Elena Papaleo

EnZight identifies and prioritizes substitution hotspots in enzymes using structural alignment, demonstrating effectiveness in improving catalytic activity and thermostability.

This study identifies five proteins whose sequential changes in plasma precede spontaneous labour onset, implicating the IL-33/ST2 axis in labour timing and identifying LMOD1 as a novel marker.

This study evaluates the impact of updating FoldX from version 5 to 5.1 on MAVISp stability predictions, finding high agreement and minimal shifts in folding free energy.

MAVISp provides a modular structural framework for analyzing protein variant effects, offering over eight million variants across 1000 proteins with curated data and a web server for accessibility.

This study predicts the impact of all missense variants in ATG9A using structural analysis and variant effect predictors, identifying potentially damaging variants and their molecular mechanisms.

This study identifies gene expression markers that distinguish pediatric B-cell and T-cell acute lymphoblastic leukemia subtypes and shows their association with patient survival.

The paper uses MAVISp and molecular dynamics simulations to analyze DNA polymerase variants, classifying VUS and predicting functional consequences of POLE and POLD1 variants.

The paper introduces Moonlight2, an updated framework that integrates transcriptomic and epigenomic data to identify cancer driver genes, finding 33, 190, and 263 epigenetically driven genes in three cancer subtypes.

The paper benchmarks protein stability predictors using a balanced dataset of AlphaFold structures, showing performance declines when balancing data and highlighting the benefits of molecular dynamics ensembles over static structures.

This study examines how point mutations in TRAP1 affect its structure, function, and pro-neoplastic activity using molecular dynamics simulations and structural analysis.

This study uses MD simulations to show that N99 glycosylation in ATG9A enhances protomer interactions and lipid scrambling, but does not significantly affect autophagy flux.

This study uses molecular dynamics simulations to investigate how acidic sphingomyelinase interacts with lysosomal membranes and how cation amphiphilic drugs like ebastine affect these interactions.

The paper creates a structural atlas of ASM variants to understand their pathogenic mechanisms and therapeutic implications.

The paper develops AVENGERS, a method using CRISPR-Cas9 to classify BRCA2 variants as functional or nonfunctional, improving the interpretation of variants of uncertain significance.

PDBminer provides an open-source tool to search and rank experimentally determined protein structures from PDB and AlphaFoldDB for computational analysis.

The paper introduces two open-source tools for analyzing protein structural ensembles using network theory, focusing on reproducibility and parameter stability.

The paper compares force fields to model NPL4's zinc-bound states and its interaction with copper and Antabuse metabolites using MD simulations and QM calculations.

RosettaDDGPrediction enables high-throughput calculation of free energy changes from amino acid substitutions using Rosetta, with tools for data aggregation and visualization.

The paper introduces Moonlight2, an updated framework that uses mutation-based mechanistic indicators to identify driver genes in cancer, applied to Basal-like breast cancer.

The paper introduces Cancermuts, a Python tool for prioritizing missense cancer variants, and validates its predictions on AMBRA1 mutations in melanoma.

MutateX automates in-silico saturation mutagenesis to predict free energy changes of protein mutations, supporting analysis of disease-associated mutations and protein design.

psntools is a Python package for analyzing Protein Structure Networks, providing PSN-agnostic tools for studying protein dynamics and interactions.

LipidDyn is a computational pipeline that analyzes MD simulations of membranes with varying compositions to extract properties like area per lipid and thickness.

The paper evaluates 43 methods for predicting intrinsically disordered regions and binding regions in proteins, finding that deep learning methods outperform older physicochemical approaches but binding prediction remains challenging.

The paper assesses ULK1 kinase alterations across 30+ cancer types using -omics data and structural analysis, finding downregulation in gynecological tumors and significant mutation impacts on protein stability.

CAMPP is an open-source R pipeline that simplifies high-throughput data analysis for non-experts, providing tools for normalization, clustering, differential expression, and biomarker prediction.

The paper analyzes the transcriptomic, mutational, and structural alterations of pro-survival Bcl-2 proteins in breast cancer to identify potential therapeutic targets.

The paper investigates Bcl-xL's allosteric communication between binding sites and evaluates how cancer mutations affect this mechanism using molecular dynamics and protein structure networks.

The paper introduces new features in the TCGAbiolinks R/Bioconductor package to improve cancer data analysis, including better differential expression analysis, tumor purity estimation, and integration of normal samples from GTEx.

The paper analyzes RNA-Seq data from lung cancer subtypes to identify molecular signatures and immune-related features associated with LUAD and LUSC.