ArticleThe Journal of chemical physics2015
CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein sequences.
Article in The Journal of chemical physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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51 citing papers in PubMed.
- Distinguishing near- versus off-critical phase behaviors of intrinsically disordered proteins.Reports on progress in physics. Physical Society (Great Britain) · 2026Article
- Computational rheometry for modeling viscoelasticity and mechanical responses of biomolecular condensates.Biophysical journal · 2026Article
- Accurate prediction of thermoresponsive phase behavior of disordered proteins.Protein science : a publication of the Protein Society · 2025Article
- How does protein aggregate structure affect mechanisms of disaggregation?Biochemical Society transactions · 2025Review
- Computational rheometry of viscoelastic networks: From random graphs to biomolecular condensates.bioRxiv : the preprint server for biology · 2025Article
- Controlled liquid-liquid phase separation via the simulation-guided, targeted engineering of the RNA-binding protein PARCL.iScience · 2025Article
- FINCHES: A Computational Framework for Predicting Intermolecular Interactions in Intrinsically Disordered Proteins.International journal of molecular sciences · 2025Review
- Martini 3 coarse-grained model of enzymes: Framework with validation by all-atom simulations and x-ray diffraction measurements.The Journal of chemical physics · 2025Article
- Accurate prediction of thermoresponsive phase behavior of disordered proteins.bioRxiv : the preprint server for biology · 2025Article
- Modeling biomolecular condensates across scales: Atomistic, coarse-grained, and data-driven approaches.Advances in physics: X · 2025Article
- Crowder titrations enable the quantification of driving forces for macromolecular phase separation.Biophysical journal · 2024Article
- Aromatic and arginine content drives multiphasic condensation of protein-RNA mixtures.Biophysical journal · 2024Article
- Biomolecular condensates form spatially inhomogeneous network fluids.Nature communications · 2024Article
- Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery.Membranes · 2023Review
- Intermolecular interactions underlie protein/peptide phase separation irrespective of sequence and structure at crowded milieu.Nature communications · 2023Article
- Phase separation of protein mixtures is driven by the interplay of homotypic and heterotypic interactions.Nature communications · 2023Article
- Investigating the Hydrogen Bond-Induced Self-Assembly of Polysulfamides Using Molecular Simulations and Experiments.Macromolecules · 2023Article
- Comparative Performance of Computer Simulation Models of Intrinsically Disordered Proteins at Different Levels of Coarse-Graining.Journal of chemical information and modeling · 2023Article
- Developments in describing equilibrium phase transitions of multivalent associative macromolecules.Current opinion in structural biology · 2023Review
- Thermodynamic origins of two-component multiphase condensates of proteins.Chemical science · 2023Article
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3 authors.
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Abstract
We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of protein sequences with block-copolymeric architectures. Our algorithm, named CAMELOT for Coarse-grained simulations Aided by MachinE Learning Optimization and Training, leverages information from converged all atom simulations that is used to determine a suitable resolution and parameterize the coarse-grained model. To parameterize a system-specific coarse-grained model, we use a combination of Boltzmann inversion, non-linear regression, and a Gaussian process Bayesian optimization approach. The accuracy of the coarse-grained model is demonstrated through direct comparisons to results from all atom simulations. We demonstrate the utility of our coarse-graining approach using the block-copolymeric sequence from the exon 1 encoded sequence of the huntingtin protein. This sequence comprises of 17 residues from the N-terminal end of huntingtin (N17) followed by a polyglutamine (polyQ) tract. Simulations based on the CAMELOT approach are used to show that the adsorption and unfolding of the wild type N17 and its sequence variants on the surface of polyQ tracts engender a patchy colloid like architecture that promotes the formation of linear aggregates. These results provide a plausible explanation for experimental observations, which show that N17 accelerates the formation of linear aggregates in block-copolymeric N17-polyQ sequences. The CAMELOT approach is versatile and is generalizable for simulating the aggregation and phase behavior of a range of block-copolymeric protein sequences.
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