Fabrizio Pucci

This study evaluates computational models for predicting pharmacogenomic variant effects, finding that StructureDCA outperforms other methods in distinguishing functional variants.

The paper links somatic mutations in cancer to DNA electronic properties by showing anticorrelations between mutation frequencies and vertical ionization potential (vIP) values of nucleobase motifs.

This paper introduces StructureDCA, a method that improves mutational landscape predictions by integrating structural information into direct coupling analysis.

The paper introduces AbAgym, a curated dataset of 335k mutations in antibody-antigen complexes, and evaluates models for predicting binding affinity changes.

This paper evaluates blind predictions of dynamic water and ion ensembles around RNA using cryo-EM densities from CASP16, finding that molecular dynamics approaches perform best.

PInteract detects aromatic-involving interactions in protein and nucleic acid complexes using geometric criteria and identifies recurrent motifs at interfaces.

The paper introduces a simple model using residue conservation and solvent accessibility to predict mutational effects, showing it performs as well as complex deep learning models.

The paper evaluates variant effect predictions for ARSA gene variants, finding that a bootcamp-trained model outperformed others and that deep learning slightly improved performance over simpler methods.

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

SOuLMuSiC predicts the impact of mutations on protein solubility using a shallow neural network, outperforming existing methods on curated and external datasets.

NucleoSeeker curates high-quality RNA structure datasets by filtering the PDB database, improving dataset quality for training and evaluating RNA structure prediction methods.

The paper evaluates how evolutionary models predict protein stability using deep mutational scanning data and finds that simple models perform as well as complex ones when combined with structural features.

FiTMuSiC predicts protein fitness effects using structural and coevolutionary data, achieving high accuracy and providing mechanistic insights through functional and structural scores.

This study benchmarks eight predictors of protein-protein binding affinity changes upon mutations, identifying biases and generalizability issues, particularly in machine learning approaches.

The paper identifies a stability-activity trade-off in enzymes, showing oscillatory patterns of stability strengths and weaknesses around catalytic sites and their evolutionary conservation.

The paper estimates the vertical ionization potential (vIP) of single-stranded DNA sequences using quantum chemistry calculations and validates the results with experimental data.

pyScoMotif identifies similar 3D structural motifs across proteins, enabling tasks like catalytic site detection and drug discovery.

BRANEart evaluates per-residue stability contributions in membrane proteins using specialized statistical potentials, showing agreement with experimental data.

CoCoNet improves RNA contact map prediction using a coevolutionary model and convolutional neural network, achieving 70% higher accuracy than DCA on 60 RNA structures.

MutaFrame provides a visualization framework to interpret deleteriousness predictions of missense variants by integrating sequence, structure, and functional data.

SpikePro predicts SARS-CoV-2 fitness based on spike protein sequence and structure, identifying variants with increased transmissibility, infectivity, and immune evasion.

SWOTein predicts stability strengths and weaknesses in proteins using statistical energy functions, showing agreement with experimental data and identifying functional regions.

The paper uses computational mutagenesis on protein structures to show that the genetic code and codon usage are optimized to enhance protein mutational robustness.

The paper evaluates DCA-based methods for RNA contact prediction using a curated dataset of RNA structures and finds minor performance differences between methods.

The paper introduces HoTMuSiC, a tool predicting the impact of point mutations on protein melting temperature, and demonstrates its use in thermal stability engineering of Rhizomucor miehei lipase.

pydca is a Python software for Direct Coupling Analysis of RNA and protein sequences, offering tools for sequence alignment, contact map visualization, and coevolutionary coupling inference.

SOLart predicts protein solubility using structure-based features, achieving high correlation with experimental values and outperforming existing predictors.

This study develops new energy functions to analyze amino acid interactions in membrane proteins, revealing differences in stability contributions between transmembrane and extramembrane regions.

The paper investigates the relationship between DNA ionization potentials, single base substitutions, and pathogenic mutations, finding correlations between ionization potentials and mutation frequency/pathogenicity.

The paper analyzes noise amplification and reduction in open chemical reaction networks using stochastic differential equations and identifies conditions under which noise is modulated in different reaction scenarios.