Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.06.03.597089

Generative AI impact on protein stability prediction in breast cancer genes

Gnanaolivu, R.·Hart, S.
View original source ↗Export metadata ↓

Abstract

The functional classification of a missense variant in cancer predisposition genes is often challenging due to how rare the variant is observed in the population. When available, clinicians utilize a combination of family history, in vitro functional assays and in silico methods to infer protein function. In silico methods, such as missense predictors (predict changes in protein function) and protein stability predictors (predict changes in free energy) have been used to help classify a missense variant in accordance with the American College of Medical Genetics and Genomics (ACMG) guideline. To measure protein stability, many in silico algorithms predict stability based on the change of free energy and most accurate protein stability predictors require a wild-type protein template. In this study, we examine the use of generative AI to predict high-resolution protein structures as templates analyzed with protein stability methods to evaluate loss of function (LOF) activity in cancer predisposition genes BRCA1, BRCA2, PALB2 and RAD51C upon the presence of missense variant. Utilizing multiplexed assay of variant effect measurements and variant classifications from ClinVar, we find that prediction of Gibbs free energy ({Delta}{Delta}G) from AlphaFold2 (AF2) structures analyzed with FoldX predicts LOF better than experimental-derived wild type structures in the BRCT domain of BRCA1 and the DNA binding domain (DBD) of BRCA2, but not in PALB2 and RAD51C. We also find that AF2 structures in the BRCT domain of BRCA1 and DBD-Dss1 domain of BRCA2 analyzed with FoldX measure homologous DNA recombination (HDR) activity significantly better than Rosetta and DDGun3D. Our study also revealed that there are other factors that contribute to predicting loss of function activity other than protein stability, with AlphaMissense ranking the best overall predictor of LOF activity in these tumor suppressor breast cancer genes. Author SummaryThe stability of a protein, often expressed in terms of Gibbs free energy ({Delta}{Delta}G), is a critical factor in predicting loss of function (LOF) activity when a missense variant is present. The effect is higher in haploinsufficient genes like the tumor suppressor genes BRCA1, BRCA2, PALB2 and RAD51C. Protein stability predictors that utilizes a wild-type structure to make its predictions is often limited by the availability of experimentally-derived protein structures. Here, in our study we show that generative AI, like AlphaFold2 (AF2) can predict structures similar to experimentally-derived structures with high similarity. Furthermore, protein stability tools such as FoldX, Rosetta, and DDGun3D can be used in conjunction to measure changes in stability. From our study, we find that complex AF2 structures representing the BRCT domain of BRCA1 and DBD domain of BRCA2 analyzed by FoldX predicts function significantly better than the experimentally-derived structures. However, predicted |{Delta}{Delta}G| does not predict function better than purpose-built in silico missense predictors for protein function. Overall, we find the AlphaMissense is the best predictor to predict function in these tumor suppressor breast cancer genes.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Gnanaolivu, R., Hart, S.. 2024-06-03. Generative AI impact on protein stability prediction in breast cancer genes. https://doi.org/10.1101/2024.06.03.597089

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗