Biology subjects

Wiesenbach, P.

Publications and source records attributed to Wiesenbach, P..

2 recordsLinked to original sources

bioRxiv2026

CAPRINI-M: An AI-curated Cardiac-Specific Atlas of Protein Interactions in Mice

MotivationProtein-protein interactions are fundamental to cardiovascular disease biology, but the corresponding knowledge is dispersed across the literature and heterogeneous databases, making systematic curation time-consuming. Moreover, many existing PPI resources may be biased and lack detailed information on structural interaction interfaces or associated thermodynamic parameters. ResultsWe present CAPRINI-M (CArdiac PRotein INteractions In Mice), a web-based tool hosting an AI-curated atlas of cardiac protein interactions. We mined 9,105 cardiobiology manuscripts and used open-source LLMs (LLaMA-3.3 70B) to extract 11,189 protein-protein interactions. We then used AlphaFold3 to infer interaction interfaces, estimate thermodynamic properties related to complex stability, and predict the likelihood that each protein pair forms a complex. In our benchmarking analysis, CAPRINI-M showed stronger performance than the comparator PPI resources tested here. Predicted interaction favourability also agreed with published experimental evidence, with lower predicted Gibbs free energy associated with experimentally preferred binding partners. Overall, CAPRINI-M provides a more comprehensive, mechanistically annotated view of cardiovascular disease-relevant protein-protein interactions by integrating literature evidence with structural, interface-level, and stability-related information. AvailabilityThe CAPRINI-M web application is available at https://shiny.dieterichlab.org/app/caprinim. The source code used in this study is linked in the manuscripts Availability section.

bioinformatics↗

bioRxiv2025

Reading papers: Extraction of molecular interaction networks with large language models

MotivationSignalling occurs within and across cells and orchestrates essential cellular processes in complex tissues. Cell signalling involves several different components, including protein-protein interactions (PPI) and transcription factors (TF), to promoter binding in gene regulatory networks (GRNs). Dynamically changing conditions oftentimes lead to the rewiring of cellular communication networks. Computational modelling approaches typically rely on databases of possible molecular interactions. Evidently, manual curation of databases is time-consuming and automatic relation extraction from scientific literature would greatly support our strive to understand molecular mechanisms. To ease this process, we reason that prompt-based data mining with Large Language Models (LLMs) could be used to extract information from relevant scientific publications. ApproachIn our work, we use open-source LLMs to mine an annotated corpus of molecular interactions. We focus on the extraction of entity relations between proteins, as exemplified in protein-protein interaction networks, and transcription factor to target gene relations, as exemplified in gene regulatory networks. ResultsWe obtain promising evaluation results as measured by precision, recall and F1-score for the extraction of PPI relations: 87%, 70% and 71% and 77%, 57% and 62% for GRN relation extraction over a large corpus of short (average 331 tokens) scientific texts. AvailabilityCodes with scripts and results have been provided in: https://github.com/dieterich-lab/LLM_Relations.

bioinformatics↗