Marianne J. Rooman

Assessing Computational Models for Pharmacogenomic Variant Interpretation

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

Linking somatic mutations in cancer to the electronic properties of DNA

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.

Structure-informed direct coupling analysis improves protein mutational landscape predictions

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

AbAgym: a well-curated dataset for the mutational analysis of antibody-antigen complexes

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

PInteract: Detecting Aromatic-Involving Motifs in Proteins and Protein-Nucleic Acid Complexes

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

Residue conservation and solvent accessibility are (almost) all you need for predicting mutational effects in proteins

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.

Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A

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.

Critical assessment of missense variant effect predictors on disease-relevant variant data

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

SOuLMuSiC: A Computational Tool for Predicting the Impact of Mutations on Protein Solubility

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

Exploring evolution to uncover insights into protein mutational stability

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: Leveraging structural and (co)evolutionary data for protein fitness prediction

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

Quantification of biases in predictions of protein-protein binding affinity changes upon mutations

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

Enzyme stability-activity trade-off: new insights from protein stability weaknesses and evolutionary conservation

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.

Estimating the vertical ionization potential of single-stranded DNA molecules

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: Discovery of similar 3D structural motifs across proteins

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

BRANEart: identify stability strength and weakness regions in membrane proteins

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

MutaFrame - an interpretative visualization framework for deleteriousness prediction of missense variants in the human exome

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

Prediction and evolution of the molecular fitness of SARS-CoV-2 variants: Introducing SpikePro

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

SWOTein: A structure-based approach to predict stability Strengths and Weaknesses of prOTEINs

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

Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness

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

Protein thermal stability engineering using HoTMuSiC

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.

SOLart: a structure-based method to predict protein solubility and aggregation

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

A comprehensive computational study of amino acid interactions in membrane proteins

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

More insights into the relation between DNA ionization potentials, single base substitutions and pathogenic mutations

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

Deciphering noise amplification and reduction in open chemical reaction networks

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.

Insights into the relation between noise and biological complexity

The paper investigates how network topology and reaction fluxes affect noise levels in chemical reaction networks, showing a relationship between noise and biological complexity.

Quantification of biases in predictions of protein stability changes upon mutations

The paper analyzes biases in protein stability prediction tools and proposes a symmetry-based solution to reduce bias towards destabilizing mutations.

Prediction and interpretation of deleterious coding variants in terms of protein structural stability

The paper develops a stability-driven classifier to predict deleterious coding variants based on protein structural stability, achieving 71% balanced accuracy and 89% positive predictive value.

Intrinsic noise modulation in closed oligomerization-type systems

The paper analyzes how intrinsic noise in oligomerization systems depends on reaction cooperativity and system complexity, finding that higher cooperativity reduces noise in products while increasing noise in reactants.