Evidence map›Paper›PMID 31863748›Full record

ArticleJournal of molecular biology2020

ClusPro LigTBM: Automated Template-based Small Molecule Docking.

Andrey Alekseenko, Sergei Kotelnikov, Mikhail Ignatov, Megan Egbert, Yaroslav Kholodov, Sandor Vajda, Dima Kozakov

Abstract read
In one paragraph

Article in Journal of molecular biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Polaris Challenge: Data-Driven Priors to Improve Docking for Pose Prediction.Journal of chemical information and modeling · 2025
    Article
  3. TEMPL: A Template-Based Protein-Ligand Pose Prediction Baseline.Journal of chemical information and modeling · 2025
    Article
  4. DockOpt: A Tool for Automatic Optimization of Docking Models.Journal of chemical information and modeling · 2024
    Article
  5. Article
  6. High Accuracy Prediction of PROTAC Complex Structures.Journal of the American Chemical Society · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Conservation of binding properties in protein models.Computational and structural biotechnology journal · 2021
    Article
  13. Article
  14. Review
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

7 authors.

Andrey AlekseenkoDepartment of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, 11794 Stony Brook, NY, USA.
Sergei KotelnikovDepartment of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, 11794 Stony Brook, NY, USA; Innopolis University, 420500, Innopolis, Russia.
Mikhail IgnatovDepartment of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, 11794 Stony Brook, NY, USA; Institute for Advanced Computational Sciences, Stony Brook University, 11794, Stony Brook, NY, USA.
Megan EgbertDepartment of Biomedical Engineering, Boston University, 02215, Boston, MA, USA.
Yaroslav KholodovInnopolis University, 420500, Innopolis, Russia.
Sandor VajdaDepartment of Biomedical Engineering, Boston University, 02215, Boston, MA, USA; Department of Chemistry, Boston University, 02215, Boston, MA, USA.
Dima KozakovDepartment of Applied Mathematics and Statistics, Stony Brook University, 11794 Stony Brook, NY, USA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, 11794 Stony Brook, NY, USA; Institute for Advanced Computational Sciences, Stony Brook University, 11794, Stony Brook, NY, USA. Electronic address: midas@laufercenter.org.

Funding

Analysis and Prediction of Molecular InteractionsR35GM118078 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI SANDOR VAJDA · 2016 to 2026
$6.5M
Fast energy evaluation for multi-protein systemsR21GM127952 · NIGMS · STATE UNIVERSITY NEW YORK STONY BROOK · PI KOZAKOV, DMYTRO · 2018 to 2019
$432k
NIGMS NIH HHS R21 GM127952NIGMS NIH HHS R35 GM118078
6 · The paper itself

Abstract

The template-based approach has been essential for achieving high-quality models in the recent rounds of blind protein-protein docking competition CAPRI (Critical Assessment of Predicted Interactions). However, few such automated methods exist for protein-small molecule docking. In this paper, we present an algorithm for template-based docking of small molecules. It searches for known complexes with ligands that have partial coverage of the target ligand, performs conformational sampling and template-guided energy refinement to produce a variety of possible poses, and then scores the refined poses. The algorithm is available as the automated ClusPro LigTBM server. It allows the user to specify the target protein as a PDB file and the ligand as a SMILES string. The server then searches for templates and uses them for docking, presenting the user with top-scoring poses and their confidence scores. The method is tested on the Astex Diverse benchmark, as well as on the targets from the last round of the D3R (Drug Design Data Resource) Grand Challenge. The server is publicly available as part of the ClusPro docking server suite at https://ligtbm.cluspro.org/.

Indexed as

Computational BiologyDatabases, ProteinMolecular Docking SimulationProtein ConformationProteinsSmall Molecule LibrariesSoftwareStructural Homology, ProteinProteinsSmall Molecule Librarieshomology modelsprotein-ligand dockingstructure refinementtemplate-based docking

Identifiers

PMID31863748
PMCPMC7890944

What OpenQuestion holds

Textmetadata
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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.