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references.bib
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@article{PhysRevLett.79.765,
title = {{Nature of Driving Force for Protein Folding: A Result From Analyzing the Statistical Potential}},
author = {Li, Hao and Tang, Chao and Wingreen, Ned S.},
journal = {Phys. Rev. Lett.},
volume = {79},
issue = {4},
pages = {765--768},
numpages = {0},
year = {1997},
month = {Jul},
publisher = {American Physical Society},
doi = {10.1103/PhysRevLett.79.765},
url = {https://link.aps.org/doi/10.1103/PhysRevLett.79.765}
}
@article{10.1016/j.bpj.2018.07.035,
author = {Anishchenko, Ivan
and Kundrotas, Petras J.
and Vakser, Ilya A.},
title = {{Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model}},
journal = {Biophysical Journal},
year = {2018},
month = {Sep},
day = {04},
publisher = {Elsevier},
volume = {115},
number = {5},
pages = {809-821},
issn = {0006-3495},
doi = {10.1016/j.bpj.2018.07.035},
url = {https://doi.org/10.1016/j.bpj.2018.07.035}
}
@article{S1359-0278(97)00041-2,
title = {{Recovery of protein structure from contact maps}},
journal = {Folding and Design},
volume = {2},
number = {5},
pages = {295-306},
year = {1997},
issn = {1359-0278},
doi = {https://doi.org/10.1016/S1359-0278(97)00041-2},
url = {https://www.sciencedirect.com/science/article/pii/S1359027897000412},
author = {Michele Vendruscolo and Edo Kussell and Eytan Domany},
keywords = {contact, distance, dynamics, map, protein, reconstruction},
abstract = {Background: Prediction of a protein's structure from its amino acid sequence is a key issue in molecular biology. While dynamics, performed in the space of two-dimensional contact maps, eases the necessary conformational search, it may also lead to maps that do not correspond to any real three-dimensional structure. To remedy this, an efficient procedure is needed to reconstruct three-dimensional conformations from their contact maps. Results: We present an efficient algorithm to recover the three-dimensional structure of a protein from its contact map representation. We show that when a physically realizable map is used as target, our method generates a structure whose contact map is essentially similar to the target. Furthermore, the reconstructed and original structures are similar up to the resolution of the contact map representation. Next, we use nonphysical target maps, obtained by corrupting a physical one; in this case, our method essentially recovers the underlying physical map and structure. Hence, our algorithm will help to fold proteins, using dynamics in the space of contact maps. Finally, we investigate the manner in which the quality of the recovered structure degrades when the number of contacts is reduced. Conclusions: The procedure is capable of assigning quickly and reliably a three-dimensional structure to a given contact map. It is well suited for use in parallel with dynamics in contact map space to project a contact map onto its closest physically allowed structural counterpart.}
}
@article{PhysRevLett.92.218101,
title = {{Reconstruction of Protein Structures from a Vectorial Representation}},
author = {Porto, Markus and Bastolla, Ugo and Roman, H. Eduardo and Vendruscolo, Michele},
journal = {Phys. Rev. Lett.},
volume = {92},
issue = {21},
pages = {218101},
numpages = {4},
year = {2004},
month = {May},
publisher = {American Physical Society},
doi = {10.1103/PhysRevLett.92.218101},
url = {https://link.aps.org/doi/10.1103/PhysRevLett.92.218101}
}
@inproceedings{10.1109/HICSS.1994.323564,
author = {Stanislav G. Galaktionov and Garland R. Marshall},
booktitle = {1994 Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences},
title = {Properties of intraglobular contacts in proteins: an approach to prediction of tertiary structure},
year = {1994},
volume = {5},
number = {},
pages = {326-335},
doi = {10.1109/HICSS.1994.323564},
url = {https://ieeexplore.ieee.org/document/323564}
}
%protein secondary structure assignment
@article{https://doi.org/10.1002/bip.360221211,
author = {Kabsch, Wolfgang and Sander, Christian},
title = {Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features},
journal = {Biopolymers},
volume = {22},
number = {12},
pages = {2577-2637},
doi = {https://doi.org/10.1002/bip.360221211},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/bip.360221211},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/bip.360221211},
abstract = {Abstract For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern-recognition process of hydrogen-bonded and geometrical features extracted from x-ray coordinates. Cooperative secondary structure is recognized as repeats of the elementary hydrogen-bonding patterns “turn” and “bridge.” Repeating turns are “helices,” repeating bridges are “ladders,” connected ladders are “sheets.” Geometric structure is defined in terms of the concepts torsion and curvature of differential geometry. Local chain “chirality” is the torsional handedness of four consecutive Cα positions and is positive for right-handed helices and negative for ideal twisted β-sheets. Curved pieces are defined as “bends.” Solvent “exposure” is given as the number of water molecules in possible contact with a residue. The end result is a compilation of the primary structure, including SS bonds, secondary structure, and solvent exposure of 62 different globular proteins. The presentation is in linear form: strip graphs for an overall view and strip tables for the details of each of 10.925 residues. The dictionary is also available in computer-readable form for protein structure prediction work.},
year = {1983}
}
@article{https://doi.org/10.1002/prot.340230412,
author = {Frishman, Dmitrij and Argos, Patrick},
title = {Knowledge-based protein secondary structure assignment},
journal = {Proteins: Structure, Function, and Bioinformatics},
volume = {23},
number = {4},
pages = {566-579},
keywords = {protein structure analysis, hydrogen bond, torsional angle, α-helix, β-sheet},
doi = {https://doi.org/10.1002/prot.340230412},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.340230412},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/prot.340230412},
abstract = {Abstract We have developed an automatic algorithm STRIDE for protein secondary structure assignment from atomic coordinates based on the combined use of hydrogen bond energy and statistically derived backbone torsional angle information. Parameters of the pattern recognition procedure were optimized using designations provided by the crystallographers as a standard-of-truth. Comparison to the currently most widely used technique DSSP by Kabsch and Sander (Biopolymers 22:2577-2637, 1983) shows that STRIDE and DSSP assign secondary structural states in 58 and 31\% of 226 protein chains in our data sample, respectively, in greater agreement with the specific residue-by-residue definitions provided by the discoverers of the structures while in 11\% of the chains, the assignments are the same. STRIDE delineates every 11th helix and every 32nd strand more in accord with published assignments. © 1995 Wiley-Liss, Inc.},
year = {1995}
}
@article{10.1002/pro.4155,
author = {Norn, Christoffer and André, Ingemar and Theobald, Douglas L.},
title = {A thermodynamic model of protein structure evolution explains empirical amino acid substitution matrices},
journal = {Protein Science},
volume = {30},
number = {10},
pages = {2057-2068},
keywords = {amino acid substitution, exchangeabilities, protein evolution, protein stability, replacement matrices},
doi = {https://doi.org/10.1002/pro.4155},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4155},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.4155},
abstract = {Abstract Proteins evolve under a myriad of biophysical selection pressures that collectively control the patterns of amino acid substitutions. These evolutionary pressures are sufficiently consistent over time and across protein families to produce substitution patterns, summarized in global amino acid substitution matrices such as BLOSUM, JTT, WAG, and LG, which can be used to successfully detect homologs, infer phylogenies, and reconstruct ancestral sequences. Although the factors that govern the variation of amino acid substitution rates have received much attention, the influence of thermodynamic stability constraints remains unresolved. Here we develop a simple model to calculate amino acid substitution matrices from evolutionary dynamics controlled by a fitness function that reports on the thermodynamic effects of amino acid mutations in protein structures. This hybrid biophysical and evolutionary model accounts for nucleotide transition/transversion rate bias, multi-nucleotide codon changes, the number of codons per amino acid, and thermodynamic protein stability. We find that our theoretical model accurately recapitulates the complex yet universal pattern observed in common global amino acid substitution matrices used in phylogenetics. These results suggest that selection for thermodynamically stable proteins, coupled with nucleotide mutation bias filtered by the structure of the genetic code, is the primary driver behind the global amino acid substitution patterns observed in proteins throughout the tree of life.},
year = {2021}
}