Empereur-Mot Charly, Pesce Luca, Doni Giovanni, Bochicchio Davide, Capelli Riccardo, Perego Claudio, Pavan Giovanni M
Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Galleria 2, Via Cantonale 2c, CH-6928 Manno, Switzerland.
Department of Applied Science and Techology, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
ACS Omega. 2020 Dec 7;5(50):32823-32843. doi: 10.1021/acsomega.0c05469. eCollection 2020 Dec 22.
We present , a versatile software for the automatic iterative parametrization of bonded parameters in coarse-grained (CG) models, ideal in combination with popular CG force fields such as MARTINI. By coupling fuzzy self-tuning particle swarm optimization to Boltzmann inversion, performs accurate bottom-up parametrization of bonded terms in CG models composed of up to 200 pseudo atoms within 4-24 h on standard desktop machines, using default settings. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect to support and facilitate the development of new CG models for the study of complex molecular systems interesting for bio- and nanotechnology. Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity, and size. is available with all its dependencies the Python Package Index (PIP package: ). Demonstration data are available at: www.github.com/GMPavanLab/SwarmCG.
我们展示了一款通用软件,用于粗粒度(CG)模型中键合参数的自动迭代参数化,与诸如MARTINI等流行的CG力场结合使用非常理想。通过将模糊自调整粒子群优化与玻尔兹曼反演相结合,该软件在标准台式机上使用默认设置,能在4至24小时内对由多达200个伪原子组成的CG模型中的键合项进行准确的自下而上参数化。该软件具有用户友好的界面和两种不同的使用模式(默认模式和高级模式)。我们特别期望它能支持并促进用于研究对生物和纳米技术有意义的复杂分子系统的新CG模型的开发。使用9个具有不同性质、结构复杂性和大小的分子进行基准测试,展示了其出色的性能。该软件及其所有依赖项可通过Python包索引(PIP包: )获取。演示数据可在以下网址获取:www.github.com/GMPavanLab/SwarmCG 。