OrgNet+: towards robust protein stability prediction with convolutional neural networks

Predicting the effect of single-point mutations on protein stability is a central problem in molecular biology and protein engineering. Recent structure-based deep learning methods, particularly 3D convolutional neural networks (3D CNNs), have achieved strong predictive performance by leveraging high-resolution protein structures. However, proteins exist as heterogeneous conformational ensembles rather than single static structures, and the impact of conformational flexibility on structure-based ΔΔG predictors remains poorly characterized. Consequently, current models may yield unstable or even contradictory predictions when evaluated across alternative, yet equally plausible, conformations of the same protein.

We introduce OrgNet+, a conformational ensemble-aware and orientation-gnostic framework that explicitly incorporates protein structure flexibility during training. OrgNet+ is trained on augmented datasets comprising diverse conformational ensembles generated using a comprehensive set of molecular modelling methods: normal mode analysis, molecular dynamics, Monte-Carlo simulations, and a generative deep learning model. Across all ensemble types, OrgNet+ substantially reduces intra-ensemble prediction variance while simultaneously improving predictive accuracy. The improved performance extends to standard single-reference-structure benchmarks, even though OrgNet+ was trained exclusively on conformational ensembles and never exposed to the reference experimental structures.

OrgNet+ is available at https://github.com/i-Molecule/OrgNet.