Biology subjects

Timucin, A. C.

Publications and source records attributed to Timucin, A. C..

3 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↗

bioRxiv2024

Experimental and Computational Insights into the Structural Dynamics of the Fc Fragment of IgG1 Subtype from Biosimilar VEGF-Trap

The constant fragment (Fc) of the Immunoglobulin G1 (IgG1) subtype is increasingly recognized as a crucial scaffold in the development of advanced therapeutics due to its enhanced specificity, efficacy, and extended half-life. A prime example is VEGF-Trap (Aflibercept), a recombinant fusion protein that merges the Fc region of the IgG1 subtype with the binding domains of vascular endothelial growth factor receptors (VEGFR)-1 and VEGFR-2. The Fc regions role in N-glycosylation is particularly important, as it significantly influences protein stability. In this study, we present the first near-physiological temperature structures of the N-glycan-bound Fc fragment of IgG1 subtype from a biosimilar VEGF-Trap, determined using the SPring-8 Angstrom Compact free electron LAser (SACLA) and the Turkish Light Source (Turkish DeLight). Comparative analysis with cryogenic structures, including our own data, reveals alternate conformations within the glycan-binding pocket. Furthermore, molecular dynamics (MD) simulations highlight an unexpected degree of structural plasticity. These findings offer new insights into the molecular basis of Fc-mediated functions and provide valuable information for the design of next-generation therapeutics.

molecular biology↗

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↗