Evidence map›Paper›PMID 26488417›Full record

ArticlePLoS computational biology2015

Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways.

Sean L Seyler, Avishek Kumar, M F Thorpe, Oliver Beckstein

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
3.0field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 67 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Prediction of αBiophysical journal · 2025
    Article
  10. SLAE: Strictly Local All-atom Environment for Protein Representation.bioRxiv : the preprint server for biology · 2025
    Article
  11. SHINE: Deterministic Many-to-Many Clustering of Molecular Pathways.Journal of chemical information and modeling · 2025
    Article
  12. Contact-Map-Driven Exploration of Heterogeneous Protein-Folding Paths.Journal of chemical theory and computation · 2024
    Article
  13. Article
  14. Article
  15. LPATH: A Semiautomated Python Tool for Clustering Molecular Pathways.Journal of chemical information and modeling · 2023
    Article
  16. LPATH: A semi-automated Python tool for clustering molecular pathways.bioRxiv : the preprint server for biology · 2023
    Article
  17. Article
  18. Article
  19. Probing Conformational Dynamics of Antibodies with Geometric Simulations.Methods in molecular biology (Clifton, N.J.) · 2023
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Sean L SeylerDepartment of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America.
Avishek KumarDepartment of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America.
M F ThorpeDepartment of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America; Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, United Kingdom.
Oliver BecksteinDepartment of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona, United States of America.
Arizona State University · USUniversity of Oxford · GB

Funding

Center for the Rational Design of Membrane Protein CrystallographyU54GM094599 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI FROMME, PETRA · 2010 to 2014
$6.8M
NIGMS NIH HHS U54 GM094599
6 · The paper itself

Abstract

Diverse classes of proteins function through large-scale conformational changes and various sophisticated computational algorithms have been proposed to enhance sampling of these macromolecular transition paths. Because such paths are curves in a high-dimensional space, it has been difficult to quantitatively compare multiple paths, a necessary prerequisite to, for instance, assess the quality of different algorithms. We introduce a method named Path Similarity Analysis (PSA) that enables us to quantify the similarity between two arbitrary paths and extract the atomic-scale determinants responsible for their differences. PSA utilizes the full information available in 3N-dimensional configuration space trajectories by employing the Hausdorff or Fréchet metrics (adopted from computational geometry) to quantify the degree of similarity between piecewise-linear curves. It thus completely avoids relying on projections into low dimensional spaces, as used in traditional approaches. To elucidate the principles of PSA, we quantified the effect of path roughness induced by thermal fluctuations using a toy model system. Using, as an example, the closed-to-open transitions of the enzyme adenylate kinase (AdK) in its substrate-free form, we compared a range of protein transition path-generating algorithms. Molecular dynamics-based dynamic importance sampling (DIMS) MD and targeted MD (TMD) and the purely geometric FRODA (Framework Rigidity Optimized Dynamics Algorithm) were tested along with seven other methods publicly available on servers, including several based on the popular elastic network model (ENM). PSA with clustering revealed that paths produced by a given method are more similar to each other than to those from another method and, for instance, that the ENM-based methods produced relatively similar paths. PSA applied to ensembles of DIMS MD and FRODA trajectories of the conformational transition of diphtheria toxin, a particularly challenging example. For the AdK transition, the new concept of a Hausdorff-pair map enabled us to extract the molecular structural determinants responsible for differences in pathways, namely a set of conserved salt bridges whose charge-charge interactions are fully modelled in DIMS MD but not in FRODA. PSA has the potential to enhance our understanding of transition path sampling methods, validate them, and to provide a new approach to analyzing conformational transitions.

Indexed as

Models, ChemicalModels, MolecularAlgorithmsComputer SimulationMotionMultiprotein ComplexesProtein ConformationProteinsMultiprotein ComplexesProteins

Identifiers

PMID26488417
PMCPMC4619321
OpenAlexW2096394554

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.