Evidence map›Paper›PMID 34621251›Full record

ArticleFrontiers in microbiology2021

The Role of Hydrophobic Nodes in the Dynamics of Class A β-Lactamases.

Edgar Olehnovics, Junqi Yin, Adrià Pérez, Gianni De Fabritiis, Robert A Bonomo, Debsindhu Bhowmik, Shozeb Haider

Open access · goldAbstract read
In one paragraph

Article in Frontiers in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.7field-weighted citation impact, top 39% 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

8 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Deciphering the Coevolutionary Dynamics of L2 β-Lactamases via Deep Learning.Journal of chemical information and modeling · 2024
    Article
  5. Article
  6. Article
  7. Article
  8. 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

7 authors at 5 institutions in 3 countries.

Edgar OlehnovicsPharmaceutical and Biological Chemistry, University College London School of Pharmacy, London, United Kingdom.
Junqi YinOak Ridge National Laboratory, National Center for Computational Sciences, Oak Ridge, TN, United States.
Adrià PérezComputational Science Laboratory, Barcelona Biomedical Research Park, Universitat Pompeu Fabra, Barcelona, Spain.
Gianni De FabritiisComputational Science Laboratory, Barcelona Biomedical Research Park, Universitat Pompeu Fabra, Barcelona, Spain.
Robert A BonomoDepartment of Molecular Biology and Microbiology, Case Western Reserve University, Cleveland, OH, United States.
Debsindhu BhowmikComputer Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States.
Shozeb HaiderPharmaceutical and Biological Chemistry, University College London School of Pharmacy, London, United Kingdom.
Oak Ridge National Laboratory · USUniversity College London · GBBarcelona Biomedical Research Park · ESLouis Stokes Cleveland VA Medical Center · USPompeu Fabra University · ES

Funding

Understanding ceftazidime resistance in SHV B-LactamasesR01AI063517 · NIAID · CASE WESTERN RESERVE UNIVERSITY · PI BONOMO, ROBERT A. · 2005 to 2023
$4.9M
BLRD VA I01 BX001974NIAID NIH HHS R01 AI063517
6 · The paper itself

Abstract

Class A β-lactamases are known for being able to rapidly gain broad spectrum catalytic efficiency against most β-lactamase inhibitor combinations as a result of elusively minor point mutations. The evolution in class A β-lactamases occurs through optimisation of their dynamic phenotypes at different timescales. At long-timescales, certain conformations are more catalytically permissive than others while at the short timescales, fine-grained optimisation of free energy barriers can improve efficiency in ligand processing by the active site. Free energy barriers, which define all coordinated movements, depend on the flexibility of the secondary structural elements. The most highly conserved residues in class A β-lactamases are hydrophobic nodes that stabilize the core. To assess how the stable hydrophobic core is linked to the structural dynamics of the active site, we carried out adaptively sampled molecular dynamics (MD) simulations in four representative class A β-lactamases (KPC-2, SME-1, TEM-1, and SHV-1). Using Markov State Models (MSM) and unsupervised deep learning, we show that the dynamics of the hydrophobic nodes is used as a metastable relay of kinetic information within the core and is coupled with the catalytically permissive conformation of the active site environment. Our results collectively demonstrate that the class A enzymes described here, share several important dynamic similarities and the hydrophobic nodes comprise of an informative set of dynamic variables in representative class A β-lactamases.

Indexed as

class Adeep learninghydrophobic nodesMarkov state modelmolecular dynamicsβ-lactamase

Identifiers

PMID34621251
PMCPMC8490755
OpenAlexW3201045256

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.