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Arnaudi, M.

Publications and source records attributed to Arnaudi, M..

5 recordsLinked to original sources

Interpreting the Effects of DNA Polymerase Variants at the Structural Level

Genetic variants in the DNA polymerase enzymes POLE and POLD1 can affect protein function by altering stability, catalysis, DNA binding, and interactions with other biomolecules. Understanding the structural basis of these variants is important for a comprehensive interpretation of variant impact. In this study, we used MAVISp, a modular structure-based framework, and molecular dynamics simulations to analyze over 60,000 missense variants of POLE and POLD1. By integrating results from changes in folding and binding free energies, local alterations in the proximity of the active or phosphorylation sites, we provided a detailed structural interpretation of variants reported across various databases, including ClinVar, COSMIC, and cBioPortal. Moreover, we predicted the functional consequences of variants not found yet in disease-related databases, thereby creating a comprehensive catalogue for future studies. Of note, our approach enabled us to classify 364 Variants of Uncertain Significance (VUS) as PP3 evidence and 323 as BP4 evidence, in accordance with the American College of Medical Genetics and Genomics (ACMG) guidelines. Additionally, we identified a group of variants that could alter the native orientation of the residues within the catalytic site of the exonuclease domain, such as POLE variants P297S and P436R. Finally, we identified a group of variants predicted to affect DNA-binding affinity and rationalized their effects in terms of different energetic contributions and structural features. Collectively, our results not only advance our understanding of protein variant effects in POLE and POLD1 at the structural level but also support future studies aimed at variant classification, variant prioritization for experimental studies, and functional interpretation across diverse biological contexts.

bioinformatics↗

AVENGERS: Analysis of Variant Effects using Next Generation sequencing to Enhance BRCA2 Stratification

Accurate interpretation of genetic variation is a critical step towards realizing the potential of precision medicine. Sequencing-based genetic tests have uncovered a vast array of BRCA2 sequence variants. Due to limited clinical, familial and/or epidemiological data, thousands of variants are considered to be variants of uncertain significance (VUS). To determine the functional impact of VUSs, here we develop AVENGERS: Analysis of Variant Effects using NGs to Enhance BRCA2 Stratification, utilizing CRISPR-Cas9-based saturation genome editing (SGE) in a humanized-mouse embryonic stem cell line. We have categorized nearly all possible missense single nucleotide variants (SNVs) encompassing the C-terminal DNA binding domain of BRCA2. We have generated the function scores for 6270 SNVs, covering 95.5% of possible SNVs in exons 15-26 spanning residues 2479-3216, including 1069 unique missense VUS, with 81% functional and 14% found to be nonfunctional. Our classification aligns strongly with pathogenicity data from ClinVar, orthogonal functional assays and computational meta predictors. Our statistical classifier exhibits 92.2% sensitivity and 96% specificity in distinguishing clinically benign and pathogenic variants recorded in ClinVar. Furthermore, we offer proactive evidence for 617 SNVs being non-functional and 3396 SNVs being functional demonstrated by impact on cell growth and response to DNA damaging drugs like cisplatin and olaparib. This classification serves as a valuable resource for interpreting unidentified variants in the population and for physicians and genetic counselors assessing BRCA2 VUSs in patients.

genetics↗

TRAP1 S-nitrosylation as a model of population-shift mechanism to study the effects of nitric oxide on redox-sensitive oncoproteins.

S-nitrosylation is a post-translational modification in which nitric oxide (NO) binds to the thiol group of cysteine, generating an S-nitrosothiol (SNO) adduct. S-nitrosylation has different physiological roles, and its alteration has also been linked to a growing list of pathologies, including cancer. SNO can affect the function and stability of different proteins, such as the mitochondrial chaperone TRAP1. Interestingly, the SNO site (C501) of TRAP1 is in the proximity of another cysteine (C527). This feature suggests that the S-nitrosylated C501 could engage in a disulfide bridge with C527 in TRAP1, resembling the well-known ability of S-nitrosylated cysteines to resolve in disulfide bridge with vicinal cysteines. We used enhanced sampling simulations and in-vitro biochemical assays to address the structural mechanisms induced by TRAP1 S-nitrosylation. We showed that the SNO site induces conformational changes in the proximal cysteine and favors conformations suitable for disulfide-bridge formation. We explored 4172 known S-nitrosylated proteins using high-throughput structural analyses. Furthermore, we carried out coarse-grain simulations of 44 proteins to account for protein dynamics in the analyses. This resulted in the identification of up to 1248 examples of proximal cysteines which could sense the redox state of the SNO site, opening new perspectives on the biological effects of redox switches. In addition, we devised two bioinformatic workflows (https://github.com/ELELAB/SNO_investigation_pipelines) to identify proximal or vicinal cysteines for a SNO site with accompanying structural annotations. Finally, we analyzed mutations in tumor suppressor or oncogenes in connection with the conformational switch induced by S-nitrosylation. We classified the variants as neutral, stabilizing, or destabilizing with respect to the propensity to be S-nitrosylated and to undergo the population-shift mechanism. The methods applied here provide a comprehensive toolkit for future high-throughput studies of new protein candidates, variant classification, and a rich data source for the research community in the NO field.

biochemistry↗

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↗

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↗