Surl-Hee Ahn

Summary / ANKLE2 is a scaffolding protein with crucial roles in neuroprogenitor cell division and embryonic brain development. Pathogenic variants in ANKLE2 cause primary microcephaly, a congenital disorder characterized by impaired neurodevelopment. Nonetheless, the underlying dysfunction of ANKLE2 is not understood. Here, we define the ANKLE2 protein interaction landscape, which includes cell division proteins and novel microcephaly candidates in the APC (anaphase-promoting complex). Analysis of six pathogenic variants reveals changes in this interactome that likely result in microcephaly. Using molecular simulations, we identify the structural consequences of five pathogenic substitutions, including disruption of important helical structures. Combined, these techniques suggest loss of interaction with the PP2A (protein phosphatase 2A) complex is a common mechanism for ANKLE2 pathogenesis. Phosphoproteomics reveals widespread alterations in ANKLE2- and PP2A-dependent phosphorylation of cell division proteins. Together, our integrative proteomics and simulation approach improves the molecular understanding of ANKLE2 function and its pathogenic changes in primary microcephaly. Highlights: ANKLE2 interacts with the APC (anaphase-promoting complex), whose loss results in microcephaly in vivo . Pathogenic ANKLE2 variants have distinct interaction networks and structural dynamics. Mutations in Caulimovirus Domain disrupt domain structure and interaction with PP2A (protein phosphatase 2A) complex. ANKLE2 regulates phosphorylation of many proteins through PP2A, including APC.

A critical step in the emergence of a virus is the ability of the viral protein to bind a host receptor and mediate cell entry. For many coronaviruses, this interaction occurs between the Spike S1 subunit and the human ACE2 receptor. Whether this binding interface can be computationally distinguished across unstudied viruses without experimentally resolved protein structures remains an open question. We predicted how 28 emerging coronaviruses may bind to human ACE2 using structural predictions, static interaction prediction programs, and molecular dynamics simulations. To screen the emerging coronaviruses, we predicted a library of S1 structures using AlphaFold. These predicted structures were then used to model the S1-ACE2 interaction with AlphaFold, ClusPro, and HADDOCK. We used known ACE2-binding sarbecoviruses as positive controls and coronaviruses that bind other receptors as negative controls to threshold predicted binding. Contact analysis quantified the predicted binding and revealed that these static interaction prediction methods varied in discriminative power. Less restrained static predictions separated binders from non-binders, whereas heavily restrained docking did not, potentially forcing an interaction where none should exist. This analysis highlighted an emerging coronavirus, Zhejiang2013, as a potential ACE2 binder. We used molecular dynamics simulations to further assess the static predictions and model the interaction over time. Overall, our results indicate that Zhejiang2013 exhibits dynamic interaction patterns consistent with ACE2 binding. Given that two ACE2-binding coronaviruses have caused global pandemics within the past two decades, identifying potential ACE2 binders is critical for early warning and pandemic preparedness.

Though "enhanced sampling methods," a class of computational tools, can help accelerate the exploration of the system's configuration space and dynamics in molecular dynamics (MD) simulations, obtaining accurate thermodynamic and kinetic properties of large systems within a computationally tractable period remains challenging. To tackle this issue, we developed a novel hybrid enhanced sampling method, parallelizable Gaussian accelerated molecular dynamics (ParGaMD), that runs many short Gaussian accelerated molecular dynamics (GaMD) simulations across multiple GPUs in parallel using the weighted ensemble method (WE) framework. Although GaMD accelerates sampling by adding a harmonic boost potential to the system, GaMD often takes weeks to run for large systems and does not parallelize well across multiple GPUs in the implemented MD simulation engines. By leveraging the WE framework's efficient GPU parallelization, we can overcome this bottleneck and additionally sample along the chosen collective variables, thereby making ParGaMD more powerful than GaMD itself. We show that ParGaMD can significantly accelerate sampling of different configuration states and dynamics across various systems, benefiting the broader scientific community.

The severe acute respiratory syndrome coronavirus 2 spike glycoprotein enables infection through a key conformational transition that exposes its receptor binding domain (RBD). Experimental evidence indicates that spike mutations, particularly the early D614G variant, alter the rate of this conformational shift, potentially increasing viral infectivity. We conducted extensive weighted ensemble simulations of the Ancestral, Delta, and Omicron BA.1 spike strains to investigate relationships between sequence mutations and RBD opening dynamics. We observe that Ancestral, Delta, and Omicron BA.1 spike RBDs open differently. Via dynamical network analysis, we identified two allosteric communication networks connecting all S1 domains: the established N2R linker and a newly investigated antiparallel R2N linker. In Delta and Omicron BA.1 variant spikes, RBD opening is facilitated by both linkers, while the Ancestral strain relies predominantly on the N2R linker. In the Ancestral spike, the D614-K854 salt bridge impedes allosteric communication through the R2N linker, whereas the loss of this salt bridge in all subsequent variants of concerns allows for increased local flexibility, thereby accelerating RBD opening. Hydrogen–deuterium mass spectrometry experiments validate these altered dynamics in the D614 region. This study unveils a “hidden” network, connecting the N-terminal domain to the RBD via the 614-proximal region, and the D614G mutation reshapes the fitness landscape of these critical viral glycoproteins.

G-protein-coupled receptors (GPCRs) are pivotal in cellular signal transduction and serve as key drug targets. Among them, the β-adrenergic receptors (β₁AR and β₂AR) regulate cardiovascular function and are activated by endogenous catecholamines, norepinephrine (NE), and epinephrine (EP). While ligand interaction kinetics, especially the dissociation rate constants (k_off), have been recognized as a key parameter for drug development, accurate and atomistic modeling of these processes remains a major challenge due to the rarity and slow time scales of unbinding events. Hence, we applied the weighted ensemble method (WE), an enhanced sampling method, in molecular dynamics (MD) simulations to sample the dissociation pathways of NE and EP from β₁AR and β₂AR. To analyze and visualize the results, we used machine learning-based techniques, such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and density-based spatial clustering of applications with noise (DBSCAN). These methods enabled unbiased sampling of ligand dissociation from membrane-embedded GPCRs while preserving kinetics. We found that NE and EP dissociate via front-side pathways in β₁AR but favor a back-side pathway in β₂AR. We also found that WE simulations could yield reasonable k_off values.

Though ``enhanced sampling methods,'' a class of computational tools, can help accelerate the exploration of the configuration space and dynamics of the system of interest in molecular dynamics (MD) simulations, obtaining accurate thermodynamic and kinetic properties of large systems (>200,000 atoms) from MD simulations in a computationally tractable period is still challenging. To tackle this issue, we develop a novel enhanced sampling method called parallelized Gaussian accelerated molecular dynamics (ParGaMD) that runs many short Gaussian accelerated molecular dynamics (GaMD) simulations over multiple GPUs in parallel by using the weighted ensemble method (WE) framework. Although GaMD accelerates sampling by adding a harmonic boost potential to the system, GaMD often takes weeks to run for large systems and does not parallelize well over multiple GPUs in specific MD simulation engines. By using the efficient GPU parallelization of the WE framework, we can overcome this bottleneck and additionally sample along the chosen collective variables, which enables ParGaMD to be more powerful than GaMD itself. We show that ParGaMD can significantly speed up sampling different configuration states and dynamics of various systems, which can benefit the broader scientific community.

APOBEC3B (A3B) is implicated in DNA mutations that facilitate tumor evolution. Although structures of its individual N- and C-terminal domains (NTD and CTD) have been resolved through X-ray crystallography, the full-length A3B (fl-A3B) structure remains elusive, limiting our understanding of its dynamics and mechanisms. In particular, the APOBEC3B C-terminal domain (A3Bctd) is frequently closed in models and structures. In this study, we built several new models of fl-A3B using integrative structural biology methods and selected a top model for further dynamical investigation. We compared the dynamics of the truncated (A3Bctd) to that of the fl-A3B via conventional and Gaussian accelerated molecular dynamics (MD) simulations. Subsequently, we employed weighted ensemble methods to explore the fl-A3B active site opening mechanism, finding that interactions at the NTD-CTD interface enhance the opening frequency of the fl-A3B active site. Our findings shed light on the structural dynamics and potential druggability of fl-A3B, including observations regarding both the active and allosteric sites, which may offer new avenues for therapeutic intervention in cancer.

The SARS-CoV-2 spike glycoprotein binds human epithelial cells and enables infection through a key conformational transition that exposes its receptor binding domain (RBD). Experimental evidence indicates that spike mutations, particularly the early D614G variant, alter the rate of this conformational shift, potentially increasing viral infectivity. To investigate how mutations reshape the conformational landscape, we conducted extensive weighted ensemble simulations of the Ancestral, Delta, and Omicron BA.1 spike strains along the RBD opening pathway. We observe that Ancestral, Delta, and Omicron BA.1 spike RBDs open differently, with Omicron BA.1 following a more direct opening profile until it reaches a “super-open” state wherein it begins to “peel”, suggesting increased S1 flexibility. Via dynamical network analysis, we identified two allosteric communication networks uniting all S1 domains: the established N2R linker and a newly discovered anti-parallel R2N linker. In Delta and Omicron BA.1 variant spikes, RBD opening is facilitated by both linkers, while the Ancestral strain relies predominantly on the N2R linker. In the ancestral spike, the D614-K854 salt bridge impedes allosteric communication through the R2N linker, whereas the loss of this salt bridge in all subsequent VOCs alleviates local frustration and, we believe, accelerates RBD opening. Hydrogen-deuterium mass spectrometry experiments validate these altered dynamics in the D614 region across Ancestral, D614G, and Omicron BA.1 spikes. This study unveils a ‘hidden’ allosteric network, connecting the NTD to the RBD via the 614-proximal region, and the D614G mutation reshapes the fitness landscape of these critical viral glycoproteins. Significance Statement: Our work reveals how the D614G mutation in the SARS-CoV-2 spike protein reshapes its internal communication pathways and speeds up receptor binding domain (RBD) opening, providing mechanistic insight into the evolution and enhanced infectivity of SARS-CoV-2 variants of concern. We also describe differences in opening pathways and relative rates of opening for Delta and Omicron BA.1 spike RBDs relative to the original (Ancestral) coronavirus strain from Wuhan, China.