Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.12.15.571846

Benchmarking of AlphaFold2 accuracy self-estimates as empirical quality measures and model ranking indicators and their comparison with independent model quality assessment programs.

Edmunds, N. S.·McGuffin, L. J.·Genc, A. G.
View original source ↗Export metadata ↓

Abstract

MotivationDespite an increase in the accuracy of predicted protein structures following the development of AlphaFold2, there remains a gap in the accuracy of predicted model quality assessment scores when compared to those generated with reference to experimental structures. The predictions of model accuracy scores generated by AlphaFold2, plDDT and pTM, have become familiar descriptors of model quality. However, at CASP15 some modelling groups noticed a variation in these scores for models of very similar observed quality, particularly for quaternary structures. There have also been a number of methods describing adaptations of the AlphaFold2 algorithm to purposes such as refinement by custom template recycling and model quality assessment using a similar method of template input. In this study we compare plDDT and pTM to their observed counterparts lDDT (including lDDT-C and lDDT-oligo) and TM-score to examine whether they retain their reliability across the whole scoring range for both tertiary and quaternary structures and in situations where the AlphaFold2 algorithm is adapted to customised functionality. In addition, we explore the accuracy with which plDDT and pTM rank AlphaFold2 tertiary and quaternary models and whether these can be improved by the independent model quality assessment programs ModFOLD9 and ModFOLDdock. ResultsFor tertiary structures it was found that plDDT was an accurate descriptor of model quality when compared to observed lDDT-C scores (Pearson {rho} = 0.97). Additionally, plDDT achieved a tertiary structure ranking agreement with observed scores of 0.34 as measured by true positive rate (TPR) and ModFOLD9 offered similar but not improved performance. However, the accuracy of plDDT (Pearson {rho} = 0.67) and pTM (Pearson {rho} = 0.70) became more variable for quaternary structures quality assessment where overprediction was seen with both scores for models of lower quality and underprediction was also seen with pTM for models of higher quality. Importantly, ModFOLDdock was able to improve upon AF2-Multimer quaternary structure model ranking as measured by both TM-score (TPR 0.34) and lDDT-oligo (TPR 0.43). Finally, evidence is presented for an increase in variability of both plDDT and pTM when custom template recycling is used, and that this variation is more pronounced for quaternary structures.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Edmunds, N. S., McGuffin, L. J., Genc, A. G.. 2023-12-15. Benchmarking of AlphaFold2 accuracy self-estimates as empirical quality measures and model ranking indicators and their comparison with independent model quality assessment programs.. https://doi.org/10.1101/2023.12.15.571846

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↗