Biology subjects

Hermans, P.

Publications and source records attributed to Hermans, P..

3 recordsLinked to original sources

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↗