Biology subjects

Hermans, P.

Publications and source records attributed to Hermans, P..

7 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

Mistakes matter, age doesn't: task outcome modulates implicit motor adaptation similarly in young and older adults

Despite several age-related processes impacting motor performance, older adults often retain the ability to implicitly adapt to sensory prediction errors. Here, we leverage the fact that implicit adaptation is not attenuated by aging to study the impact of aging on responses to motor errors. In other domains, such as reinforcement learning, aging has been shown to influence how task outcomes or rewards are processed and used to guide subsequent actions, with some studies emphasizing that older adults react more strongly to a miss than to a hit. We aimed to extend these reinforcement learning findings to the motor domain with two preregistered experiments testing whether missing the target leads to larger implicit adaptation in young and older adults to the same extent. In addition, we compared these results to one reinforcement learning task in the motor domain (Boolean feedback after reaching in the absence of visual feedback) and one in the cognitive domain (reward-based decision making). While we found age-related effects in the cognitive domain, we did not observe a consistent effect of age on the modulation of reaching direction or motor adaptation by task outcomes. These results suggest a domain-specific nature of age-related changes in sensitivity to task outcomes.

neuroscience↗

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

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↗

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↗

bioRxiv2023

Not fleeting but lasting: Limited influence of aging on implicit adaptative motor learning and its short-term retention.

In motor adaptation, learning is thought to rely on a combination of several processes. Two of these are implicit learning (incidental updating of the sensory prediction error) and explicit learning (intentional adjustment to reduce target error). The explicit component is thought to be fast adapting, while the implicit one is slow. The dynamic integration of these components can lead to an adaptation rebound, called spontaneous recovery: the trace of a first, longer learned adaptation reappears after it is extinguished by a shorter period of de-adaptation. The slow implicit process is still decaying from the first adaptation, resulting in the before mentioned adaptation rebound. Trewartha et al. (2014) found that older adults show less spontaneous recovery than their younger controls, indicating impairments in implicit learning. This disagrees with evidence suggesting that the implicit component and its retention does not decline with aging. To clarify this discrepancy, we performed a conceptual replication of that result. Twenty-eight healthy young and 20 healthy older adults learned to adapt to a forcefield perturbation in a paradigm known to elicit spontaneous recovery. Both groups adapted equally well to the perturbation. Implicit adaptation of the older subjects was indistinguishable from their younger counterparts. In addition, our conceptual replication failed to reproduce the result of Trewartha et al. (2014) and found that the spontaneous recovery was also similar across groups. Our results reconcile previous studies by showing that both spontaneous recovery and implicit adaptation are unaffected by aging.

neuroscience↗

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↗