pyPept: a python']python library to generate atomistic 2D and 3D representations of peptides

被引:4
|
作者
Ochoa, Rodrigo [1 ]
Brown, J. B. [1 ]
Fox, Thomas [1 ]
机构
[1] Boehringer Ingelheim Pharm GmbH & Co KG, Med Chem, D-88397 Biberach, Germany
关键词
Peptide; !text type='Python']Python[!/text; Conformer; BILN; RDKit; Cheminformatics; Molecule depiction; PROTEIN; HELM;
D O I
10.1186/s13321-023-00748-2
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
We present pyPept, a set of executables and underlying python-language classes to easily create, manipulate, and analyze peptide molecules using the FASTA, HELM, or recently-developed BILN notations. The framework enables the analysis of both pure proteinogenic peptides as well as those with non-natural amino acids, including support to assemble a customizable monomer library, without requiring programming. From line notations, a peptide is transformed into a molecular graph for 2D depiction tasks, the calculation of physicochemical properties, and other systematic analyses or processing pipelines. The package includes a module to rapidly generate approximate peptide conformers by incorporating secondary structure restraints either given by the user or predicted via pyPept, and a wrapper tool is also provided to automate the generation and output of 2D and 3D representations of a peptide directly from the line notation. HELM and BILN notations that include circular, branched, or stapled peptides are fully supported, eliminating errors in structure creation that are prone during manual drawing and connecting. The framework and common workflows followed in pyPept are described together with illustrative examples. pyPept has been released at: https://github.com/Boehringer-Ingelheim/pyPept.
引用
收藏
页数:10
相关论文
共 50 条
  • [41] DeepPack3D: A Python']Python package for online 3D bin packing optimization by deep reinforcement learning and constructive heuristics
    Tsang, Y. P.
    Mo, D. Y.
    Chung, K. T.
    Lee, C. K. M.
    SOFTWARE IMPACTS, 2025, 23
  • [42] Depth processing in 2D representations of 3D objects: Is there any?
    Ray, RH
    AUSTRALIAN JOURNAL OF PSYCHOLOGY, 2002, 54 (01) : 51 - 52
  • [43] ON AUTOMATIC RECOGNITION OF 3D STRUCTURES FROM 2D REPRESENTATIONS
    ALDEFELD, B
    COMPUTER-AIDED DESIGN, 1983, 15 (02) : 59 - 64
  • [44] Colored anchored visibility representations in 2D and 3D space
    Binucci, Carla
    Di Giacomo, Emilio
    Hong, Seok-Hee
    Liotta, Giuseppe
    Meijer, Henk
    Sacristan, Vera
    Wismath, Stephen
    COMPUTATIONAL GEOMETRY-THEORY AND APPLICATIONS, 2020, 89
  • [45] VPython']Python: 3D interactive scientific graphics for students
    Scherer, D
    Dubois, P
    Sherwood, B
    COMPUTING IN SCIENCE & ENGINEERING, 2000, 2 (05) : 56 - 62
  • [46] Matching 3D shapes using 2D conformal representations
    Gu, XF
    Vemuri, BC
    MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2004, PT 1, PROCEEDINGS, 2004, 3216 : 771 - 780
  • [47] Comparison of 2D and 3D representations for visualising telecommunication usage
    Hicks, M
    O'Malley, C
    Nichols, S
    Anderson, B
    BEHAVIOUR & INFORMATION TECHNOLOGY, 2003, 22 (03) : 185 - 201
  • [48] On strategies and automation in upgrading 2D to 3D landscape representations
    Prechtel, Nikolas
    CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE, 2015, 42 (03) : 244 - 258
  • [49] A comparison of usefulness of 2D and 3D representations of urban planning
    Herbert, Grant
    Chen, Xuwei
    CARTOGRAPHY AND GEOGRAPHIC INFORMATION SCIENCE, 2015, 42 (01) : 22 - 32
  • [50] 2D and 3D representations of solution spaces for CO problems
    Nowicki, E
    Smutnicki, C
    COMPUTATIONAL SCIENCE - ICCS 2004, PT 2, PROCEEDINGS, 2004, 3037 : 483 - 490