Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning.

Conotoxins are a family of highly toxic neurotoxins composed of cysteine-rich peptides produced by marine cone snails. The most lethal cone snail species to humans is with fatality rates of up to ∼65% from a single sting, which is caused mostly by the activity of α-conotoxins against human nicotinic acetylcholine receptors (nAChRs). While sequence-based machine learning (ML) classifiers have been trained to identify targets of conotoxins binding voltage-gated ion channels, no ML model has been built to predict the subtype-specific nAChR targets of α-conotoxins.

Distinct binding conformations of epinephrine with α- and β-adrenergic receptors.

Agonists targeting α-adrenergic receptors (ARs) are used to treat diverse conditions, including hypertension, attention-deficit/hyperactivity disorder, pain, panic disorders, opioid and alcohol withdrawal symptoms, and cigarette cravings. These receptors transduce signals through heterotrimeric Gi proteins. Here, we elucidated cryo-EM structures that depict α-AR in complex with Gi proteins, along with the endogenous agonist epinephrine or the synthetic agonist dexmedetomidine.

Structural basis of agonist specificity of α-adrenergic receptor.

α-adrenergic receptors (α-ARs) play critical roles in the cardiovascular and nervous systems where they regulate blood pressure, cognition, and metabolism. However, the lack of specific agonists for all α subtypes has limited our understanding of the physiological roles of different α-AR subtypes, and led to the stagnancy in agonist-based drug development for these receptors. Here we report cryo-EM structures of α-AR in complex with heterotrimeric G-proteins and either the endogenous common agonist epinephrine or the α-AR-specific synthetic agonist A61603.

Structures of β-adrenergic receptor in complex with Gs and ligands of different efficacies.

G-protein-coupled receptors (GPCRs) receive signals from ligands with different efficacies, and transduce to heterotrimeric G-proteins to generate different degrees of physiological responses. Previous studies revealed how ligands with different efficacies activate GPCRs. Here, we investigate how a GPCR activates G-proteins upon binding ligands with different efficacies.

Mechanism of Tripeptide Trimming of Amyloid β-Peptide 49 by γ-Secretase.

The membrane-embedded γ-secretase complex processively cleaves within the transmembrane domain of amyloid precursor protein (APP) to produce 37-to-43-residue amyloid β-peptides (Aβ) of Alzheimer's disease (AD). Despite its importance in pathogenesis, the mechanism of processive proteolysis by γ-secretase remains poorly understood. Here, mass spectrometry and Western blotting were used to quantify the efficiency of tripeptide trimming of wild-type (WT) and familial AD (FAD) mutant Aβ49.

Challenges and frontiers of computational modelling of biomolecular recognition.

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modeling. Here, we review the challenges and computational approaches developed to characterize biomolecular binding, including molecular docking, Molecular Dynamics (MD) simulations (especially enhanced sampling) and Machine Learning.