Protein Conformation Generation via Force-Guided SE(3) Diffusion Models
Authors: Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, Quanquan Gu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a variety of protein conformation prediction tasks, including 12 fast-folding proteins and the Bovine Pancreatic Trypsin Inhibitor (BPTI), demonstrate that our method surpasses the state-of-the-art method. |
| Researcher Affiliation | Collaboration | 1Byte Dance Research 2School of Mathematical Sciences, Tongji University, Shanghai (this work was done during Yan s internship at Byte Dance Research) 3Department of Computer Science, University of California, Los Angeles (this work was done during Huizhuo and Yue s internship at Byte Dance Research). Correspondence to: Quanquan Gu <quanquan.gu@bytedance.com>. |
| Pseudocode | Yes | Algorithm 1 Force-guided CONFDIFF |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Training datasets. We train CONFDIFF using all available single-chain protein structures deposited to the PDB on or before Dec 31, 2021... fast-folding proteins comprise MD simulation data for 12 small proteins with fast folding-unfolding dynamics (Lindorff Larsen et al., 2021)... bovine pancreatic trypsin inhibitor (BPTI) contains MD simulation data for BPTI exhibiting five metastable states (Shaw et al., 2010). |
| Dataset Splits | Yes | We train CONFDIFF using all available single-chain protein structures deposited to the PDB on or before Dec 31, 2021, and perform validation using structures deposited between Jan 1, 2022 and Dec 31, 2022. |
| Hardware Specification | Yes | We used a single NVIDIA-V100 GPU to benchmark model performance. |
| Software Dependencies | No | The paper mentions several software tools like pyemma, Deeptime, Open MM, and faspr, along with citations, but does not specify their version numbers (e.g., 'pyemma 2.x' or 'Open MM 7.x'). |
| Experiment Setup | Yes | Table S3. Hyperparameter choices of CONFDIFF... Batch Size 32 Learning Rate 1 10 4... During minimization, we keep the conformations by applying independent harmonic restraints on all heavy (non-hydrogen) atoms with spring constant of 10 kcal/mol A 2, the tolerance is set to 2.39 kcal/mol A 2 without maximal step limits. |