Biology subjects

Guallar, V.

Publications and source records attributed to Guallar, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Iterative computational bioprospecting of hydroxymethylfurfural oxidases combining molecular simulation and machine learning

The enzymatic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) is a key step in the sustainable production of polyethylene furanoate (PEF), yet the final oxidation of 5-formyl-2-furancarboxylic acid (FFCA) to FDCA remains the principal bottleneck for industrial application. Although hydroxymethylfurfural oxidases (HMFOs) can catalyze this transformation, naturally occurring enzymes with high FFCA-oxidizing activity remain largely unexplored. Here, we present an iterative computational bioprospecting campaign to identify natural HMFOs with improved activity toward this rate-limiting reaction. Across four successive rounds, the computational workflow evolved from conservative homology-based screening to large-scale sequence mining assisted by machine learning, with each iteration redesigned according to the principal limitations identified through molecular simulations and experimental characterization. This adaptive strategy led to the discovery of several natural HMFOs with substantially improved FFCA oxidation activity, including candidates whose performance under the tested conditions approached that of the engineered benchmark 8BxHMFO. Beyond the identification of promising biocatalysts, the study demonstrates the value of continuous integration of computational prediction and experimental feedback for refining enzyme discovery campaigns, and provides practical insights into balancing catalytic prioritization, sequence diversity, developability, and computational scalability in future simulation-guided bioprospecting efforts.

bioinformatics↗

bioRxiv2026

Site-Specific Entry Factors Define Cellular Susceptibility to SARS-CoV-2 in Human Tissues

SARS-CoV-2 primarily infects the respiratory epithelium through entry factors ACE2 and TMPRSS2. However, alternative viral entry receptors and/or modulators of viral entry factors expression may contribute to infection in respiratory as well as non-respiratory tissues, including the kidneys and colon. Using single-cell analyses, we identified novel candidates for spike-mediated viral entry in epithelial cells from diverse fresh human tissues exposed to a labeled spike-pseudotyped virus. Systematic viral tracking revealed tissue-specific membrane receptors that contribute to viral entry, functionally validated in human tissues and partially predicted by molecular modeling. We found ADAM17 and IL1R1, together with canonical molecules, to significantly facilitate viral entry in the lung parenchyma, while entry into the renal cortex was less dependable on canonical factors, but strongly modulated by JAK1/2 engagement. These findings uncover distinct viral entry mechanisms across tissues and suggest novel organ-specific therapeutic targets for patients at risk.

microbiology↗

bioRxiv2026

Multi-objective Engineering of Trimethylamine Monooxygenase for Improved Thermostability and Cofactor Use

Trimethylamine (TMA) is a major contributor to undesirable odours in protein hydrolysates derived from marine by-products, limiting their industrial use. Flavin-containing monooxygenases (FMOs) catalyse the conversion of TMA to the odourless trimethylamine N-oxide (TMAO); however, industrial applications demand enzymes that are both thermally stable and compatible with cost-effective cofactors. A thermostable variant of the Methylophaga aminisulfidivorans FMO (mFMO_20) can function at elevated temperatures but depends exclusively on the expensive and unstable cofactor NADPH. In this study, we investigated whether it is possible to simultaneously enhance thermostability and NADH compatibility using a multi-objective engineering strategy. We first targeted residues in the cofactor binding site of mFMO_20 to restore NADH activity, which had been completely lost despite the wild type enzyme being naturally active with both cofactors. Variants derived from the thermostable scaffold partially recovered NADH activity but showed reduced NADPH activity. Given the wild types inherent NADH compatibility, we next pursued a stability-improvement approach, introducing highly conserved stabilizing mutations. This preserved cofactor competence but produced only modest improvements in thermostability. Finally, by combining physical, evolutionary, and statistical metrics, we obtained variants that retained higher NADPH activity after heat treatment than any previously reported thermostable mutants, while a subset also retained measurable NADH activity before heat treatment. These findings show that combining complementary scoring strategies helps navigate the trade-off between stability and activity; while, robust NADH function under thermal stress remains elusive, with only one variant retaining detectable NADH activity after heat treatment, the results provide valuable insight into the underlying constraints linking stability and cofactor usage and highlights possible directions for engineering FMOs with both enhanced thermostability and cofactor compatibility. Author summaryIn this work, we aimed to improve an enzyme that could be useful in industrial applications but is limited by two common constraints: poor stability at high temperatures and dependence on an expensive cofactor. To make the enzyme more suitable for large-scale applications, we sought to engineer variants that are both more thermostable and compatible with a cheaper cofactor, NADH. For enzyme engineering, we used a strategy that balances several properties rather than prioritizing a single trait. We combined tools that capture evolutionary patterns, protein physics, and AI-based predictions to explore which mutations might provide the right combination of stability and function. Through this approach, we obtained variants with improved heat resistance and higher cofactor activity retention.

molecular biology↗

bioRxiv2025

A Unified Framework for TCR-pMHC Structural Model Assessment

T cell receptor (TCR) recognition of peptide-MHC complexes (pMHCs) is a central determinant of cellular immunity. Structural insights are key to understanding TCR-pMHC interactions, however progress is limited by the scarcity of experimental structures (PDB = 232 TCR-pMHC class I complexes, date 01/2025). Although protein modelling tools have rapidly advanced, their accuracy on TCR-pMHC complexes is hampered by CDR loop hypervariability, conformational flexibility, and the lack of reliable quality assessment without experimental references. To evaluate the performance of recent protein modelling algorithms on TCR-pMHC complexes, we benchmarked three general-purpose (AlphaFold3, Boltz-2, Chai-1) and two TCR-specific (tFold-TCR, TCRmodel2) modelling tools using a benchmark set of 20 experimentally determined PDB structures, demonstrating AlphaFold3 superior performance. To expand structural coverage beyond the existing experimental landscape, we present a framework that enables quality assessment of TCR-pMHC structural models without requiring experimental reference structures. To this end, we integrate multiple modelling and interface confidence metrics (pLDDT, ipTM, iPAE, pDockQ) into explainable random forest classifiers trained on 1160 models of 232 experimentally determined PDB TCR-pMHC class I complexes. This approach reliably distinguishes low-, acceptable-, medium-, and high-quality models, outperforming the combination of these metrics using literature-established thresholds. Leveraging our quality tier framework, we used AlphaFold3 to generate and evaluate the largest synthetic dataset of TCR-pMHC class I structural models to date from VDJdb-annotated sequences, comprising 33,820 complexes (169,100 models, 5 models per complex) and representing a >70-fold expansion of available structures. We demonstrate the applicability of our quality tier stratification in three settings: 1) filtering validating vs. non-validating TCR-pMHC interaction in sequence databases (VDJdb), 2) enrichment of biologically validated interactions versus synthetic negatives amongst higher quality complexes, and 3) enhancing the predictive performance of TCR-pMHC pairing models. Altogether our TCR-pMHC structural quality tier framework provides a scalable and interpretable approach for improving modelling and functional analyses of TCR-pMHC class I complexes, with translational applications in T-cell and TCR-based immunotherapies.

bioinformatics↗

bioRxiv2025

Structural insights into a conserved mechanism of choline translocation through CHT

The essential nutrient choline is critical for cellular homeostasis across all domains of life. In humans, choline uptake in cholinergic neurons for its recycling into acetylcholine is mediated by the high-affinity Na-dependent transporter SLC5A7 (also known as CHT1). Prokaryotes also depend on choline as an osmo-protectant and as metabolite, raising the possibility that bacteria also possess choline transporters akin to CHT1. Here, we present a bacterial Na+-dependent choline transporter (sfCHT) with high sequence identity to CHT1. sfCHT transport activity can be blocked by the choline transport inhibitor hemicholinium-3. Cryo-EM structures of Na+- and choline-bound sfCHT reveal a 14 transmembrane helix topology with a LeuT-fold architecture and Na+ coordination geometry similar to CHT1. Captured in an inward-facing conformation, in sfCHT choline is found at a site near the cytoplasmic side. Computational analysis and transport assays reveal local conformational changes along a choline translocation pathway to the cytosolic site. Transport assays with CHT1 variants, carrying substitutions at conserved residues along the proposed translocation pathway in sfCHT, reveal a conserved mechanism of choline transport between the bacterial and human choline transporters.

biophysics↗

bioRxiv2025

Horus: Reproducible Workflow Manager for Biomolecular Modelling

Horus is a versatile, multi-platform reproducible-workflow management system designed for biomolecular modelling and computational life sciences. Available as both a desktop application and a web-based service, Horus offers researchers an intuitive graphical interface alongside a robust Python API, democratising the creation, execution, and management of complex scientific workflows. Its modular architecture uses individual blocks that can be easily customised and extended, allowing for an integration with diverse computational tools and resources. Horus supports remote execution via SSH to efficiently dispatch tasks to high-performance computing clusters in remote machines. The platform also features integrated 3D and 2D molecular visualizers for interactive structure analysis, which enhances the interpretability of simulation results. Its design emphasises reproducibility, scalability, and user-friendliness, positioning Horus as a valuable tool to advance research in structural bioinformatics and related fields.

bioinformatics↗

bioRxiv2025

Characterization of PROTACs by Ternary Complex Landscape Exploration using Monte Carlo simulations

PROTACs (Proteolysis-Targeting Chimeras) have emerged as a powerful modality for targeted protein degradation, yet their optimization still relies heavily on trial-and-error methods. A key factor in PROTAC degradation efficiency is the formation of the ternary complex (TC) between the PROTAC and its target proteins. However, due to their dynamic nature, PROTAC-mediated TCs can adopt multiple conformations, making their characterization challenging. Computational methods that account for this flexibility can provide more accurate predictions aligned with experimental results. Here, we explore the dynamic nature of TCs by analyzing their energy landscapes using protein-protein docking coupled with Monte Carlo sampling. This approach enables the identification of energetically relevant TC conformations, including those observed in experimental crystal structures, and allows estimation of thermodynamic and kinetic stability, as shown for a set of VHL-WDR5 PROTACs. Insights from these landscapes could support the screening and optimization of tens of similar PROTACs based on TC stability.

biophysics↗

bioRxiv2025

Efficient Design of Affilin(R) Protein Binders for HER3

Engineered scaffold-based proteins that bind to concrete targets with high affinity offer significant advantages over traditional antibodies in theranostic applications. Their development often relies on display methods, where large libraries of variants are physically contacted with the desired target protein and pools of binding variants can be selected. Herein, we use a combined artificial intelligence/physics-based computational framework and phage display approach to obtain ubiquitin based Affilin(R) proteins targeting the HER3 extracellular domain, a relevant tumor target. We demonstrate that the developed in silico pipeline can generate de novo Affilin(R) proteins with high experimental success rate using a small training set of sequences (<1000 sequences). The classical phage display yielded primary candidates with low nanomolar affinities. These binders could be further optimized by phage display and computational maturation alike. These combined efforts resulted in four HER3 ligands with high affinity, cell binding, and serum stability that have theranostic potential.

biochemistry↗

bioRxiv2023

VLP-mediated delivery of structure-selected neoantigens demonstrates immunogenicity and antitumoral activity in mice

BackgroundNeoantigens are patient- and tumor-specific peptides that arise from somatic mutations. They stand as promising targets for personalized therapeutic cancer vaccines. The identification process for neoantigens has evolved with the use of next-generation sequencing technologies and bioinformatic tools in tumor genomics. However, in silico strategies for selecting immunogenic neoantigens still have very low accuracy rates, since they mainly focus on predicting peptide binding to Major Histocompatibility Complex (MHC) molecules, which is key but not the sole determinant for immunogenicity. MethodsWe developed a novel neoantigen selection pipeline based on existing software combined with a novel prediction method, the Neoantigen Optimization Algorithm (NOAH), which takes into account structural features of the peptide/MHC-I interaction in its prediction strategy. Moreover, to maximize neoantigens therapeutic potential, neoantigen-based vaccines should be manufactured in an optimal delivery platform that elicits robust de novo immune responses and bypasses central and peripheral tolerance. ResultsWe generated a highly immunogenic vaccine platform based on engineered HIV-1 Gag-based Virus-Like Particles (VLPs) expressing a high copy number of each in silico selected neoantigen. We tested different neoantigen-loaded VLPs (neoVLPs) in a B16-F10 melanoma mouse model to evaluate their capability to generate new immunogenic specificities. NeoVLPs were used in in vivo immunogenicity and tumor challenge experiments. ConclusionsNeoVLPs can promote the generation of de novo antitumor-specific immune responses, resulting in a delay in tumor growth. Vaccination with the neoVLP platform is a robust alternative to current therapeutic vaccine approaches and a promising candidate for future personalized immunotherapy. WHAT IS ALREADY KNOWN ON THIS TOPICIdentification of highly immunogenic neoantigens is still challenging, currently available pipelines base their prediction on MHC-I binding affinity. Moreover, neoantigen-based vaccine delivery needs to be improved to increase the potency of anti-tumor immune response. WHAT THIS STUDY ADDSNOAH is a novel pipeline for the identification and selection of neoantigens that combines binding affinity and structural features of the peptide/MHC-I interaction. Preclinical studies show highly immunogenic vaccine platform based on HIV-1 Gag based VLPs (neoVLPs) generates antitumor-specific immune responses, delaying tumor growth. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYThe combination of NOAH and neoVLP platform represents an alternative to current therapeutic vaccine approaches and a promising candidate for future personalized immunotherapy.

immunology↗

bioRxiv2023

Efficient and accurate sequence generation with small-scale protein language models

Large Language Models (LLMs) have demonstrated exceptional capabilities in understanding contextual relationships, outperforming traditional methodologies in downstream tasks such as text generation and sentence classification. This success has been mirrored in the realm of protein language models (pLMs), where proteins are encoded as text via their amino acid sequences. However, the training of pLMs, which involves tens to hundreds of millions of sequences and hundreds of millions to billions of parameters, poses a significant computational challenge. In this study, we introduce a Small-Scale Protein Language Model (SS-pLM), a more accessible approach that requires training on merely millions of representative sequences, reducing the number of trainable parameters to 14.8M. This model significantly reduces the computational load, thereby democratizing the use of foundational models in protein studies. We demonstrate that the performance of our model, when fine-tuned to a specific set of sequences for generation, is comparable to that of larger, more computationally demanding pLM.

bioinformatics↗

bioRxiv2023

Novel Spike-stabilized trimers with improved production protect K18-hACE2 mice and golden Syrian hamsters from the highly pathogenic SARS-CoV-2 Beta variant.

Most COVID-19 vaccines are based on the SARS-CoV-2 Spike glycoprotein (S) or their subunits. However, the S shows some structural instability that limits its immunogenicity and production, hampering the development of recombinant S-based vaccines. The introduction of the K986P and V987P (S-2P) mutations increases the production of the recombinant S trimer and, more importantly, its immunogenicity, suggesting that these two parameters are related. However, S-2P still shows some molecular instability and it is produced with low yield. Thus, S-2P production can be further optimized. Here we described a novel set of mutations identified by molecular modelling and located in the S2 region of the Spike that increase S-2P production up to five-fold. Besides their immunogenicity, the efficacy of two representative S-2P-based mutants, S-29 and S-21, protecting from a heterologous SARS-CoV-2 Beta variant challenge was assayed in K18-hACE2 mice (an animal model of severe SARS-CoV-2 disease) and golden Syrian hamsters (GSH) (a moderate disease model). S-21 induced higher level of WH1 and Delta variants neutralizing antibodies than S-2P in K18-hACE2 mice three days after challenge. Viral load in nasal turbinate and oropharyngeal samples were reduced in S-21 and S-29 vaccinated mice. Despite that, only the S-29 protein protected 100% of K18-hACE2 mice from severe disease. When GSH were analyzed, all immunized animals were protected from disease development irrespectively of the immunogen they received. Therefore, the higher yield of S-29, as well as its improved immunogenicity and efficacy protecting from the highly pathogenic SARS-CoV-2 Beta variant, pinpoint the S-29 spike mutant as an alternative to the S-2P protein for future SARS-CoV-2 vaccine development. Authors summaryThe rapid development of SARS-CoV-2 vaccines have been pivotal in the control of the COVID-19 pandemic worldwide. Most of these vaccines include the S glycoprotein as the main immunogen since this protein, and particularly its receptor binding domain (RBD), is the major target of neutralizing antibodies. SARS-CoV-2 have been evolving from the beginning of the pandemic and several variants with increased transmissibility, pathogenicity or resistance to infection- or vaccine-induced immunity have emerged. Different strategies have been adopted to improve vaccine protection including additional booster doses or the adaptation of the S immunogens to the novel SARS-CoV-2 variants. As a complementary strategy we have identified a combination of non-proline mutations that increase S production by 5-fold (S-29 protein). Despite the sequence of this novel S-29 immunogen is based on the ancestral SARS-CoV-2 WH1 variant, it effectively protects animal model from the highly pathogenic and neutralization resistant SARS-CoV-2 Beta variant. Thus, we describe a novel set of mutations that can increase the production and efficacy of S-based COVID-19 vaccines.

immunology↗

bioRxiv2022

Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures

Machine learning protein structure prediction, such as RosettaFold and AlphaFold2, have impacted the structural biology field, raising a fair amount of discussion around its potential role in drug discovery. While we find some preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a target with low sequence identity. In order to address this, we have developed an AlphaFiold2 version where we exclude all structural templates with more than 30% sequence identity. In a previous study, we used those models in conjunction with state of the art free energy perturbation methods. In this work we focus on using them in rigid receptor ligand docking. Our results indicate that using out-of-the-box Alphafold2 models is not an ideal scenario; one might think in including some post processing modeling to drive the binding site into a more realistic holo target model.

biochemistry↗

bioRxiv2022

Deep Learning Structural Models Sufficiently Accurate for Free Energy Calculations? Application of FEP+ to AlphaFold2 Predicted Structures

The availability of AlphaFold2 has led to great excitement in the scientific community - particularly among drug hunters - due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with >30% identity to the target protein from the training set. We observed that in most cases, the {Delta}{Delta}G values for ligand transformations calculated with FEP, using these prospective AlphaFold2 structures, were comparable in accuracy to the corresponding calculations previously carried out using X-ray structures. We conclude that under the right circumstances, AlphaFold2 modeled structures are accurate enough to be used by physics-based methods such as FEP, in typical lead optimization stages of a drug discovery program.

biochemistry↗

bioRxiv2021

SARS-CoV-2 B.1.351 (beta) variant shows enhanced infectivity in K18-hACE2 transgenic mice and expanded tropism to wildtype mice compared to B.1 variant

SARS-CoV-2 variants display enhanced transmissibility and/or immune evasion and can be generated in humans or animals, like minks, thus generating new reservoirs. The continuous surveillance of animal susceptibility to new variants is necessary to predict pandemic evolution. In this study we demonstrate that, compared to the B.1 SARS-CoV-2 variant, K18-hACE2 transgenic mice challenged with the B.1.351 variant displayed a faster progression of infection. Furthermore, we also report that B.1.351 can establish infection in wildtype mice, while B.1 cannot. B.1.351-challenged wildtype mice showed a milder infection than transgenic mice, confirmed by detectable viral loads in oropharyngeal swabs and tissues, lung pathology, immunohistochemistry and serology. In silico models supported these findings by demonstrating that the Spike mutations in B.1.351 resulted in increased affinity for both human and murine ACE2 receptors. Overall, this study highlights the plasticity of SARS-CoV-2 animal susceptibility landscape, which may contribute to viral persistence and expansion.

microbiology↗

bioRxiv2021

Siglec-1 on dendritic cells mediates SARS-CoV-2 trans-infection of target cells while on macrophages triggers proinflammatory responses

COVID-19 pandemic is not yet under control by vaccination, and effective antivirals are critical for preparedness. Here we report that macrophages and dendritic cells, key antigen presenting myeloid cells (APCs), are largely resistant to SARS-CoV-2 infection. APCs effectively captured viruses within cellular compartments that lead to antigen degradation. Macrophages sense SARS-CoV-2 and released higher levels of cytokines, including those related to cytokine storm in severe COVID-19. The sialic acid-binding Ig-like lectin 1 (Siglec-1/CD169) present on APCs, which interacts with sialylated gangliosides on membranes of retroviruses or filoviruses, also binds SARS-CoV-2 via GM1. Blockage of Siglec-1 receptors by monoclonal antibodies reduces SARS-CoV-2 uptake and transfer to susceptible target cells. APCs expressing Siglec-1 and carrying SARS-CoV-2 are found in pulmonary tissues of non-human primates. Single cell analysis reveals the in vivo induction of cytokines in those macrophages. Targeting Siglec-1 could offer cross-protection against SARS-CoV-2 and other enveloped viruses that exploit APCs for viral dissemination, including those yet to come in future outbreaks.

microbiology↗

bioRxiv2021

Previous SARS-CoV-2 infection increases B.1.1.7 cross-neutralization by vaccinated individuals.

To assess the potential impact of predominant circulating SARS-CoV-2 variants on neutralizing activity of infected and/or vaccinated individuals, we analyzed neutralization of pseudoviruses expressing the spike of the original Wuhan strain, the D614G and B.1.1.7 variants. Our data show that parameters of natural infection (time from infection and infecting variant) determined cross-neutralization. Importantly, upon vaccination, previously infected individuals developed equivalent B.1.1.7 and Wuhan neutralizing responses. In contrast, uninfected vaccinees showed reduced neutralization against B.1.1.7. FundingThis study was funded by Grifols, the Departament de Salut of the Generalitat de Catalunya, the Spanish Health Institute Carlos III, CERCA Programme/Generalitat de Catalunya, and the crowdfunding initiatives #joemcorono, BonPreu/Esclat and Correos.

immunology↗

bioRxiv2021

Protection against reinfection with D614- or G614-SARS-CoV-2 isolates in hamsters

Reinfections with SARS-CoV-2 have already been documented in humans, although its real incidence is currently unknown. Besides having great impact on public health, this phenomenon raises the question if immunity generated by a single infection is sufficient to provide sterilizing/protective immunity to a subsequent SARS-CoV-2 re-exposure. The Golden Syrian hamster is a manageable animal model to explore immunological mechanisms able to counteract COVID-19, as it recapitulates pathological aspects of mild to moderately affected patients. Here, we report that SARS-CoV-2-inoculated hamsters resolve infection in the upper and lower respiratory tracts within seven days upon inoculation with the Cat01 (G614) SARS-CoV-2 isolate. Three weeks after primary challenge, and despite high titers of neutralizing antibodies, half of the animals were susceptible to reinfection by both identical (Cat01, G614) and variant (WA/1, D614) SARS-CoV-2 isolates. However, upon re-inoculation, only nasal tissues were transiently infected with much lower viral replication than those observed after the first inoculation. These data indicate that a primary SARS-CoV-2 infection is not sufficient to elicit a sterilizing immunity in hamster models but protects against lung disease.

pathology↗

bioRxiv2020

Stable neutralizing antibody levels six months after mild and severe COVID-19 episode

Understanding mid-term kinetics of immunity to SARS-CoV-2 is the cornerstone for public health control of the pandemic and vaccine development. However, current evidence is rather based on limited measurements, thus losing sight of the temporal pattern of these changes1-6. In this longitudinal analysis, conducted on a prospective cohort of COVID-19 patients followed up to 242 days, we found that individuals with mild or asymptomatic infection experienced an insignificant decay in neutralizing activity that persisted six months after symptom onset or diagnosis. Hospitalized individuals showed higher neutralizing titers, which decreased following a two-phase pattern, with an initial rapid decline that significantly slowed after day 80. Despite this initial decay, neutralizing activity at six months remained higher among hospitalized individuals. The slow decline in neutralizing activity at mid-term contrasted with the steep slope of antibody titers change, reinforcing the hypothesis that the quality of immune response evolves over the post-convalescent stage4,5.

immunology↗