Evidence map›Paper›PMID 23382659›Full record

ArticleJournal of chemical theory and computation2012

Rigid Body Energy Minimization on Manifolds for Molecular Docking.

Hanieh Mirzaei, Dmitri Beglov, Ioannis Ch Paschalidis, Sandor Vajda, Pirooz Vakili, Dima Kozakov

Abstract read
In one paragraph

Article in Journal of chemical theory and computation, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Improved cluster ranking in protein-protein docking using a regression approach.Computational and structural biotechnology journal · 2021
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. The impact of side-chain packing on protein docking refinement.Journal of chemical information and modeling · 2015
    Article
  10. Energy Minimization on Manifolds for Docking Flexible Molecules.Journal of chemical theory and computation · 2015
    Article
  11. A Subspace Semi-Definite programming-based Underestimation (SSDU) method for stochastic global optimization in protein docking.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2014
    Article
  12. Optimization on the space of rigid and flexible motions: an alternative manifold optimization approach.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2014
    Article
  13. Article
  14. Modeling protein association mechanisms and kinetics.Current opinion in structural biology · 2013
    Review
  15. A New Distributed Algorithm for Side-Chain Positioning in the Process of Protein DockingProceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2013
    Article
  16. Flexible Refinement of Protein-Ligand Docking on Manifolds.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2013
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Hanieh MirzaeiDivision of Systems Engineering, Department of Biomedical Engineering, Department of Electrical and Computer Engineering, and Department of Mechanical Engineering, Boston University, Boston, USA.
Dmitri Beglov
Ioannis Ch Paschalidis
Sandor Vajda
Pirooz Vakili
Dima Kozakov

Funding

Computational mapping of proteins for the binding of ligandsR01GM064700 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI VAJDA, SANDOR · 2003 to 2015
$4.3M
A MULTISTAGE APPROACH TO PROTEIN-PROTEIN DOCKINGR01GM061867 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI VAJDA, SANDOR · 2000 to 2016
$4.0M
Refinement Methods for Protein Docking based on Exploring Multi-Dimensional EnergR01GM093147 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI KOZAKOV, DMYTRO, PASCHALIDIS, IOANNIS · 2011 to 2014
$1.2M
NIGMS NIH HHS R01 GM061867NIGMS NIH HHS R01 GM064700NIGMS NIH HHS R01 GM093147
6 · The paper itself

Abstract

Virtually all docking methods include some local continuous minimization of an energy/scoring function in order to remove steric clashes and obtain more reliable energy values. In this paper, we describe an efficient rigid-body optimization algorithm that, compared to the most widely used algorithms, converges approximately an order of magnitude faster to conformations with equal or slightly lower energy. The space of rigid body transformations is a nonlinear manifold, namely, a space which locally resembles a Euclidean space. We use a canonical parametrization of the manifold, called the exponential parametrization, to map the Euclidean tangent space of the manifold onto the manifold itself. Thus, we locally transform the rigid body optimization to an optimization over a Euclidean space where basic optimization algorithms are applicable. Compared to commonly used methods, this formulation substantially reduces the dimension of the search space. As a result, it requires far fewer costly function and gradient evaluations and leads to a more efficient algorithm. We have selected the LBFGS quasi-Newton method for local optimization since it uses only gradient information to obtain second order information about the energy function and avoids the far more costly direct Hessian evaluations. Two applications, one in protein-protein docking, and the other in protein-small molecular interactions, as part of macromolecular docking protocols are presented. The code is available to the community under open source license, and with minimal effort can be incorporated into any molecular modeling package.

Identifiers

PMID23382659
PMCPMC3561712

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Registered trials

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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.