Browsing by Author "Jin, Shikai"

Now showing 1 - 6 of 6
  • 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
    CrysFormer: Protein structure determination via Patterson maps, deep learning, and partial structure attention
    (AIP Publishing LLC, 2024) Pan, Tom; Dun, Chen; Jin, Shikai; Miller, Mitchell D.; Kyrillidis, Anastasios; Phillips, George N., Jr.
    Determining the atomic-level structure of a protein has been a decades-long challenge. However, recent advances in transformers and related neural network architectures have enabled researchers to significantly improve solutions to this problem. These methods use large datasets of sequence information and corresponding known protein template structures, if available. Yet, such methods only focus on sequence information. Other available prior knowledge could also be utilized, such as constructs derived from x-ray crystallography experiments and the known structures of the most common conformations of amino acid residues, which we refer to as partial structures. To the best of our knowledge, we propose the first transformer-based model that directly utilizes experimental protein crystallographic data and partial structure information to calculate electron density maps of proteins. In particular, we use Patterson maps, which can be directly obtained from x-ray crystallography experimental data, thus bypassing the well-known crystallographic phase problem. We demonstrate that our method, CrysFormer, achieves precise predictions on two synthetic datasets of peptide fragments in crystalline forms, one with two residues per unit cell and the other with fifteen. These predictions can then be used to generate accurate atomic models using established crystallographic refinement programs.
  • Thumbnail Image
    Item
    Modeling protein structural ensembles using AWSEM-Suite
    (2023-04-11) Jin, Shikai; Tao, Jane; Wolynes, Peter
    Proteins are the driving force behind most cellular processes. Traditional methods for determining protein structure are limited to probing only a few static structures of a given protein. However, molecular dynamics simulation presently allows for the determination of the dynamics of a protein. In this thesis, I introduce AWSEM-Suite, a coarse-grained force field that has recently shown good performance in protein structure prediction experiments. This force field is based on physical principles and neural network-based machine learning. I describe the composition of the force field, as well as two new energy terms: the template-based and coevolutionary-guided terms. After discussing the problem of protein structure prediction, I showcase how we built an online server for AWSEM-Suite, describing the path between the input and output. Additionally, I discuss other techniques for structural analysis, including frustration protein structure refinement, a physics-based method that complements current methods. In the last part of this thesis, I introduce two applications: solving phase problems in X-ray crystallography and exploring the translocation mechanism of bacteriophage T7 helicase gp4 using AWSEM-Suite. Our results show that AWSEM-Suite provides better results than the state-of-the-art program I-TASSER-MR in finding phase information for a given protein. Furthermore, AWSEM-Suite can successfully predict the key loops that interact with ssDNA during gp4 translocation. We explore the intermediate structures of action, highlighting the possible mechanism of the role of electrostatic effects exerted by the binding and release of ATP molecules. In these chapters, we explore several related questions concerning protein structure prediction, protein structure refinement, and specific biological questions using AWSEM-Suite. In summary, our studies demonstrate that AWSEM-Suite is a powerful technology for exploring protein dynamics and predicting protein structure.
  • 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
    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.
  • Thumbnail Image
    Item
    Unleashing the potential of noncanonical amino acid biosynthesis to create cells with precision tyrosine sulfation
    (Springer Nature, 2022) Chen, Yuda; Jin, Shikai; Zhang, Mengxi; Hu, Yu; Wu, Kuan-Lin; Chung, Anna; Wang, Shichao; Tian, Zeru; Wang, Yixian; Wolynes, Peter G.; Xiao, Han; Center for Theoretical Biological Physics
    Despite the great promise of genetic code expansion technology to modulate structures and functions of proteins, external addition of ncAAs is required in most cases and it often limits the utility of genetic code expansion technology, especially to noncanonical amino acids (ncAAs) with poor membrane internalization. Here, we report the creation of autonomous cells, both prokaryotic and eukaryotic, with the ability to biosynthesize and genetically encode sulfotyrosine (sTyr), an important protein post-translational modification with low membrane permeability. These engineered cells can produce site-specifically sulfated proteins at a higher yield than cells fed exogenously with the highest level of sTyr reported in the literature. We use these autonomous cells to prepare highly potent thrombin inhibitors with site-specific sulfation. By enhancing ncAA incorporation efficiency, this added ability of cells to biosynthesize ncAAs and genetically incorporate them into proteins greatly extends the utility of genetic code expansion methods.