Biology subjects

Poblete, S.

Publications and source records attributed to Poblete, S..

8 recordsLinked to original sources

bioRxiv2026

Topology-aware multiscale modeling of viral genomes reveals stability determinants in circoviruses

Understanding how viral genomes are organized under extreme spatial confinement remains a fundamental challenge in structural virology. Icosahedral viruses, despite offering high-resolution capsid structures through cryo-electron microscopy and X-ray crystallography, present a major obstacle: their genomes do not obey icosahedral symmetry and are thus averaged out during standard reconstruction procedures, leaving genome topology largely unresolved. Computational modeling offers a complementary avenue, but existing approaches often rely on simplified polymer representations that fail to capture sequence-specific features and the extreme compaction observed in small DNA viruses. Here, we focus on Porcine Circovirus type 2 (PCV2), a member of the Circoviridae family and one of the smallest autonomous mammalian viruses, which packages a circular ~1.7 kb single-stranded DNA genome into a ~20 nm T=1 icosahedral capsid at one of the highest DNA packing densities found in nature. We introduce an integrative methodology combining AI-based structural prediction, lattice Monte Carlo simulations, and multiscale molecular dynamics to generate and simulate three-dimensional topological models of the complete PCV2 virion. Our results demonstrate that multiple distinct genome arrangements can produce virions with indistinguishable external morphology, yet differ substantially in their internal stress distributions and predicted particle stability. These findings suggest that PCV2 populations comprise energetically heterogeneous assemblies with implications for infectivity, uncoating, and environmental persistence, while providing a generalizable framework for modeling genome topology in other confined viral systems.

biophysics↗

bioRxiv2026

Exploring Conformational Transitions of RNA Dimers via Machine Learning Potentials

RNA is a flexible biopolymer that adopts diverse conformations while forming structural motifs essential for its function. Classical RNA force fields often show limited transferability and inefficient sampling of transitions between stable states, particularly in moderately large RNA. To address these limitations, quantum-informed machine learning (ML) potentials have recently emerged as a promising alternative, offering improved accuracy and transferability relative to classical force fields. Here, we assess ML potentials for exploring RNA conformations using the adenine-adenine dinucleoside monophosphate (ApA) dimer, a fundamental RNA building block. We generated an extensive quantum-mechanical (QM) dataset for ApA conformations obtained from temperature replica exchange molecular dynamics (TREMD) simulations. Despite its small size, the ApA dimer exhibits six conformations in which quantum effects and solvent-mediated interactions play crucial roles. Using this dataset, we parameterized ML potentials based on the equivariant MACE architecture and informed by both ab-initio and semi-empirical data. The resulting potentials reproduce key conformational features of the ApA system, including base stacking, sugar puckering, and backbone flexibility, and provide broader coverage of structural transitions than the general-purpose SO3LR and MACE-OFF24 models. These findings highlight the importance of quantum-accurate RNA force fields towards the structural and energetic characterization of RNA complexes.

biophysics↗

bioRxiv2026

Ultra-deep sequencing reveals intra-host diversity and co-infection-driven evolution of SARS-CoV-2

As COVID-19 enters an endemic phase, SARS-CoV-2 continues to diversify under ongoing immune pressure, with Omicron sublineages and episodic emergent variants sustaining reinfections worldwide. Intra-host evolution represents the earliest stage of this diversification, yet remains undercharacterized, particularly in regions with limited genomic surveillance. Here, we conducted high-throughput sequencing on 96 nasopharyngeal swab samples from Chilean individuals (2020-2022), achieving an average per-base genome coverage of [~]60,000x across the viral genome. This ultra-deep sequencing coverage enabled the identification of intra-host single-nucleotide variants (iSNVs) and co-infection events with high sensitivity and accuracy. Co-infections, especially with Omicron, significantly increased iSNV frequency and recombination, driving viral diversity. Evolutionary analysis based on the non-synonymous to synonymous ratio (dN/dS) shows that Omicron is under extensive purifying selection (global dN/dS [~] 0.55). However, Omicron co-infection cases exhibited higher dN/dS ratios ([~]0.58), suggesting a lower level of purifying selection and increased genetic diversity. Notably, the Spike gene showed dN/dS ratios indicative of positive selection (dN/dS > 1), which are more pronounced in co-infection cases than in Omicron alone. This suggests that co-infections are providing the substrate for the emergence of new variants with enhanced transmissibility and immune evasion capabilities. Together, these findings demonstrate that ultra-deep sequencing is crucial for mapping the evolutionary forces driving SARS-CoV-2 intra-host adaptation and the emergence of new variants.

genetics↗

bioRxiv2025

Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16

Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and thus hidden from experiments and evaluation challenges that represent biomolecules as single, ordered structures. Here, we compare blindly predicted ensembles of water and ion structure to the cryo-EM densities observed around the Tetrahymena ribozyme at 2.2-2.3 [A] resolution, collected through target R1260 in the CASP16 competition. 26 groups participated in this solvation cryo-ensemble prediction challenge, submitting over 350 million atoms in total, offering the first opportunity to compare blind predictions of dynamic solvent shell ensembles to cryo-EM density. Predicted atomic ensembles were converted to density through local alignment and these densities were compared to the cryo-EM densities using Pearson correlation, Spearman correlation, mutual information, and precision-recall curves. These predictions show that an ensemble representation is able to capture information of transient or dynamic water and ions better than traditional atomic models, but there remains a large accuracy gap to the performance ceiling set by experimental uncertainty. Overall, molecular dynamics approaches best matched the cryo-EM density, with blind predictions from bussilab_plain_md, SoutheRNA, bussilab_replex, coogs2, and coogs3 outperforming the baseline molecular dynamics prediction. This study indicates that simulations of water and ions can be quantitatively evaluated with cryo-EM maps. We propose that further community-wide blind challenges can drive and evaluate progress in modeling water, ions and other previously hidden components of biomolecular systems.

biophysics↗

bioRxiv2025

AbAgym: a well-curated dataset for the mutational analysis of antibody-antigen complexes

With monoclonal antibodies becoming one of the largest classes of biopharmaceuticals, it is important to have curated data to train computational models that can accelerate their design. Despite the massive amount of mutagenesis data generated on antibody-antigen interactions, only a few small, well-curated datasets are available. This paper introduces AbAgym, a manually curated dataset comprising approximately 335k mutations in antibody-antigen complexes, including one tenth of interface mutations, whose effects on antibody-antigen binding have been experimentally quantified through deep mutational scanning (DMS) experiments. We collected and curated 67 DMS datasets from the literature together with the three-dimensional structure of each antibody-antigen complex. We benchmarked the performance of established force field methods as well as recent machine learning models that predict the change in binding affinity upon mutation. The former achieved modest performance, whereas the latter performed only marginally better than random. Finally, our analysis of hotspot residues responsible for immune evasion highlights the importance of accounting for biological complexities, such as conformational changes or oligomeric states that influence antibody-antigen binding, which are often overlooked. Abagym is freely available for academic use at https://github.com/3BioCompBio/Abagym.

bioinformatics↗

bioRxiv2023

RNA multiscale simulations as an interplay of electrostatic, mechanical properties, and structures inside viruses

Multiscale simulations have broadened our understanding of RNA structure and function. Various methodologies have enabled the quantification of electrostatic and mechanical interactions of RNA at the nanometer scale. Atom-by-atom simulations, coarse-grained strategies, and continuum models of RNA and its environment provide physical insight and allow to interpret diverse experiments in a systematic way. In this chapter, we present and discuss recent advances in a set of methods to study nucleic acids at different scales. In particular, we introduce details of their parameterization, recent applications, and current limitations. We discuss the interaction of the proteinacous virus capsid, RNA with substrates, compare the properties of RNA and DNA and their interaction with the environment, and analyze the application of these methods to reconstruct the structure of the virus genome structure. Finally, the last lines are dedicated to future developments and challenges ahead.

biophysics↗

bioRxiv2022

Sampling globally and locally correct RNA 3D structures using ERNWIN, SPQR and experimental SAXS data

The determination of the three-dimensional structure of large RNA macromolecules in solution is a challenging task that often requires the use of several experimental and computational techniques. Small-angle X-ray spectroscopy can provide insight into some geometrical properties of the probed molecule, but this data must be properly interpreted in order to generate a three-dimensional model. Here, we propose a multiscale pipeline which introduces SAXS data into modelling the global shape of RNA in solution, which can be hierarchically refined until reaching atomistic precision in explicit solvent. The low-resolution helix model (ERNWIN) deals with the exploration of the huge conformational space making use of the SAXS data, while a nucleotide-level model (SPQR) removes clashes and disentangles the proposed structures, leading the structure to an all-atom representation in explicit solvent. We apply the procedure on five different structures up to 126 nucleotides with promising results.

molecular biology↗

bioRxiv2021

RNA secondary structure regulates fragments' adsorption onto flat substrates

RNA is a functionally rich molecule with multilevel, hierarchical structures whose role in the adsorption to molecular substrates is only beginning to be elucidated. Here, we introduce a multiscale simulation approach that combines a tractable coarse-grained RNA structural model with an interaction potential of a structureless flat adsorbing substrate. Within this approach, we study the specific role of stem-hairpin and multibranch RNA secondary structure motifs on its adsorption phenomenology. Our findings identify a dual regime of adsorption for short RNA fragments with and without secondary structure, and underline the adsorption efficiency in both cases as a function of the surface interaction strength. The observed behavior results from an interplay between the number of contacts formed at the surface and the conformational entropy of the RNA molecule. The adsorption phenomenology of RNA seems to persist also for much longer RNAs as qualitatively observed by comparing the trends of our simulations with a theoretical approach based on an ideal semiflexible polymer chain.

biophysics↗