Biology subjects

Gallego, O.

Publications and source records attributed to Gallego, O..

8 recordsLinked to original sources

bioRxiv2026

ModCRE-NN: Interpretable Deep Learning Harnesses Structural and Evolutionary Synergy to Predict Transcription Factor Binding Specificity

We present ModCRE-NN, a machine-learning framework and server for predicting transcription-factor (TF) DNA-binding motifs through the integration of structural and evolutionary information. The method combines structure-derived Position Weight Matrices (PWMs) together with PWMs of homologous spanning multiple evolutionary sequence-identity intervals, which are integrated into a unified 20-channel tensor representation. Benchmark datasets were constructed on experimental databases of TF motifs, showing DNA binding specificity, while redundancy reduction and strict train/test partitioning minimized homology leakage. Prediction quality was evaluated on an independent separated set of TFs using the similarity analysis of profiles. Three complementary architectures were implemented and evaluated: an interpretable regression-based model, a convolutional neural network (CNN), and a Transformer-based architecture using self-attention mechanisms. The regression model achieved strong performance in high-homology regimes dominated by closely related PWMs, whereas CNN and Transformer architectures showed superior robustness under low evolutionary similarity and increased structural uncertainty. Importantly, AI-generated motifs consistently improved the similarity-scores while reducing prediction variance relative to the original structural and evolutionary input motifs, indicating that the models effectively denoise heterogeneous motif assemblies and reconstruct stable consensus DNA-binding representations rather than simply transferring PWMs from the nearest homolog. The CNN model exhibited the most balanced attribution profile, suggesting enhanced ability to combine weak structural and evolutionary signals into coherent motif representations. Additionally, we implemented a prediction-reliability framework combining Random Forest regression, exponential interpolation, and hybrid residual-corrected modeling to estimate the quality and uncertainty of the PWMs as functions of evolutionary similarity, motif-cluster consistency, and TF-family context. Overall, our results demonstrate that integrating structural information with deep learning provides a robust framework for large-scale TF-binding specificity prediction under conditions of substantial evolutionary divergence and motif uncertainty.

bioinformatics↗

bioRxiv2026

ExoFILT: Transfer learning for robust and accelerated analysis of exocytosis single-particle tracking data

MotivationUnderstanding constitutive exocytosis at the molecular level requires quantitative characterization of protein dynamics during the process. Single-particle tracking allows the measurement of protein dynamics in living cells. However, identifying bona fide exocytic events requires extensive manual annotation, limiting throughput and introducing personal biases that affect reproducibility. ResultsWe present ExoFILT, a deep learning-based classifier designed to identify exocytic events in single-particle tracking data, using the exocyst complex as a reference. Trained via transfer learning on simulated and experimental data, ExoFILT reduces the time required for manual annotation by ten-fold while improving measurement consistency across researchers. When applied to simultaneous dual-color time-lapse movies, ExoFILT enabled the systematic quantification of temporal relationships between exocytic proteins. The increased throughput uncovered distinct subpopulations of exocytic events with differential molecular composition (e.g., events with and without detectable levels of Sec1), underscoring the potential of ExoFILT to reveal mechanistic insights into exocytosis.

bioinformatics↗

bioRxiv2026

ModCRElib: A standalone package to model cis-regulatory elements.

SummaryThe ModCRElib package provides various tools for the analysis and modelling of transcription factor(TF)-DNA and regulatory complex inter-protein interactions. It takes structural information on these interactions to predict TF binding motifs, generate binding profiles along DNA sequences that score the binding affinity, predict TF binding sites and model the structure of higher order regulatory complexes. It is capable of working with a variety of input data formats and sources. The user may follow the analysis pipeline as outlined in the documentation or the user can make use of any of the multiple functionalities in an isolated manner. The package takes the service offered by the ModCRE server and enables users to apply its tools in an unrestricted and customizable manner. In this paper we provide 5 example uses of ModCRElib. This includes (i) TF binding affinity prediction, (ii) TF binding aggregation, (iii) characterization of specificity in TF binding sites along target DNA sequences,(iv) modelling TF bound to predicted binding sites, and (v) the generation of statistical potential derived scoring profiles of TF interacting with DNA. Availabilityhttps://github.com/structuralbioinformatics/ModCRElib Contactbaldo.oliva@upf.edu Supplementary informationAvailable at https://github.com/structuralbioinformatics/ModCRElib. doi:10.5281/zenodo.17484081

bioinformatics↗

bioRxiv2025

Continuum architecture dynamics of vesicle tethering in exocytosis

Essential for eukaryotic organisms, multiple copies of the exocyst complex tether each secretory vesicle to the plasma membrane (PM) in constitutive exocytosis. The exocyst higher-order structure (ExHOS) that coordinates the action of these multiple exocysts remains unexplored. We integrated particle-tracking, super-resolution microscopy and cryo-electron tomography in a model that time-resolves the continuum conformational landscape of the ExHOS and functionally annotates its different conformations. We found that 7 exocysts form flexible ring-shaped ExHOS that tether vesicles at <45 nm from the PM. The ExHOS, initially 19 nm in radius, rapidly expands while it pulls the vesicle towards the PM. Subsequently, the ExHOS stabilizes, securing the vesicle at [~]4 nm from the PM. After fusion, Sec18 mediates the ExHOS disassembly when its radius is 38 nm. By resolving the fundamental biophysical principles of tethering we bridged the gap between static isolated structures and the dynamic and multimeric nature of exocytosis.

cell biology↗

bioRxiv2024

Use of Protein interactions from Imaging Complexes after Translocation (PICT) to characterise in situ the spatial configuration of proteins interacting with the exocyst

Although the structure of the exocyst has been successfully resolved by cryo-electron microscopy, multiple studies showed that exocyst function requires the transient interaction with additional proteins. Unfortunately, the exocyst-interacting network could not be collectively reconstituted, challenging the understanding of how the exocyst complex is coordinated within the network of proteins involved in exocytosis. In a previous work, we described an approach that combines Protein interactions from Imaging Complexes after Translocation (PICT) and centroid localization analysis of diffraction-limited fluorescence signals to estimate the distance between a labelled protein and a spatial reference. This approach allows resolving the spatial organisation of protein interactions directly in living cells, both for intra-complex (i.e. between exocyst subunits) and inter-complex (i.e. between exocyst and transient binding proteins) interactions. In this chapter, we present the protocol to reproduce the sample preparation and image acquisition for PICT experiments. We also describe the computational image analysis pipeline to estimate the distance in PICT experiments. As illustration of the approach, we measure the distance from the spatial reference where the exocyst is anchored to 1) an intra-complex interaction (i.e. Sec5 exocyst subunit) and 2) an inter-complex interaction (Sec2, a guanyl-nucleotide exchange factor mediating vesicle tethering).

cell biology↗

bioRxiv2024

PyF2F: A robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments

SummaryStructural knowledge of protein assemblies in their physiological environment is paramount to understand cellular functions at the molecular level. Protein interactions from Imaging Complexes after Translocation (PICT) is a live-cell imaging technique for the structural characterization of macromolecular assemblies in living cells. PICT relies on the measurement of the separation between labelled molecules using fluorescence microscopy and cell engineering. Unfortunately, the required computational tools to extract molecular distances involve a variety of sophisticated software that challenge reproducibility and limit their implementation by highly specialised researchers. Here we introduce PyF2F, a Python-based software that provides a workflow for measuring molecular distances from PICT data, with minimal user programming expertise. We used a published dataset to validate PyF2F performance.

bioinformatics↗

bioRxiv2022

ModCRE: a structure homology-modeling approach to predict TF binding in cis-regulatory elements

Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ~25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the state-of-art nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs that are then used to scan a DNA sequence for occurrences. The best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Cooperativity is modelled by: i) the co-localization of TFs; and ii) the structural modeling of protein-protein interactions between TFs and with co-factors. As case examples, we apply our approach to automatically model the interferon-{beta} enhanceosome and the pioneering complex of OCT4, SOX2 and SOX11 with a nucleosome, which are compared with the experimentally known structures.

bioinformatics↗

bioRxiv2020

The P4-ATPase Drs2 regulates the homeostasis of Atg9

Multisubunit Tethering Complexes (MTCs) are a set of conserved protein complexes that tether transported vesicles at the acceptor membrane. Interactions with other components of the trafficking machinery regulate MTCs through mechanisms that are just partially understood. Here we systematically investigate the interactome that regulates the function of MTCs. We found that P4-ATPases, a family of lipid transporters involved in the biogenesis of vesicles, interact with MTCs that participate in the anterograde and retrograde transport at the Golgi, such as TRAPPIII. We used the lipid flippase Drs2 as a model to investigate the mechanism and biological relevance of such interplay during the transport of Atg9 vesicles. Binding to the N-terminal tail of Drs2 stabilizes TRAPPIII on membrane compartments loaded with Atg9 and it is required for the delivery of Atg9 during selective autophagy, a role that is independent of previously reported functions of the P4-ATPase. This mechanism relies on the I(S/R)TTK motif nested in the N-terminal tail cavity of Drs2, a motif that is required for the interaction with MTCs.

cell biology↗