Biology subjects

Utichi, M.

Publications and source records attributed to Utichi, M..

6 recordsLinked to original sources

bioRxiv2026

Decoding ATG9A Variation: A Comprehensive Structural Investigation of All Missense Variants

Macroautophagy (hereafter autophagy) is a cellular recycling pathway that requires different ATG (autophagy-related) proteins to generate double-membraned autophagosomes. ATG9A, a multi-spanning membrane protein, plays a crucial role in this process as the only transmembrane component of the core autophagy machinery. ATG9A functions as a lipid scramblase, redistributing lipids between membrane leaflets for the expanding autophagosome membrane. Structural studies have revealed that ATG9A forms a homotrimer with an interlocked domain-swapped architecture and a network of internal hydrophilic cavities. This configuration underlies its role in lipid transfer and membrane remodeling together with the lipid transporter ATG2A. ATG9A dysfunction has also been linked to human disease, as specific ATG9A mutations cause neurodevelopmental or neurodegenerative phenotypes. Additionally, ATG9A is altered in cancer, promoting pro-tumorigenic traits. However, most missense variants in ATG9A remain uncharacterized, posing a significant challenge for interpreting genomic data. In this study, we employed in silico saturation mutagenesis approach using the MAVISp (Multi-layered Assessment of VarIants by Structure) framework to predict the impact of every missense mutation in ATG9A. By analyzing multiple structural assemblies of ATG9A (monomer, trimer, and the ATG9A-ATG2A complex), we evaluated diverse mechanistic indicators of variant impact, including protein stability, long-range conformational changes, effects on multimerization interfaces, and alterations in post-translational modifications. We integrated the structure-based predictions with Variant Effect Predictors from recent deep-learning or evolutionary-based models and cross-referenced known variants catalogued in ClinVar, COSMIC, and cBioPortal. Finally, we predicted mechanistic indicators for all possible variants with structural coverage not yet reported in the disease-related databases supported by MAVISp. Our analyses identified a group of potentially damaging variants in ATG9A and the possible molecular mechanisms underlying their effects. Together, this work provides a roadmap for interpreting missense variants in autophagy regulators and highlights specific ATG9A mutations that deserve further investigation in the context of human disease.

bioinformatics↗

bioRxiv2025

In-Silico Stability Predictors: Investigation of Performance Towards balanced Experimental Data

Change in protein stability, quantified as the change in Gibbs free energy of folding ({Delta}{Delta}G) in kcal/mol, plays a crucial role in functional alterations of proteins, with misfolding and destabilization commonly associated with pathogenicity. The past two decades have brought the development of bioinformatics tools leveraging evolutionary knowledge, empirical force fields, and machine learning to predict stability alterations. However, existing tools are often optimized towards or trained on limited experimental data, leading to unbalanced datasets and potential overfitting. The research objective is to benchmark selected stability predictors using an unbiased and balanced dataset, with AlphaFold structures as input. We demonstrate a performance decline when balancing data across amino acids, stabilizing and destabilizing mutations, and protein representatives, highlighting that redundancy alone is insufficient for benchmarking correction. Additionally, we illustrate that a protein structure ensemble from molecular dynamics acts as a superior input compared to a single static structure. At the same time, coarse-grained methodologies tend to decrease the output quality.

bioinformatics↗

bioRxiv2025

Role of N-glycosylation as a determinant of ATG9A conformations and activity

In this study we investigate the effects of glycosylation at position N99 on the structural dynamics and lipid scrambling activity of ATG9A, a key autophagy protein, using microsecond all-atom molecular dynamics (MD) simulations. ATG9A is an integral membrane protein involved in autophagosome biogenesis, and glycosylation at N99 is known to play a critical, yet poorly understood role in its function. The MD simulations revealed that the hydrophilic central cavity of ATG9A supports lipid reorientation and partial transbilayer movements, consistent with its lipid scrambling activity observed experimentally. N-glycosylation at N99 was found to enhance cooperative interactions between protomers, facilitating lipid insertion and traversal within the central cavity. These findings align with the proposed mechanism of ATG9A role in lipid redistribution across the phagophore membrane during autophagy. However, mutagenesis experiments that abolish N-glycosylation in ATG9A (ATG9AN99A and ATG9AN99D mutants) did not show a significant change in autophagy flux, suggesting that further experimental approaches, such as lipid scramblase assays, are needed to pinpoint the function of glycosylation. In this study we also observed an asymmetric protomer conformations in ATG9A, contrasting with symmetric structures obtained from cryo-EM, suggesting that the structural heterogeneity of the protein could be further explored in cryo-EM datasets. Overall, the study highlights the importance of incorporating glycosylation in computational studies of membrane proteins and offers valuable insights into the molecular mechanisms of lipid transport in autophagy, with potential implications for other lipid scramblases and flippases.

biophysics↗

bioRxiv2023

Phosphorylation-induced rewiring of intermolecular interactions with LC3B: local and long-range effects on short linear motifs

Short Linear Motifs (SLiMs) play a pivotal role in mediating interactions between intrinsically disordered proteins and their binding partners. SLiMs exhibit sequence degeneracy and undergo regulation through post-translational modifications, including phosphorylation. The flanking regions surrounding the core motifs also exert a crucial role in shaping the modes of interaction. In this study, we aimed to integrate biomolecular simulations, in silico high-throughput mutational scans, and biophysical experiments to elucidate the structural details of phospho-regulation in a class of SLiMs crucial for autophagy, known as LC3 interacting regions (LIRs). As a case study, we investigated the interaction between optineurin and LC3B. Optineurin LIR perfectly exemplify a class of LIR where there is a complex interplay of different phosphorylations and a N-terminal helical flanking region to be disentangled. Our work unveils the unexplored role of the N-terminal flanking region upstream of the LIR core motif in contributing to the interaction interface. The results offer an atom-level perspective on the structural mechanisms and conformational alterations induced by phosphorylation in optineurin and LC3B recognition, along with of effects of mutations on the background of the phosphorylated form of the protein. Additionally, we assessed the impact of disease-related mutations on optineurin, accounting for different functional features. Notably, we established an approach based on Microfluidic Diffusional Sizing as a novel method to investigate the binding affinity of SLiMs to target proteins, enabling precise measurements of the dissociation constant for a selection of variants identified in the in silico mutational screening. Overall, our work provides a versatile toolkit to characterize other LIR-containing proteins and their modulation by phosphorylation or other phospho-regulated SLiMs, thereby advancing the understanding of important cellular processes.

biochemistry↗

bioRxiv2022

MAVISp: Multi-layered Assessment of VarIants by Structure for proteins

The role of genomic variants in disease has expanded significantly with the advent of advanced sequencing techniques. The rapid increase in identified genomic variants has led to many variants being classified as Variants of Uncertain Significance or as having conflicting evidence, posing challenges for their interpretation and characterization. Additionally, current methods for predicting pathogenic variants often lack insights into the underlying molecular mechanisms. Here, we introduce MAVISp (Multi-layered Assessment of VarIants by Structure for proteins), a modular structural framework for variant effects, accompanied by a web server (https://services.healthtech.dtu.dk/services/MAVISp-1.0/) to enhance data accessibility, consultation, and reusability. MAVISp currently provides data over 1000 proteins, encompassing more than eight million variants. A team of biocurators regularly analyzes and updates protein entries using standardized workflows, incorporating free energy calculations or biomolecular simulations. We illustrate the utility of MAVISp through selected case studies. The framework facilitates the analysis of variant effects at the protein level and has the potential to advance the understanding and application of mutational data in disease research.

bioinformatics↗

bioRxiv2022

RosettaDDGPrediction for high-throughput mutational scans: from stability to binding

Reliable prediction of free energy changes upon amino acidic substitutions ({Delta}{Delta}Gs) is crucial to investigate their impact on protein stability and protein-protein interaction. Moreover, advances in experimental mutational scans allow high-throughput studies thanks to sophisticated multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput calculations of {Delta}{Delta}Gs. In this context, the Rosetta modeling suite implements effective approaches to predict the change in the folding free energy in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. Their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for {Delta}{Delta}G prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. RosettaDDGPrediction assists with checking whether the runs are completed successfully aggregates raw data for multiple variants, and generates publication-ready graphics. We showed the potential of the tool in selected case studies, including variants of unknown significance found in children who developed cancer, proteins with known experimental unfolding {Delta}{Delta}Gs values, interactions between target proteins and a disordered functional motif, and phospho-mimetic variants. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

bioinformatics↗