Mechanisms of γ-Secretase Activation and Substrate Processing

Bhattarai, Apurba; Devkota, Sujan; Bhattarai, Sanjay; Wolfe, Michael S.; Miao, Yinglong

doi:10.1101/2020.03.09.984534

Cited by 5 publications

(10 citation statements)

References 63 publications

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“…The method adds a harmonic boost potential to the simulation, favoring transitions between low‐energy states. In this way, it is possible to capture micro‐to‐millisecond (and in some cases beyond) conformational changes without any predefined collective variable in large biomolecular systems 48–51 . These simulations extensively sampled the possible configurations of the HNH domain, and were first in identifying a putative structure of the active state, which was shown to be thermodynamically stable (Figure 4a).…”

Section: Information Transfer Between the Rec And Nuc Lobes Governs Dmentioning

confidence: 99%

“…In this way, it is possible to capture micro-to-millisecond (and in some cases beyond) conformational changes without any predefined collective variable in large biomolecular systems. [48][49][50][51] These simulations extensively sampled the possible configurations of the HNH domain, and were first in identifying a putative structure of the active state, which was shown to be thermodynamically stable (Figure 4a). 52 This configuration predicted the conformation of the active state before structural data were made available, enabling also early investigations of the active site chemistry though quantum-classical methods.…”

Section: Information Transfer Between the Rec And Nuc Lobes Governsmentioning

confidence: 99%

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Establishing the allosteric mechanism in CRISPR‐Cas9

Nierzwicki

Arantes

Saha

et al. 2020

WIREs Comput Mol Sci

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Allostery is a fundamental property of proteins, which regulates biochemical information transfer between spatially distant sites. Here, we report on the critical role of molecular dynamics (MD) simulations in discovering the mechanism of allosteric communication within CRISPR‐Cas9, a leading genome editing machinery with enormous promises for medicine and biotechnology. MD revealed how allostery intervenes during at least three steps of the CRISPR‐Cas9 function: affecting DNA recognition, mediating the cleavage and interfering with the off‐target activity. An allosteric communication that activates concerted DNA cleavages was found to led through the L1/L2 loops, which connect the HNH and RuvC catalytic domains. The identification of these “allosteric transducers” inspired the development of novel variants of the Cas9 protein with improved specificity, opening a new avenue for controlling the CRISPR‐Cas9 activity. Discussed studies also highlight the critical role of the recognition lobe in the conformational activation of the catalytic HNH domain. Specifically, the REC3 region was found to modulate the dynamics of HNH by sensing the formation of the RNA:DNA hybrid. The role of REC3 was revealed to be particularly relevant in the presence of DNA mismatches. Indeed, interference of REC3 with the RNA:DNA hybrid containing mismatched pairs at specific positions resulted in locking HNH in an inactive “conformational checkpoint” conformation, thereby hampering off‐target cleavages. Overall, MD simulations established the fundamental mechanisms underlying the allosterism of CRISPR‐Cas9, aiding engineering strategies to develop new CRISPR‐Cas9 variants for improved genome editing. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics

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Section: Information Transfer Between the Rec And Nuc Lobes Governs Dmentioning

confidence: 99%

Section: Information Transfer Between the Rec And Nuc Lobes Governsmentioning

confidence: 99%

Establishing the allosteric mechanism in CRISPR‐Cas9

Nierzwicki

Arantes

Saha

et al. 2020

WIREs Comput Mol Sci

View full text Add to dashboard Cite

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“…In this review, we will present the principles and the most recent applications of GaMD. Robust GaMD has been established for advanced simulation studies of a wide range of biomolecular systems, especially the protein–nucleic acid interactions 63–65 such as the CRISPR (clustered regularly interspaced short palindromic repeats)–Cas9 gene‐editing system, 66,67 protein–protein/peptide interactions, 68–72 protein‐ligand binding, 55,56,73–76 protein folding, 77 protein enzymes, 52,58,78–92 membrane proteins (including GPCRs, 68,69,73,76,93–95 ion channels, 53,96 and γ‐secretase 97 ) and carbohydrates, 98–100 as well as drug design 101,102 …”

Section: Introductionmentioning

confidence: 99%

Gaussian accelerated molecular dynamics: Principles and applications

Wang

Arantes

Bhattarai

et al. 2021

WIREs Comput Mol Sci

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201

204

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Gaussian accelerated molecular dynamics (GaMD) is a robust computational method for simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost potential to smooth biomolecular potential energy surface and reduce energy barriers. GaMD greatly accelerates biomolecular simulations by orders of magnitude. Without the need to set predefined reaction coordinates or collective variables, GaMD provides unconstrained enhanced sampling and is advantageous for simulating complex biological processes. The GaMD boost potential exhibits a Gaussian distribution, thereby allowing for energetic reweighting via cumulant expansion to the second order (i.e., “Gaussian approximation”). This leads to accurate reconstruction of free energy landscapes of biomolecules. Hybrid schemes with other enhanced sampling methods, such as the replica‐exchange GaMD (rex‐GaMD) and replica‐exchange umbrella sampling GaMD (GaREUS), have also been introduced, further improving sampling and free energy calculations. Recently, new “selective GaMD” algorithms including the Ligand GaMD (LiGaMD) and Peptide GaMD (Pep‐GaMD) enabled microsecond simulations to capture repetitive dissociation and binding of small‐molecule ligands and highly flexible peptides. The simulations then allowed highly efficient quantitative characterization of the ligand/peptide binding thermodynamics and kinetics. Taken together, GaMD and its innovative variants are applicable to simulate a wide variety of biomolecular dynamics, including protein folding, conformational changes and allostery, ligand binding, peptide binding, protein–protein/nucleic acid/carbohydrate interactions, and carbohydrate/nucleic acid interactions. In this review, we present principles of the GaMD algorithms and recent applications in biomolecular simulations and drug design. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Molecular and Statistical Mechanics > Free Energy Methods

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“…Amyloid precursor protein (APP) processing by membraneassociated α-, β-, and γ-secretase is strongly dependent on membrane fluidity (Eckert et al, 2010;Bhattarai et al, 2020). Aβ has been shown to upregulate APP amyloidogenic processing by binding and reducing membrane fluidity, which increases its own production in HEK293 and SH-SY5Y cells, and this effect was postulated to be mediated by gangliosides due to their strong affinity to Aβ (Ariga et al, 2011).…”

Section: Alterations In Ganglioside and Sialic Acid Metabolismmentioning

confidence: 99%

Sialometabolism in Brain Health and Alzheimer’s Disease

Rawal

Zhao

2021

Front. Neurosci.

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Sialic acids refer to a unique family of acidic sugars with a 9-carbon backbone that are mostly found as terminal residues in glycan structures of glycoconjugates including both glycoproteins and glycolipids. The highest levels of sialic acids are expressed in the brain where they regulate neuronal sprouting and plasticity, axon myelination and myelin stability, as well as remodeling of mature neuronal connections. Moreover, sialic acids are the sole ligands for microglial Siglecs (sialic acid-binding immunoglobulin-type lectins), and sialic acid-Siglec interactions have been indicated to play a critical role in the regulation of microglial homeostasis in a healthy brain. The recent discovery of CD33, a microglial Siglec, as a novel genetic risk factor for late-onset Alzheimer’s disease (AD), highlights the potential role of sialic acids in the development of microglial dysfunction and neuroinflammation in AD. Apart from microglia, sialic acids have been found to be involved in several other major changes associated with AD. Elevated levels of serum sialic acids have been reported in AD patients. Alterations in ganglioside (major sialic acid carrier) metabolism have been demonstrated as an aggravating factor in the formation of amyloid pathology in AD. Polysialic acids are linear homopolymers of sialic acids and have been implicated to be an important regulator of neurogenesis that contributes to neuronal repair and recovery from neurodegeneration such as in AD. In summary, this article reviews current understanding of neural functions of sialic acids and alterations of sialometabolism in aging and AD brains. Furthermore, we discuss the possibility of looking at sialic acids as a promising novel therapeutic target for AD intervention.

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Mechanisms of γ-Secretase Activation and Substrate Processing

Abstract: activation and substrate processing, which should facilitate rational computer-aided drug design targeting this functionally important enzyme.

Cited by 5 publications

References 63 publications

Establishing the allosteric mechanism in CRISPR‐Cas9

Establishing the allosteric mechanism in CRISPR‐Cas9

Gaussian accelerated molecular dynamics: Principles and applications

Sialometabolism in Brain Health and Alzheimer’s Disease

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