Biology subjects

Tusnady, G. E.

Publications and source records attributed to Tusnady, G. E..

7 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↗

bioRxiv2024

Importance of updated benchmark sets for statistically correct AlphaFold applications

AlphaFold2 changed structural biology by providing high-quality structure predictions for all possible proteins. Since its inception, a plethora of applications were built on AlphaFold2, expediting discoveries in virtually all areas related to protein science. In many cases, however, optimism seems to have made scientists forget about data leakage, a serious issue that needs to be addressed when evaluating machine learning methods. Here we provide a rigorous benchmark set that can be used in a broad range of applications built around AlphaFold2/3. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC="FIGDIR/small/606297v3_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@145b4deorg.highwire.dtl.DTLVardef@1656df0org.highwire.dtl.DTLVardef@14a94dorg.highwire.dtl.DTLVardef@773bd2_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIWhen building applications on AlphaFold, scientists should consider the possibility of data leakage between AlphaFold training set and the independent test set of their method C_LIO_LIBETA provides multiple datasets with structures and sequences that were not used during the training of AlphaFold C_LIO_LIThese datasets provide a diverse range of use cases C_LIO_LIThe protocol was applied when building a simple disordered prediction method, showing different parameters required to optimize disordered prediction for proteins not used in AlphaFold training C_LI

bioinformatics↗

bioRxiv2023

How AlphaFold shaped the structural coverage of the human transmembrane proteome

AlphaFold2 (AF2) provides structures for every protein, opening up prospects for virtually every field in structural biology. However, transmembrane proteins pose a challenge for experimental scientists, resulting in a limited number of solved structures. Consequently, algorithms trained on this finite training set also face difficulties. To address this issue, we recently launched the TmAlphaFold database, where predicted AlphaFold2 structures are embedded into the membrane and a quality assessment is provided for each prediction using geometrical evaluation. In this paper, we analyze how AF2 has changed the structural coverage of membrane proteins compared to earlier years when only experimental structures were available, and high-throughput structure prediction was greatly limited. We also evaluate how AF2 can be used to search for (distant) homologs in highly diverse protein families. By combining quality assessment and homology search, we can pinpoint protein families where AF2 accuracy is limited, and experimental approaches are still desired.

bioinformatics↗

bioRxiv2023

LeishMANIAdb: an integrative structural resource of Leishmania proteins

Leishmaniasis is a detrimental disease causing serious changes in quality of life and some forms lead to death. The disease is spread by the parasite Leishmania transmitted by sandfly vectors and their primary hosts are vertebrates including humans. The pathogen penetrates host cells and secretes proteins (the secretome) to repurpose cells for pathogen growth and to alter cell signaling via host-pathogen Protein-Protein Interactions (PPIs). Here we present LeishMANIAdb, a database specifically designed to investigate how Leishmania virulence factors may interfere with host proteins. Since the secretomes of different Leishmania species are only partially characterized, we collected various experimental evidence and used computational predictions to identify Leishmania secreted proteins to generate a user-friendly unified web resource allowing users to access all information available on experimental and predicted secretomes. In addition, we manually annotated host-pathogen interactions of 211 proteins, and the localization/function of 3764 transmembrane (TM) proteins of different Leishmania species. We also enriched all proteins with automatic structural and functional predictions that can provide new insights in the molecular mechanisms of infection. Our database, available at https://leishmaniadb.ttk.hu may provide novel insights into Leishmania host-pathogen interactions and help to identify new therapeutic targets for this neglected disease.

bioinformatics↗

bioRxiv2020

Putative linear motifs mediate the trafficking to apical and basolateral membranes

Cell polarity refers to the asymmetric organisation of cellular components in various cells. Epithelial cells are the best known examples of polarized cells, featuring apical and basolateral membrane domains. Despite huge efforts, the exact rules governing the protein distribution in such domains are still elusive. In this study we examined linear motifs accumulating in these parts and based on the results we prepared Classical and Convolutional Neural Networks to classify human transmembrane proteins localizing into apical/basolateral membranes. Asymmetric expression of drug transporters results in vectorial drug transport, governing the pharmacokinetics of numerous substances, yet the data on how proteins are sorted in epithelial cells is very scattered. The provided dataset may offer help to experimentalists to characterize novel molecular targets to regulate transport processes more precisely.

bioinformatics↗

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↗