Biology subjects

Timucin, E.

Publications and source records attributed to Timucin, E..

9 recordsLinked to original sources

bioRxiv2026

Systematic Evaluation of AlphaFold2 and OpenFold3 on Protein-Peptide Complexes

Protein-peptide interactions are important mediators of diverse biological processes. While deep learning has revolutionized protein structure prediction, comparative evaluation of these methods, specifically for protein-peptide complexes, remains an area of active investigation. Here, we present a systematic benchmarking of AlphaFold2 (AF2) and OpenFold3 (OF3) on a curated, non-redundant dataset of 271 protein-peptide complexes evaluated under CAPRI peptide criteria, partitioned into disordered (IDR) and structured (Non-IDR) peptide subsets. Results show that AF2 consistently outperformed OF3 across both subsets in overall success rate and proportion of high-quality models, while both methods exhibited comparable global fold prediction accuracy. We further demonstrate that AF2 exhibited memorization on a large set of protein-peptide complexes that were in its training data. Analysis of built-in and post-hoc confidence scores demonstrated that PAE-derived metrics, particularly pDockQ2, LIS, and ipSAE, provided the most reliable proxies for structural accuracy in AF2 predictions, whereas OF3s PAE distributions substantially diminished the discriminative power of its derived scores. Furthermore, we find that canonical DockQ threshold cutoffs for protein-protein complexes are not directly transferable to protein-peptide complexes, underscoring the need for method- and dataset-specific calibration. Peptide sequence composition and length were identified as potential modulators of prediction success, with glycine-rich short peptides and long receptors posing challenges to both methods. Collectively, these findings establish a peptide-specific evaluation framework and highlight the need for dataset/method-calibrated metrics to support the continued development of structure prediction tools for protein-peptide interactions.

bioinformatics↗

bioRxiv2026

ULK1-linked mitophagy promotes cardiac hypoxia tolerance in the blind mole-rat

Blind mole-rats (BMRs) thrive in chronically hypoxic subterranean environments, displaying exceptional cardiac resilience to conditions that rapidly induce failure in other mammals. Here, we integrate in vivo physiology, multi-omics profiling, mitochondrial analyses, and genome editing to uncover an evolved cardioprotective program in BMRs. Under acute 0% O2 exposure, BMRs exhibit markedly prolonged survival compared to mouse. At the molecular level, BMR hearts undergo coordinated metabolic remodeling, restrained inflammatory signaling, and enhanced genome maintenance. Functionally, BMR cardiac mitochondria suppress high-flux oxidative phosphorylation and reverse electron transport-associated ROS following hypoxia, indicating intrinsic adaptation to oxygen collapse. Hypoxia selectively activates AMPK-mTOR-ULK1 dependent mitophagy, and pharmacological manipulation demonstrates that mitophagy is required for BMR cardiomyocyte survival during hypoxia-reoxygenation stress. Finally, we identify a BMR-specific insertion in ULK1 and demonstrate that introduction of this sequence into rat cardiomyocytes enhances hypoxia tolerance in a mitophagy-dependent manner. These findings reveal an evolutionarily tuned mitochondrial quality-control strategy that enables extreme cardiac resilience to hypoxia.

cell biology↗

bioRxiv2025

Aggregation-Prone Region Mapping in Olfactomedin Domain of Myocilin through Classical and Enhanced Sampling Molecular Dynamics Simulations

The aggregation of the myocilin olfactomedin (OLF) domain, generally driven by genetic mutations, is the leading cause of primary open-angle glaucoma (POAG). Developing therapeutic strategies requires a detailed understanding its initial unfolding events that expose aggregation-prone regions (APRs). However, it has been a challenge, as the slow conformational dynamics of OLF hinders classical molecular dynamics (MD) simulations from capturing aggregation-prone OLF intermediates. To overcome this, we employed a multi-pronged computational strategy, integrating over 15 {micro}s of simulation time across diverse conditions, including high-temperature, enhanced sampling, chemical denaturation, and simulations of the pathogenic I499F mutant. Our results reveal that OLF unfolding is not random but initiates at specific structural regions pertinent to the terminal blade A and E. Specifically, the blade interfaces between A-B and A-E showed unique regions rich in aromatic/hydrophobic residues as aggregation hotspots. Overall, our simulations proved effective to generate a detailed map of seven distinct APRs. The accuracy of these APRs is partially validated by the close localization of these predicted regions with both previously identified amyloid peptides and the sites of known disease-causing mutations. By scrutinizing the OLF structure and dynamics under different MD settings, our study provides potential molecular targets for developing new therapeutic interventions against POAG.

bioinformatics↗

bioRxiv2025

Assessing the Performance of AF2 and AF3-Implementations on Antibody-Antigen Complexes

Accurate determination of antibody-antigen (Ab-Ag) complex structures is critical for therapeutic development. While AI-based methods, beginning with AlphaFold2 (AF2), have revolutionized multimer predictions, the optimal strategies for Ab-Ag modeling and the reliability of their confidence scores remain active areas of research. This study evaluates the performance of AF2, Boltz-1, Boltz-1x, Boltz-2, Chai-1, Protenix and ESMFold, on a curated dataset of 200 antibody-antigen (Ab-Ag) complexes. Our findings reveal that AF2 remains a strong predictor against newer AlphaFold3 (AF3)-inspired methods in Ab-Ag complex prediction. Chai-1 consistently ranks as the second-best performer across multiple success metrics. We observed diverse effects of recycling iterations, with AF2, Chai-1 and Protenix benefiting from increased cycles, unlike Boltz variants. We analyzed various model confidence scores, noting high precision from pDockQ2 and high recall from pTM. By integrating these two scores, we developed AntiConf, a novel metric that achieves superior performance for all methods in terms of precision and recall. These strengths make AntiConf a valuable post score for both computational predictions and downstream experimental workflows, reflecting its potential to improve Ab-Ag complex predictions by AF2 and AF3 architectures. Altogether, this study addresses current limitations in AI-based Ab-Ag complex prediction, showcasing the potential of AntiConf for future predictions and assessments of these complexes, and providing a guideline for improving the accuracy of Ab-Ag complex prediction.

bioinformatics↗

bioRxiv2024

Computational Modeling of the Anti-Inflammatory Complexes of IL37

Interleukin (IL) 37 is an anti-inflammatory cytokine belonging to the IL1 protein family. Owing to its pivotal role in modulating immune responses, particularly through interfering with the IL18 signaling, elucidating the IL37 complex structures holds substantial therapeutic promise for various autoimmune disorders and cancers. Although the structural homology between IL37 and IL18 suggests a common binding mechanism with the primary members of IL18 signaling, the structures of IL37 complexes have not been experimentally resolvet yet. This computational study aims to address this gap through molecular modeling and classical molecular dynamics simulations, revealing the structural underpinnings of its modulatory effects on the IL18 signaling pathway. All IL37 protein-protein complexes, including both receptordependent and receptor-independent pairs, were modeled using a range of methods from homology modeling to AlphaFold2 multimer predictions. The models that successfully captured experimental features were subjected to molecular dynamics simulations. As positive controls, binary and ternary PDB complexes of IL18 were also included. The comparative look on the IL37 and IL18 complexes revealed a highly dynamic nature for the IL37 complexes. Repeated simulations of IL37-IL18R showed altered receptor conformations capable of accommodating IL37 in its dimeric form without clashes, providing a structural basis for the failure of IL18R{beta} to be recruited to the IL37-IL18R complex. Simulations of receptor complexes involving various mature forms of IL37 revealed that the N-terminal loop of IL37 is pivotal in modulating receptor dynamics. Additionally, the glycosyl chains on the primary receptor residue N297 act as a steric block against the IL37s N-terminal loop. The interactions between IL37 and IL18BP were also investigated, and our dynamical models indicated that a homologous binding mode was unlikely, suggesting an alternative mechanism by which IL37 functions as an anti-inflammatory cytokine upon binding to IL18BP. Altogether this study accesses to the structure and dynamics of IL37 complexes, offering molecular insights into IL37s inhibitory function within the IL18 signaling pathway and informing future experimental research. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/613817v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@ab5419org.highwire.dtl.DTLVardef@1e7126org.highwire.dtl.DTLVardef@968436org.highwire.dtl.DTLVardef@1c1fec2_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

immunology↗

bioRxiv2024

AFFIPred: AlphaFold2 Structure-based Functional Impact Prediction of Missense Variations

Structural information holds immense potential for pathogenicity prediction of missense variations, albeit structure-based pathogenicity classifiers are limited compared to their sequence-based counterparts due to the well-known gap between sequence and structure data. Leveraging the highly accurate protein structure prediction method, AlphaFold2 (AF2), we introduce AFFIPred, an ensemble machine learning classifier that combines established sequence and AF2-based structural characteristics to predict disease-causing missense variant pathogenicity. Based on the assessments on unseen datasets, AFFIPred reached a comparable level of performance with the state-of-the-art predictors such as AlphaMissense and Rhapsody. We also showed that the recruitment of AF2 structures that are full-length and represent the unbound states ensures more precise SASA calculations compared to the recruitment of experimental structures. Second, in line with the the completeness of the AF2 structures, their use provide a more comprehensive view of the structural characteristics of the missense variation datasets by capturing all variants. AFFIPred maintains high-level accuracy without the well-known limitations of structure-based pathogenicity classifiers, paving the way for the development of more sophisticated structure-based methods without PDB dependence. AFFIPred has predicted over 210 million variations of the human proteome, which are accessible at https://affipred.timucinlab.com/.

bioinformatics↗

bioRxiv2024

Identification of Interaction Partners of Outer Inflammatory Protein A: Computational and Experimental Insights into How Helicobacter pylori Infects Host Cells

Adherence to the gastric epithelium is an essential feature of Helicobacter pylori for its colonization. Outer membrane proteins (OMPs) play a pivotal role in adherence potentiating the survival of the microbe in the gastric tissue. Among these proteins, Outer inflammatory protein A (OipA) is a critical protein that is known to help bacteria to colonize on the host gastric epithelial cell surface. Although the role of OipA in the H. pylori attachment and the association between OipA-positive H. pylori strains and clinical outcomes have been demonstrated, there is limited information on the structural mechanism of the OipA action in the adherence of H. pylori to the gastric epithelial cell surface. Our study utilizes experimental and computational methodologies to investigate the interaction partners of OipA on the gastric epithelial cell surface. Initially, we performed a proteomic analysis to decipher the OipA interactome in the human gastric epithelial cells using a pull-down assay of the recombinant OipA and the membrane proteins of the gastric epithelial cells. Proteomic analysis has revealed 704 unique proteins that interacted with OipA. We have further analyzed 16 partners of OipA using molecular modeling tools. Structural findings obtained from the prediction of the protein-protein complexes of OipA and candidate partners unraveled 3 human proteins whose OipA interactions could base an explanation about how H. pylori recruits OipA for adherence. Altogether, the findings presented here provide insights into novel mechanisms of H. pylori and host interactions through OipA, reflecting the potential of these mechanisms and interactions as therapeutic targets to combat H. pylori infection. Key pointsO_LIOuter membrane proteins (OMPs) are an emerging topic in bacterial infection. C_LIO_LIOipA is a candidate for an adherence-receptor network on the gastric epithelial cell surface with H. pylori. C_LIO_LIOipA interactome partners on gastric epithelial cell surfaces are valuable therapeutic targets for the H. pylori infection. C_LI

microbiology↗

bioRxiv2024

Rational Design of Monomeric IL37 Variants Guided by Stability and Dynamical Analyses of IL37 Dimers

IL37 plays important roles in the regulation of innate immunity and its oligomeric status is critical to these roles. In its monomeric state, IL37 can effectively inhibit the inflammatory response triggered by IL18 through binding to the IL18 receptor , a capability lost in its dimeric form. This paradigm underscores the pivotal role of IL37s dimer structure in the design of novel anti-inflammatory therapeutics. Hitherto, two IL37 dimer structures were deposited in PDB, reflecting the potential use of their binding interface in the design of IL37 variants with altered dimerization tendencies. Inspection of these static structures suggested a substantial difference in their dimer interfaces. Prompted by this discrepancy, we analyzed the PDB structures of IL37 dimer (PDB: 6ncu and 5hn1) along with a predicted structure by AF2-multimer by molecular dynamics (MD) simulations to unravel whether and how IL37 can form homodimers through distinct interfaces. Results showed that the 5hn1 and AF2 dimers, which shared the same interface, stably maintained their initial conformations throughout the simulations whilst the recent IL37 dimer (PDB ID: 6ncu) with a different interface, did not. These findings underscored that the recent IL37 dimer (6ncu) structure is likely to contain an error, probably in its biological assembly record, otherwise it was not a stable assembly in silico. Next, focusing on the stable dimer structure of 5hn1, we have identified five critical positions of V71/Y85/I86/E89/S114 that would altogether reduce dimer stability without affecting the monomer fold. Two quintet mutations were tested similarly by MD simulations and both mutations showed either partial or complete dissociation of the dimeric form. Overall, this work contributes to the development of IL37-based therapeutics by accurately representing the dimer interface in the PDB structures and identifying five potential substitutions to effectively inhibit the inflammatory response triggered by IL18.

immunology↗

bioRxiv2021

Towards Compilation of Balanced Protein Stability Datasets: Flattening the ΔΔG Curve through Systematic Under-sampling

Protein stability datasets contain neutral mutations that are highly concentrated in a much narrower {Delta}{Delta}G range than destabilizing and stabilizing mutations. Notwith-standing their high density, often studies analyzing stability datasets and/or predictors ignore the neutral mutations and use a binary classification scheme labeling only destabilizing and stabilizing mutations. Recognizing that highly concentrated neutral mutations would affect the quality of stability datasets, we have explored three protein stability datasets; S2648, PON-tstab and the symmetric Ssym that differ in size and quality. A characteristic leptokurtic shape in the {Delta}{Delta}G distributions of all three datasets including the curated and symmetric ones were reported due to concentrated neutral mutations. To further investigate the impact of neutral mutations on {Delta}{Delta}G predictions, we have comprehensively assessed the performance of eleven predictors on the PON-tstab dataset. Correlation and error analyses showed that all of the predictors performed the best on the neutral mutations while their performance became gradually worse as the {Delta}{Delta}G of the mutations departed further from the neutral zone regardless of the direction, implying a bias towards dense mutations. To this end, after unraveling the role of concentrated neutral mutations in biases of stability datasets, we described a systematic under-sampling approach to balance the {Delta}{Delta}G distributions. Before under-sampling, mutations were clustered based on their biochemical and/or structural features and then three mutations were systematically selected from every 2 kcal/mol of each cluster. Upon implementation of this approach by distinct clustering schemes, we generated five subsets varying in size and {Delta}{Delta}G distributions. All subsets notably showed amelioration of not only the shape of {Delta}{Delta}G distributions but also other pre-existing imbalances in the frequency distributions. We also reported differences in the performance of the predictors between the parent and under-sampled subsets due to the enrichment of previously under-represented mutations in the subsets. Altogether, this study not only elaborated the pivotal role of concentrated mutations in the dataset biases but also contemplated and realized a rational strategy to tackle this and other forms of biases. Under-sampling code is available on GitHub (https://github.com/narodkebabci/gRoR).

bioinformatics↗