Biology subjects

Di Rienzo, L.

Publications and source records attributed to Di Rienzo, L..

7 recordsLinked to original sources

bioRxiv2026

HERCULES: an integrative deep-learning framework for predicting RNA-binding propensity and mutation effects at single-residue resolution

RNA-binding proteins (RBPs) regulate essential aspects of RNA metabolism, yet accurately identifying RNA-binding domains (RBDs) and quantifying the impact of sequence variation on RNA-binding ability remain challenging. Here, we present HERCULES (Hybrid framEwoRk for RNA-binding domain loCalization and mUtation anaLysis using physicochemical and languagE modelS), a unified sequence-based framework for simultaneous RBD localization, global RNA-binding propensity prediction and mutation effect assessment. HERCULES integrates a fine-tuned protein language model with an explicit residue-level physicochemical module, combining global contextual representations with local mutation-sensitive descriptors. On an independent test set, the HERCULES global score discriminates RBPs from non-RBPs with an AUROC of 0.86. At residue resolution, HERCULES outperforms state-of-the-art sequence-based predictors in identifying canonical, non-canonical and putative RBDs across Pfam-annotated proteins. Using a curated dataset of experimentally validated RNA-binding-disrupting mutations, HERCULES correctly classifies 87% of deleterious variants, including single-amino acid substitutions. Evaluation on experimentally resolved protein-RNA complexes further demonstrates robust residue-level performance and improved generalization when contact annotations are augmented with AlphaFold3-predicted complexes. By unifying domain localization and mutation sensitivity within a single sequence-only framework, HERCULES provides a mechanistically interpretable approach for studying RNA-protein interactions. HERCULES is freely available at https://tools.tartaglialab.com/hercules and as an open-source Python package at https://github.com/tartaglialabIIT/hercules.git.

biochemistry↗

bioRxiv2025

Preventing pathogenic dimerization in a misfolded antibody light chain through the design of an inhibitory peptide

Immunoglobulin light chain (AL) amyloidosis is the most common form of systemic amyloidosis. The disease correlates with the formation of insoluble aggregates mostly composed by patient-specific antibody light chains, whose hypervariable regions make each case unique and highlight the need for personalized therapeutics. In this study, we focused on a pathogenic homodimer we previously obtained from a patient-derived light chain. By analyzing the dynamics and the interface of this dimer, we identified a 15-residue peptide with potential inhibitory activity. The peptide was then refined using a computational mutagenesis protocol that iteratively improved its sequence to maximize complementarity with the protein interface, taking into account shape, electrostatics, and hydropathy. The resulting optimized peptide is found to bind the monomer with a binding affinity comparable to that of the full pathogenic interface. These results suggest that the designed peptide could act as an effective antagonist of the pathogenic dimer, and demonstrate that our computational strategy could provide a general framework for designing patient-specific inhibitory peptides against aggregation-prone proteins.

biochemistry↗

bioRxiv2025

Influence of neck tissue conductivities on the phrenic nerve activation threshold during non-invasive electrical stimulation

Phrenic nerve stimulation can be used as an artificial ventilation method to reduce the adverse effects of mechanical ventilation. Detailed computational models and electromagnetic simulations are used to determine appropriate stimulation parameters. Therefore, tissue parameters have to be selected, but they vary widely in the literature. Here, we evaluated the phrenic nerve activation threshold using minimum and maximum electrical conductivity values found in the literature of each modeled neck tissue type. To calculate the phrenic nerve activation threshold, an anatomical detailed finite element model of the neck and a biophysiological nerve model were used. Considerable changes in nerve activation thresholds were found for the following tissue conductivities (with decreasing effects): muscle, skin, soft tissue, subcutaneous fat, and nerve tissue. Changes in the nerve activation threshold due to changes in skin conductivity occurred due to the bridging effect, which is an unwanted and avoidable effect during stimulation. In conclusion, fat, muscle, nerve, and soft tissue require the most accurate tissue properties and geometric representation within the model.

neuroscience↗

bioRxiv2022

Molecular dynamics analysis of Superoxide Dismutase 1 mutations suggests decoupling between mechanisms underlying ALS onset and progression

Mutations in the superoxide dismutase 1 (SOD1) gene are the second most common known cause of ALS. SOD1 variants express high phenotypic variability and over 200 have been reported in people with ALS. Investigating how different SOD1 variants affect the protein dynamics might help in understanding their pathogenic mechanism and explaining their heterogeneous clinical presentation. It was previously proposed that variants can be broadly classified in two groups, wild-type like (WTL) and metal binding region (MBR) variants, based on their structural location and biophysical properties. MBR variants are associated with a loss of SOD1 enzymatic activity. In this study we used molecular dynamics and large clinical datasets to characterise the differences in the structural and dynamic behaviour of WTL and MBR variants with respect to the wild-type SOD1, and how such differences influence the ALS clinical phenotype. Our study identified marked structural differences, some of which are observed in both variant groups, while others are group specific. Moreover, applying graph theory to a network representation of the proteins, we identified differences in the intramolecular contacts of the two classes of variants. Finally, collecting clinical data of approximately 500 SOD1 ALS patients carrying variants from both classes, we showed that the survival time of patients carrying an MBR variant is generally longer (~6 years median difference, p < 0.001) with respect to patients with a WTL variant. In conclusion, our study highlights key differences in the dynamic behaviour of the WTL and MBR SOD1 variants, and wild-type SOD1 at an atomic and molecular level. We identified interesting structural features that could be further investigated to explain the associated phenotypic variability. Our results support the hypothesis of a decoupling between mechanisms of onset and progression of SOD1 ALS, and an involvement of loss-of-function of SOD1 with the disease progression.

neuroscience↗

bioRxiv2021

Inferring the stabilization effects of SARS-CoV-2 variants on the binding with ACE2 receptor

With the progression of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic, several variants of the virus are emerging with mutations distributed all over the viral sequence. While most of them are expected to have little to no effects at the phenotype level, some of these variants presenting specific mutations on the Spike protein are rapidly spreading, making urgent the need of characterizing their effects on phenotype features like contagiousness and antigenicity. With this aim, we performed extensive molecular dynamics simulations on a selected set of possible Spike variants in order to assess the stabilizing effect of particular amino acid substitutions, with a special focus on the mutations that are both characteristic of the top three most worrying variants at the moment, i.e the English, South African and Amazonian ones, and that occur at the molecular interface between SARS-CoV-2 Spike protein and its human ACE2 receptor. We characterize these variants effect in terms of (i) residues mobility, (ii) compactness, studying the network of interactions at the interface, and (iii) variation of shape complementarity via expanding the molecular surfaces in the Zernike basis. Overall, our analyses highlighted greater stability of the three variant complexes with respect to both the wild type and two negative control systems, especially for the English and Amazonian variants. In addition, in the three variants, we investigate the effects a not-yet observed mutation in position 501 could provoke on complex stability. We found that a phenylalanine mutation behaves similarly to the English variant and may cooperate in further increasing the stability of the South African one, hinting at the need for careful surveillance for the emergence of such kind of mutations in the population. Ultimately, we show that the observables we propose describe key features for the stability of the ACE2-spike complex and can help to monitor further possible spike variants.

biophysics↗

bioRxiv2020

In-Silico evidence for two receptors based strategy of SARS-CoV-2

We propose a novel numerical method able to determine efficiently and effectively the relationship of complementarity between portions of protein surfaces. This innovative and general procedure, based on the representation of the molecular iso-electron density surface in terms of 2D Zernike polynomials, allows the rapid and quantitative assessment of the geometrical shape complementarity between interacting proteins, that was unfeasible with previous methods. We first tested the method with a large dataset of known protein complexes obtaining an overall area under the ROC curve of 0.76 in the blind recognition of binding sites and then applied it to investigate the features of the interaction between the Spike protein of SARS-CoV-2 and human cellular receptors. Our results indicate that SARS-CoV-2 uses a dual strategy: its spike protein could also interact with sialic acid receptors of the cells in the upper airways, in addition to the known interaction with Angiotensin-converting enzyme 2.

biophysics↗

bioRxiv2018

Insights on protein thermal stability: a graph representation of molecular interactions

Understanding the molecular mechanisms of thermal stability is a challenge in protein biology. Indeed, knowing the temperature at which proteins are stable has important theoretical implications, which are intimately linked with properties of the native fold, and a wide range of potential applications from drug design to the optimization of enzyme activity.\n\nHere, we present a novel graph-theoretical framework to assess thermal stability based on the structure without any a priori information. In our approach we describe proteins as energy-weighted graphs and compare them using ensembles of interaction networks. Investigating the position of specific interactions within the 3D native structure, we developed a parameter-free network descriptor that permits to distinguish thermostable and mesostable proteins with an accuracy of 76% and Area Under the Roc Curve of 78%.

bioinformatics↗