremy Curuksu |
| Abstract |
Computer simulations of biomolecules such as molecular dynamics simulations are limited by the time scale of conformational rearrangements. Several sampling techniques are available to search the multi-minima free energy landscape but most efficient, time-dependent methods do generally not produce a canonical ensemble. A sampling algorithm based on a self-regulating ladder of searching copies in the dihedral subspace is developped in this paper. The learning process using short- and long-term memory functions allows an efficient search in phase space while combining a deterministic dynamics and stochastic swaps with the searching copies conserves a canonical limit. The sampling efficiency and accuracy are indicated by comparing the ansatz with conventional molecular dynamics and replica exchange simulations. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 6% |
| Brazil | 1 | 6% |
| Unknown | 14 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 50% |
| Student > Master | 2 | 13% |
| Researcher | 2 | 13% |
| Professor | 1 | 6% |
| Student > Bachelor | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 1 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 31% |
| Chemistry | 5 | 31% |
| Biochemistry, Genetics and Molecular Biology | 2 | 13% |
| Computer Science | 1 | 6% |
| Medicine and Dentistry | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 1 | 6% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 August 2012.
All research outputs
#18,312,024
of 22,673,450 outputs
Outputs from Journal of Mathematical Biology
#440
of 654 outputs
Outputs of similar age
#95,363
of 112,449 outputs
Outputs of similar age from Journal of Mathematical Biology
#4
of 4 outputs
Altmetric has tracked 22,673,450 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 654 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 112,449 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.