Evidence map›Paper›PMID 36562610›Full record

ArticleeLife2022

Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases.

Joan Gizzio, Abhishek Thakur, Allan Haldane, Ronald M Levy

Open access · goldAbstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
2.3field-weighted citation impact, top 10% 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

22 citing papers in PubMed, 31 citations in OpenAlex.

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  16. Kinetic coevolutionary models predict the temporal emergence of HIV-1 resistance mutations under drug selection pressure.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  17. Article
  18. Classifying protein kinase conformations with machine learning.Protein science : a publication of the Protein Society · 2024
    Article
  19. Article
  20. In vivo functional phenotypes from a computational epistatic model of evolution.Proceedings of the National Academy of Sciences of the United States of America · 2024
    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

4 authors at 1 institution in 1 country.

Joan Gizzio *Center for Biophysics and Computational Biology, Temple University, Philadelphia, United States.ORCID 0000-0002-4127-2294
Abhishek Thakur *Center for Biophysics and Computational Biology, Temple University, Philadelphia, United States.ORCID 0000-0002-4827-7602
Allan HaldaneCenter for Biophysics and Computational Biology, Temple University, Philadelphia, United States.ORCID 0000-0002-8343-1994
Ronald M LevyCenter for Biophysics and Computational Biology, Temple University, Philadelphia, United States.ORCID 0000-0001-8696-5177
Temple University · US

Funding

Mapping Fitness & Free Energy Landscapes of ProteinsR35GM132090 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI Ronald Levy · 2019 to 2026
$3.2M
NIGMS NIH HHS R35 GM132090
6 · The paper itself

Abstract

Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved 'type-II inhibitors' in the treatment of cancers. Tyrosine kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while serine/threonine kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale (~17-20 Å) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical 'folded activation loop' DFG-out conformation relative to the active conformation, that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly 'anchored' against the catalytic loop motif in the active conformation and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and have pharmacological implications for the target selectivity of type-II inhibitors.

Indexed as

Protein Serine-Threonine KinasesTyrosineMolecular Dynamics SimulationProtein ConformationProtein Kinase InhibitorsSerineThreonineProtein Kinase InhibitorsProtein Serine-Threonine KinasesSerineThreonineTyrosinecoevolutioncomputational biologyconformational landscapefree-energy simulationsmolecular biophysicsnoneprotein kinasesstructural biologysystems biologytype-II inhibitorstyrosine kinase evolution

Identifiers

PMID36562610
PMCPMC9822262
OpenAlexW4312115132

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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.