Broyles, B. K. | bioRxiv Science

bioRxiv2025

evo3D: a generalised framework for structure-informed evolutionary analysis, implemented in R

At the molecular level, selection pressures often act on protein structural features, yet most evolutionary analyses remain confined to linear sequences. Early structure-informed approaches improved interpretability by mapping single-site metrics onto protein structures, and later methods introduced 3D sliding windows to capture spatially clustered signals missed by linear window approaches. These frameworks, however, are restricted to predefined statistics and narrowly defined 3D window types, limiting the scope of questions that can be addressed. We developed an R package, evo3D, as a new framework for structure-informed evolutionary analysis that supports a wide range of downstream statistics and scales from simple to complex structures. evo3D extracts structure-informed multiple sequence alignment subsets (spatial haplotypes), making the structure-informed unit of analysis directly available to users. The framework supports fixed-count and fixed-distance spatial windows, introduces residue and codon analysis modes, and extends to multimers, interfaces, and multiple structural models through a single wrapper, run_evo3d(). We demonstrate evo3Ds utility by performing an epitope-level diversity scan of Hepatitis C virus E1/E2 complex, identifying conserved spatial neighbourhoods missed by linear sliding windows, and by evaluating evo3Ds scalability on the octameric Chikungunya virus E1/E2 assembly. Importantly, evo3D formalises the core components of structure-informed analysis of molecular evolution and removes technical barriers. As a result, the framework streamlines the evaluation of evolutionary patterns directly within 3D structural contexts, and we anticipate its wide application in molecular evolution studies. The package is available at github.com/bbroyle/evo3D.

bioinformatics↗

bioRxiv2024

Predicting transcriptional activation domain function using Graph Neural Networks

Analysis of factors that lead to the functionality of transcriptional activation domains remains a crucial and yet challenging task owing to the significant diversity in their sequences and their intrinsically disordered nature. Almost all existing methods that have aimed to predict activation domains have involved traditional machine learning approaches, such as logistic regression, that are unable to capture complex patterns in data or plain convolutional neural networks and have been limited in exploration of structural features. However, there is a tremendous potential in the inspection of the structural properties of activation domains, and an opportunity to investigate complex relationships between features of residues in the sequence. To address these, we have utilized the power of graph neural networks which can represent structural data in the form of nodes and edges, allowing nodes to exchange information among themselves. We have experimented with two kinds of graph formulations, one involving residues as nodes and the other assigning atoms to be the nodes. A logistic regression model was also developed to analyze feature importance. For all the models, several feature combinations were experimented with. The residue-level GNN model with amino acid type, residue position, acidic/basic/aromatic property and secondary structure feature combination gave the best performing model with accuracy, F1 score and AUROC of 97.9%, 71% and 97.1% respectively which outperformed other existing methods in the literature when applied on the dataset we used. Among the other structure-based features that were analyzed, the amphipathic property of helices also proved to be an important feature for classification. Logistic regression results showed that the most dominant feature that makes a sequence functional is the frequency of different types of amino acids in the sequence. Our results consistent have shown that functional sequences have more acidic and aromatic residues whereas basic residues are seen more in non-functional sequences.

bioinformatics↗