Boltzmann Graph Ensemble Embeddings for Aptamer Libraries

TL;DR

This paper introduces a Boltzmann-weighted graph ensemble embedding for aptamer molecules, improving robustness in predicting ligand affinity amidst experimental biases in SELEX datasets.

Contribution

It presents a novel thermodynamic ERGM embedding that models molecules as ensembles, enhancing analysis of aptamer-ligand interactions under biased experimental conditions.

Findings

Enables robust community detection in aptamer graphs

Provides subgraph explanations for ligand affinity

Helps identify low-abundance aptamer candidates

Abstract

Machine-learning methods in biochemistry commonly represent molecules as graphs of pairwise intermolecular interactions for property and structure predictions. Most methods operate on a single graph, typically the minimal free energy (MFE) structure, for low-energy ensembles (conformations) representative of structures at thermodynamic equilibrium. We introduce a thermodynamically parameterized exponential-family random graph (ERGM) embedding that models molecules as Boltzmann-weighted ensembles of interaction graphs. We evaluate this embedding on SELEX datasets, where experimental biases (e.g., PCR amplification or sequencing noise) can obscure true aptamer-ligand affinity, producing anomalous candidates whose observed abundance diverges from their actual binding strength. We show that the proposed embedding enables robust community detection and subgraph-level explanations for aptamer ligand affinity, even in the presence of biased observations. This approach may be used to identify low-abundance aptamer candidates for further experimental evaluation.