KRAS-G12D-DeepLearning: code and data for deep learning analysis of KRAS G12D conformations
Year: 2026
Type: article
Abstract: This record contains the code, data and trained models for the deep learning analysis of KRAS G12D conformational states in the accompanying manuscript. Dataset: 790 human KRAS chains from 415 Protein Data Bank entries (260 WT, 174 G12D, 195 G12C, 120 G12V), from a PDB snapshot of 1 January 2026. The full list of entries with annotations is in data/PDB_entries_used.csv. Methods: A convolutional neural network (CNN) trained on Cα distance maps, evaluated with 5-fold cross-validation grouped by PDB entry and compared against logistic regression and random forest baselines. Attribution analysis of the CNN, which identifies Switch I as the main driver of nucleotide-state prediction. A variational autoencoder (VAE) that maps the conformational landscape. Asp12 active-site geometry, elastic network model with perturbation response scanning (ENM/PRS), and pocket analysis. Key results: the CNN predicts GTP vs GDP state with AUC 0.97. For G12D vs WT, random forest reaches AUC 0.92 in the GTP state and 0.72 in the GDP state. Contents: raw Cα coordinates, processed descriptors, cross-validation predictions, trained network weights (models/*.pt), analysis scripts and figure-generation scripts (Figures 8–13). Run run_all.sh to reproduce all results. The environment is in environment.yml and requirements.txt.
Author: shahab, Muhammad
Institution: Beijing University of Chemical Technology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7225345527