Biology subjects

Rooman, M.

Publications and source records attributed to Rooman, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Assessing Computational Models for Pharmacogenomic Variant Interpretation

Accurately predicting the effects of pharmacogenomic variants is essential for the development of personalized therapeutic strategies, as genetic variability can influence drug response differently across patients. Here, we assessed several computational approaches using a dataset of pharmacogenomic variants with either clinical annotations or functional characterization by deep mutational scanning, compiled from the literature, with an additional focus on CYP2C9, a clinically relevant drug-metabolizing enzyme. Our results show that, despite recent methodological advances, substantial room for improvement remains. In particular, current methods struggle to distinguish gain-of-function variants associated with increased drug clearance and fast-metabolizer phenotypes from neutral variants, whereas loss-of-function variants that reduce drug clearance are predicted more accurately. The integration of structural and evolutionary information appears to be a key strategy for improving performance, with the coevolution-based StructureDCA method achieving the highest accuracy compared with classical genetic variant-effect predictors and recent deep learning approaches, including the pathogenic-variant predictor AlphaMissense and general protein language model-based methods. Finally, our results indicate that computational models can complement in vitro experiments in clinical variant interpretation, as StructureDCA predictions showed better agreement with clinically annotated phenotypes than large-scale deep mutational scanning data in several cases.

bioinformatics↗

bioRxiv2026

Structure-informed direct coupling analysis improves protein mutational landscape predictions

Direct Coupling Analysis has been instrumental over the past decade in leveraging evolutionary information and advancing our understanding of biomolecular structure and function. Here, we introduce sparse extensions of this method that explicitly incorporate structural information. StructureDCA focuses on physically relevant interactions by selectively retaining couplings between residues in spatial contact, and StructureDCA[RSA] additionally incorporates per-residue relative solvent accessibility. These models outperform state-of-the-art approaches in describing mutational landscapes, as they more effectively integrate structural context. Moreover, their sparse formulation enables orders-of-magnitude improvements in computational efficiency while preserving interpretability, providing a powerful framework for gaining mechanistic insights into mutation effects and advancing protein design. The StructureDCA models are available as a user-friendly Python package via the PyPI repository. The source code is freely accessible at https://github.com/3BioCompBio/StructureDCA, which also includes a Colab Notebook interface.

bioinformatics↗

bioRxiv2026

Linking somatic mutations in cancer to the electronic properties of DNA

Oxidative stress, generated by both endogenous and exogenous agents, can cause DNA lesions that, if not repaired, accumulate as somatic mutations and can contribute to cancer initiation. Here, we explored this problem through the lens of DNA electronic properties, quantified by the vertical ionization potential (vIP) of nucleobase motifs, which reflects their susceptibility to oxidation. We analyzed genome-wide experimental data on oxidative DNA damage and found that the highest damage levels occur in regions with low vIP values, supporting a causal link between them. The analysis of cancer mutational signatures and their annotated aetiologies revealed strong anticorrelations between mutation frequency and vIP values, particularly in cancers driven by oxidative DNA damage, such as lung cancer. We further computed anticorrelations between vIP values and the frequencies of mutated motifs across coding and non-coding regions and across different mutation types, observing the strongest anticorrelations for silent mutations, consistent with their reduced selective pressure. Moreover, similar anticorrelations were observed for somatic mutations in cancer and normal tissues, as well as for germline mutations, suggesting that they arise from similar mutagenesis processes. This work clarifies how oxidative damage, DNA electronic properties and carcinogenesis are related and help identify genomic regions more prone to mutations.

bioinformatics↗

bioRxiv2025

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

With the recent development of accurate protein structure prediction tools, virtually all protein sequences now have an experimental or a modeled structure. It has therefore become essential to develop fast algorithms capable of detecting non-covalent interactions not only within proteins but also in protein-protein, protein-DNA, protein-RNA, and protein-ligand complexes. Interactions involving aromatic compounds, particularly their{pi} molecular orbitals, hold unique significance among molecular interactions due to the electron density delocalization, which is known to play a key role in processes such as protein aggregation. In this paper, we present PInteract, an algorithm that detects{pi} -involving interactions in input structures based on geometric criteria, including{pi} -{pi}, cation-{pi}, amino-{pi}, His-{pi}, and sulfur-{pi} interactions. In addition, it is capable of detecting chains and clusters of{pi} interactions as well as particular recurrent motifs at protein-DNA and protein-RNA interfaces, called stair motifs, consisting of a particular combination of{pi} -{pi} stacking, cation/amino/His-{pi} and H-bond interactions. PInteract is freely available for download at https://github.com/3BioCompBio/PInteract.

bioinformatics↗

bioRxiv2025

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

With monoclonal antibodies becoming one of the largest classes of biopharmaceuticals, it is important to have curated data to train computational models that can accelerate their design. Despite the massive amount of mutagenesis data generated on antibody-antigen interactions, only a few small, well-curated datasets are available. This paper introduces AbAgym, a manually curated dataset comprising approximately 335k mutations in antibody-antigen complexes, including one tenth of interface mutations, whose effects on antibody-antigen binding have been experimentally quantified through deep mutational scanning (DMS) experiments. We collected and curated 67 DMS datasets from the literature together with the three-dimensional structure of each antibody-antigen complex. We benchmarked the performance of established force field methods as well as recent machine learning models that predict the change in binding affinity upon mutation. The former achieved modest performance, whereas the latter performed only marginally better than random. Finally, our analysis of hotspot residues responsible for immune evasion highlights the importance of accounting for biological complexities, such as conformational changes or oligomeric states that influence antibody-antigen binding, which are often overlooked. Abagym is freely available for academic use at https://github.com/3BioCompBio/Abagym.

bioinformatics↗

bioRxiv2025

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

MotivationPredicting 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. ResultsWe showed that a simple evolutionary score, based on the log-odd ratio (LOR) of wild-type and mutated residue frequencies in evolutionary related proteins, when scaled by the residues relative solvent accessibility (RSA), performs on par with or slightly outperforms most of the benchmarked predictors, many of which are considerably more complex. The evaluation is performed on mutations from the ProteinGym deep mutational scanning dataset collection, which measures various properties such as stability, activity or fitness. This raises further questions about what these complex models actually learn and highlights their limitations in addressing prediction of mutational landscape. AvailabilityThe RSALOR model is available as a user-friendly Python package that can be installed from the PyPI repository. The code is freely available at https://github.com/3BioCompBio/RSALOR. Contactmatsvei.tsishyn@ulb.be, fabrizio.pucci@ulb.be

bioinformatics↗

bioRxiv2025

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

Protein solubility problems arise in a wide range of applications, from antibody development to enzyme production, and are linked to several major disorders, including cataracts and Alzheimers diseases. To assist scientists in designing proteins with improved solubility and better understand solubility-related diseases, we introduce SOuLMuSiC, a computational tool for the fast and accurate prediction of the impact of mutations on protein solubility. Our model is based on a simple shallow artificial neural network that takes as input a series of features, including biophysical properties of wild-type and mutated residues, energetic values computed using various statistical potentials, and mutational scores derived from protein language models. SOuLMuSiC has been trained on a curated dataset of about seven hundred mutations with known solubility values, collected and manually verified from original literature. It significantly outperforms current state-of-the-art predictors in strict cross-validation and shows good performance on external datasets containing high-throughput enzyme solubility-related data as well as protein aggregation propensities. In summary, SOuLMuSiC is a valuable tool for identifying mutations that impact protein solubility, and can play a major role in the rational design of proteins with improved solubility and in understanding genetic variants effect. It is freely available for academic use at http://babylone.ulb.ac.be/SoulMuSiC/.

bioinformatics↗

bioRxiv2024

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

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

genetics↗

bioRxiv2024

Exploring evolution to enhance mutational stability prediction

Determining the impact of mutations on the thermodynamic stability of proteins is essential for a wide range of applications such as rational protein design and genetic variant interpretation. Since protein stability is a major driver of evolution, evolutionary data are often used to guide stability predictions. Many state-of-the-art stability predictors extract evolutionary information from multiple sequence alignments (MSA) of proteins homologous to a query protein, and leverage it to predict the effects of mutations on protein stability. To evaluate the power and the limitations of such methods, we used the massive amount of stability data recently obtained by deep mutational scanning to study how best to construct MSAs and optimally extract evolutionary information from them. We tested different evolutionary models and found that, unexpectedly, independent-site models achieve similar accuracy to more complex epistatic models. A detailed analysis of the latter models suggests that their inference often results in noisy couplings, which do not appear to add predictive power over the independent-site contribution, at least in the context of stability prediction. Interestingly, by combining any of the evolutionary features with a simple structural feature, the relative solvent accessibility of the mutated residue, we achieved similar prediction accuracy to supervised, machine learning-based, protein stability change predictors. Our results provide new insights into the relationship between protein evolution and stability, and show how evolutionary information can be exploited to improve the performance of mutational stability prediction.

bioinformatics↗

bioRxiv2024

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

Continued advances in variant effect prediction are necessary to demonstrate the ability of machine learning methods to accurately determine the clinical impact of variants of unknown significance (VUS). Towards this goal, the ARSA Critical Assessment of Genome Interpretation (CAGI) challenge was designed to characterize progress by utilizing 219 experimentally assayed missense VUS in the Arylsulfa-tase A (ARSA) gene to assess the performance of community-submitted predictions of variant functional effects. The challenge involved 15 teams, and evaluated additional predictions from established and recently released models. Notably, a model developed by participants of a genetics and coding bootcamp, trained with standard machine-learning tools in Python, demonstrated superior performance among sub-missions. Furthermore, the study observed that state-of-the-art deep learning methods provided small but statistically significant improvement in predictive performance compared to less elaborate techniques. These findings underscore the utility of variant effect prediction, and the potential for models trained with modest resources to accurately classify VUS in genetic and clinical research.

genetics↗

bioRxiv2023

pyScoMotif: Discovery of similar 3D structural motifs across proteins

MotivationThe fast and accurate detection of similar geometrical arrangements of protein residues, known as 3-dimensional (3D) structural motifs, is highly relevant for many applications such as binding region and catalytic site detection, drug discovery and structure conservation analyses. With the recent publication of new protein structure prediction methods, the number of available protein structures is exploding, which makes efficient and easy-to-use tools for identifying 3D structural motifs essential. ResultsWe present an open-source Python package that enables the search for both exact and mutated motifs with position-specific residue substitutions. The tool is fast, accurate and suitable to run both on computer clusters and personal laptops. Successful application of pyScoMotif to catalytic site identification is showcased. AvailabilityThe pyScoMotif package can be installed from the PyPI repository and is also available at https://github.com/3BioCompBio/pyScoMotif. It is free to use for non-commercial purposes; for commercial purposes, please contact us.

bioinformatics↗

bioRxiv2023

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

Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the SARS-CoV-2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases towards specific mutation properties, the most marked being towards destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.

bioinformatics↗

bioRxiv2023

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

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was one of the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6). To further demonstrate FiTMuSiCs robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiCs qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at http://babylone.ulb.ac.be/FiTMuSiC/, which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community.

bioinformatics↗

bioRxiv2023

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

A general limitation of the use of enzymes in biotechnological processes under sometimes non-physiological conditions is the complex interplay between two key quantities, enzyme activity and stability, where the increase of one is often associated with the decrease of the other. A precise stability-activity trade-off is necessary for the enzymes to be fully functional, but its weight in different protein regions and its dependence on environmental conditions is not yet elucidated. To advance this issue, we used the formalism that we have recently developed to effectively identify stability strength and weakness regions in protein structures, and applied it to a large set of globular enzymes with known experimental structure and catalytic sites. Our analysis showed a striking oscillatory pattern of free energy compensation centered on the catalytic region. Indeed, catalytic residues are usually non-optimal with respect to stability, but residues in the first shell around the catalytic site are, on the average, stability strengths and thus compensate for this lack of stability; residues in the second shell are weaker again, and so on. This trend is consistent across all enzyme families. It is accompanied by a similar, but less pronounced, pattern of residue conservation across evolution. In addition, we analyzed cold- and heat-adapted enzymes separately and highlighted different patterns of stability strengths and weaknesses, which provide insight into the longstanding problem of catalytic rate enhancement in cold environments. The successful comparison of our stability and conservation results with experimental fitness data, obtained by deep mutagenesis scanning, led us to propose criteria for improving catalytic activity while maintaining enzyme stability, a key goal in enzyme design.

bioinformatics↗

bioRxiv2023

Estimating the vertical ionization potential of single-stranded DNA molecules

The electronic properties of DNA molecules, defined by the sequence-dependent ionization potentials of nucleobases, enable long-range charge transport along the DNA stacks. This has been linked to a range of key physiological processes in the cells and to the triggering of nucleobase substitutions, some of which may cause diseases. To gain molecular-level understanding of the sequence dependence of these phenomena, we estimated the vertical ionization potential (vIP) of all possible nucleobase stacks in B-conformation, containing one to four Gua, Ade, Thy, Cyt or methylated Cyt. To do this, we used quantum chemistry calculations and more precisely the second-order Moller-Plesset perturbation theory (MP2) and three double-hybrid density functional theory (DFT) methods, combined with several basis sets for describing atomic orbitals. The calculated vIP of single nucleobases were compared to experimental data and those of nucleobase pairs, triplets and quadruplets, to observed mutability frequencies in the human genome, reported to be correlated with vIP values. This comparison selected MP2 with the 6-31G* basis set as the best of the tested calculation levels. These results were exploited to set up a recursive model, called vIPer, which estimates the vIP of all possible single-stranded DNA sequences of any length based on the calculated vIPs of overlapping quadruplets. vIPers vIP values correlate well with oxidation potentials measured by cyclic voltammetry and activities obtained through photoinduced DNA cleavage experiments, further validating our approach. vIPer is freely available on the github.com/3BioCompBio/vIPer repository.

bioinformatics↗

bioRxiv2021

BRANEart: identify stability strength and weakness regions in membrane proteins

Understanding the role of stability strengths and weaknesses in proteins is a key objective for rationalizing their dynamical and functional properties such as conformational changes, catalytic activity, and protein-protein and protein-ligand interactions. We present BRANEart, a new, fast and accurate method to evaluate the per-residue contributions to the overall stability of membrane proteins. It is based on an extended set of recently introduced statistical potentials derived from membrane protein structures, which better describe the stability properties of this class of proteins than standard potentials derived from globular proteins. We defined a per-residue membrane propensity index from combinations of these potentials, which can be used to identify residues which strongly contribute to the stability of the transmembrane region or which would, on the contrary, be more stable in extramembrane regions, or vice versa. Large-scale application to membrane and globular proteins sets and application to tests cases show excellent agreement with experimental data. BRANEart thus appears as a useful instrument to analyze in detail the overall stability properties of a target membrane protein, to position it relative to the lipid bilayer, and to rationally modify its biophysical characteristics and function. BRANEart can be freely accessed from http://babylone.3bio.ulb.ac.be/BRANEart.

bioinformatics↗

bioRxiv2021

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

MotivationHigh-throughput experiments are generating ever increasing amounts of various -omics data, so shedding new light on the link between human disorders, their genetic causes, and the related impact on protein behavior and structure. While numerous bioinformatics tools now exist that predict which variants in the human exome cause diseases, few tools predict the reasons why they might do so. Yet, understanding the impact of variants at the molecular level is a prerequisite for the rational development of targeted drugs or personalized therapies. ResultsWe present the updated MutaFrame webserver, which aims to meet this need. It offers two deleteriousness prediction softwares, DEOGEN2 and SNPMuSiC, and is designed for bioinformaticians and medical researchers who want to gain insights into the origins of monogenic diseases. It contains information at two levels for each human protein: its amino acid sequence and its 3-dimensional structure; we used the experimental structures whenever available, and modeled structures otherwise. MutaFrame also includes higher-level information, such as protein essentiality and protein-protein interactions. It has a user-friendly interface for the interpretation of results and a convenient visualization system for protein structures, in which the variant positions introduced by the user and other structural information are shown. In this way, MutaFrame aids our understanding of the pathogenic processes caused by single-site mutations and their molecular and contextual interpretation. AvailabilityMutaframe webserver at http://mutaframe.com

bioinformatics↗

bioRxiv2021

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

The understanding of the molecular mechanisms driving the fitness of the SARS-CoV-2 virus and its mutational evolution is still a critical issue. We built a simplified computational model, called SpikePro, to predict the SARS-CoV-2 fitness from the amino acid sequence and structure of the spike protein. It contains three contributions: the viral transmissibility predicted from the stability of the spike protein, the infectivity computed in terms of the affinity of the spike protein for the ACE2 receptor, and the ability of the virus to escape from the human immune response based on the binding affinity of the spike protein for a set of neutralizing antibodies. Our model reproduces well the available experimental, epidemiological and clinical data on the impact of variants on the biophysical characteristics of the virus. For example, it is able to identify circulating viral strains that, by increasing their fitness, recently became dominant at the population level. SpikePro is a useful instrument for the genomic surveillance of the SARS-CoV-2 virus, since it predicts in a fast and accurate way the emergence of new viral strains and their dangerousness. It is freely available in the GitHub repository github.com/3BioCompBio/SpikeProSARS-CoV-2.

bioinformatics↗