{"id":19401143,"url":"https://github.com/google-research/slip","last_synced_at":"2025-04-24T07:30:35.595Z","repository":{"id":37895817,"uuid":"431606967","full_name":"google-research/slip","owner":"google-research","description":"SLIP is a sandbox environment for engineering protein sequences with synthetic fitness functions.","archived":true,"fork":false,"pushed_at":"2024-01-17T07:29:23.000Z","size":135,"stargazers_count":19,"open_issues_count":5,"forks_count":9,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-04-03T01:01:49.788Z","etag":null,"topics":["computational-biology","machine-learning","protein-design"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/google-research.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-11-24T19:36:36.000Z","updated_at":"2025-03-24T12:17:38.000Z","dependencies_parsed_at":"2023-01-21T12:32:02.259Z","dependency_job_id":null,"html_url":"https://github.com/google-research/slip","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google-research%2Fslip","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google-research%2Fslip/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google-research%2Fslip/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/google-research%2Fslip/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/google-research","download_url":"https://codeload.github.com/google-research/slip/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250582783,"owners_count":21453913,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computational-biology","machine-learning","protein-design"],"created_at":"2024-11-10T11:17:20.176Z","updated_at":"2025-04-24T07:30:35.302Z","avatar_url":"https://github.com/google-research.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"This is not an officially supported Google product.\n\n# SLIP - Synthetic Landscape Inference for Proteins\n![](https://github.com/google-research/slip/workflows/Build/badge.svg)\n\nSLIP is a sandbox environment for engineering protein sequences with\nsynthetic fitness functions. See our [preprint](https://www.biorxiv.org/content/10.1101/2022.10.28.514293v1)\n\n## Installation instructions\n\nTested on python \u003e= 3.7\n\nWe recommend installing into a [virtual environment](https://docs.python.org/3/library/venv.html) to isolate dependencies.\n\n```\npython3 -m venv env\nsource env/bin/activate\n```\n\nTo install:\n```\npip3 install -q -r requirements.txt\n```\n\nTo run the unit tests:\n```\nbash -c 'for f in *_test.py; do python3 $f || exit 1; done'\n```\n\n## Example landscape usage\n\nSee this [colab](https://colab.research.google.com/drive/1BkR2KvvjgzUTJg5VO3BsuTPSDjQisnbJ) for an example of using a landscape.\n\n## Constructing a new landscape\n\nAll landscapes were constructed using [Mogwai](https://github.com/songlab-cal/mogwai). See that repo's [example](https://github.com/songlab-cal/mogwai/blob/main/examples/gremlin_train.ipynb), which shows how to train a new Potts model and how to (optionally) examine contact accuracy after training. All that is required is an alignment in .a3m format, true contacts are not required (e.g. as in this [colab](https://github.com/songlab-cal/slc22a5/blob/main/slc22a5_train_potts.ipynb)). \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle-research%2Fslip","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgoogle-research%2Fslip","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoogle-research%2Fslip/lists"}