Biology subjects

Zonta, F.

Publications and source records attributed to Zonta, F..

6 recordsLinked to original sources

bioRxiv2025

ANUBI: A Platform for Affinity Optimization of Proteins and Peptides in Drug Design

The increasing availability of computational power is opening unprecedented opportunities in computational biology and drug design. Computer simulations based on physical models can now reproduce or replace critical biophysical experiments such as binding affinity evaluations in the drug screening process. Here we present ANUBI (ANUBI Nexus for Understanding Binding Interactions), a software package that automates sequence space exploration and binding free energy calculations to optimize protein or peptide drug candidates for improved target binding. Starting from a user-provided molecular model of the drug-target interaction, ANUBI systematically evaluates point mutations in selected regions using Monte Carlo methodology, retaining favorable mutations based on calculated binding affinity differences. We demonstrate that this approach efficiently samples sequence space, generating dozens of optimized variants in timeframes comparable to experimental approaches at substantially reduced cost. As proof of concept, we applied ANUBI to an antibody-antigen complex and a peptide-protein interaction, identifying variants with significantly improved predicted binding energy (approximately 20 kcal/mol, calculated using the MMPBSA method), within 20 days of computation on a single GPU. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/683353v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@17fa242org.highwire.dtl.DTLVardef@75cf1dorg.highwire.dtl.DTLVardef@70888aorg.highwire.dtl.DTLVardef@14412d_HPS_FORMAT_FIGEXP M_FIG TOC GRAPHIC C_FIG

biophysics↗

bioRxiv2025

Assessing computational strategies for the evaluation of antibody binding affinities.

Accurate evaluation of binding affinity is critical in drug discovery to identify molecules that bind strongly to their targets while minimizing off-target effects. Although binding affinity calculations are theoretically well-defined, they require exhaustive sampling of configurational space, a step that often requires significant computational resources. In this study, we compare different methods for calculating the binding energy of antibodies targeting a peptide derived from the N-terminus of CXCR2, a GPCR-family protein. Contrary to some previous reports, we find that equilibrium MMPBSA calculations yield better agreement with experimental binding affinities than non-equilibrium potential of mean force evaluations, underscoring the system-dependent performance of these methods. We also observed a modest improvement in accuracy when MMPBSA is combined with replica exchange molecular dynamics, albeit at a significantly higher computational cost. Calculation based on Rosetta force field, instead, produced results that did not correlate with the experimental data. We attribute these findings to two factors, which could limit the applicability of some methodologies that are widely used in the computation the binding energy: the high potency of the antibodies studied and the dominance of hydrophobic interactions between the antibodies and the peptide. Overall, this work provides important insights for optimizing in silico antibody screening strategies.

biophysics↗

bioRxiv2025

Enhanced cellular and transdermal delivery of the modified chromatin using gH625 cell-penetrating peptide

The transdermal drug delivery system has been used as a noninvasive route for drug administration, but often requires specialized equipment or invasive treatment. To examine recombinant synthetic chromatin as a non-invasive transdermal delivery vehicle, a modified histone H2A fused with a cell-penetrating peptide (gH625) was examined for its utility. Both in vitro porcine skin-based test and in vivo mice studies showed significantly enhanced passive skin penetration of gH625-modified chromatin compared to control groups. The gH625-H2A chromatin was examined as a delivery vehicle of EGF by displaying it at the carboxy terminus of histone H2B to enhance stability and skin penetration. The experimental results showed that the biological activities of EGF-displayed chromatin are comparable to those of the isolated EGF peptide in the HaCa-T cell line. The enhanced skin penetration capabilities of the gH625-H2A-modified chromatin offer great potential for developing non-invasive transdermal delivery of therapeutic proteins or peptides.

synthetic biology↗

bioRxiv2022

RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity

The spread of SARS-CoV-2 has fueled the COVID-19 pandemic with its enduring medical and socioeconomic challenges due to subsequent waves and long-term consequences of great concern. Here we charted the molecular basis of COVID-19 pathogenesis, by analysing patients immune response at single-cell resolution across disease course and severity. This approach uncovered cell subpopulation-specific dysregulation in COVID-19 across disease course and severity and identified a severity-associated activation of the receptor for advanced glycation endproduct (RAGE) pathway in monocytes. In vitro experiments confirmed that monocytes bind the SARS-CoV-2 S1-RBD via RAGE and that RAGE-Spike interactions drive monocyte infection. Our results demonstrate that RAGE is a novel functional receptor of SARS-CoV-2 contributing to COVID-19 severity. One-Sentence SummaryMonocyte SARS-CoV-2 infection via the receptor for advanced glycation endproduct triggers severe COVID-19.

immunology↗

bioRxiv2021

Construction of a neural network energy function for protein physics

Classical potentials are widely used to describe protein physics, due to their simplicity and accuracy, but they are continuously challenged as real applications become more demanding with time. Deep neural networks could help generating alternative ways of describing protein physics. Here we propose an unsupervised learning method to derive a neural network energy function for proteins. The energy function is a probability density model learned from plenty of 3D local structures which have been extensively explored by evolution. We tested this model on a few applications (assessment of protein structures, protein dynamics and protein sequence design), showing that the neural network can correctly recognize patterns in protein structures. In other words, the neural network learned some aspects of protein physics from experimental data.

biophysics↗

bioRxiv2020

Dissecting the role of glutamine in seeding peptide aggregation.

Poly glutamine and glutamine-rich peptides play a central role in a plethora of pathological aggregation events. However, biophysical characterization of soluble oligomers --the most toxic species involved in these processes-- remains elusive due to their structural heterogeneity and dynamical nature. Here, we exploit the high spatio-temporal resolution of simulations as a computational microscope to characterize the aggregation propensity and morphology of a series of polyglutamine and glutamine-rich peptides. Comparative analysis of ab-initio aggregation pinpointed a double role for glutamines. In the first phase, glutamines mediate seeding by pairing monomeric peptides, which serve as primers for higher-order nucleation. According to the glutamine content, these low molecular-weight oligomers may then proceed to create larger aggregates. Once within the aggregates, buried glutamines continue to play a role in their maturation by optimizing solvent-protected hydrogen bonds networks. TOC / Abstract Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/381632v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@251f33org.highwire.dtl.DTLVardef@123506borg.highwire.dtl.DTLVardef@174d9cdorg.highwire.dtl.DTLVardef@875f00_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗