Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.08.26.672300

Ensemblify: a user-friendly tool for generating ensembles of intrinsically disordered regions of AlphaFold and user-defined models

Abstract

MotivationIntrinsically disordered proteins (IDPs) and regions (IDRs) challenge structural characterization due to their dynamic conformational ensembles and lack of stable structure. Existing computational approaches for modelling these ensembles are either computationally intensive, limited in flexibility, or inaccessible to non-experts, especially when dealing with multi-domain or multi-chain proteins. ResultsWe present Ensemblify, an open-source, user-friendly Python package for generating and analyzing conformational ensembles of IDPs/IDRs. Ensemblify uses a Monte Carlo algorithm coupled with neighbour-aware sampling of dihedral angles from curated or user-defined fragment libraries to explore conformational space. It directly incorporates information from AlphaFolds confidence metrics as flexible energy restraints in PyRosetta to guide the sampling. It supports multi-chain and multi-domain proteins and can sample N-terminal, C-terminal, and inter-domain linkers while preserving folded regions. Ensemble quality can be validated and refined against experimental data such as SAXS via Bayesian/Maximum Entropy (BME) reweighting. Interactive dashboards provide in-depth structural analysis and comparison. Testing across 10 diverse proteins demonstrated Ensemblifys accuracy, flexibility, and ability to recover experimentally observed structural features. Incorporating AlphaFold confidence metrics shows potential to improve the ensemble-data agreement. AvailabilityEnsemblify is freely available at https://github.com/CordeiroLab/ensemblify, along with detailed installation instructions and usage tutorials. Ensemblify can be used for scripting through its Python API or directly through the provided command-line interface (CLI). Complete documentation is available within the source-code and CLI and on Ensemblifys official documentation page (https://ensemblify.readthedocs.io). Contacttiago.gomes@itqb.unl.pt, tiago.cordeiro@itqb.unl.pt Supplementary informationSupplementary data is available online.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fernandes, N., Gomes, T. L., Cordeiro, T. N.. 2025-08-30. Ensemblify: a user-friendly tool for generating ensembles of intrinsically disordered regions of AlphaFold and user-defined models. https://doi.org/10.1101/2025.08.26.672300

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

KEEP EXPLORING

Related preprints

A meta-interaction basis for cell-cell communication in tissues

Tissue function depends on signals exchanged between cells and the responses they elicit. Yet whether diverse cell-cell interactions in situ form recurrent sender-receiver programs remains unclear. We present SpiderNet, an interpretable representation-learning framework that discovers such directed programs as a compact basis of cell-cell meta-interactions (MIs) from spatial transcriptomics. SpiderNet jointly learns which sender regulators, ligand-receptor pairs, and receiver targets define each MI and where each program is active across neighboring cell pairs. The resulting representation traces multicellular relays and links communication to cell states, perturbation responses, and phenotypes. SpiderNet recovers ground-truth MIs and their molecular components in simulations and, in real tissues, shows stronger direction-specific agreement with independently curated regulatory programs in senders and receivers than alternative methods. Across more than 5.8 million spatially profiled cells, SpiderNet resolves an SPP1-THBS relay linking monocytes, fibroblasts, and tumor cells within an immune-suppressive ovarian cancer niche, predicts T-cell responses to held-out melanoma-cell perturbations, and identifies a T-cell-associated brain-aging program and age-predictive signals that transfer across regions and platforms. It reveals a recurrent pan-cancer COLLAGEN-linked fibroblast-tumor program whose projected abundance in independent cohorts is associated with poorer survival and non-response to immunotherapy. SpiderNet thus establishes MIs as a reusable organizational layer between molecular interactions and tissue phenotypes, providing a framework to resolve, compare, trace, and perturb multicellular regulation in situ.

bioinformatics↗

Heterogeneous Graph Contrastive Learning for Drug-Gene-Disease Motif Prediction

Drug repurposing and target discovery offer critical strategies for advancing therapeutic development by uncovering the potential biological pathways and novel associations among drugs, genes, and diseases. However, experimental discovery remains expensive and time-consuming, which limits the scalability of large-scale studies. In addition, existing computational approaches often struggle to effectively integrate heterogeneous biomedical data, capture the complex higher-order topological signatures of biological interactomes, and generalize to unseen entities. Here, we present HANAMI (Heterogeneous grAph coNtrastive leArning for drug-gene-disease Motif predIction), a multi-view deep graph learning framework designed to model complex interactions among drugs, genes, and diseases. HANAMI integrates diverse heterogeneous biomedical knowledge, including chemical structures, genomic sequences, and clinical phenotypes, and leverages relation-aware topology encoding, structure-aware aggregation, and contrastive learning to enable accurate motif prediction with biological context from the network. Systematic evaluation on benchmark datasets shows that HANAMI achieves up to 6% improvements over existing state-of-the-art methods in predicting drug-gene-disease motifs. The framework further demonstrates strong inductive generalization, maintaining an [~]18% performance advantage in zero-shot settings involving previously unseen entities. Beyond predictive performance, HANAMI effectively prioritizes drug-disease relationships investigated in Phase II or III trials while identifying candidate genes that suggest plausible mechanistic links. Together, HANAMI provides a computational framework for interpreting complex biomedical interactions, offering a scalable foundation to accelerate drug repurposing and therapeutic innovation.

bioinformatics↗

PTMExplorer: A Multi-Dimensional Integrative Visualization Platform for Protein Post-Translational Modification Function and Structure

Deciphering the functions of post-translational modifications (PTMs) is a critical bridge connecting large-scale modification proteomics data to mechanistic studies. However, most existing tools for visualizing PTM omics data are limited to site catalogs or single-dimensional feature displays. They lack the capability to simultaneously map user-derived differential modification sites onto multi-dimensional contexts, including protein three-dimensional (3D) structure, evolutionary conservation, functional sites, and disease associations. This limitation makes it difficult for researchers to rapidly assess the biological importance of candidate sites from among a vast number of differentially modified sites. Here, we present PTMExplorer, an interactive platform for the multi-dimensional visualization of protein PTMs. PTMExplorer comprises three core modules: PTM Inspector, built upon ProtVista, provides a multi-track, sequence-feature integrated view incorporating intrinsically disordered region (IDR) prediction (via flDPnn), surface accessibility calculation (via FreeSASA), and UniProt functional annotations; PTM 3D Locator, leveraging the Nightingale/Mol* engine, anchors modification sites onto AlphaFold/Protein Data Bank (PDB) 3D structures through residue mapping via PDBe-SIFTS; and PTM Overview, utilizing the R circlize package, presents a panoramic polar circos plot illustrating modification distribution and inter-group differential regulation. Additionally, three major disease-associated modification databases (PTMD, qPTM, and PhosCancer) are integrated as PTM-Disease Nexus, enabling co-localization comparison between user-defined differential sites and reported disease-related sites. PTMExplorer currently supports eight model organisms, accepts user-uploaded differential analysis results, and provides multi-dimensional annotations and various visualization options (https://www.bioladder.cn/PTMExplorer/). Using a multi-omics dataset from hepatocellular carcinoma (18 patients, 9 modification types) as a case study, we demonstrate the practical utility of PTMExplorer in screening potential biomarkers, revealing multi-modification coordination mechanisms, and distinguishing between absolute and relative quantification patterns.

bioinformatics↗