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is the work of team CSU_CHINA-iDEC:\n\n![Team Logo](https://github.com/Peldom/Peplib_Generator/blob/main/README_Support/Teamlogo.png)\n\n- The one model for peptide genesis\n  \n## [`Team wiki`](https://idec2021.github.io/CSU_CHINA/idec/home.html)\n\n## [`iDEC wiki`](http://idec.io)\n\nThis program is also a team-competition work in ***International Directed Evolution Competition***(abbr. into ***iDEC***)\n\n## Pre-Print\n\n[Peplib Generator: In silico directed evolution and de novo generation for\npeptide drug powered by AlphaFold2](https://arxiv.idec.io/article/000004/) || [Supplementary Data](https://arxiv.idec.io/pdf/a000004.01.pdf)  \nOther work-related paper will be listed later\n\n## Workflow\n\nDue to job adjustment, our work has been modified and integrated into a new pipeline for high-throughput in silico generation and screening:\n\n- Rif sampling: by [RifGen](https://github.com/LongxingCao/rifdock_v4.2)/our sampling scripts(unreleased yet)\n- Scaffold generation: WeFold(unreleased yet)\n- Scaffold filtering: Molpacker(unreleased yet)\n- Sequence design: [FastDesign](https://www.rosettacommons.org/docs/latest/scripting_documentation/RosettaScripts/Movers/movers_pages/FastDesignMover)/**ProteinMPNN special edition** edited from [ProteinMPNN](https://github.com/dauparas/ProteinMPNN)\n- Sequence Filtering: **AlphaFold2-turbo**(from **Peplib Generator** edited from [AlphaFold2](https://github.com/lucidrains/alphafold2), unreleased yet)\n\nIt takes 10-15 days to generate ideal 20k-100k sequences \u0026 structures.\n\n## Hardware Dependencies\n\n- Hardware(supported SLURM)\n     * CPU: \u003e=1000 * x86-64 cores; arm64 based is not supported\n     * GPU: \u003e=10 * NVIDIA® Tesla® P100 or higher, with 16GB+ VRAM\n     * RAM: 15 GB each GPU core, 4GB each CPU core\n     * ROM: 500GB or 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