Biology subjects

Vant, J.

Publications and source records attributed to Vant, J..

6 recordsLinked to original sources

bioRxiv2023

Structural mechanism of HP1-dependent transcriptional repression and chromatin compaction

Heterochromatin protein 1 (HP1) plays a central role in establishing and maintaining constitutive heterochromatin. However, the mechanisms underlying HP1-nucleosome interactions and their contributions to heterochromatin functions remain elusive. In this study, we employed a multidisciplinary approach to unravel the interactions between human HP1 and nucleosomes. We have elucidated the cryo-EM structure of an HP1 dimer bound to an H2A.Z nucleosome, revealing that the HP1 dimer interfaces with nucleosomes at two distinct sites. The primary binding site is located at the N-terminus of histone H3, specifically at the trimethylated K9 (K9me3) region, while a novel secondary binding site is situated near histone H2B, close to nucleosome superhelical location 4 (SHL4). Our biochemical data further demonstrates that HP1 binding influences the dynamics of DNA on the nucleosome. It promotes DNA unwrapping near the nucleosome entry and exit sites while concurrently restricting DNA accessibility in the vicinity of SHL4. This study offers a model that explains how HP1 functions in heterochromatin maintenance and gene silencing, particularly in the context of H3K9me-dependent mechanisms. Additionally, it sheds light on the H3K9me-independent role of HP1 in responding to DNA damage.

biochemistry↗

bioRxiv2023

Glycolytic Metabolon Assembly on Mitochondria via Hexokinase O-GlcNAcylation Promotes Metabolic Efficiency

Glucose, the primary cellular energy source, is metabolized through glycolysis initiated by the rate-limiting enzyme Hexokinase (HK). In energy-demanding tissues like the brain, HK1 is the dominant isoform, primarily localized on mitochondria, crucial for efficient glycolysis-oxidative phosphorylation coupling and optimal energy generation. This study unveils a unique mechanism regulating HK1 activity, glycolysis, and the dynamics of mitochondrial coupling, mediated by the metabolic sensor enzyme O-GlcNAc transferase (OGT). OGT catalyzes reversible O-GlcNAcylation, a post-translational modification, influenced by glucose flux. Elevated OGT activity induces dynamic O-GlcNAcylation of HK1s regulatory domain, subsequently promoting the assembly of the glycolytic metabolon on the outer mitochondrial membrane. This modification enhances HK1s mitochondrial association, orchestrating glycolytic and mitochondrial ATP production. Mutations in HK1s O-GlcNAcylation site reduce ATP generation, affecting synaptic functions in neurons. The study uncovers a novel pathway that bridges neuronal metabolism and mitochondrial function via OGT and the formation of the glycolytic metabolon, offering new prospects for tackling metabolic and neurological disorders.

cell biology↗

bioRxiv2022

Learning Free Energy Pathways through Reinforcement Learning of Adaptive Steered Molecular Dynamics

In this paper, we develop a formulation to utilize reinforcement learning and sampling-based robotics planning to derive low free energy transition pathways between two known states. Our formulation uses Jarzynskis equality and the stiffspring approximation to obtain point estimates of energy, and construct an informed path search with atomistic resolution. At the core of this framework, is our first ever attempt we use a policy driven adaptive steered molecular dynamics (SMD) to control our molecular dynamics simulations. We show that both the reinforcement learning and robotics planning realization of the RL-guided framework can solve for pathways on toy analytical surfaces and alanine dipeptide.

biophysics↗

bioRxiv2021

Energy Landscape of the SARS-CoV-2 Reveals Extensive Conformational Heterogeneity

Cryo-electron microscopy (cryo-EM) has produced a number of structural models of the SARS-CoV-2 spike, already prompting biomedical outcomes. However, these reported models and their associated electrostatic potential maps represent an unknown admixture of conformations stemming from the underlying energy landscape of the spike protein. As for any protein, some of the spikes conformational motions are expected to be biophysically relevant, but cannot be interpreted only by static models. Using experimental cryo-EM images, we present the energy landscape of the spike protein conformations, and identify molecular rearrangements along the most-likely conformational path in the vicinity of the open (so called 1RBD-up) state. The resulting global and local atomic refinements reveal larger movements than those expected by comparing the reported 1RBD-up and 1RBD-down cryo-EM models. Here we report greater degrees of "openness" in global conformations of the 1RBD-up state, not revealed in the single-model interpretations of the density maps, together with conformations that overlap with the reported models. We discover how the glycan shield contributes to the stability of these conformations along the minimum free-energy pathway. A local analysis of seven key binding pockets reveals that six out them, including those for engaging ACE2, therapeutic mini-proteins, linoleic acid, two different kinds of antibodies, and protein-glycan interaction sites, switch conformations between their known apo- and holo-conformations, even when the global spike conformation is 1RBD-up. This is reminiscent of a conformational pre-equilibrium. We found only one binding pocket, namely antibody AB-C135 to remain closed along the entire minimum free energy path, suggesting an induced fit mechanism for this enzyme.

biophysics↗

bioRxiv2020

Mind reading of the proteins: Deep-learning to forecast molecular dynamics

Molecular dynamics (MD) simulations have emerged to become the back-bone of todays computational biophysics. Simulation tools such as, NAMD, AMBER and GROMACS have accumulated more than 100,000 users. Despite this remarkable success, now also bolstered by compatibility with graphics processor units (GPUs) and exascale computers, even the most scalable simulations cannot access biologically relevant timescales - the number of numerical integration steps necessary for solving differential equations in a million-to-billion-dimensional space is computationally in-tractable. Recent advancements in Deep Learning has made it such that patterns can be found in high dimensional data. In addition, Deep Learning have also been used for simulating physical dynamics. Here, we utilize LSTMs in order to predict future molecular dynamics from current and previous timesteps, and examine how this physics-guided learning can benefit researchers in computational biophysics. In particular, we test fully connected Feed-forward Neural Networks, Recurrent Neural Networks with LSTM / GRU memory cells with TensorFlow and PyTorch frame-works trained on data from NAMD simulations to predict conformational transitions on two different biological systems. We find that non-equilibrium MD is easier to train and performance improves under the assumption that each atom is independent of all other atoms in the system. Our study represents a case study for high-dimensional data that switches stochastically between fast and slow regimes. Applications of resolving these sets will allow real-world applications in the interpretation of data from Atomic Force Microscopy experiments.

biophysics↗

bioRxiv2019

CryoFold: Ab-initio structure determination from electron density maps using molecular dynamics

Cryo-EM is a powerful method for determining protein structures. But it requires computational assistance. Physics-based computations have the power to give low-free-energy structures and ensembles of populations, but have been computationally limited to only small soluble proteins. Here, we introduce CryoFold. By integrating data of varying sparsity from electron density maps of 3-5 [A] resolution with coarse-grained physical knowledge of secondary and tertiary interactions, CryoFold determines ensembles of protein structures directly from sequence. We give six examples showing its broad capabilities, over proteins ranging from 72 to 2000 residues, including membrane and multi-domain proteins, and including results from two EMDB competitions. The ensembles CryoFold predicts starting from the density data of a single known protein conformation encompass multiple low-energy conformations, all of which are experimentally validated and biologically relevant.

biophysics↗