DIPS-Plus: The enhanced database of interacting protein structures for interface prediction
Abstract In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.
- Research Organization:
- Donald Danforth Plant Science Center, St. Louis, MO (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- National Institutes of Health (NIH); National Science Foundation (NSF); USDOE; USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- Grant/Contract Number:
- AC05-00OR22725; AR0001213; SC0020400; SC0021303
- OSTI ID:
- 1993653
- Journal Information:
- Scientific Data, Journal Name: Scientific Data Journal Issue: 1 Vol. 10; ISSN 2052-4463
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
Similar Records
A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers
Local dynamics of proteins and DNA evaluated from crystallographic B factors