2021/06/06 by Alex Morehead, Chen Chen, Morehead, Alex +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomolecules (q-bio.BM) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #I.2.1 #J.3 #Machine Learning (cs.LG) #Microbial Metabolic Engineering and Bioproduction #Protein Structure and Dynamics #Quantitative Methods (q-bio.QM) #cs.LG #q-bio.BM #q-bio.QM
paper · pdf · doi:10.48550/arxiv.2106.04362
19 pages, 1 figure, and 4 tables. Updated URLs
openalex publication_date 2021/06/06 · arxiv created 2021/10/06 · arxiv updated 2021/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
How and where proteins interface with one another can ultimately impact the proteins' functions along with a range of other biological processes. As such, precise computational methods for protein interface prediction (PIP) come highly sought after as they could yield significant advances in drug discovery and design as well as protein function analysis. However, the traditional benchmark dataset for this task, Docking Benchmark 5 (DB5), contains only a modest 230 complexes for training, validating, and testing different machine learning algorithms. In this work, we expand on a dataset recently introduced for this task, the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for geometric deep learning of protein interfaces. The previous version of DIPS contains only the Cartesian coordinates and types of the atoms comprising a given protein complex, whereas DIPS-Plus now includes a plethora of new residue-level features including protrusion indices, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, giving researchers a large, well-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 SOTA results, surpassing the performance of all other models trained on residue-level and atom-level encodings of protein complexes to date.