Evidence map›Paper›PMID 28693470›Full record

ArticleBMC bioinformatics2017

Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein-small molecule docking.

Hongrui Wang, Hongwei Liu, Leixin Cai, Caixia Wang, Qiang Lv

Abstract read
In one paragraph

Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Hongrui WangSchool of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, People's Republic of China. riihon@yeah.net.ORCID http://orcid.org/0000-0002-2678-4670
Hongwei LiuSchool of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, People's Republic of China.
Leixin CaiSchool of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, People's Republic of China.
Caixia WangSchool of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, People's Republic of China.
Qiang LvSchool of Computer Science and Technology, Soochow University, 1 Shizi Street, Suzhou, 215006, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein-small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange.

resultsThe Pareto front information generated to select lower energy conformations as representative conformation structure replicas can facilitate the convergence of the available conformational space, including available near-native structures. Furthermore, our approach directly provides min-min scenario Pareto optimal solutions, as well as a hybrid of the min-min and max-min scenario Pareto optimal solutions with lower energy conformations for use as structure templates in the REMC sampling method. These methods were validated based on a thorough analysis of a benchmark data set containing 16 benchmark test cases. An in-depth comparison between MC, REMC, multi-objective optimization-REMC (MO-REMC), and hybrid MO-REMC (HMO-REMC) sampling methods was performed to illustrate the differences between the four conformational search strategies.

conclusionsOur findings demonstrate that the MO-REMC and HMO-REMC conformational sampling methods are powerful approaches for obtaining protein-small molecule docking conformational predictions based on the binding energy of complexes in RosettaLigand.

Indexed as

AlgorithmsMonte Carlo MethodLigandsMolecular Docking SimulationProtein BindingProteinsSmall Molecule LibrariesLigandsProteinsSmall Molecule LibrariesComplex structure predictionEnhanced sampling methodMonte CarloMulti-objective optimizationProtein–small molecule docking

Identifiers

PMID28693470
PMCPMC5504647

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.