Evidence map›Paper›PMID 30996777›Full record

ArticleACS medicinal chemistry letters2019

Coupling Supervised Molecular Dynamics (SuMD) with Entropy Estimations To Shine Light on the Stability of Multiple Binding Sites.

Shailesh Kumar Panday, Mattia Sturlese, Veronica Salmaso, Indira Ghosh, Stefano Moro

Open access · greenAbstract read
In one paragraph

Article in ACS medicinal chemistry letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

5 authors at 2 institutions in 2 countries.

Shailesh Kumar PandaySchool of Computational and Integrative Sciences (SCIS), Jawaharlal Nehru University, New Delhi 110067, India.
Mattia SturleseMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35122 Padova, Italy.
Veronica SalmasoMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35122 Padova, Italy.
Indira GhoshSchool of Computational and Integrative Sciences (SCIS), Jawaharlal Nehru University, New Delhi 110067, India.
Stefano MoroMolecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, via Marzolo 5, 35122 Padova, Italy.
University of Padua · ITJawaharlal Nehru University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Exploring at the molecular level, all possible ligand-protein approaching pathways and, consequently, identifying the energetically favorable binding sites is considered crucial to depict a clear picture of the whole scenario of ligand-protein binding. In fact, a ligand can recognize a protein in multiple binding sites, adopting multiple conformations in every single binding site and inducing protein modifications upon binding. In the present work, we would like to present how it is possible to couple a supervised molecular dynamics (SuMD) approach to explore, from an unbound state, the most energetically favorable recognition pathways of the ligand to its protein, with an enthalpic and entropic characterization of the most stable ligand-protein bound states, using the protein kinase CK2α as a prototype study. We identified two accessory binding pockets surrounding the ATP-binding site having a strong enthalpic contribution but a different configurational entropy contribution, suggesting that they play a different role.

Identifiers

PMID30996777
PMCPMC6466520
OpenAlexW2911604085

What OpenQuestion holds

Textmetadata
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.