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Karaca, E.

Publications and source records attributed to Karaca, E..

At least 19 recordsLinked to original sources

Enhanced BCMA Antigen Density Increases Trogocytosis and Attenuates CAR T cell Function

BackgroundChimeric Antigen Receptor (CAR) T cell therapy targeting B-cell maturation antigen (BCMA) has demonstrated impressive clinical efficacy in relapsed/refractory multiple myeloma (MM). Nonetheless, disease relapse limits durable response for most patients. Trogocytosis of target antigen by effector cells has emerged as a potential contributor to reduced surface antigen density, CAR T cell dysfunction, and fratricide. Although {gamma}-secretase inhibitors (GSI) significantly increase cell surface BCMA density and decrease soluble BCMA (sBCMA), their effects on BCMA trogocytosis and the resulting impact on CAR T-cell function remain incompletely understood. MethodsWe investigated the effects of GSI on BCMA-directed CAR T cell function and trogocytosis using in vitro co-culture systems with MM cell lines across a spectrum of BCMA expression. We validated findings using confocal microscopy and cytotoxicity assays. Trogocytosis and fratricide were assessed in time-resolved functional studies. Phenotypic and functional differences between trogocytosis-positive (CAR T Trogo+) and trogocytosis-negative (CAR T) cells were evaluated using multiparametric flow cytometry, proteomic profiling, single-cell RNA sequencing (scRNA-seq), T-cell receptor (TCR) sequencing, and in vitro rechallenge assays. We also interrogated clinical samples from two Phase I trials (NCT03338972 and NCT03502577) which employed the identical CAR T cell construct with or without GSI respectively, to evaluate the relationship between trogocytosis, CAR T cell persistence, and treatment outcome. ResultsGSI driven increases in BCMA density on MM cell lines enhanced CAR T cell cytotoxicity but concomitantly increased trogocytosis, particularly in high-antigen-density cell lines-(H929+GSI vs H929; 30 min (P<0.0001), 1 h (P<0.0001), 2 h (P<0.0001), 6 h (P<0.0001), and 24 h (P<0.0001) and in CD4 CAR T cells (K562mCherry+GSI, CD4 vs CD8 CAR T cells;10 min (P=0.01), 2 h (P=0.01), and 6 h (P=0.004). Following BCMA acquisition, CAR T cells (CAR T Trogo) exhibited reduced proliferative capacity, diminished cytotoxic function (CAR T Trogo+ vs CAR T; (P=0.01), and an increase in markers of exhaustion/activation (CD4+ CAR T Trogo+ vs CD4 CAR T and CD8+ CAR T Trogo+ vs CAR T; PD-1+LAG-3+, PD-1+TIM-3+, and TOX+TIM-3 co-expression, (P=0.007, P<0.0001, P=0.006 and P=0.004, P=0.03, P=0.006). In fratricide assays, CAR T Trogo cells were susceptible to killing by naive CAR T cells. Single cell RNA-seq supports the phenotypic findings revealing transcriptional features of heightened activation and accelerated exhaustion in CAR T Trogo+ cells. Clinical phase I trial data confirm BCMA trogocytosis in patient samples. ConclusionsOur findings highlight the paradoxical effects of increased BCMA density on BCMA CAR T cell therapy: enhancement of initial tumor targeting and promotion of trogocytosis-associated dysfunction. Trogocytosis may contribute to antigen modulation, CAR T cell exhaustion, and fratricide, potentially muting the therapeutic benefits of enhanced antigen density. To optimize GSI and mitigate trogocytosis-associated resistance mechanism, future clinical trial designs should incorporate early time-point sampling, a sample size providing sufficient statistical power to determine an impact on CAR T cell persistence and treatment response, and mechanistic assessments.

cancer biology↗

Archaic translation initiation factor eIF5B supports KSHV late lytic replication and viral oncogenesis by mimicking a hypoxic cellular landscape

Kaposis Sarcoma (KS) Herpesvirus (KSHV) is the etiological agent of KS, an AIDS-defining illness. Recent studies have demonstrated that even during normoxic conditions, KSHV facilitates replication by modulating hypoxia-inducible factors (HIFs), creating a hypoxia-like environment that promotes cellular transformation. KSHV lytic viral genes are favored for protein synthesis during infection by upregulating HIF2 and utilizing the hypoxic eIF4E2 translation initiation complex in oxygen-replete conditions. This translation initiation plasticity (TRIP) links viral replication strategies to angiogenic signaling and oncogenicity. However, the molecular basis of this plasticity remains poorly understood. This study reveals that viral inhibition of eukaryotic initiation factor 2 (eIF2) induces the use of another alternative initiation factor, eIF5B, which canonically mediates delivery of initiator methionine-tRNAiMet during hypoxia. We demonstrate through ribosome density fractionation that eIF2 accumulates in translationally inactive monosome fractions while eIF5B redistributes its translation activity toward polysomes during lytic replication. Progressive dependence on eIF5B during KSHV infection was illustrated by impaired late lytic gene expression, diminished virion production, and altered polysome profiles following eIF5B knockdown. Transcriptomic analyses further reveal that the mRNA landscape in KSHV-infected cells depleted of eIF5B mirrors that of uninfected hypoxic cells. Moreover, silencing of eIF5B in a natural infection model reduces both VEGF secretion and anchorage-independent growth--two hallmarks of KSHV viral oncogenicity. These results demonstrate that eIF5B functions as an essential component of alternative translation initiation machinery activated in response to eIF2 inactivation during KSHV lytic replication, emphasizing its key role in viral pathogenesis and its potential as a novel therapeutic target. IMPORTANCEKaposis Sarcoma Herpesvirus (KSHV) is a human cancer virus that causes severe malignancies in immunocompromised individuals, including the blood vessel cancer Kaposis Sarcoma and a highly aggressive body-cavity lymphoma. Although cells mount a response to infection by shutting down global protein synthesis, how viruses like KSHV continue making viral proteins when this cellular machinery is shutdown remains unclear. This study uncovers that KSHV exploits an ancient cellular factor typically used for protein production under low-oxygen conditions. We identify a previously unrecognized role for the translation factor, eIF5B, in supporting KSHV replication and select production of viral proteins during infection. Our work further demonstrates that KSHVs utilization of eIF5B contributes to a hypoxia-like environment during infection, ultimately contributing to cancer-promoting changes within the cell. These findings highlight a new strategy for tumor-virus reprogramming of host cells, opening an avenue for novel therapeutic targets to eliminate virus-associated cancers.

microbiology↗

DynaPIN: A Tool for Characterizing Dynamic Protein Interfaces

Static structural models often fail to capture the dynamic mechanisms of protein interactions. To address this, we introduce DynaPIN, an open-source pipeline for extracting dynamic interface fingerprints from molecular simulations. DynaPIN unifies quality control metrics, interface prediction accuracy assessment, and atomistic interaction analysis into a single automated workflow, accessible at https://github.com/CSB-KaracaLab/DynaPIN. A key feature is our interface-specific analysis centered on a Dynamic Interface definition, which classifies residues based on the persistence of their interaction status over the simulation. We applied DynaPIN to representative rigid, medium, and difficult targets from the DynaBench dataset, an MD simulation resource for Docking Benchmark 5.5. Our results show that interface flexibility diverges from static accuracy classifications established in Docking Benchmark 5.5, as explored before. All in all, by providing standardized, frame-resolved outputs, DynaPINs aim is to facilitate mechanistic studies and generate standardized unbiased data for future dynamics-aware artificial intelligence models.

bioinformatics↗

Co-targeting menin and LSD1 dismantles oncogenic programs and restores differentiation in MLL-rearranged AML

Acute myeloid leukemia (AML) harboring MLL (MLL1, KMT2A) rearrangement (MLL-r) remains a lethal subtype with limited durable responses to single-agent menin inhibition. To define rational combination strategies, we performed a high-throughput screen of >900 epigenetic modulators in combination with menin inhibition in MLL-r AML models. This uncovered consistent synergy between menin and lysine-specific demethylase 1 (LSD1) inhibition, including with the clinical agent iadademstat. Mechanistically, LSD1 was found to interact with LEDGF/p75 (PSIP1), a chromatin-anchoring cofactor of the menin-MLL complex at H3K36me3 marked euchromatin. Chromatin profiling revealed extensive co-occupancy of LSD1 and menin-MLL components at leukemogenic loci in MLL-r AML cells. Dual inhibition of menin and LSD1 dismantled this chromatin complex, evicted H3K36me3 from LEDGF-bound sites, and reprogrammed transcription toward myeloid differentiation. Combined menin and LSD1 blockade repressed canonical MLL targets, including HOXA9, MYC, FLT3, PBX3, and CDK6, while restoring H3K36me3 and H3K4me3 and activating differentiation-associated genes. In vivo, the combination produced potent antileukemic effects in both MOLM-13 and MLL-r patient-derived xenografts, markedly reducing leukemic burden and extending survival without overt toxicity. These findings identify LSD1 as a critical cofactor of the menin-MLL-LEDGF axis and establish concurrent menin and LSD1 inhibition as a mechanistically informed combinatorial therapeutic approach in MLL-r AML.

cancer biology↗

E2-Regulated Transcriptome Complexity Revealed by Long-Read Direct RNA Sequencing: From Isoform Discovery to Truncated Proteins

Estrogen receptor alpha (ER)-positive (ER+) breast cancers are driven by 17{beta}-estradiol (E2) binding to ER, which transcriptionally regulates downstream target genes. Although microarrays and conventional RNA sequencing have identified E2 target genes, pre-designed probes and short read lengths are limited in accurately capturing complex transcript structures. Long-read RNA sequencing offers a solution by spanning entire transcripts, providing a more complete view of the transcriptome. Here, we employed nanopore long-read direct RNA sequencing (DRS) complemented with 3-end sequencing, in vitro experiments, and deep learning-based protein modeling to explore the intricate landscape of E2-responsive transcriptome and protein level implications. Our analysis revealed a range of E2-responsive non-coding and coding isoforms, including intronically polyadenylated (IPA) mRNAs. One of these IPA isoforms was detected for TLE1 (Transducin-like enhancer protein 1), which positively assists ER-chromatin interactions for a subset of E2 target genes. The IPA isoform produces a C-terminus truncated protein, lacking the WDR interaction domain, but retains dimerization/tetramerization capacity through its intact N-terminus Q-domain. Structural modeling and protein-based assays confirmed the truncated proteins dimerization potential and nuclear localization. Functional assays showed that overexpression of truncated TLE1 reduces the E2-induced upregulation of GREB1, an E2-responsive gene, thereby disrupting transcriptional regulation. Importantly, a lower IPA isoform ratio is associated with worse survival in ER+ patients, highlighting clinical relevance. Our study uncovers new layers of complexity in the E2-regulated transcriptome, providing insights into truncated proteins. These findings contribute to a deeper understanding of gene regulation and may help the development of new therapeutic strategies.

cancer biology↗

Nuclear rerouting of paracrine Fgf3 in source cells represses target genes to pattern morphogen responses

Morphogen gradients direct tissue patterning by inducing dose-dependent transcriptional responses, yet ligand-producing cells often respond differently from their neighbors. Using the zebrafish lateral line organogenesis model, we uncover a cell-autonomous role for the paracrine ligand Fgf3. Transcriptomic profiling and quantitative single-molecule imaging identify target genes, including the chemokine scavenger cxcr7b, whose expression decreases both when FGF receptor signaling is inhibited and when Fgf3 is overexpressed. High-resolution live imaging reveals nuclear accumulation of Fgf3 in producing cells, whereas neighbors receive only extracellular ligand, a feature also observed in other embryonic tissues. Mosaic gain- of-function and nanobody-mediated degradation demonstrate that the nuclear pool of Fgf3 autonomously represses specific targets without impairing canonical receptor signaling. Structure-guided comparative assays indicate nuclear targeting as a latent property of several paracrine FGFs. Dual secreted-nuclear functionality of FGF ligands may represent an intrinsic symmetry-breaking mechanism during organogenesis.

developmental biology↗

Exploring the Potential of AlphaFold Distograms for Flexibility Assignment in Cryo-EM Maps

AlphaFold models provide static structural predictions, limiting their use in interpreting flexible regions in low-resolution cryo-EM maps. Here, we assess whether AlphaFold-generated distograms can instead reveal conformational flexibility, focusing on binding-induced hinge motions. For this, we examined the key metabolic AK2/AIFM1 complex, where molecular dynamics and cryo-EM confirm a hinge motion in AK2 upon binding. Notably, this motion is captured in the AlphaFold2/3 distograms of apo AK2, even though it is absent in the predicted structures. By extending our analysis to other systems, we demonstrate that distograms may offer a valuable, model-independent method for interpreting ambiguous regions in cryo-EM maps.

bioinformatics↗

Towards a greener AlphaFold2 protocol for protein complex modeling: Insights from CAPRI Round 55

In the 55th round of CAPRI, we used enhanced AlphaFold2 (AF2) sampling and data-driven docking. Our AF2 protocol relies on Wallners massive sampling approach, which combines different AF2 versions and sampling parameters to produce thousands of models per target. For T231 (an antibody peptide complex) and T232 (PP2A:TIPRL complex), we employed a 50-fold reduced MinnieFold sampling and a custom ranking approach, leading to a top-ranking medium prediction in both cases. For T233 and T234 (two antibody bound MHC I complexes), we followed data-driven docking, which did not lead to an acceptable model. Our post-CAPRI55 analysis showed that if we would have used our MinnieFold approach on T233 and T234, we could have submitted a medium-quality model for T233 as well. In the scoring challenge, we utilized the scoring function of FoldX, which was effective in selecting acceptable models for T231 and medium quality models for T232. Our success, especially in predicting and ranking a medium quality model for T231 and potentially for T233 underscores the feasibility of green and accurate enhanced AF2 sampling in antibody complex prediction.

bioinformatics↗

Identification of targetable epigenetic vulnerabilities in uveal melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, with a strong predilection for hepatic metastasis, occurring in approximately 50% of cases. Metastatic UM remains highly resistant to therapy and is almost invariably fatal. The strongest genetic drivers of UM metastasis are loss-of-function mutations in tumor suppressor BAP1, an epigenetic regulator that serves as the ubiquitin hydrolase subunit of the polycomb repressive deubiquitinase (PR-DUB) complex, and a key player in global epigenetic regulation. Inactivation of BRCA Associated Protein 1 (BAP1) has been shown to induce widespread epigenetic alterations across multiple model systems. To identify novel therapeutic strategies, we investigated whether targeting the epigenome could reveal new vulnerabilities in UM. We performed high-throughput compound screening using a curated epigenetic inhibitor library and identified BET (bromodomain and extra-terminal domain) inhibition as a particularly promising approach. Interestingly, we observed significant heterogeneity in the efficacy of different BET inhibitors in UM. While previous clinical trials with two BET inhibitors have failed to show efficacy in UM, our findings highlight substantial differences in the potency of specific BET inhibitors for this malignancy. Notably, the BET inhibitor mivebresib (ABBV-075) significantly improved survival rates by 50% in a metastatic UM xenograft mouse model and completely prevented detectable metastases in the bones, spinal cord, and brain. Unexpectedly, RNA sequencing revealed a strong transcriptional overlap between BET inhibition and histone deacetylase (HDAC) inhibition--an approach currently under clinical evaluation for UM treatment. Both BET and HDAC inhibitors reversed gene expression signatures associated with high metastatic risk and induced a neuronal differentiation-like phenotype in UM cells. Together, our findings demonstrate that UM cells exhibit a distinct vulnerability to BET inhibition and establish BET inhibitors as promising candidates for further clinical evaluation for metastatic UM.

cancer biology↗

AlphaFold2 and AlphaFold3 leads to significantly different results in human-parasite interaction prediction

MotivationParasitic diseases pose a significant global health challenge with substantial socioeconomic impact, especially in developing countries (Dampier et al., 2009; Mehmood et al., 2017; Merrifield et al., 2016). Combatting these diseases is difficult due to two factors: the resistance developed by parasites to existing drugs and the underfunded research efforts to resolve host-parasite interactions (Cuesta-Astroz & Oliveira, 2018). To address the latter, we focused on 276 human-parasite domain-domain interaction predictions, involving 15 parasitic species known to cause infections in humans (Table S1, Cuesta-Astroz et al., 2019). We aimed to validate these interactions by using all AlphaFold2-Multimer (AF2) and AlphaFold3 (AF3) (Jumper et al., 2021; Abramson et al., 2024). Our initial hypothesis was that the interactions with an AF confidence score [&ge;] 0.8 could serve as high confident templates for further research. However, through systematic modeling of these interactions, we observed a striking discrepancy: AF3 confidence assignment behaves significantly differently from that of AF2. In this study, we investigate the underlying causes of this difference.

bioinformatics↗

An NADH-controlled gatekeeper of ATP synthase

ATP fuels crucial cellular processes and is obtained mostly by oxidative phosphorylation (OXPHOS) at the inner mitochondrial membrane. While significant progress has been made in mechanistic understanding of ATP production, critical aspects surrounding its substrate supply logistics are poorly understood. We identify an interaction between mitochondrial apoptosis-inducing factor 1 (AIFM1) and adenylate kinase 2 (AK2) as gatekeeper of ATP synthase. This interaction is NADH-dependent and influenced by glycolysis, linking it to the cells metabolic state. Genetic interference with AIFM1/AK2 association impedes the ability of Caenorhabditis elegans animals to handle altered metabolic rates and nutrient availability. Together, the results imply AIFM1 as a cellular NADH sensor, placing AK2 next to the OXPHOS complexes for local ADP regeneration as the substrate for ATP synthesis. This metabolic signal relay balances ATP synthase substrate supply against ATP conservation, enabling cells to adapt to fluctuating energy availability, with possible implications for AIFM1-related mitochondrial diseases. HighlightsO_LIDiscovery of AIFM1/AK2 interaction as a gatekeeper of mitochondrial ATP synthase C_LIO_LIAIFM1/AK2 interaction is NADH-dependent and linked to the cells metabolic state C_LIO_LIDisrupting AIFM1/AK2 impairs metabolic adaptation in Caenorhabditis elegans C_LIO_LIAIFM1 as NADH sensor, influencing ATP synthesis with mitochondrial disease implications C_LI

biochemistry↗

A direct readout mechanism regulates the sequence selectivity of human de novo DNA methyltransferases

DNMT3A and DNMT3B are closely related DNA methyltransferases that catalyze de novo CpG methylation yet exhibit distinct preferences for flanking sequences. Despite sharing 91% sequence similarity within their catalytic domains, these paralogs show non-overlapping genomic targeting and divergent biological roles. To uncover the mechanistic basis of this specificity, we performed 16{micro}s of all-atom molecular dynamics simulations on DNMT3A and DNMT3B complexes bound to CpG substrates with varied +2 flanking bases (i.e., CGX). To resolve their base- and shape-readout mechanisms at atomistic detail, we introduce a Comparative Dynamics Analysis (CDA) framework. Our CDA analysis reveals that DNMT3A relies on a rigid, sequence-specific hydrogen bonding network and shape-constrained electrostatic anchoring, whereas DNMT3B employs a more flexible and distributed interface, allowing broader substrate tolerance. This represents the first systematic analysis of shape readout in DNMT3 enzymes and demonstrates how flanking sequence specificity is dynamically encoded by two nearly identical proteins. Our findings not only clarify how closely related DNA-modifying enzymes diverge in recognition strategies, but also lay the foundation for future efforts to engineer paralog-specific protein-DNA interactions.

biophysics↗

Dynamic proximity interaction profiling suggests that YPEL2 is involved in cellular stress surveillance

YPEL2 is a member of the evolutionarily conserved YPEL family involved in cellular proliferation, mobility, differentiation as well as senescence and death. However, the mechanism by which YPEL2, or YPEL proteins, mediates its effects is yet unknown. Proteins perform their functions in a network of proteins whose identities, amounts, and compositions change spatiotemporally in a lineage-specific manner in response to internal and external stimuli. We here explored interaction partners of YPEL2 by using dynamic TurboID-coupled mass spectrometry analyses to infer a function for the protein. Our results using inducible transgene expressions in COS7 cells indicate that proximity interaction partners of YPEL2 are largely involved in RNA and mRNA metabolic processes, ribonucleoprotein complex biogenesis, regulation of gene silencing by miRNA, and cellular responses to stress. We showed that YPEL2 interacts with RNA binding protein ELAVL1 and selective autophagy receptor SQSTM1. We also found that YPEL2 participates in events associated with the formation/disassembly of stress granules in response to sodium arsenite an oxidative stress inducer. Establishing a point of departure in the delineation of structural/functional features of YPEL2, our results suggest that YPEL2 may be involved in stress surveillance mechanisms.

cell biology↗

The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15

In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMinds AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes remains also challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved the 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

bioinformatics↗

Truncated KDM6A exhibits differential chromatin occupancy and regulatory interactions

Epigenetic deregulation is a critical theme which needs further investigation for bladder cancer research. One of the highly mutated genes in bladder cancer is KDM6A, functioning as a H3K27 demethylase and is part of the MLL3/4 complexes. To decipher the role of KDM6A in normal versus tumor setting, we identified the genomic landscape of KDM6A in normal, immortalized and cancer bladder cells. Our results showed differential KDM6A occupancy at the genes involved in cell differentiation, chromatin organization and Notch signaling depending on the cell type and the mutation status of KDM6A. Transcription factor motif analysis revealed HES1 to be enriched at KDM6A peaks identified for T24 bladder cancer cell line, which has a truncating mutation in KDM6A, lacking demethylase domain. Our co-immunoprecipitation experiments reveal TLE co-repressors and HES1 as potential truncated and wild type KDM6A interactors. With the aid of structural modeling, we explored how the truncated KDM6A could interact with TLE, HES1, as well RUNX, HHEX transcription factors. These structures provide a solid mean to study the functions of KDM6A independent of its demethylase activity. Collectively, our work provides important contributions to the understanding of KDM6A malfunction in bladder cancer.

cancer biology↗

Rapid and High Resolution Ambient Temperature Structure Determination at Turkish Light Source

High-resolution biomacromolecular structure determination is essential to better understand protein function and dynamics. Serial crystallography is an emerging structural biology technique which has fundamental limitations due to either sample volume requirements or immediate access to the competitive X-ray beamtime. Obtaining a high volume of well-diffracting, sufficient-size crystals while mitigating radiation damage remains a critical bottleneck of serial crystallography. As an alternative, we introduce the plate-reader module adapted for using a 72-well Terasaki plate for biomacromolecule structure determination at a convenience of a home X-ray source. We also present the first ambient temperature lysozyme structure determined at the Turkish Light Source (Turkish DeLight). The complete dataset was collected in 18.5 mins with resolution extending to 2.39 [A] and 100% completeness. Combined with our previous cryogenic structure (PDB ID: 7Y6A), the ambient temperature structure provides invaluable information about the structural dynamics of the lysozyme. Turkish DeLight provides robust and rapid ambient temperature biomacromolecular structure determination with limited radiation damage.

biophysics↗

Cryogenic X-ray crystallographic studies of biomacromolecules at Turkish Light Source: Turkish DeLight

X-ray crystallography is a robust and powerful structural biology technique that provides high-resolution atomic structures of biomacromolecules. Scientists use this technique to unravel mechanistic and structural details of biological macromolecules (e.g. proteins, nucleic acids, protein complexes, protein-nucleic acid complexes, or large biological compartments). Since its inception, single-crystal cryo-crystallography has never been performed in Turkiye due to the lack of a single-crystal X-ray diffractometer. The X-ray diffraction facility recently established at the University of Health Sciences, Istanbul, Turkiye will enable Turkish and international researchers to easily perform high-resolution structural analysis of biomacromolecules from single crystals. Here, we describe the technical and practical outlook of a state-of-the-art home-source X-ray, using lysozyme as a model protein. The methods and practice described in this article can be applied to any biological sample for structural studies. Therefore, this article will be a valuable practical guide from sample preparation to data analysis.

biophysics↗

Benchmarking the Widely Used Structure-based Binding Affinity Predictors on the Spike-ACE2 Deep Mutational Interaction Set

Since the start of COVID-19 pandemic, a huge effort has been devoted to understanding the Spike (SARS-CoV-2)-ACE2 recognition mechanism. To this end, two deep mutational scanning studies traced the impact of all possible mutations across Receptor Binding Domain (RBD) of Spike and catalytic domain of human ACE2. By concentrating on the interface mutations of these experimental data, we benchmarked six commonly used structure-based binding affinity predictors (FoldX, EvoEF1, MutaBind2, SSIPe, HADDOCK, and UEP). These predictors were selected based on their user-friendliness, accessibility, and speed. As a result of our benchmarking efforts, we observed that none of the methods could generate a meaningful correlation with the experimental binding data. The best correlation is achieved by FoldX (R = -0.51). Also, when we simplified the prediction problem to a binary classification, i.e., whether a mutation is enriching or depleting the binding, we showed that the highest accuracy is achieved by FoldX with 64% success rate. Surprisingly, on this set, simple energetic scoring functions performed significantly better than the ones using extra evolutionary-based terms, as in Mutabind and SSIPe. Furthermore, we also demonstrated that recent AI approaches, mmCSM-PPI and TopNetTree, yielded comparable performances to the force field-based techniques. These observations suggest plenty of room to improve the binding affinity predictors in guessing the variant-induced binding profile changes of a host-pathogen system, such as Spike-ACE2. To aid such improvements we provide our benchmarking data at https://github.com/CSB-KaracaLab/RBD-ACE2-MutBench with the option to visualize our mutant models at https://rbd-ace2-mutbench.github.io/

bioinformatics↗