Biology subjects

Biagini, T.

Publications and source records attributed to Biagini, T..

9 recordsLinked to original sources

bioRxiv2026

Dynamic consensus pocket detection across molecular dynamics ensembles reveals persistent and transient druggable sites

The traditional "one drug, one target" paradigm assumes that drugs interact with a single specific binding site. Modern pharmacology has proven this definition overly simplistic and, instead, recognizes that drugs operate within complex biological systems and often interact with multiple targets. In this context, proteins cannot be viewed as possessing a single functional binding site, but rather as dynamic entities capable of accommodating ligands at multiple regions, including transient and cryptic pockets. Here, we review and repurpose representative pocket detection tools across geometry-based, energy-based, and machine/deep learning approaches, originally designed to work on static conformations, to evaluate their agreement on molecular dynamics-derived conformational ensembles. Using GLUT1 protein as a dynamic transporter model and Aldose reductase as a cryptic-pocket reference system, we combine inter-tool concordance, HDBSCAN-based spatial clustering, volumetric IoU analysis, and temporal persistence scoring. Our results show that different algorithmic classes capture complementary aspects of pocket dynamics, with energy-based methods showing stronger sensitivity to transient cryptic regions and geometry-based approaches depending more strongly on pre-formed cavities. This work proposes a consensus-oriented framework for identifying conserved and transient druggable pockets in dynamic protein systems.

bioinformatics↗

bioRxiv2026

STRmie-HD enables interruption-aware HTT repeat genotyping and somatic mosaicism profiling across sequencing platforms

Short tandem repeat expansions in exon 1 of the HTT gene drive Huntingtons disease (HD) pathogenesis, with disease onset and progression heavily influenced by somatic mosaicism and sequence interruptions. While sequencing technologies enable repeat sizing, many computational tools lack the resolution to capture subtle interruption motifs and allele-specific somatic variation. We present STRmie-HD, an alignment-free, de novo framework for interruption-aware genotyping and quantitative profiling of somatic mosaicism at single-read resolution. The tool parses individual reads to quantify uninterrupted CAG tract length, CCG repeat content, and critical interruption variants, including Loss of Interruption (LOI) and Duplication of Interruption (DOI). Validated across Illumina, PacBio SMRT, and Oxford Nanopore platforms, STRmie-HD demonstrates high concordance with reference genotypes and superior sensitivity in identifying rare interruption patterns that conventional tools often overlook. Furthermore, it implements somatic mosaicism metrics to characterize repeat dynamics, successfully distinguishing the higher somatic expansion burden in brain tissues compared to peripheral blood. STRmie-HD offers a comprehensive and extensible solution for high-resolution molecular characterization of HTT variation, providing a robust framework for patient stratification and genetic research in HD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=72 SRC="FIGDIR/small/713334v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@17a54aforg.highwire.dtl.DTLVardef@4dcfc5org.highwire.dtl.DTLVardef@8398edorg.highwire.dtl.DTLVardef@1acefde_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract: STRmie-HD flowchart. STRmie-HD is a comprehensive analytical framework that processes sequencing reads to analyze CAG/CCG trinucleotide repeats, interruption variants, and somatic mosaicism in the HTT gene. The workflow begins with sequencing reads (FASTA/FASTQ) that can undergo optional custom processing eq]based on the sequencing design. These reads are then fed into a regular expression-based engine (STRmie-HD) to identify CAG and CCG motifs. The identified motifs lead to the estimation of CAG/CCG alleles, visualized as distinct peaks representing different allele sizes, interruption variant assessment, and somatic mosaicism quantification. STRmie-HD produces an HTML output that wraps this information into a report. C_FIG

bioinformatics↗

bioRxiv2025

nAPOGEE: A machine-learning platform for clinically actionable pathogenicity assessment of all mitochondrial noncoding variants

Mitochondrial noncoding variants, particularly those in tRNA and rRNA genes, pose significant challenges for clinical interpretation due to heteroplasmy, broad phenotypic heterogeneity where symptoms can overlap with other conditions, and the limited availability of well-established genotype-phenotype correlations. Despite their central role in mitochondrial translation, these variants have remained largely unexplored by the existing variant-effect predictors. Here, we present nAPOGEE, a novel machine-learning framework specifically designed to assess the pathogenicity of all possible single-nucleotide variants in human mitochondrial noncoding RNAs. nAPOGEE integrates two specialized predictors: tAPOGEE, which outperforms existing tools for tRNAs, and rAPOGEE, the first dedicated classifier for mitochondrial rRNA variants. Using curated training datasets, phylogenetic conservation metrics, secondary structure modeling, RNA-specific embeddings, and thermodynamic features, nAPOGEE provides biologically interpretable predictions and posterior probabilities aligned with the ACMG/AMP guidelines. Applied to both curated variant sets and population-scale data, nAPOGEE revealed consistent spatial correlation of the predicted pathogenicity, reflecting underlying structural and evolutionary constraints. This study addresses a longstanding gap in mitochondrial genomics and offers a clinically applicable tool for variant prioritization, reclassification, and research into mitochondrial disease mechanisms.

bioinformatics↗

bioRxiv2025

NetMD: Unsupervised Synchronization of Molecular Dynamics Trajectories via Graph Embedding and Time Warping

Molecular dynamics (MD) simulations yield detailed atomistic views of biomolecular processes, yet comparing independent trajectories is hindered by stochastic divergence. Here, we introduce NetMD, a computational approach that synchronizes and analyzes MD trajectories by combining graph-based representations with dynamic time warping. Frames are transformed into residue-contact graphs, entropy-filtered to retain variable interactions, and embedded as low-dimensional vectors. NetMD then uses time-warping barycenter averaging to align these vector trajectories, yielding a consensus "average" trajectory while pruning the outlier simulations. Applied to diverse systems, such as transporters, demethylases, and protein complexes, NetMD revealed shared multiphase dynamics and pinpointed mutation- or ligand-specific deviations. Thus, this method enables an unsupervised, time-resolved comparison of MD ensembles across conditions. It is robust, broadly applicable, and available as an open-source software, offering a powerful tool for uncovering common patterns and critical divergences in biomolecular dynamics.

bioinformatics↗

bioRxiv2024

Group social dynamics in a semi-natural setup reveal an adaptive value for aggression in male mice

BackgroundMaladaptive aggression in humans is associated with several psychiatric conditions and lacks effective treatment. Nevertheless, aggression constitutes an essential behavior throughout the animal kingdom as long as it is tightly regulated. Studying how social dominance hierarchies (SDH) regulate aggression and access to resources in an enriched environment (EE) can narrow the translational gap between aggression in animal models and humans normal and pathological behavior. MethodsThe social box (SB) is a semi-natural setup for automatic and prolonged monitoring of mouse group dynamics. We utilized the SB to decipher complex tradeoffs between aggression, social avoidance, resource allocation, and dominance in two mouse models of increased aggression: (i) a model of early exposure to EE and (ii) a model of oxytocin receptor deficiency (OxtR-/-). While EE increases aggression as an adaptive response to external stimuli, hyper-aggression in OxtR-/- mice is accompanied by marked abnormalities in social behavior. ResultsEE groups exhibited significant social avoidance, and an increased proportion of their encounters developed into aggressive interactions, resulting in lower levels of exploratory activity and overall aggression. The hierarchy in EE was more stable than in control groups, and dominance was correlated with access to resources. In OxtR-/- groups, mice engaged in excessive social encounters and aggressive chasing, accompanied by increased overall activity. In OxtR-/- groups, dominance hierarchies existed but were not correlated with access to resources. ConclusionMeasuring aggression and social dominance hierarchies in a semi-natural setup reveals the adaptive value of aggression in EE and OxtR-/- mice, respectively. This approach can enhance translational research of pathological aggression.

neuroscience↗

bioRxiv2023

APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

APOGEE 2 is a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP), APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2s ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

bioinformatics↗

bioRxiv2023

Investigating mitochondrial gene expression patterns in Drosophila melanogaster using network analysis to understand aging mechanisms

The process of aging is a complex phenomenon that involves a progressive decline in physiological functions required for survival and fertility. To better understand the mechanisms underlying this process, the scientific community has utilized several tools. Among them, mitochondrial DNA has emerged as a crucial factor in biological aging, given that mitochondrial dysfunction is thought to significantly contribute to this phenomenon. Additionally, Drosophila melanogaster has proven to be a valuable model organism for studying aging due to its low cost, capacity to generate large populations, and ease of genetic manipulation and tissue dissection. Moreover, graph theory has been employed to understand the dynamic changes in gene expression patterns associated with aging and to investigate the interactions between aging and aging-related diseases. In this study, we have integrated these approaches to examine the patterns of gene co-expression in Drosophila melanogaster at various stages of development. By applying graph-theory techniques, we have identified modules of co-expressing genes, highlighting those that contain a significantly high number of mitochondrial genes. We found important mitochondrial genes involved in aging and age-related diseases in Drosophila melanogaster, including UQCR-C1, ND-B17.2, ND-20, and Pdhb. Our findings shed light on the role of mitochondrial genes in the aging process and demonstrate the utility of Drosophila melanogaster as a model organism and graph theory in aging research.

bioinformatics↗

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↗

bioRxiv2018

Systematic analysis of mouse genome reveals distinct evolutionary and functional properties among circadian and ultradian genes

In living organisms, biological clocks regulate 24 h (circadian) molecular, physiological, and behavioral rhythms to maintain homeostasis and synchrony with predictable environmental changes. Harmonics of these circadian rhythms having periods of 8 hours and 12 hours (ultradian) have been documented in several species. In mouse liver, harmonics of the 24-hour period of gene transcription hallmarked genes oscillating with a frequency two or three times faster than the circadian circuitry. Many of these harmonic transcripts enriched pathways regulating responses to environmental stress and coinciding preferentially with subjective dawn and dusk. We hypothesized that these stress anticipatory genes would be more evolutionarily conserved than background circadian and non-circadian genes. To investigate this issue, we performed broad computational analyses of genes/proteins oscillating at different frequency ranges across several species and showed that ultradian genes/proteins, especially those oscillating with a 12-hour periodicity, are more likely to be of ancient origin and essential in mice. In summary, our results show that genes with ultradian transcriptional patterns are more likely to be phylogenetically conserved and associated with the primeval and inevitable dawn/dusk transitions.

bioinformatics↗