Biology subjects

Utichi, M.

Publications and source records attributed to Utichi, M..

3 recordsLinked to original sources

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↗