• Corpus ID: 248863086

What geometrically constrained protein models can tell us about real-world protein contact maps

@inproceedings{Molkenthin2022WhatGC,
  title={What geometrically constrained protein models can tell us about real-world protein contact maps},
  author={Nora Molkenthin and J. Guven and Steffen Muhle and Antonia Mey},
  year={2022}
}
The mechanisms by which a protein’s 3D structure can be determined based on its amino acid sequence have long been one of the key mysteries of biophysics. Often simplistic models, e.g. derived from geometric constraints, capture bulk real-world 3D protein–protein properties well. One approach is using protein contact maps to better understand protein’s properties. Here, we investigate the emergent behavior of protein contact maps derived from a geometrically constrained random interaction model… 

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References

SHOWING 1-10 OF 37 REFERENCES
Self-organized emergence of folded protein-like network structures from geometric constraints
TLDR
A simple model of structure formation that takes into account only fundamental geometric constraints and otherwise assumes randomly paired connections is proposed and finds that despite its simplicity, the model results in a network ensemble consistent with key overall features of the ensemble of Protein Residue Networks.
The effect of backbone on the small-world properties of protein contact maps
TLDR
By randomly mimicking the protein collapse, the covalent structure of the protein chain significantly contributes to the small-world behavior of the inter-residue contact graphs.
Highly accurate protein structure prediction with AlphaFold
TLDR
This work validated an entirely redesigned version of the neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods.
Establishing a Framework of Using Residue-Residue Interactions in Protein Difference Network Analysis
TLDR
The optimal dc = 4.5 Å is revealed, which defines the upper bound of the first shell of residue-residue packing in proteins, whereas nc is found to have little effects on performance.
Protein-folding dynamics: overview of molecular simulation techniques.
TLDR
This review presents algorithms for MD and their extensions and applications to protein-folding studies, using all-atom models with explicit and implicit solvent as well as reduced models of polypeptide chains.
Small-world communication of residues and significance for protein dynamics.
The protein folding problem.
TLDR
There is now a testable explanation for how a protein can fold so quickly: A protein solves its large global optimization problem as a series of smaller local optimization problems, growing and assembling the native structure from peptide fragments, local structures first.
Protein structure prediction from sequence variation
TLDR
Computation of covariation patterns are expected to complement experimental structural biology in elucidating the full spectrum of protein structures, their functional interactions and evolutionary dynamics.
Small-world view of the amino acids that play a key role in protein folding.
TLDR
Geometrical considerations are used to provide a different perspective on the fact that a few selected amino acids act as nucleation centers for protein folding and show that they have the "small-world" feature of having a limited set of vertices with large connectivity.
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