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Cake day: July 10th, 2023
  • Xylogx@lemmy.mltoAsklemmy@lemmy.ml•Has any scientific breakthrough come out of the Folding@Home project?
    236·
    4 months ago

    Here is an AI based summary of top breakthroughs:

    Here are some of the biggest breakthroughs mentioned in the provided references:

    1. Exascale Simulations for SARS-CoV-2:

      • Conducted exascale simulations of SARS-CoV-2 spike protein, revealing dramatic spike opening and cryptic pockets, which have implications for drug design and understanding viral infectivity (Zimmerman et al., 2021) .

    2. Ab Initio Protein Folding Simulations:

      • Achieved molecular simulations of ab initio protein folding for the NTL9 protein, providing insights into the protein folding process (Voelz et al., 2010) .

    3. Markov State Models for Protein Dynamics:

      • Developed Markov State Models (MSMs) to study protein folding kinetics and dynamics, providing a framework to understand protein conformational changes over long timescales (Bowman et al., 2009; Lane et al., 2011) .

    4. RNA Polymerase II Dynamics:

      • Investigated the dynamics of RNA polymerase II translocation at atomic resolution, elucidating mechanisms of transcription elongation (Silva et al., 2014) .

    5. Ligand Modulation of GPCR Activation:

      • Used cloud-based simulations to reveal how ligands modulate G protein-coupled receptor (GPCR) activation pathways, advancing the understanding of GPCR function and drug targeting (Kohlhoff et al., 2014) .

    6. Nanotube Confinement Effects on Proteins:

      • Demonstrated that nanotube confinement can denature protein helices, providing insights into the effects of nanoscale environments on protein structure (Sorin & Pande, 2006) .

    7. Simulation and Experiment in Protein Folding:

      • Combined simulation and experimental approaches to reveal slow unfolded-state structuring in acyl-CoA binding protein folding, highlighting the interplay between simulations and experiments (Voelz et al., 2012) .

    8. Advances in Markov State Models:

      • Improved coarse-graining and adaptive sampling techniques in MSMs, enhancing the modeling of biomolecular dynamics (Bowman, 2012; Zimmerman et al., 2018) .

    9. Insights into Allosteric Sites:

      • Identified potential cryptic allosteric sites within folded proteins using equilibrium fluctuation analysis, suggesting new targets for drug discovery (Bowman & Geissler, 2012) .

    10. GPCR Activation Pathways:

      • Revealed ligand modulation of GPCR activation pathways through extensive simulations, providing insights into receptor function (Kohlhoff et al., 2014) .