Browsing by Author "Wang, Wei"

Now showing 1 - 6 of 6
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    Emx2 underlies the development and evolution of marsupial gliding membranes
    (Springer Nature, 2024) Moreno, Jorge A.; Dudchenko, Olga; Feigin, Charles Y.; Mereby, Sarah A.; Chen, Zhuoxin; Ramos, Raul; Almet, Axel A.; Sen, Harsha; Brack, Benjamin J.; Johnson, Matthew R.; Li, Sha; Wang, Wei; Gaska, Jenna M.; Ploss, Alexander; Weisz, David; Omer, Arina D.; Yao, Weijie; Colaric, Zane; Kaur, Parwinder; Leger, Judy St; Nie, Qing; Mena, Alexandria; Flanagan, Joseph P.; Keller, Greta; Sanger, Thomas; Ostrow, Bruce; Plikus, Maksim V.; Kvon, Evgeny Z.; Aiden, Erez Lieberman; Mallarino, Ricardo; Center for Theoretical Biological Physics
    Phenotypic variation among species is a product of evolutionary changes to developmental programs1,2. However, how these changes generate novel morphological traits remains largely unclear. Here we studied the genomic and developmental basis of the mammalian gliding membrane, or patagium—an adaptative trait that has repeatedly evolved in different lineages, including in closely related marsupial species. Through comparative genomic analysis of 15 marsupial genomes, both from gliding and non-gliding species, we find that the Emx2 locus experienced lineage-specific patterns of accelerated cis-regulatory evolution in gliding species. By combining epigenomics, transcriptomics and in-pouch marsupial transgenics, we show that Emx2 is a critical upstream regulator of patagium development. Moreover, we identify different cis-regulatory elements that may be responsible for driving increased Emx2 expression levels in gliding species. Lastly, using mouse functional experiments, we find evidence that Emx2 expression patterns in gliders may have been modified from a pre-existing program found in all mammals. Together, our results suggest that patagia repeatedly originated through a process of convergent genomic evolution, whereby regulation of Emx2 was altered by distinct cis-regulatory elements in independently evolved species. Thus, different regulatory elements targeting the same key developmental gene may constitute an effective strategy by which natural selection has harnessed regulatory evolution in marsupial genomes to generate phenotypic novelty.
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    Exploration of Tikhonov regularization for the fusion of experimental data and computational fluid dynamics
    (1999) Wang, Wei; Meade, Andrew J., Jr.
    A method is developed to fuse Computational Fluid Dynamics (CFD) simulations and experimental data through the use of Tikhonov regularization. Inviscid-Viscous Interaction and Thin-Layer Navier-Stokes Equation models are used to provide CFD solutions for the flow past NACA 0012 and RAE 2822 airfoils, respectively. The velocity profile within the boundary layer and the pressure coefficient on the surface of the airfoil are merged with the corresponding experimental data. A finite element approach is applied to accomplish the numerical solution of the Tikhonov regularization method. By using over- or under-relaxation technique, relatively few iterations are needed to achieve the convergence of the fusion method. The results demonstrate that a-priori CFD solutions of low fidelity can be improved by the experimental data with less computational cost compared with more sophisticated CFD models. Alternatively, the sparse and scattered experimental data are efficiently processed by utilizing CFD models as regularization. The limitations of the Tikhonov regularization method have been examined. The result shows that the fusion method has significant advantages over a nonlinear least-square polynomial approach for interpolating and extrapolating experimental data.
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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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    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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    Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis
    (Elsevier, 2020) Roussarie, Jean-Pierre; Yao, Vicky; Rodriguez-Rodriguez, Patricia; Oughtred, Rose; Rust, Jennifer; Plautz, Zakary; Kasturia, Shirin; Albornoz, Christian; Wang, Wei; Schmidt, Eric F.; Dannenfelser, Ruth; Tadych, Alicja; Brichta, Lars; Barnea-Cramer, Alona; Heintz, Nathaniel; Hof, Patrick R.; Heiman, Myriam; Dolinski, Kara; Flajolet, Marc; Troyanskaya, Olga G.; Greengard, Paul
    A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neuron-type-specific molecular profiles across the lifetime of the healthy mouse, which we generated using bacTRAP, with postmortem human functional genomics and quantitative genetics data. We demonstrate human-mouse conservation of cellular taxonomy at the molecular level for neurons vulnerable and resistant in AD, identify specific genes and pathways associated with AD neuropathology, and pinpoint a specific functional gene module underlying selective vulnerability, enriched in processes associated with axonal remodeling, and affected by amyloid accumulation and aging. We have made all cell-type-specific profiles and functional networks available at http://alz.princeton.edu. Overall, our study provides a molecular framework for understanding the complex interplay between A?, aging, and neurodegeneration within the most vulnerable neurons in AD.
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    Synergistic Gold-Bismuth Catalysis for Non-Mercury Hydrochlorination of Acetylene to Vinyl Chloride Monomer
    (American Chemical Society, 2014) Zhou, Kai; Wang, Wei; Zhao, Zhun; Luo, Guohua; Miller, Jeffrey T.; Wong, Michael S.; Wei, Fei
    Gold has been proposed as an environmentally friendly catalyst for acetylene hydrochlorination for vinyl chloride monomer synthesis by replacing the commercially used mercury catalyst. However, long life with excellent activity is difficult to achieve because gold is readily reduced to metallic nanoparticles. The stability of gold limits its industrial application. In this paper, we promoted gold with bismuth for the hydrochlorination of acetylene. It was found that the Bi promotion leads to partial reduction to AuCl, rather than the complete reduction of Au to metallic nanoparticles in the absence of Bi. The optimized catalyst with a molar ratio of Bi/Au = 3:1 (0.3 wt % Au) showed comparable reactivity to 1.0 wt % Au catalyst and significantly improved stability. Furthermore, the gold-bismuth catalyst had higher activity and stability than the commercial mercury catalyst, is less toxic and more environmental-friendly, making it a potentially green, mercury-free industrial catalyst for acetylene hydrochlorination.