Finding an Optimal Pathway on a Multidimensional Free-Energy Landscape Article Swipe
Haohao Fu
,
Haochuan Chen
,
Xin’ao Wang
,
Hao Chai
,
Xueguang Shao
,
Wensheng Cai
,
Christophe Chipot
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.0c00279
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1021/acs.jcim.0c00279
An ad-hoc, yet widely adopted approach to investigate complex molecular objects in motion using importance-sampling schemes involves two steps, namely (i) mapping the multidimensional free-energy landscape that characterizes the movements in the molecular object at hand and (ii) finding the most probable transition path connecting basins of the free-energy hyperplane. To achieve this goal, we turn to an importance-sampling algorithm, coined well-tempered metadynamics-extended adaptive biasing force (WTM-eABF), aimed at mapping rugged free-energy landscapes, combined with a path-searching algorithm, which we call multidimensional lowest energy (MULE), to identify the underlying minimum free-energy pathway in the collective-variable space of interest. First, the well-tempered feature of the importance-sampling scheme confers to the latter an asymptotic convergence, while the overall algorithm inherits the advantage of high sampling efficiency of its predecessor, meta-eABF, making its performance less sensitive to user-defined parameters. Second, the Dijkstra algorithm implemented in MULE is able to identify with utmost efficiency a pathway that satisfies minimum free energy of activation among all the possible routes in the multidimensional free-energy landscape. Numerical simulations of three molecular assemblies indicate that association of WTM-eABF and MULE constitutes a reliable, efficient and robust approach for exploring coupled movements in complex molecular objects. On account of its ease of use and intrinsic performance, we expect WTM-eABF and MULE to become a tool of choice for both experts and nonexperts interested in the thermodynamics and the kinetics of processes relevant to chemistry and biology.
Related Topics
Concepts
Energy landscape
Metadynamics
Hyperplane
Computer science
Energy (signal processing)
Sampling (signal processing)
Algorithm
Path (computing)
Molecular dynamics
Theoretical computer science
Mathematics
Chemistry
Statistics
Computer vision
Computational chemistry
Combinatorics
Filter (signal processing)
Programming language
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.jcim.0c00279
- https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00279
- OA Status
- bronze
- Cited By
- 93
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3025988887
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3025988887Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1021/acs.jcim.0c00279Digital Object Identifier
- Title
-
Finding an Optimal Pathway on a Multidimensional Free-Energy LandscapeWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-13Full publication date if available
- Authors
-
Haohao Fu, Haochuan Chen, Xin’ao Wang, Hao Chai, Xueguang Shao, Wensheng Cai, Christophe ChipotList of authors in order
- Landing page
-
https://doi.org/10.1021/acs.jcim.0c00279Publisher landing page
- PDF URL
-
https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00279Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
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https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00279Direct OA link when available
- Concepts
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Energy landscape, Metadynamics, Hyperplane, Computer science, Energy (signal processing), Sampling (signal processing), Algorithm, Path (computing), Molecular dynamics, Theoretical computer science, Mathematics, Chemistry, Statistics, Computer vision, Computational chemistry, Combinatorics, Filter (signal processing), Programming language, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
93Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 15, 2024: 18, 2023: 14, 2022: 22, 2021: 22Per-year citation counts (last 5 years)
- References (count)
-
47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.96730008 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |