{"id":37,"date":"2019-04-24T20:59:07","date_gmt":"2019-04-25T00:59:07","guid":{"rendered":"https:\/\/sites.temple.edu\/voelzlab\/?page_id=37"},"modified":"2025-07-29T15:31:16","modified_gmt":"2025-07-29T19:31:16","slug":"code","status":"publish","type":"page","link":"https:\/\/sites.temple.edu\/voelzlab\/code\/","title":{"rendered":"Code and Datasets"},"content":{"rendered":"\n

Code<\/h3>\n\n\n\n

https:\/\/github.com\/vvoelz\/voelzlab<\/a> – Voelz Lab code repository (private)<\/p>\n\n\n\n

https:\/\/github.com\/vvoelz\/biceps<\/a> – Bayesian inference of conformational populations (BICePs), an method to infer populations from a combination of computational models and sparse experimental observables. <\/p>\n\n\n\n

https:\/\/github.com\/vvoelz\/hpsandbox<\/a> – simple (and slow) python library for experimenting with the two-dimensional HP lattice model of proteins.   <\/p>\n\n\n\n

https:\/\/github.com\/vvoelz\/ratespec<\/a> – python code for calculating rate spectra (inverse Laplace transforms with Bayesian regularization) from noisy time course data<\/p>\n\n\n\n

Datasets<\/h3>\n\n\n\n

Folding@home<\/a> COVID-19 protein simulation data as an AWS Open Data Set<\/a>: https:\/\/registry.opendata.aws\/foldingathome-covid19<\/a> <\/p>\n\n\n\n

Trajectory data<\/strong><\/p>\n\n\n\n

Marshall, T., Raddi, R., & Voelz, V. (2024). MD simulation trajectory data for “An Evaluation of Force Field Accuracy for the Mini-Protein Chignolin using Markov State Models” (1.0.0) [Data set]. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.10681926<\/a> <\/p>\n\n\n\n

FOXO1 transcription factor simulation data (Novack et al. 2022<\/a>) at OSF: https:\/\/doi.org\/10.17605\/OSF.IO\/T7H5B<\/a> <\/p>\n\n\n\n

Molecular dynamics trajectories of apomyoglobin at pH 4 (Wan et al. 2020<\/a>) at Zenodo: https:\/\/doi.org\/10.5281\/zenodo.16541773<\/a> <\/p>\n\n\n\n

p53 TAD peptide binding to MDM2 receptor (Zhou et al. 2017<\/a>) at OSF: https:\/\/doi.org\/10.17605\/OSF.IO\/8SC9G<\/a><\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Code https:\/\/github.com\/vvoelz\/voelzlab – Voelz Lab code repository (private) https:\/\/github.com\/vvoelz\/biceps – Bayesian inference of conformational populations (BICePs), an method to infer populations from a combination of computational models and sparse experimental observables. https:\/\/github.com\/vvoelz\/hpsandbox – simple (and slow) python library for experimenting with the two-dimensional HP lattice model of proteins.    https:\/\/github.com\/vvoelz\/ratespec – python code for calculating … <\/p>\n

Continue reading “Code and Datasets”<\/span><\/a><\/p>\n","protected":false},"author":1258,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-37","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sites.temple.edu\/voelzlab\/wp-json\/wp\/v2\/pages\/37","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.temple.edu\/voelzlab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.temple.edu\/voelzlab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/voelzlab\/wp-json\/wp\/v2\/users\/1258"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.temple.edu\/voelzlab\/wp-json\/wp\/v2\/comments?post=37"}],"version-history":[{"count":4,"href":"https:\/\/sites.temple.edu\/voelzlab\/wp-json\/wp\/v2\/pages\/37\/revisions"}],"predecessor-version":[{"id":658,"href":"https:\/\/sites.temple.edu\/voelzlab\/wp-json\/wp\/v2\/pages\/37\/revisions\/658"}],"wp:attachment":[{"href":"https:\/\/sites.temple.edu\/voelzlab\/wp-json\/wp\/v2\/media?parent=37"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}