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.

Diverse toxins exhibit a common binding mode to the nicotinic acetylcholine receptors.

Nicotinic acetylcholine receptors (nAChRs) are critical ligand-gated ion channels in the human nervous system. They are targets for various neurotoxins produced by algae, plants, and animals. While many structures of nAChRs bound by neurotoxins have been published, the binding mechanism of toxins to the nAChRs remains unclear.

Dynamics and activation of membrane-bound B cell receptor assembly.

B-cell receptor (BCR) complexes are expressed on the surface of a B-cell and are critical in antigen recognition and modulating the adaptive immune response. Even though the relevance of antibodies has been known for almost a hundred years, the antigen-dependent activation mechanism of B-cells has remained elusive. Several models have been proposed for BCR activation, including cross-linking, conformation-induced oligomerization, and dissociation activation models.

Insertion and Anchoring of the HIV-1 Fusion Peptide into a Complex Membrane Mimicking the Human T-Cell.

A fundamental understanding of how the HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells.

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.

Insertion and Anchoring of HIV-1 Fusion Peptide into Complex Membrane Mimicking Human T-cell.

A fundamental understanding of how HIV-1 envelope (Env) protein facilitates fusion is still lacking. The HIV-1 fusion peptide, consisting of 15 to 22 residues, is the N-terminus of the gp41 subunit of the Env protein. Further, this peptide, a promising vaccine candidate, initiates viral entry into target cells by inserting and anchoring into human immune cells.

PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations.

Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering.

Dynamics and Activation of Membrane-Bound B Cell Receptor Assembly.

B-cell receptor complexes (BCR) are expressed on the surface of a B-cell and are critical in antigen recognition and modulating the adaptive immune response. Even though the relevance of antibodies has been known for almost a hundred years, the antigen-dependent activation mechanism of B-cells has remained elusive. Several models have been proposed for BCR activation, including cross-linking, conformation-induced oligomerization, and dissociation activation models.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery.

Introduction: For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.

Areas Covered: End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery.

Deep Learning Dynamic Allostery of G-Protein-Coupled Receptors.

G-protein-coupled receptors (GPCRs) make up the largest superfamily of human membrane proteins and represent primary targets of ∼1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonists and antagonists. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon the binding of positive and negative allosteric modulators (PAMs and NAMs).

Molecular Dynamics Activation of γ-Secretase for Cleavage of the Notch1 Substrate.

γ-Secretase is an intramembrane aspartyl protease complex that cleaves the transmembrane domain of over 150 peptide substrates, including amyloid precursor protein (APP) and the Notch family of receptors, via two conserved aspartates D257 and D385 in the presenilin-1 (PS1) catalytic subunit. However, while the activation of γ-secretase for cleavage of APP has been widely studied, the cleavage of Notch by γ-secretase remains poorly explored. Here, we combined Gaussian accelerated molecular dynamics (GaMD) simulations and mass spectrometry (MS) analysis of proteolytic products to present the first dynamic models for cleavage of Notch by γ-secretase.

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.

Deep Boosted Molecular Dynamics: Accelerating Molecular Simulations with Gaussian Boost Potentials Generated Using Probabilistic Bayesian Deep Neural Network.

We have developed a new deep boosted molecular dynamics (DBMD) method. Probabilistic Bayesian neural network models were implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, thereby allowing for accurate energetic reweighting and enhanced sampling of molecular simulations. DBMD was demonstrated on model systems of alanine dipeptide and the fast-folding protein and RNA structures.

Deep Boosted Molecular Dynamics (DBMD): Accelerating molecular simulations with Gaussian boost potentials generated using probabilistic Bayesian deep neural network.

We have developed a new Deep Boosted Molecular Dynamics (DBMD) method. Probabilistic Bayesian neural network models were implemented to construct boost potentials that exhibit Gaussian distribution with minimized anharmonicity, thereby allowing for accurate energetic reweighting and enhanced sampling of molecular simulations. DBMD was demonstrated on model systems of alanine dipeptide and the fast-folding protein and RNA structures.

Effects of presenilin-1 familial Alzheimer's disease mutations on γ-secretase activation for cleavage of amyloid precursor protein.

Presenilin-1 (PS1) is the catalytic subunit of γ-secretase which cleaves within the transmembrane domain of over 150 peptide substrates. Dominant missense mutations in PS1 cause early-onset familial Alzheimer's disease (FAD); however, the exact pathogenic mechanism remains unknown. Here we combined Gaussian accelerated molecular dynamics (GaMD) simulations and biochemical experiments to determine the effects of six representative PS1 FAD mutations (P117L, I143T, L166P, G384A, L435F, and L286V) on the enzyme-substrate interactions between γ-secretase and amyloid precursor protein (APP).

Deep Learning Dynamic Allostery of G-Protein-Coupled Receptors.

G-protein-coupled receptors (GPCRs) are the largest superfamily of human membrane proteins and represent primary targets of ~1/3 of currently marketed drugs. Allosteric modulators have emerged as more selective drug candidates compared with orthosteric agonists and antagonists. However, many X-ray and cryo-EM structures of GPCRs resolved so far exhibit negligible differences upon binding of positive and negative allosteric modulators (PAMs and NAMs).

Gaussian Accelerated Molecular Dynamics in OpenMM.

Gaussian accelerated molecular dynamics (GaMD) is a computational technique that provides both unconstrained enhanced sampling and free energy calculations of biomolecules. Here, we present the implementation of GaMD in the OpenMM simulation package and validate it on model systems of alanine dipeptide and RNA folding. For alanine dipeptide, 30 ns GaMD production simulations reproduced free energy profiles of 1000 ns conventional molecular dynamics (cMD) simulations.

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.

Molecular Simulations and Drug Discovery of Adenosine Receptors.

G protein-coupled receptors (GPCRs) represent the largest family of human membrane proteins. Four subtypes of adenosine receptors (ARs), the AAR, AAR, AAR and AAR, each with a unique pharmacological profile and distribution within the tissues in the human body, mediate many physiological functions and serve as critical drug targets for treating numerous human diseases including cancer, neuropathic pain, cardiac ischemia, stroke and diabetes. The AAR and AAR preferentially couple to the G proteins, while the AAR and AAR prefer coupling to the G proteins.

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.

GLOW: A Workflow Integrating Gaussian-Accelerated Molecular Dynamics and Deep Learning for Free Energy Profiling.

We introduce a Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and free energy profiling workflow (GLOW) to predict molecular determinants and map free energy landscapes of biomolecules. All-atom GaMD-enhanced sampling simulations are first performed on biomolecules of interest. Structural contact maps are then calculated from GaMD simulation frames and transformed into images for building DL models using a convolutional neural network.

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.

Unique features of different classes of G-protein-coupled receptors revealed from sequence coevolutionary and structural analysis.

G-protein-coupled receptors (GPCRs) are the largest family of human membrane proteins and represent the primary targets of about one third of currently marketed drugs. Despite the critical importance, experimental structures have been determined for only a limited portion of GPCRs and functional mechanisms of GPCRs remain poorly understood. Here, we have constructed novel sequence coevolutionary models of the A and B classes of GPCRs and compared them with residue contact frequency maps generated with available experimental structures.