Biology subjects

Ficho, E.

Publications and source records attributed to Ficho, E..

4 recordsLinked to original sources

bioRxiv2026

Positive selection tends to act on exposed extracellular regions and delineate interaction modules targeted by pathogens

Background: Positive selection shapes protein function by favoring amino acid substitutions that increase fitness. Although numerous studies have identified positively selected genes (PSGs), the structural principles underlying residue-level positive selection remain incompletely understood and previous studies have produced conflicting conclusions, particularly regarding the role of intrinsically disordered regions. In order to resolve these issues and to better understand the driving forces underlying positive selection in human proteins in general, we performed a comprehensive analysis on a manually curated, high quality collection of PSGs and positively selected residues (PSRs) as well as on a large-scale PSR dataset restricted to proteins with experimental structures. Results: Human PSRs were significantly enriched in secreted and cell membrane proteins and preferentially localized to extracellular regions, solvent-exposed surfaces, coil structures, and protein-protein interaction interfaces, whereas neither domains nor intrinsically disordered regions showed an enrichment. Spatial clustering analysis reinforced that PSRs accumulate within localized surface patches. Integration of host-pathogen interaction data highlighted that adaptive changes preferentially affect extracellular molecular recognition surfaces, recurrently appearing in certain pathogen-interacting membrane protein families and particular domain types. Additionally, PSRs showed an enrichment in the residues of human-pathogen interaction interfaces that are in direct contact with pathogenic proteins, implying that neutralizing pathogen attacks is one of the driving forces behind the adaptive evolution of human proteins. By analysing human variation data we found that PSRs are enriched in benign but depleted in pathogenic substitutions, extending previous observations that positively selected genes contain elevated levels of missense variation. Notably, PSGs of the large-scale dataset were also enriched among clinical-stage drug targets, suggesting a potential link between positive selection and pharmacological relevance. Conclusion: Together, our results provide a refined structural model of adaptive evolution in human proteins and identify extracellular exposed molecular recognition surfaces and human-pathogen interaction interfaces as recurrent hotspots of positive selection. Our results point to the direction that precise identification of human PSRs could delineate important host-pathogen interfaces that exert evolutionary selection pressure on humans and appoint novel targets with therapeutic potential.

molecular biology↗

bioRxiv2026

Reliable prediction of short linear motifs in the human proteome

Short linear motifs (SLiMs) are small interaction modules within intrinsically disordered regions of proteins that interact with specific domains, and thereby regulate numerous biological processes. Their limited sequence information leads to frequent false positive hits in computational and experimental SLiM identification methods. We present SLiMMine, a deep learning-based method to identify SLiMs in the human proteome. By refining the annotations of known motif classes, we created a high-quality training dataset. Using protein embeddings and neural networks, SLiMMine reliably predicts novel SLiM candidates in known classes, eliminates ~80% of the pattern matching-based hits as false-positives, furthermore, it also functions as a discovery tool to find uncharacterized SLiMs based on optimal sequence environment. Finally, narrowing the broad interactor-domain definitions of known SLiM classes to specific human proteins enables more precise linking of predicted SLiMs to known protein-protein interactions. SLiMMine is available as a user-friendly, multipurpose web server at https://slimmine.pbrg.hu/.

bioinformatics↗

bioRxiv2025

Digital Twin Approaches for Interpretable Side Effect Prediction in Drug Discovery

Artificial intelligence plays an ever-greater role in preclinical drug development, ranging from target identification and molecule design to ADME-Tox prediction; however, predicting side effects before performing clinical trials is still lagging behind. The best performing side effect predictors in the literature use either ATC codes, which are expert-derived features not even available at early stages, or graph neural networks based on chemical similarity, which - although use readily available features - are "black boxes" that do not deliver actionable insights. We argue that a paradigm shift is needed. Instead of using the latest neural network architectures that have proved worthy in other domains with a plethora of available data, one could use the off-targets of the compounds and build simple and interpretable predictors of side effects. To add another layer of biological realism, intricate biophysical mechanisms within the cells could also be simulated and used as features for training. Although not outperforming the current methods by a great margin, this digital twin-based model has the benefit of being interpretable, i.e., it puts biology behind the predictions. We showcase, with real-world examples, how the side effects predicted by this model can be interpreted and traced back to off-target proteins, and the complexes and signaling pathways in which they partake. In this way, the proposed model not only provides actionable insights, but in the future, may contribute to the amendment of secondary pharmacology assays. HighlightsO_LINo standard tool is available to predict side effects in early-phase drug discovery. C_LIO_LIAs publicly available side effect data is scarce, only simple models should be trained. C_LIO_LISimple models trained on biorealistic features, such as off-target proteins, are interpretable. C_LIO_LIInterpreting predictions can highlight critical off-targets and are therefore actionable. C_LI

pharmacology and toxicology↗

bioRxiv2017

Interplay between folding and binding modulates protein sequences, structures, functions and regulation

Intrinsically Disordered Proteins (IDPs) fulfill critical biological roles without having the potential to fold on their own. While lacking inherent structure, the majority of IDPs do reach a folded state via interaction with a protein partner, presenting a deep entanglement of the folding and binding process. Protein disorder has been recognized as a major determinant in several properties of proteins; yet the way the binding process is reflected in these features in general lacks this detail of description. Recent advances in database development enabled us to identify three basic scenarios of the interplay between folding and binding in unprecedented detail. These scenarios have fundamentally different properties in terms of protein sequence, structure, function and regulation, depending on the structural properties of the interacting partners. Strikingly, the existence of a binding partner and its structural properties influence all analyzed properties of proteins to the same extent as the divide between inherent order or disorder. The appreciation of this interplay between folding and binding is the basis for the successful charting of unknown territories in the protein interactome, the understanding of how different binding modes assemble regulatory networks, and the development of future pharmaceutical applications.

bioinformatics↗