PeptideBuilder: A simple Python library to generate model peptides

Tien, Matthew; Sydykova, Dariya K.; Meyer, Austin G.; Wilke, Claus O.

doi:10.7717/peerj.80

Cited by 86 publications

(86 citation statements)

References 10 publications

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“…A structural database consisting of regular poly-L-alanine HCO-(Ala) 11 -CONH 2 peptides defined by a single pair of φ and ψ torsion angles for each peptide was created using Biopython 30,31 with the peptide builder by Tien et al 32 The database was initially constructed using 3035 of such regular structures, by selecting possible combinations of φ and ψ torsion angles across the entire Ramachandran plot. The φ and ψ grid is created with a spacing of 5° in both directions as can be seen in Figure 1.…”

Section: Experimental Computational Methodology: Database Constructionmentioning

confidence: 99%

Ramachandran mapping of peptide conformation using a large database of computed Raman and Raman optical activity spectra

Mensch

Barron

Johannessen

2016

Phys. Chem. Chem. Phys.

136

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The past decades, Raman optical activity (ROA) spectroscopy has been shown to be very sensitive to the solution structure of peptides and proteins. A major and urgent challenge remains the need to make detailed assignments of experimental ROA patterns and relate those to the solution structure adopted by the protein. The past years, theoretical developments and implementations of ROA theory have made it possible to use quantum chemical methods to compute ROA spectra of peptides. In this work, a large database of ROA spectra of peptide model structures describing the allowed backbone conformations of proteins were systematically calculated and used to make unprecedented detailed assignments of experimental ROA patterns to the conformational elements of the peptide in solution. By using a similarity index to compare an experimental spectrum to the database spectra (2902 theoretical spectra), the conformational preference of the peptide in solution can be assigned to a very specific region in the Ramachandran space. For six (poly)peptides this approach was validated and gives excellent agreement between experiment and theory. Additionally, hydrogen/deuterium exchanged structures and the conformational dependence of the amide modes in Raman spectra can be analysed using the new database. The excellent agreement between experiment and theory demonstrates the power of the newly developed database as a tool to study Raman and ROA patterns of peptides and proteins. The interpretation of experimental ROA patterns of different proteins published in scientific literature is discussed based on the spectral trends observed in the database.

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Section: Experimental Computational Methodology: Database Constructionmentioning

confidence: 99%

Ramachandran mapping of peptide conformation using a large database of computed Raman and Raman optical activity spectra

Mensch

Barron

Johannessen

2016

Phys. Chem. Chem. Phys.

136

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“…A similar approach to avoid spurious conformations has been adopted by Vila et al in the creation of the CheShift chemical shifts predictor, which is parametrized from quantum mechanical calculations on model peptides [Vila et al, 2009]. Here bond angles and lengths are simply set to the standard values of the ECEPP/3 force field [Nemethy et al, 1992].…”

Section: Optimizationmentioning

confidence: 99%

“…Naturally, an efficient and stable method is needed in order to generate such a number of peptide models. Two recent programs that can generate peptide structures are the Ribosome program [Srinivasan, 2013] and the PeptideBuilder library [Tien et al, 2013]. The Ribosome program is written in FORTRAN and thus difficult to extend and therefore not ideal for use in an automated, scripting fashion.…”

Section: Introductionmentioning

confidence: 99%

“…Although theoretical methods to treat large systems are being developed, it is computationally more feasible to investigate properties of small, representative, protein-like structures. For example, calculations on peptides have been used to parametrize protein-specific molecular mechanics force fields, and NMR properties of proteins such as chemical shifts and spin-spin coupling constants [Mackerell, 2004, Vila et al, 2009, Case et al, 2000.…”

Section: Introductionmentioning

confidence: 99%

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FragBuilder: An efficient Python library to setup quantum chemistry calculations on peptides models

Christensen

Hamelryck

Jensen

2013

Preprint

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We present a powerful Python library to quickly and efficiently generate realistic peptide model structures. The library makes it possible to quickly set up quantum mechanical calculations on model peptide structures. It is possible to manually specify a specific conformation of the peptide. Additionally the library also offers sampling of backbone conformations and side chain rotamer conformations from continuous distributions. The generated peptides can then be geometry optimized by the MMFF94 molecular mechanics force field via convenient functions inside the library. Finally, it is possible to output the resulting structures directly to files in XYZ and PDB formats, or optionally directly as input files for a quantum chemistry program. FragBuilder is freely available at https://github.com/jensengroup/fragbuilder/ under the terms of the BSD open source license.

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“…Although theoretical methods to treat large systems are being developed, it is computationally more feasible to investigate properties of small, representative, protein-like structures, such as peptides. For example, calculations on peptides have been used to parametrize protein-specific molecular mechanics force fields, and models for NMR properties of proteins such as chemical shifts and spin-spin coupling constants [Mackerell, 2004, Vila et al, 2009, Case et al, 2000.…”

Section: Introductionmentioning

confidence: 99%

FragBuilder: An efficient Python library to setup quantum chemistry calculations on peptide models

Christensen

Hamelryck

Jensen

2014

Preprint

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View the peer-reviewed version (peerj.com/articles/277), which is the preferred citable publication unless you specifically need to cite this preprint. We present a powerful Python library to quickly and efficiently generate realistic peptide model structures. The library makes it possible to quickly set up quantum mechanical calculations on model peptide structures. It is possible to manually specify a specific conformation of the peptide. Additionally the library also offers sampling of backbone conformations and side chain rotamer conformations from continuous distributions. The generated peptides can then be geometry optimized by the MMFF94 molecular mechanics force field via convenient functions inside the library. Finally, it is possible to output the resulting structures directly to files in a variety of useful formats, such as XYZ or PDB formats, or directly as input files for a quantum chemistry program. FragBuilder is freely available at https://github.com/jensengroup/fragbuilder/ under the terms of the BSD open source license. 1PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.169v3 | CC-BY 3.0

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PeptideBuilder: A simple Python library to generate model peptides

Cited by 86 publications

References 10 publications

Ramachandran mapping of peptide conformation using a large database of computed Raman and Raman optical activity spectra

Ramachandran mapping of peptide conformation using a large database of computed Raman and Raman optical activity spectra

FragBuilder: An efficient Python library to setup quantum chemistry calculations on peptides models

FragBuilder: An efficient Python library to setup quantum chemistry calculations on peptide models

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