Substrate binding to Src: A new perspective on tyrosine kinase substrate recognition from NMR and molecular dynamics

Joshi, Mehul; Burton, Robert P.; Wu, Heng; Lipchik, Andrew M.; Craddock, Barbara P.; Mo, Huaping; Parker, Laurie L.; Miller, W. Todd; Post, Carol Beth

doi:10.1002/pro.3777

Cited by 11 publications

(17 citation statements)

References 42 publications

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“…The “RD-pocket” (Figure 4B) plays a distinctive functional role in STKs compared with TKs. Similar to what is seen for the N-terminal anchor in STKs, the RD-pocket in the active conformation of STKs directly interfaces with co-crystallized substrates, in contrast to TKs which bind peptides in a different binding mode 16 . The RD-pocket is a dynamically assembled and positively charged pocket formed by a cluster of basic and hydrophobic sidechains originating from the HRD-Arg (R123 HRD ), the activation loop N-terminus (145 DFG+1 through 147 DFG+3 ) and C-terminal anchor (159 APE-8 – 161 APE-7 ).…”

Section: Resultssupporting

confidence: 54%

“…The three active-state motifs we identified to control the active → inactive conformational bias in TKs and STKs have a second functional role: they are involved in the binding of substrate peptides in trans which bind in different orientations in PDB co-crystal structures of TKs compared with distantly related STKs 16 (Figure 6A). This leads us to hypothesize that the evolutionary divergence in substrate binding functionality that distinguishes holo TKs from STKs has also altered the stability of the active, apo conformation.…”

Section: Discussionmentioning

confidence: 99%

“…This large backbone reorganization appears to propagate from a "flip" of the DFG motif at the beginning of the activation loop from "DFG-in" to "DFG-out", and in this way downstream residues are placed in proximity to a peptide binding surface in the C-lobe which has been functionalized by TKs. 13,16,17 Previously it was confirmed 15 that the stability of this "DFG-out activation loop-folded" basin for TKs relative to STKs is greatly enhanced by contacts in cis that allow the folded/inactive activation loop to mimic a Tyrcontaining protein substrate that would otherwise bind to the active conformation in trans 17,23 . This appears to be a complex form of TK autoregulation in which the very selection pressures that have optimized TKs for binding tyrosine substrates also stabilize the substrate-competitive DFG-out activation loop-folded conformation.…”

Section: Introductionmentioning

confidence: 90%

“…Additionally, in TKs the sidechains of contact-pair 161APE-6 and 166APE-1 in the Cterminal anchor form part of the "C-lobe" binding site located "underneath" the activation loop where substrate peptides are found co-crystallized with TKs in the active conformation. 16 The distinct C-terminal anchors of TKs enable substrate mimicry in the inactive conformation.…”

Section: Role Of the C-terminal Anchor In The Active Conformation Of Tksmentioning

confidence: 99%

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Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Gizzio,

Thakur,

Haldane

et al. 2024

Preprint

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Protein kinases are molecular machines with rich sequence variation that distinguishes the two main evolutionary branches – tyrosine kinases (TKs) from serine/threonine kinases (STKs). Using a sequence co-variation Potts statistical energy model we previously concluded that TK catalytic domains are more likely than STKs to adopt an inactive conformation with the activation loop in an autoinhibitory “folded” conformation, due to intrinsic sequence effects. Here we investigated the structural basis for this phenomenon by integrating the sequence-based model with structure-based molecular dynamics (MD) to determine the effects of mutations on the free energy difference between active and inactive conformations, using a novel thermodynamic cycle involving many (n=108) protein-mutation free energy perturbation (FEP) simulations in the active and inactive conformations. The sequence and structure-based results are consistent and support the hypothesis that the inactive conformation “DFG-out Activation Loop Folded”, is a functional regulatory state that has been stabilized in TKs relative to STKs over the course of their evolution via the accumulation of residue substitutions in the activation loop and catalytic loop that facilitate distinct substrate binding modesin transand additional modes of regulationin cisfor TKs.

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Section: Resultssupporting

confidence: 54%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

Section: Role Of the C-terminal Anchor In The Active Conformation Of Tksmentioning

confidence: 99%

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Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases

Gizzio,

Thakur,

Haldane

et al. 2024

Preprint

Self Cite

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“…Xie et al used NMR to identify conformational states occupied by Abl kinase and were then able to fit their chemical exchange data to a model in order to predict populations and kinetics of those states for the wildtype and various mutants [122]. MD simulations have also been important to develop a perspective on substrate recognition integrating information from NMR [123].…”

Section: Relating Computational Studies To Functional Assaysmentioning

confidence: 99%

Tyrosine kinases: complex molecular systems challenging computational methodologies

Thomas

Roux

2021

Eur. Phys. J. B

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Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology, and chemistry. Nonetheless, generating genuine knowledge about biological systems using MD simulations remains challenging. Protein tyrosine kinases are important cellular signaling enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Due to the large conformational changes and long timescales involved in their function, these kinases present particularly challenging problems to modern computational and theoretical frameworks aimed at elucidating the dynamics of complex biomolecular systems. Markov state models have achieved limited success in tackling the broader conformational ensemble and biased methods are often employed to examine specific long timescale events. Recent advances in machine learning continue to push the limitations of current methodologies and provide notable improvements when integrated with the existing frameworks. A broad perspective is drawn from a critical review of recent studies.

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