Browsing by Author "Chen, Mingchen"

Now showing 1 - 8 of 8
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    Assemblies of calcium/calmodulin-dependent kinase II with actin and their dynamic regulation by calmodulin in dendritic spines
    (National Academy of Sciences, 2019) Wang, Qian; Chen, Mingchen; Schafer, Nicholas P.; Bueno, Carlos; Song, Sarah S.; Hudmon, Andy; Wolynes, Peter G.; Waxham, M. Neal; Cheung, Margaret S.
    The structural dynamics of the dendritic synapse, arising from the remodeling of actin cytoskeletons, has been widely associated with memory and cognition. The remodeling is regulated by intracellular Ca2+ levels. Under low Ca2+ concentration, actin filaments are bundled by a calcium signaling protein, CaMKII. When the Ca2+ concentration is raised, CaMKII dissociates from actin and opens the window for actin remodeling. At present, the molecular details of the actin bundling and regulation are elusive. Herein we use experimental tools along with molecular simulations to construct a model of how CaMKII bundles actin and how the CaMKII–actin architecture is regulated by Ca2+ signals. In this way, our results explain how Ca2+ signals ultimately change the structure of the dendritic synapse.
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    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.
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    Frustration and the Kinetic Repartitioning Mechanism of Substrate Inhibition in Enzyme Catalysis
    (American Chemical Society, 2022) Zhang, Yangyang; Chen, Mingchen; Lu, Jiajun; Li, Wenfei; Wolynes, Peter G.; Wang, Wei; Center for Theoretical Biological Physics
    Substrate inhibition, whereby enzymatic activity decreases with excess substrate after reaching a maximum turnover rate, is among the most elusive phenomena in enzymatic catalysis. Here, based on a dynamic energy landscape model, we investigate the underlying mechanism by performing molecular simulations and frustration analysis for a model enzyme adenylate kinase (AdK), which catalyzes the phosphoryl transfer reaction ATP + AMP ⇋ ADP + ADP. Intriguingly, these reveal a kinetic repartitioning mechanism of substrate inhibition, whereby excess substrate AMP suppresses the population of an energetically frustrated, but kinetically activated, catalytic pathway going through a substrate (ATP)-product (ADP) cobound complex with steric incompatibility. Such a frustrated pathway plays a crucial role in facilitating the bottleneck product ADP release, and its suppression by excess substrate AMP leads to a slow down of product release and overall turnover. The simulation results directly demonstrate that substrate inhibition arises from the rate-limiting product-release step, instead of the steps for populating the catalytically competent complex as often suggested in previous works. Furthermore, there is a tight interplay between the enzyme conformational equilibrium and the extent of substrate inhibition. Mutations biasing to more closed conformations tend to enhance substrate inhibition. We also characterized the key features of single-molecule enzyme kinetics with substrate inhibition effect. We propose that the above molecular mechanism of substrate inhibition may be relevant to other multisubstrate enzymes in which product release is the bottleneck step.
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    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.
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    Predicting protein conformational motions using energetic frustration analysis and AlphaFold2
    (National Academy of Sciences, 2024) Guan, Xingyue; Tang, Qian-Yuan; Ren, Weitong; Chen, Mingchen; Wang, Wei; Wolynes, Peter G.; Li, Wenfei; Center for Theoretical Biological Physics
    Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.
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    Protein Aggregation in the Formation of Long Term Memory and Neurodegenerative Diseases
    (2019-07-02) Chen, Mingchen; Wolynes, Peter Guy; Levine, Herbert
    The formation of intra- and extra-cellular amyloid fibres through protein aggregation are traditionally coupled to a series of devastating and incurable neurodegenerative disorders, as well as functional implications. Computational simulations of protein aggregation has been challenging due to the relative long time scale and the involvement of larger systems. In this thesis, we summarized the efforts of using a coarse-grained model, the associative memory, water-mediated interactions, structure and energy model (AWSEM), in the study of protein aggregation and structures of amyloids. In the first part, we explored the aggregation free energy landscapes of a mechanical prion, CPEB protein. While CPEB is aggregation prone, the aggregation process is only favorable after exerting mechanical forces. We propose that an active cytoskeleton can be the origin of this mechanical force, and this mechanical catalysis makes possible a positive feedback loop that would localize the formation of CPEB fibres to active synapse areas and mark the formation of long-term memory. In the second part, we explored the aggregation landscapes of polyglutamine repeats, which are involved in over 9 neurodegenerative diseases. Free energy analyses show the length dependence of the aggregation of polyglutamine repeats, and this length-dependence property arises from the intrinsic properties of polyglutamine repeats to form β-hairpins. Following this the aggregation of full-length Huntington Exon1 encoded protein fragments, which is the culprit in Huntington’s Diseases is explored. The simulations show that the addition of the N-terminal 17-residue sequence (NT17) facilitates polyQ aggregation by encouraging the formation of prefibrillar oligomers, while adding the C-terminal polyproline sequence (P10) inhibits aggregation. The combination of both terminal additions in HTT exon 1 fragment leads to a complex aggregation mechanism, whose basic core resembles that found for the aggregation of pure polyQ repeats. At the extrapolated physiological concentration, while the grand canonical free energy profiles are uphill for HTT exon1 fragments having 20 or 30 glutamines, the aggregation landscape for fragments with 40 repeats has become downhill, thus explaining the correlation between length and Huntington disease onset age seen clinically. After elucidating the mechanisms of protein aggregation in the above cases, the structure of amyloidogenic peptides is examined under the framework of energy landscape theory. A predictive tool, the "AWSEM-Amylometer" was developed to predict the topology and aggregation propensity of peptides. The AWSEM-Amylometer notably performs better than other software in terms of the prediction of amyloidogenic sequences, and it also predicts the amyloid topology of existing peptide amyloids accurately. Nevertheless, the tertiary assembly of those amyloidogenic segments in full-length proteins is still largely unknown. So in the final part of this thesis, another tool, the AWSEM-Ribbon model, with layered constraints on each protein monomers, in the study of amyloid structures is introduced. Adopting this view of fiber architecture leads to a practical method of predicting stable protofilament structures for arbitrary peptide sequences. We apply this scheme to variants of Aβ, the amyloid forming peptide that is characteristically associated with Alzheimer’s disease. Consistent with what is known from experiment, Aβ protofibrils are found to be polymorphic. The polymorph landscape of Aβ also suggests some evolutionary aspects of amyloid protein assembly. Overall, the simulations presented here not only provide novel indications of detailed molecular mechanisms for the formation of long-term memory and the progress of neurodegenerative diseases, but also indicate the capability of energy landscape analyses to address protein misfolding/aggregation.
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    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.
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    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.