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    Structured Review

    Databank Inc swissprot databank
    a , The structural potential operates on the dihedral and two consecutive bond angles of four consecutive amino acids. b , The potential is made from two Gaussian potentials centered at the geometry for a perfect helix. The positive Gaussian provides a kinetic barrier while the negative Gaussian biases helix formation. c , The width of the well and the location of the helix barrier was determined by overlap with α-carbon coordinates of helices extracted from PDB structures. d , The kinetics of the helix–coil transition is set by enforcing a barrier height of 4.0 kcal/mol. Standard errors are calculated across 150 independent 10 µs simulations of an A11 peptide. e , A multiple sequence alignment (MSA) is extracted from all helical sequences from predicted AlphaFold 2.0 structures in the <t>SwissProt</t> database that is clustered with mmSeqs2. Boltzmann machine learning with gradient descent is used on the MSA to produce a Potts model. f , The natural log of the helix to coil frames for segments of 4 consecutive amino acids within 75 separate 11 length peptides simulated at an atomistic resolution. Block averaging on 3 equally-sized blocks from 1.5 µs of simulation time at the coldest replica (298.15 K) is used for error. g , h , i , A Gaussian process workflow for parameterizing well depths to recapitulate helical propensities from atomistic simulations and NMR measurements . j , The final well depth to Potts score trend combines the y-intercept from fitting the NMR experimental datasets with the slope of a fitting to all non helix-breaking sequences. Errors in Potts scores are calculated using the standard deviation of all Potts scores along the peptide sequence. k , Mpipi-Helix models α-helices in a sequence-dependent manner.
    Swissprot Databank, supplied by Databank Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    1) Product Images from "Biomolecular Condensates Dictate the Folding Landscape of Proteins"

    Article Title: Biomolecular Condensates Dictate the Folding Landscape of Proteins

    Journal: bioRxiv

    doi: 10.64898/2026.01.12.699095

    a , The structural potential operates on the dihedral and two consecutive bond angles of four consecutive amino acids. b , The potential is made from two Gaussian potentials centered at the geometry for a perfect helix. The positive Gaussian provides a kinetic barrier while the negative Gaussian biases helix formation. c , The width of the well and the location of the helix barrier was determined by overlap with α-carbon coordinates of helices extracted from PDB structures. d , The kinetics of the helix–coil transition is set by enforcing a barrier height of 4.0 kcal/mol. Standard errors are calculated across 150 independent 10 µs simulations of an A11 peptide. e , A multiple sequence alignment (MSA) is extracted from all helical sequences from predicted AlphaFold 2.0 structures in the SwissProt database that is clustered with mmSeqs2. Boltzmann machine learning with gradient descent is used on the MSA to produce a Potts model. f , The natural log of the helix to coil frames for segments of 4 consecutive amino acids within 75 separate 11 length peptides simulated at an atomistic resolution. Block averaging on 3 equally-sized blocks from 1.5 µs of simulation time at the coldest replica (298.15 K) is used for error. g , h , i , A Gaussian process workflow for parameterizing well depths to recapitulate helical propensities from atomistic simulations and NMR measurements . j , The final well depth to Potts score trend combines the y-intercept from fitting the NMR experimental datasets with the slope of a fitting to all non helix-breaking sequences. Errors in Potts scores are calculated using the standard deviation of all Potts scores along the peptide sequence. k , Mpipi-Helix models α-helices in a sequence-dependent manner.
    Figure Legend Snippet: a , The structural potential operates on the dihedral and two consecutive bond angles of four consecutive amino acids. b , The potential is made from two Gaussian potentials centered at the geometry for a perfect helix. The positive Gaussian provides a kinetic barrier while the negative Gaussian biases helix formation. c , The width of the well and the location of the helix barrier was determined by overlap with α-carbon coordinates of helices extracted from PDB structures. d , The kinetics of the helix–coil transition is set by enforcing a barrier height of 4.0 kcal/mol. Standard errors are calculated across 150 independent 10 µs simulations of an A11 peptide. e , A multiple sequence alignment (MSA) is extracted from all helical sequences from predicted AlphaFold 2.0 structures in the SwissProt database that is clustered with mmSeqs2. Boltzmann machine learning with gradient descent is used on the MSA to produce a Potts model. f , The natural log of the helix to coil frames for segments of 4 consecutive amino acids within 75 separate 11 length peptides simulated at an atomistic resolution. Block averaging on 3 equally-sized blocks from 1.5 µs of simulation time at the coldest replica (298.15 K) is used for error. g , h , i , A Gaussian process workflow for parameterizing well depths to recapitulate helical propensities from atomistic simulations and NMR measurements . j , The final well depth to Potts score trend combines the y-intercept from fitting the NMR experimental datasets with the slope of a fitting to all non helix-breaking sequences. Errors in Potts scores are calculated using the standard deviation of all Potts scores along the peptide sequence. k , Mpipi-Helix models α-helices in a sequence-dependent manner.

    Techniques Used: Sequencing, Blocking Assay, Standard Deviation



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    a , The structural potential operates on the dihedral and two consecutive bond angles of four consecutive amino acids. b , The potential is made from two Gaussian potentials centered at the geometry for a perfect helix. The positive Gaussian provides a kinetic barrier while the negative Gaussian biases helix formation. c , The width of the well and the location of the helix barrier was determined by overlap with α-carbon coordinates of helices extracted from PDB structures. d , The kinetics of the helix–coil transition is set by enforcing a barrier height of 4.0 kcal/mol. Standard errors are calculated across 150 independent 10 µs simulations of an A11 peptide. e , A multiple sequence alignment (MSA) is extracted from all helical sequences from predicted AlphaFold 2.0 structures in the <t>SwissProt</t> database that is clustered with mmSeqs2. Boltzmann machine learning with gradient descent is used on the MSA to produce a Potts model. f , The natural log of the helix to coil frames for segments of 4 consecutive amino acids within 75 separate 11 length peptides simulated at an atomistic resolution. Block averaging on 3 equally-sized blocks from 1.5 µs of simulation time at the coldest replica (298.15 K) is used for error. g , h , i , A Gaussian process workflow for parameterizing well depths to recapitulate helical propensities from atomistic simulations and NMR measurements . j , The final well depth to Potts score trend combines the y-intercept from fitting the NMR experimental datasets with the slope of a fitting to all non helix-breaking sequences. Errors in Potts scores are calculated using the standard deviation of all Potts scores along the peptide sequence. k , Mpipi-Helix models α-helices in a sequence-dependent manner.
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    a , The structural potential operates on the dihedral and two consecutive bond angles of four consecutive amino acids. b , The potential is made from two Gaussian potentials centered at the geometry for a perfect helix. The positive Gaussian provides a kinetic barrier while the negative Gaussian biases helix formation. c , The width of the well and the location of the helix barrier was determined by overlap with α-carbon coordinates of helices extracted from PDB structures. d , The kinetics of the helix–coil transition is set by enforcing a barrier height of 4.0 kcal/mol. Standard errors are calculated across 150 independent 10 µs simulations of an A11 peptide. e , A multiple sequence alignment (MSA) is extracted from all helical sequences from predicted AlphaFold 2.0 structures in the SwissProt database that is clustered with mmSeqs2. Boltzmann machine learning with gradient descent is used on the MSA to produce a Potts model. f , The natural log of the helix to coil frames for segments of 4 consecutive amino acids within 75 separate 11 length peptides simulated at an atomistic resolution. Block averaging on 3 equally-sized blocks from 1.5 µs of simulation time at the coldest replica (298.15 K) is used for error. g , h , i , A Gaussian process workflow for parameterizing well depths to recapitulate helical propensities from atomistic simulations and NMR measurements . j , The final well depth to Potts score trend combines the y-intercept from fitting the NMR experimental datasets with the slope of a fitting to all non helix-breaking sequences. Errors in Potts scores are calculated using the standard deviation of all Potts scores along the peptide sequence. k , Mpipi-Helix models α-helices in a sequence-dependent manner.

    Journal: bioRxiv

    Article Title: Biomolecular Condensates Dictate the Folding Landscape of Proteins

    doi: 10.64898/2026.01.12.699095

    Figure Lengend Snippet: a , The structural potential operates on the dihedral and two consecutive bond angles of four consecutive amino acids. b , The potential is made from two Gaussian potentials centered at the geometry for a perfect helix. The positive Gaussian provides a kinetic barrier while the negative Gaussian biases helix formation. c , The width of the well and the location of the helix barrier was determined by overlap with α-carbon coordinates of helices extracted from PDB structures. d , The kinetics of the helix–coil transition is set by enforcing a barrier height of 4.0 kcal/mol. Standard errors are calculated across 150 independent 10 µs simulations of an A11 peptide. e , A multiple sequence alignment (MSA) is extracted from all helical sequences from predicted AlphaFold 2.0 structures in the SwissProt database that is clustered with mmSeqs2. Boltzmann machine learning with gradient descent is used on the MSA to produce a Potts model. f , The natural log of the helix to coil frames for segments of 4 consecutive amino acids within 75 separate 11 length peptides simulated at an atomistic resolution. Block averaging on 3 equally-sized blocks from 1.5 µs of simulation time at the coldest replica (298.15 K) is used for error. g , h , i , A Gaussian process workflow for parameterizing well depths to recapitulate helical propensities from atomistic simulations and NMR measurements . j , The final well depth to Potts score trend combines the y-intercept from fitting the NMR experimental datasets with the slope of a fitting to all non helix-breaking sequences. Errors in Potts scores are calculated using the standard deviation of all Potts scores along the peptide sequence. k , Mpipi-Helix models α-helices in a sequence-dependent manner.

    Article Snippet: To build this MSA, we collected bioinformatic data using the entire SwissProt Databank [ ] with corresponding AlphaFold 2.0 [ ] structures.

    Techniques: Sequencing, Blocking Assay, Standard Deviation