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Allosteric Regulation

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Structural insights into gating mechanism and allosteric regulation of NMDA receptors.

Current opinion in neurobiology
N-methyl-d-aspartate receptors (NMDARs) belong to the ionotropic glutamate receptors (iGluRs) superfamily and act as coincidence detectors that are crucial to neuronal development and synaptic plasticity. They typically assemble as heterotetramers of...

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors.

Journal of biomolecular structure & dynamics
Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are sear...

ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Nucleic acids research
Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible onli...

Machine learning approaches in predicting allosteric sites.

Current opinion in structural biology
Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked t...

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

Biochemistry
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...

Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR.

Nature communications
Allosteric drugs offer a new avenue for modern drug design. However, the identification of cryptic allosteric sites presents a formidable challenge. Following the allostery nature of residue-driven conformation transition, we propose a state-of-the-a...

Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning.

Molecules (Basel, Switzerland)
The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and im...

MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.

Genome biology
We present MoCHI, a tool to fit interpretable models using deep mutational scanning data. MoCHI infers free energy changes, as well as interaction terms (energetic couplings) for specified biophysical models, including from multimodal phenotypic data...

Identification of Novel Fourth-Generation Allosteric Inhibitors Targeting Inactive State of EGFR T790M/L858R/C797S and T790M/L858R Mutations: A Combined Machine Learning and Molecular Dynamics Approach.

The journal of physical chemistry. B
Targeted therapy with an allosteric inhibitor (AIs) is an important area of research in patients with epidermal growth factor receptor (EGFR) mutations. Current treatment of nonsmall cell lung cancer patients with EGFR mutations using orthosteric inh...

Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?

Journal of chemical information and modeling
Allosteric compounds offer an alternative mode of inhibition to orthosteric compounds with opportunities for selectivity and noncompetition. Structure-based drug design (SBDD) of allosteric compounds introduces complications compared to their orthost...