A5085366265- Protein Structure and Dynamics
- RNA and protein synthesis mechanisms
- Enzyme Structure and Function
- Quantum Chromodynamics and Particle Interactions
- Genomics and Phylogenetic Studies
- Particle physics theoretical and experimental studies
- Black Holes and Theoretical Physics
- Evolution and Genetic Dynamics
- Gene Regulatory Network Analysis
- Microbial Metabolic Engineering and Bioproduction
- High-Energy Particle Collisions Research
- SARS-CoV-2 and COVID-19 Research
- Genomics and Rare Diseases
- Bioinformatics and Genomic Networks
- RNA modifications and cancer
- DNA and Nucleic Acid Chemistry
- CRISPR and Genetic Engineering
- Computational Drug Discovery Methods
- vaccines and immunoinformatics approaches
- RNA Research and Splicing
- Mass Spectrometry Techniques and Applications
- COVID-19 Clinical Research Studies
- Noncommutative and Quantum Gravity Theories
- Machine Learning in Bioinformatics
- Lipid Membrane Structure and Behavior
Université Libre de Bruxelles
2016-2025
Institute of Bioinformatics
2024-2025
University of Pisa
2016-2024
Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria
2024
John von Neumann Institute for Computing
2019-2021
Forschungszentrum Jülich
2019-2021
Sapienza University of Rome
2019
Dune (Italy)
2019
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa
2016
Joint Institute for Nuclear Research
2016
Abstract Motivation Bioinformatics tools that predict protein stability changes upon point mutations have made a lot of progress in the last decades and become accurate fast enough to make computational mutagenesis experiments feasible, even on proteome scale. Despite these achievements, they still suffer from important issues must be solved allow further improving their performances utilizing them deepen our insights into folding mechanisms. One problems is bias toward learning datasets...
Abstract The accurate prediction of the impact an amino acid substitution on thermal stability a protein is central issue in science, and key relevance for rational optimization various bioprocesses that use enzymes unusual conditions. Here we present one first computational tools to predict change melting temperature Δ T m upon point mutations, given structure and, when available, wild-type protein. ingredients our model are standard temperature-dependent statistical potentials, which...
The molecular bases of protein stability remain far from elucidated even though substantial progress has been made through both computational and experimental investigations. One the most challenging goals is development accurate prediction tools temperature dependence standard folding free energy ΔG(T). Such predictors have an enormous series potential applications, which range drug design in biopharmaceutical sector to optimization enzyme activity for biofuel production. There thus...
The classification of human genetic variants into deleterious and neutral is a challenging issue, whose complexity rooted in the large variety biophysical mechanisms that can be responsible for disease conditions. For non-synonymous mutations structured proteins, one these protein stability change, which lead to loss structure or function. We developed stability-driven knowledge-based classifier uses structure, artificial neural networks solvent accessibility-dependent combinations...
Abstract Motivation The solubility of a protein is often decisive for its proper functioning. Lack major bottleneck in high-throughput structural genomic studies and high-concentration production, the formation aggregates causes wide variety diseases. Since measurements are time-consuming expensive, there strong need prediction tools. Results We have recently introduced solubility-dependent distance potentials that able to unravel role residue–residue interactions promoting or decreasing...
The solubility of globular proteins is a basic biophysical property that usually prerequisite for their functioning. In this study, we probed the with help statistical potential formalism, in view objectifying connection structural and energetic properties solubility-dependence specific amino acid interactions. We started by setting up two independent datasets containing either soluble or aggregation-prone known structures. From these datasets, computed solubility-dependent distance...
A general limitation of the use enzymes in biotechnological processes under sometimes nonphysiological conditions is complex interplay between two key quantities, enzyme activity and stability, where increase one often associated with decrease other. precise stability-activity trade-off necessary for to be fully functional, but its weight different protein regions dependence on environmental not yet elucidated. To advance this issue, we used formalism that have recently developed effectively...
Abstract Motivation The accurate prediction of how mutations change biophysical properties proteins or RNA is a major goal in computational biology with tremendous impacts on protein design and genetic variant interpretation. Evolutionary approaches such as coevolution can help solving this issue. Results We present pycofitness, standalone Python-based software package for the silico mutagenesis sequences. It based and, more specifically, popular inverse statistical approach, namely direct...
Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells self-restriction. The interactions between MHCII determine the specificity response are crucial in immunotherapy cancer vaccine design. With ever-increasing amount MHCII-peptide binding data available, many computational approaches have been developed for interaction prediction over last decade. There is thus an urgent need provide up-to-date...
Abstract Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity missense variants is necessary evaluate their clinical research utility suggest directions for future improvement. Here, as part sixth edition Critical Assessment Genome Interpretation (CAGI) challenge, we assess variant effect predictors (or impact predictors) on an evaluation dataset rare from disease-relevant databases. Our evaluates submitted CAGI6 Annotate-All-Missense...
Abstract Delineating cohesive ecological units and determining the genetic basis for their environmental adaptation are among most important objectives in microbiology. In last decade, many studies have been devoted to characterizing diversity microbial populations address these issues. However, impact of extreme conditions, such as temperature salinity, on ecology evolution remains unclear so far. order better understand mechanisms adaptation, we studied (pan)genome Exiguobacterium, a...
The ability to rationally modify targeted physical and biological features of a protein interest holds promise in numerous academic industrial applications paves the way towards de novo design. In particular, bioprocesses that utilize remarkable properties enzymes would often benefit from mutants remain active at temperatures are either higher or lower than physiological temperature, while maintaining activity. Many silico methods have been developed recent years for predicting thermodynamic...
Abstract Transmembrane proteins play a fundamental role in wide series of biological processes but, despite their importance, they are less studied than globular proteins, essentially because embedding lipid membranes hampers experimental characterization. In this paper, we improved our understanding structural stability through the development new knowledge-based energy functions describing amino acid pair interactions that prevail transmembrane and extramembrane regions membrane proteins....
We have set up and manually curated a dataset containing experimental information on the impact of amino acid substitutions in protein its thermal stability. It consists repository experimentally measured melting temperatures (Tm) their changes upon point mutations (ΔTm) for proteins having well-resolved x-ray structure. This high-quality is designed being used training or benchmarking silico stability prediction methods. also reports other thermodynamic quantities when available, i.e.,...
RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA three-dimensional structure prediction. With agreement from structural biologists, structures are predicted by modeling groups before publication experimental structures. We report large-scale set predictions 18 for 23 RNA-Puzzles: 4 elements, 2 Aptamers, Viral 5 Ribozymes 8 Riboswitches. describe automatic assessment protocols comparisons between prediction experiment. Our analyses reveal some critical...
Protein solubility problems arise in a wide range of applications, from antibody development to enzyme production, and are linked several major disorders, including cataracts Alzheimer's diseases. To assist scientists designing proteins with improved better understand solubility-related diseases, we introduce SOuLMuSiC, computational tool for the fast accurate prediction impact mutations on protein solubility. Our model is based simple shallow artificial neural network that takes as input...
Predicting how mutations impact protein biophysical properties remains a significant challenge in computational biology. In recent years, numerous predictors, primarily deep learning models, have been developed to address this problem; however, issues such as their lack of interpretability and limited accuracy persist. We showed that simple evolutionary score, based on the log-odd ratio (LOR) wild-type mutated residue frequencies related proteins, when scaled by residue's relative solvent...
Abstract The structural prediction of biomolecules via computational methods complements the often involved wet-lab experiments. Unlike protein structure prediction, RNA remains a significant challenge in bioinformatics, primarily due to scarcity annotated data and its varying quality. Many have used this limited train deep learning models but redundancy, leakage bad quality hampers their performance. In work, we present NucleoSeeker, tool designed curate high-quality, tailored datasets from...
Abstract Regular, systematic, and independent assessments of computational tools that are used to predict the pathogenicity missense variants necessary evaluate their clinical research utility guide future improvements. The Critical Assessment Genome Interpretation (CAGI) conducts ongoing Annotate-All-Missense (Missense Marathon) challenge, in which variant effect predictors (also called impact predictors) evaluated on added disease-relevant databases following prediction submission...