NMR tools to detect protein allostery

Gampp, Olivia; Kadavath, Harindranath; Riek, Roland

doi:10.1016/j.sbi.2024.102792

Cited by 5 publications

(5 citation statements)

References 75 publications

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“…Besides the various advancing experimental techniques such as X-ray crystallography, 11,12 cryo-EM, 13,14 nuclear magnetic resonance (NMR), 15,16 and others, 17,18 computational investigations have provided powerful insights into allosteric mechanisms either by powering up experimental observations (for e.g., NMR CHESCA, CHESPA) or independently. 5,19,20 Molecular dynamics (MD) simulation has served as a fundamental, independent starting tool for comparing the dynamic behavior of proteins under different regulatory scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…Besides the various advancing experimental techniques such as X-ray crystallography, , cryo-EM, , nuclear magnetic resonance (NMR), , and others, , computational investigations have provided powerful insights into allosteric mechanisms either by powering up experimental observations (for e.g., NMR CHESCA, CHESPA) or independently. ,, Molecular dynamics (MD) simulation has served as a fundamental, independent starting tool for comparing the dynamic behavior of proteins under different regulatory scenarios . Combined with suitable analytical techniques, this has helped conceptualize how the dynamics would conform to one or more classical models of allostery, such as conformational selection, induced fit, and dynamic allostery. ,− Stochastic approaches like the Markov state modeling (MSM) have been quite successful in revealing such insights by building kinetic models of a protein’s sampled conformational space as a series of discrete metastable states with descriptions of kinetic rates of their allosteric transitions .…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1′s Allosteric Network

Ray Chaudhuri,

Ghosh Dastidar

2024

Biochemistry

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Allostery is a fundamental mechanism driving biomolecular processes that holds significant therapeutic concern. Our study rigorously investigates how two distinct machine-learning algorithms uniquely classify two already close-to-active DFG-in states of TAK1, differing just by the presence or absence of its allosteric activator TAB1, from an ensemble mixture of conformations (obtained from 2.4 μs molecular dynamics (MD) simulations). The novelty, however, lies in understanding the deeper algorithmic potentials to systematically derive a diverse set of differential residue connectivity features that reconstruct the essential mechanistic architecture for TAK1-TAB1 allostery in such a close-to-active biochemical scenario. While the recursive, random forest-based workflow displays the potential of conducting discretized, hierarchical derivation of allosteric features, a multilayer perceptron-based approach gains considerable efficacy in revealing fluid connected patterns of features when hybridized with mutual information scoring. Interestingly, both pipelines benchmark similar directions of functional conformational changes for TAK1′s activation. The findings significantly advance the depth of mechanistic understanding by highlighting crucial activation signatures along a directed C-lobe → activation loop → ATP pocket channel of information flow, including (1) the αF-αE biterminal alignments and (2) the “catalytic” drift of the activation loop toward kinase active site. Besides, some novel allosteric hotspots (K253, Y206, N189, etc.) are further recognized as TAB1 sensors, transducers, and responders, including a benchmark E70 mutation site, precisely mapping the important structural segments for sequential allosteric execution. Hence, our work demonstrates how to navigate through greater structural depths and dimensions of dynamic allosteric machineries just by leveraging standard ML methods in suitable streamlined workflows adaptive to the specific system and objectives.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1′s Allosteric Network

Ray Chaudhuri,

Ghosh Dastidar

2024

Biochemistry

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“…The enzyme dynamically transitions between a closed (basal) state, where the N-SH2 domain occludes the PTP catalytic cleft, and an open (active) state, triggered by activating mutations or ligand binding C-SH2 domain, represents a conformational change of unusual magnitude that has garnered significant research attention [11][12][13][14]. Nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics simulations have been instrumental in elucidating the key transition states and pathways underlying SHP2's allosteric regulation [12,[15][16][17]. Recent investigations, employing molecular dynamics simulations and small-angle X-ray scattering (SAXS), have revealed unexpected flexibility within the open state of the E76K mutant, a model system for studying SHP2 activation [16,[18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…This allosteric transition, characterized by a substantial 120-degree rotation of the C-SH2 domain, represents a conformational change of unusual magnitude that has garnered significant research attention [ 11 , 12 , 13 , 14 ]. Nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics simulations have been instrumental in elucidating the key transition states and pathways underlying SHP2’s allosteric regulation [ 12 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy

Rehman,

Zhao,

et al. 2024

IJMS

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SHP2, a pivotal component downstream of both receptor and non-receptor tyrosine kinases, has been underscored in the progression of various human cancers and neurodevelopmental disorders. Allosteric inhibitors have been proposed to regulate its autoinhibition. However, oncogenic mutations, such as E76K, convert SHP2 into its open state, wherein the catalytic cleft becomes fully exposed to its ligands. This study elucidates the dynamic properties of SHP2 structures across different states, with a focus on the effects of oncogenic mutation on two known binding sites of allosteric inhibitors. Through extensive modeling and simulations, we further identified an alternative allosteric binding pocket in solution structures. Additional analysis provides insights into the dynamics and stability of the potential site. In addition, multi-tier screening was deployed to identify potential binders targeting the potential site. Our efforts to identify a new allosteric site contribute to community-wide initiatives developing therapies using multiple allosteric inhibitors to target distinct pockets on SHP2, in the hope of potentially inhibiting or slowing tumor growth associated with SHP2.

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“…Allosteric regulation is widespread in biology, governing processes ranging from trans-membrane signaling, enzyme activity and gene expression [6][7][8][9].There is considerable interest in understanding the mechanisms of allosteric regulation, as this could provide means of manipulating essential processes, or engineering new desired regulatory systems [4,5,10,11]. NMR spectroscopy has advantages for studying allostery due to is unique ability to provide information on both structure and dynamics at multiple sites, detecting lowly populated conformational states, and providing site-specific information on conformational entropy [1,[12][13][14][15]. Of paramount interest is detailing how ligand binding at an allosteric site is transmitted through the protein to sites responsible for its activity.…”

Section: Introductionmentioning

confidence: 99%

Insights into Ligand-Mediated Activation of an Oligomeric Ring-Shaped Gene-Regulatory Protein from Solution- and Solid-State NMR

Muzquiz,

Jamshidi,

Conroy

et al. 2024

Preprint

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The 91 kDa oligomeric ring-shaped ligand binding protein TRAP (trp RNA binding attenuation protein) regulates the expression of a series of genes involved in tryptophan (Trp) biosynthesis in bacilli. When cellular Trp levels rise, the free amino acid binds to sites buried in the interfaces between each of the 11 (or 12, depending on the species) protomers in the ring. Crystal structures of Trp-bound TRAP show the Trp ligands are sequestered from solvent by a pair of loops from adjacent protomers that bury the bound ligand via polar contacts to several threonine residues. Binding of the Trp ligands occurs cooperatively, such that successive binding events occur with higher apparent affinity but the structural basis for this cooperativity is poorly understood. We used solution methyl-TROSY NMR relaxation experiments focused on threonine and isoleucine sidechains, as well as magic angle spinning solid-state NMR13C-13C and15N-13C chemical shift correlation spectra on uniformly labeled samples recorded at 800 and 1200 MHz, to characterize the structure and dynamics of the protein. Methyl13C relaxation dispersion experiments on ligand-free apo TRAP revealed concerted exchange dynamics on the μs-ms time scale, consistent with transient sampling of conformations that could allow ligand binding. Cross-correlated relaxation experiments revealed widespread disorder on fast timescales. Chemical shifts for methyl-bearing side chains in apo- and Trp-bound TRAP revealed subtle changes in the distribution of sampled sidechain rotameric states. These observations reveal a pathway and mechanism for induced conformational changes to generate homotropic Trp-Trp binding cooperativity.

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NMR tools to detect protein allostery

Cited by 5 publications

References 75 publications

Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1′s Allosteric Network

Adaptive Workflows of Machine Learning Illuminate the Sequential Operation Mechanism of the TAK1′s Allosteric Network

Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy

Insights into Ligand-Mediated Activation of an Oligomeric Ring-Shaped Gene-Regulatory Protein from Solution- and Solid-State NMR

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