Navigating protein landscapes with a machine-learned transferable coarse-grained model

General information Datasets and code for training the CG transferable model for proteins described in Navigating protein landscapes with a machine-learned transferable coarse-grained model paper. Each folder contains a detailed description of the contents Acknowledgements We gratefully acknowledge funding from the European Commission (Grant No. ERC CoG 772230 “ScaleCell”), the International Max Planck Research School for Biology and Computation (IMPRS–BAC), the BMBF (Berlin Institute for Learning and Data, BIFOLD), the Berlin Mathematics center MATH+ (AA1-6, EF1-2) and the Deutsche Forschungsgemeinschaft DFG (NO 825/2, NO 825/3, NO 825/4, GRK DAEDALUS, SFB/TRR 186, Project A12; SFB 1114, Projects B03, B08, and A04; SFB 1078, Project C7; and RTG 2433, Project Q05, Q04), the National Science Foundation (CHE-1900374, and PHY-2019745), and the Einstein Foundation Berlin (Project 0420815101). The authors gratefully acknowledge the computing time provided on the supercomputer Lise at NHR@ZIB as part of the NHR infrastructure. The authors thank volunteers at GPUGRID.net for contributing computational resources and Acellera for funding.