Skip to content
Log in
Open access

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

A. Sarycheva Aleksandr Shumilov Petr Popov
Jul 2026 · Bioinformatics · Vol 42 · 0 citations · 43 references
Medicine

TL;DR

OrgNet+, a conformational ensemble-aware and orientation-gnostic framework that explicitly incorporates protein structure flexibility during training, is introduced, which substantially reduces intra-ensemble prediction variance while simultaneously improving predictive accuracy.

Abstract

Abstract Motivation 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. Results 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. Availability and implementation OrgNet+ is available at https://github.com/i-Molecule/OrgNet.

DOI PubMed Semantic Scholar
Read PDF

Similar papers

Open access Aug 2026

An ensemble learning framework for protein stability prediction with enhanced recognition of stabilizing mutations

Three modeling frameworks are developed, including models based on handcrafted features, models using embedding representations extracted from ProteinMPNN, and ensemble models integrating a diverse set of state‐of‐the‐art predictors integrating a diverse set of state‐of‐the‐art predictors.

Yang Liu, Jian Zhang, Minghui Li · 0 citations
Open access Jul 2026

LoMuS: low-rank adaptation with sequence multi-representation improves protein stability prediction

LoMuS is introduced, a multi-representation-based deep learning model that predicts dataset-provided protein stability scores directly from the primary sequence that consistently gains across standard experimental stability benchmarks.

Samuel Infante, Akash Singh, Anowarul Kabir · 0 citations
Open access Jun 2026

Inferring dynamic information from protein structures by Gaussian integrals and deep learning

Abstract Motivation Protein dynamics are central to function, but experiments and molecular dynamics (MD) simulations remain costly, low-throughput, and difficult to compare across protocols. Scalable structure-based methods are needed to infer dynamics from static protein structures. Results We present a deep learning framework that predicts protein dynamics from 30-dimensional Gaussian integral (GI) descriptors of Cα backbone topology. Using 1374 ATLAS protein chains with MD-derived RMSF, GI stratified proteins into fold-relevant clusters enriched for secondary structure, sequence homology, and ECOD families. An attention-based 1D-CNN classified flexible versus non-flexible proteins with test AUC = 0.772 and separated slow-mode– from fast-mode–dominated dynamics with AUC = 0.91. Regression models recovered mean RMSF (Pearson r = 0.72; R² = 0.46) and slow-mode RMSF more accurately (Pearson r = 0.83; R² = 0.62), supporting rapid inference of flexibility and collective-motion bias. Availability and implementation Code and data are available on GitHub at: https://github.com/fvilicich/gaussian_integral/blob/main/gaussian_integral_classification.ipynb.

F. Vilicich, Nicolás Bottino, Zhaoqian Su et al. · 0 citations
Open access Jun 2026

Hybrid Approach to Protein–Protein Complex Affinity Prediction Based on Language Models and Molecular Dynamics

HyBind-NN is developed, a multimodal graph neural network that integrates protein language models (PLMs) with 3D structural and dynamic datasets to predict protein–protein and protein–peptide affinity, and it is demonstrated that combining ESM-2 sequence embeddings with precise 3D Voronoi spatial geometry enables accurate affinity predictions across diverse structural datasets.

E. A. Bogdanova, A. Chernukhin, Alexey K. Shaytan · 0 citations
Open access Aug 2026

A unified predictor of protein stability changes across all mutation types via implicit structure learning

UniStab is introduced, an end-to-end framework for predicting stability changes across all mutation types by leveraging the implicit geometric reasoning of a pre-trained folding model and demonstrates state-of-the-art performance, particularly in the challenging scenarios of multi-point mutations and indels.

Hong Tan, Shenggeng Lin, Yi Xiong · 0 citations