Browsing by Author "Chen, Xun"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Computationally exploring the mechanism of bacteriophage T7 gp4 helicase translocating along ssDNA
    (National Academy of Sciences, 2022) Jin, Shikai; Bueno, Carlos; Lu, Wei; Wang, Qian; Chen, Mingchen; Chen, Xun; Wolynes, Peter G.; Gao, Yang; Center for Theoretical Biological Physics
    Bacteriophage T7 gp4 helicase has served as a model system for understanding mechanisms of hexameric replicative helicase translocation. The mechanistic basis of how nucleoside 5′-triphosphate hydrolysis and translocation of gp4 helicase are coupled is not fully resolved. Here, we used a thermodynamically benchmarked coarse-grained protein force field, Associative memory, Water mediated, Structure and Energy Model (AWSEM), with the single-stranded DNA (ssDNA) force field 3SPN.2C to investigate gp4 translocation. We found that the adenosine 5′-triphosphate (ATP) at the subunit interface stabilizes the subunit–subunit interaction and inhibits subunit translocation. Hydrolysis of ATP to adenosine 5′-diphosphate enables the translocation of one subunit, and new ATP binding at the new subunit interface finalizes the subunit translocation. The LoopD2 and the N-terminal primase domain provide transient protein–protein and protein–DNA interactions that facilitate the large-scale subunit movement. The simulations of gp4 helicase both validate our coarse-grained protein–ssDNA force field and elucidate the molecular basis of replicative helicase translocation.
  • Thumbnail Image
    Item
    Exploring the mechanism of functional binding & disease-related self-assembly of biomolecules using energy landscape theory
    (2022-04-15) Chen, Xun; Wolynes, Peter G
    Proteins are large biomolecules that consist of one or multiple chains of amino acid residues. They play vital roles in biological function in the biosystem, including transcription, DNA replication, transporting, etc. It is believed that the structure determines the function. Here we explores the mechanism of functional binding and disease-related self-assembly of biomolecules using energy landscape theory from three aspects: Tau aggregation, PU.1 transcription process, and Heme in protein. First, we used energy landscape theory to investigate the mechanisms of tau aggregation. Tau isoforms, involved in Alzheimer's and Pick's disease, often aggregate to amorphous phase separation and firbilization. The free energy distributions of these Tau isoforms showed two channels in the aggregation: one leading to more ordered amyloid fibrils and the other non-fibrillar channels leading to an amorphous phase. The different structural properties of the species in the two channels suggest that the interconversion between the two channels will act as a backtrack, suggesting that the two channels are kinetically independent. Then, we focus on the indirect readout role in PU.1 DNA recognition. The accurate recognition of the binding site is essential for transcription. Many proteins recognize the binding region through "indirect readout in the transcription factor family." The indirect readout is subtle and acts as a long-range function. Here, we investigate the origin of the binding specificity of the PU.1. A nonspecific electrostatically driven DNA mechanism mainly achieves the binding specificity of PU.1. The electrostatic interactions between PU.1 and DNA cause both changes in elastic properties of the DNA and complex DNA conformational/dynamics. PU.1 affects the elastic properties of DNA through second-order mechanical effects. When PU.1 binds to the non-binding region of the DNA, the DNA becomes stiffer. Then, PU.1 then slides along the DNA, which becomes softer as the protein recognizes its specific binding site. This recognition process is driven by configurational entropy effects, suggesting a general mechanism for indirect readout. At last, we focus on heme proteins, where heme act as an active center of proteins. Here, we explore the role of the heme in protein folding and protein structure computationally. First, we model heme proteins using a hybrid model, which combines the AWSEM Hamiltonian for the protein, and coarse-grained forcefield, with AMBER Hamiltonian for the heme, an all-atom forcefield. Here, we show that both heme b and heme c improve protein structure predictions' accuracy. In the folding process, coordinated covalent bonds for both heme b and heme c drive the heme toward the native pocket. Thioester covalent bonds also drive heme c to the binding pocket. In addition, electrostatics also helps to search binding sites. We explore the mechanism of functional binding and disease-related self-assembly of biomolecules using energy landscape theory from these three aspects.
  • Thumbnail Image
    Item
    OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations
    (Public Library of Science, 2021) Lu, Wei; Bueno, Carlos; Schafer, Nicholas P.; Moller, Joshua; Jin, Shikai; Chen, Xun; Chen, Mingchen; Gu, Xinyu; Davtyan, Aram; Pablo, Juan J. de; Wolynes, Peter G.; Center for Theoretical Biological Physics
    We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMM’s Custom Forces software framework. By utilizing GPUs, we achieve around a 30-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a single CPU core. We showcase the benefits of OpenMM’s Custom Forces framework by devising and implementing two new potentials that allow us to address important aspects of protein folding and structure prediction and by testing the ability of the combined OpenAWSEM and Open3SPN2 to model protein-DNA binding. The first potential is used to describe the changes in effective interactions that occur as a protein becomes partially buried in a membrane. We also introduced an interaction to describe proteins with multiple disulfide bonds. Using simple pairwise disulfide bonding terms results in unphysical clustering of cysteine residues, posing a problem when simulating the folding of proteins with many cysteines. We now can computationally reproduce Anfinsen’s early Nobel prize winning experiments by using OpenMM’s Custom Forces framework to introduce a multi-body disulfide bonding term that prevents unphysical clustering. Our protein-DNA simulations show that the binding landscape is funneled towards structures that are quite similar to those found using experiments. In summary, this paper provides a simulation tool for the molecular biophysics community that is both easy to use and sufficiently efficient to simulate large proteins and large protein-DNA systems that are central to many cellular processes. These codes should facilitate the interplay between molecular simulations and cellular studies, which have been hampered by the large mismatch between the time and length scales accessible to molecular simulations and those relevant to cell biology.
  • Thumbnail Image
    Item
    Surveying biomolecular frustration at atomic resolution
    (Springer Nature, 2020) Chen, Mingchen; Chen, Xun; Schafer, Nicholas P.; Clementi, Cecilia; Komives, Elizabeth A.; Ferreiro, Diego U.; Wolynes, Peter G.; Center for Theoretical Biological Physics
    To function, biomolecules require sufficient specificity of interaction as well as stability to live in the cell while still being able to move. Thermodynamic stability of only a limited number of specific structures is important so as to prevent promiscuous interactions. The individual interactions in proteins, therefore, have evolved collectively to give funneled minimally frustrated landscapes but some strategic parts of biomolecular sequences located at specific sites in the structure have been selected to be frustrated in order to allow both motion and interaction with partners. We describe a framework efficiently to quantify and localize biomolecular frustration at atomic resolution by examining the statistics of the energy changes that occur when the local environment of a site is changed. The location of patches of highly frustrated interactions correlates with key biological locations needed for physiological function. At atomic resolution, it becomes possible to extend frustration analysis to protein-ligand complexes. At this resolution one sees that drug specificity is correlated with there being a minimally frustrated binding pocket leading to a funneled binding landscape. Atomistic frustration analysis provides a route for screening for more specific compounds for drug discovery.
  • Thumbnail Image
    Item
    The marionette mechanism of domain–domain communication in the antagonist, agonist, and coactivator responses of the estrogen receptor
    (PNAS, 2023) Chen, Xun; Jin, Shikai; Chen, Mingchen; Bueno, Carlos; Wolynes, Peter G.; Center for Theoretical Biological Physics; Systems, Synthetic, and Physical Biology
    The human estrogen receptor α (hERα) is involved in the regulation of growth, development, and tissue homeostasis. Agonists that bind to the receptor’s ligand-binding domain (LBD) lead to recruitment of coactivators and the enhancement of gene expression. In contrast, antagonists bind to the LBD and block the binding of coactivators thus decreasing gene expressions. In this work, we carry out simulations using the AWSEM (Associative memory, Water mediated, Structure and Energy Model)-Suite force field along with the 3SPN.2C force field for DNA to predict the structure of hERα and study its dynamics when binding to DNA and coactivators. Using simulations of antagonist-bound hERα and agonist-bound hERα by themselves and also along with bound DNA and coactivators, principal component analyses and free energy landscape analyses capture the pathway of domain–domain communication for agonist-bound hERα. This communication is mediated through the hinge domains that are ordinarily intrinsically disordered. These disordered segments manipulate the hinge domains much like the strings of a marionette as they twist in different ways when antagonists or agonists are bound to the ligand-binding domain.