Acetylation Blocks cGAS Activity and Inhibits Self-DNA-Induced Autoimmunity.

The presence of DNA in the cytoplasm is normally a sign of microbial infections and is quickly detected by cyclic GMP-AMP synthase (cGAS) to elicit anti-infection immune responses. However, chronic activation of cGAS by self-DNA leads to severe autoimmune diseases for which no effective treatment is available yet. Here we report that acetylation inhibits cGAS activation and that the enforced acetylation of cGAS by aspirin robustly suppresses self-DNA-induced autoimmunity. We find that cGAS acetylation on either Lys384, Lys394, or Lys414 contributes to keeping cGAS inactive. cGAS is deacetylated in response to DNA challenges. Importantly, we show that aspirin can directly acetylate cGAS and efficiently inhibit cGAS-mediated immune responses. Finally, we demonstrate that aspirin can effectively suppress self-DNA-induced autoimmunity in Aicardi-Goutières syndrome (AGS) patient cells and in an AGS mouse model. Thus, our study reveals that acetylation contributes to cGAS activity regulation and provides a potential therapy for treating DNA-mediated autoimmune diseases.

Publisher Correction: K33-linked polyubiquitination of Zap70 by Nrdp1 controls CD8+ T cell activation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

K33-linked polyubiquitination of Zap70 by Nrdp1 controls CD8(+) T cell activation.

The key molecular mechanisms that control signaling via T cell antigen receptors (TCRs) remain to be fully elucidated. Here we found that Nrdp1, a ring finger-type E3 ligase, mediated Lys33 (K33)-linked polyubiquitination of the signaling kinase Zap70 and promoted the dephosphorylation of Zap70 by the acidic phosphatase-like proteins Sts1 and Sts2 and thereby terminated early TCR signaling in CD8(+) T cells. Nrdp1 deficiency significantly promoted the activation of naive CD8(+) T cells but not that of naive CD4(+) T cells after engagement of the TCR. Nrdp1 interacted with Zap70 and with Sts1 and Sts2 and connected K33 linkage of Zap70 to Sts1- and Sts2-mediated dephosphorylation. Our study suggests that Nrdp1 terminates early TCR signaling by inactivating Zap70 and provides new mechanistic insights into the non-proteolytic regulation of TCR signaling by E3 ligases.

AptaDB: a comprehensive database integrating aptamer-target interactions.

Aptamers have emerged as research hotspots of the next generation due to excellent performance benefits and application potentials in pharmacology, medicine, and analytical chemistry. Despite the numerous aptamer investigations, the lack of comprehensive data integration has hindered the development of computational methods for aptamers and the reuse of aptamers. A public access database named AptaDB, derived from experimentally validated data manually collected from the literature, was hence developed, integrating comprehensive aptamer-related data, which include six key components: (i) experimentally validated aptamer-target interaction information, (ii) aptamer property information, (iii) structure information of aptamer, (iv) target information, (v) experimental activity information, and (vi) algorithmically calculated similar aptamers. AptaDB currently contains 1350 experimentally validated aptamer-target interactions, 1230 binding affinity constants, 1293 aptamer sequences, and more. Compared to other aptamer databases, it contains twice the number of entries found in available databases. The collection and integration of the above information categories is unique among available aptamer databases and provides a user-friendly interface. AptaDB will also be continuously updated as aptamer research evolves. We expect that AptaDB will become a powerful source for aptamer rational design and a valuable tool for aptamer screening in the future. For access to AptaDB, please visit http://lmmd.ecust.edu.cn/aptadb/.

admetSAR3.0: a comprehensive platform for exploration, prediction and optimization of chemical ADMET properties.

Absorption, distribution, metabolism, excretion and toxicity (ADMET) properties play a crucial role in drug discovery and chemical safety assessment. Built on the achievements of admetSAR and its successor, admetSAR2.0, this paper introduced the new version of the series, admetSAR3.0, as a comprehensive platform for chemical ADMET assessment, including search, prediction and optimization modules. In the search module, admetSAR3.0 hosted over 370 000 high-quality experimental ADMET data for 104 652 unique compounds, and supplemented chemical structure similarity search function to facilitate read-across. In the prediction module, we introduced comprehensive ADMET endpoints and two new sections for environmental and cosmetic risk assessments, empowering admetSAR3.0 to provide prediction for 119 endpoints, more than double numbers compared to the previous version. Furthermore, the advanced multi-task graph neural network framework offered robust and reliable support for ADMET prediction. In particular, a module named ADMETopt was added to automatically optimize the ADMET properties of query molecules through transformation rules or scaffold hopping. Finally, admetSAR3.0 provides user-friendly interfaces for multiple types of input data, such as SMILES string, chemical structure and batch molecule file, and supports various output types, including digital, chart displays and file downloads. In summary, admetSAR3.0 is anticipated to be a valuable and powerful tool in drug discovery and chemical safety assessment at http://lmmd.ecust.edu.cn/admetsar3/.

Identification of vital chemical information via visualization of graph neural networks.

Qualitative or quantitative prediction models of structure-activity relationships based on graph neural networks (GNNs) are prevalent in drug discovery applications and commonly have excellently predictive power. However, the network information flows of GNNs are highly complex and accompanied by poor interpretability. Unfortunately, there are relatively less studies on GNN attributions, and their developments in drug research are still at the early stages. In this work, we adopted several advanced attribution techniques for different GNN frameworks and applied them to explain multiple drug molecule property prediction tasks, enabling the identification and visualization of vital chemical information in the networks. Additionally, we evaluated them quantitatively with attribution metrics such as accuracy, sparsity, fidelity and infidelity, stability and sensitivity; discussed their applicability and limitations; and provided an open-source benchmark platform for researchers. The results showed that all attribution techniques were effective, while those directly related to the predicted labels, such as integrated gradient, preferred to have better attribution performance. These attribution techniques we have implemented could be directly used for the vast majority of chemical GNN interpretation tasks.

RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification.

MOTIVATION: Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another. RESULTS: Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80 038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification. AVAILABILITY AND IMPLEMENTATION: The source code for the model is available at https://github.com/01dadada/RT-Transformer. The web server is available at https://huggingface.co/spaces/Xue-Jun/RT-Transformer.

Developing pan-ß-coronavirus vaccines against emerging SARS-CoV-2 variants of concern.

The concurrent prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV) raises the concern for the emergence of potential new ß-CoV clades via genetic recombination, bearing high SARS-CoV-2-like transmissibility and high MERS-CoV-like mortality rates. Therefore, we argue that there is an urgent need to develop pan-ß-CoV vaccines that can target not only current SARS-CoV-2 variants of concern, but also future putative SARS-CoV-3- or MERS-CoV-2-like coronavirus.

Regulation of PDF receptor signaling controlling daily locomotor rhythms in Drosophila.

Each day and in conjunction with ambient daylight conditions, neuropeptide PDF regulates the phase and amplitude of locomotor activity rhythms in Drosophila through its receptor, PDFR, a Family B G protein-coupled receptor (GPCR). We studied the in vivo process by which PDFR signaling turns off, by converting as many as half of the 28 potential sites of phosphorylation in its C terminal tail to a non-phosphorylatable residue (alanine). We report that many such sites are conserved evolutionarily, and their conversion creates a specific behavioral syndrome opposite to loss-of-function phenotypes previously described for pdfr. That syndrome includes increases in the amplitudes of both Morning and Evening behavioral peaks, as well as multi-hour delays of the Evening phase. The precise behavioral effects were dependent on day-length, and most effects mapped to conversion of only a few, specific serine residues near the very end of the protein and specific to its A isoform. Behavioral phase delays of the Evening activity under entraining conditions predicted the phase of activity cycles under constant darkness. The behavioral phenotypes produced by the most severe PDFR variant were ligand-dependent in vivo, and not a consequence of changes to their pharmacological properties, nor of changes in their surface expression, as measured in vitro. The mechanisms underlying termination of PDFR signaling are complex, subject to regulation that is modified by season, and central to a better understanding of the peptidergic modulation of behavior.

Hierarchically Structured Nanocomposites via Mixed-Graft Block Copolymer Templating: Achieving Controlled Nanostructure and Functionality.

Integrating inorganic and polymerized organic functionalities to create composite materials presents an efficient strategy for the discovery and fabrication of multifunctional materials. The characteristics of these composites go beyond a simple sum of individual component properties; they are profoundly influenced by the spatial arrangement of these components and the resulting homo-/hetero-interactions. In this work, we develop a facile and highly adaptable approach for crafting nanostructured polymer-inorganic composites, leveraging hierarchically assembling mixed-graft block copolymers (mGBCPs) as templates. These mGBCPs, composed of diverse polymeric side chains that are covalently tethered with a defined sequence to a linear backbone polymer, self-assemble into ordered hierarchical structures with independently tuned nano- and mesoscale lattice features. Through the coassembly of mGBCPs with diversely sized inorganic fillers such as metal ions (ca. 0.1 nm), metal oxide clusters (0.5-2 nm), and metallic nanoparticles (>2 nm), we create three-dimensional filler arrays with controlled interfiller separation and arrangement. Multiple types of inorganic fillers are simultaneously integrated into the mGBCP matrix by introducing orthogonal interactions between distinct fillers and mGBCP side chains. This results in nanocomposites where each type of filler is selectively segregated into specific nanodomains with matrix-defined orientations. The developed coassembly strategy offers a versatile and scalable pathway for hierarchically structured nanocomposites, unlocking new possibilities for advanced materials in the fields of optoelectronics, sensing, and catalysis.

Modulation of lipid profile by secretory phospholipase A2 group IIA: Verification with a transgenic mouse model.

We previously demonstrated a positive relation of secretory phospholipase A2 group IIA (sPLA2-IIA) with circulating high-density lipoprotein cholesterol (HDL-C) in patients with coronary artery disease, and sPLA2-IIA increased cholesterol efflux in THP-1 cells through peroxisome proliferator-activated receptor-γ (PPAR-γ)/liver X receptor α/ATP-binding cassette transporter A1 (ABCA1) signaling pathway. The aim of the present study was to examine the role of sPLA2-IIA over-expression on lipid profile in a transgenic mouse model. Fifteen apoE-/- and C57BL/7 female mice received bone marrow transplantation from transgenic SPLA2-IIA mice, and treated with specific PPAR-γ inhibitor GW9662. High fat diet was given after one week of bone marrow transplantation, and animals were sacrificed after twelve weeks. Immunohistochemical staining showed over-expression of sPLA2-IIA protein in the lung and spleen. The circulating level of HDL-C, but not that of low-density lipoprotein cholesterol (LDL-C), total cholesterol, or total triglyceride, was increased by sPLA2-IIA over-expression, and was subsequently reversed by GW9662 treatment. Over-expression of sPLA2-IIA resulted in augmented expression of cholesterol transporter ABCA1 at mRNA level in the aortas, and at protein level in macrophages, co-localized with macrophage specific antigen CD68. GW9662 exerted potent inhibitory effects on sPLA2-IIA-induced ABCA1 expression. Conclusively, we demonstrated the effects of sPLA2-IIA on circulating HDL-C level and the expression of ABCA1, possibly through regulation of PPAR-γ signaling in transgenic mouse model, that is in concert with the conditions in patients with coronary artery disease.

ADENet: a novel network-based inference method for prediction of drug adverse events.

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and improve drug safety assessment. With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32-54% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.

Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening.

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.

Constitutive MHC class I molecules negatively regulate TLR-triggered inflammatory responses via the Fps-SHP-2 pathway.

The molecular mechanisms that fine-tune Toll-like receptor (TLR)-triggered innate inflammatory responses remain to be fully elucidated. Major histocompatibility complex (MHC) molecules can mediate reverse signaling and have nonclassical functions. Here we found that constitutively expressed membrane MHC class I molecules attenuated TLR-triggered innate inflammatory responses via reverse signaling, which protected mice from sepsis. The intracellular domain of MHC class I molecules was phosphorylated by the kinase Src after TLR activation, then the tyrosine kinase Fps was recruited via its Src homology 2 domain to phosphorylated MHC class I molecules. This led to enhanced Fps activity and recruitment of the phosphatase SHP-2, which interfered with TLR signaling mediated by the signaling molecule TRAF6. Thus, constitutive MHC class I molecules engage in crosstalk with TLR signaling via the Fps-SHP-2 pathway and control TLR-triggered innate inflammatory responses.

Liquid-based cytology specimens for next-generation sequencing in lung adenocarcinoma: challenges and evaluation of targeted therapy.

BACKGROUND: To explore challenges of liquid-based cytology (LBC) specimens for next-generation sequencing (NGS) in lung adenocarcinoma and evaluate the efficacy of targeted therapy. METHODS: A retrospective analysis was conducted on the NGS test of 357 cases of advanced lung adenocarcinoma LBC specimens and compared with results of histological specimens to assess the consistency. The impact of tumor cellularity on NGS test results was evaluated. The utility of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) was collected. Clinical efficacy evaluation was performed and survival curve analysis was conducted using the Kaplan-Meier method. RESULTS: There were 275 TKI-naive and 82 TKI-treated specimens, the mutation rates of cancer-related genes detected in both groups were similar (86.2% vs. 86.6%). The EGFR mutation rate in the TKI treated group was higher than that in the TKI-naive group (69.5% > 54.9%, P = 0.019). There was no significant difference in the EGFR mutation frequency among different tumor cellularity in the TKI-naive group. However, in the TKI treated group, the frequency of EGFR sensitizing mutation and T790M resistance mutation in specimens with < 20% tumor cellularity was significantly lower than that in specimens with ≥ 20% tumor cellularity. Among 22 cases with matched histological specimens, 72.7% (16/22) of LBC specimens were completely consistent with results of histological specimens. Among 92 patients with EGFR-mutant lung adenocarcinoma treated with EGFR-TKIs in the two cohorts, 88 cases experienced progression, and the median progression-free survival (PFS) was 12.1 months. CONCLUSIONS: Cytological specimens are important sources for gene detection of advanced lung adenocarcinoma. When using LBC specimens for molecular testing, it is recommended to fully evaluate the tumor cellularity of the specimens.

Prohibitin 2 deficiency in photoreceptors leads to progressive retinal degeneration and facilitated Müller glia engulfing microglia debris.

Müller glia and microglia are capable of phagocytosing fragments of retinal cells in response to retinal injury or degeneration. However, the direct evidence for their mutual interactions between Müller glia and microglia in the progression of retinal degeneration (RD) remains largely unclear. This study aims to construct a progressive RD mouse model and investigate the activated pattern of Müller glia and the interplay between Müller glia and microglia in the early stage or progression of RD. A Prohibitin 2 (Phb2) photoreceptor-specific knockout (RKO) mouse model was generated by crossing Phb2flox/flox mice with Rhodopsin-Cre mice. Optical Coherence Tomography (OCT), histological staining, and Electroretinography (ERG) assessed retinal structure and function, and RKO mice exhibited progressive RD from six weeks of age. In detail, six-week-old RKO mice showed no significant retinal impairment, but severe vision dysfunction and retina thinning were shown in ten-week-old RKO mice. Furthermore, RKO mice were sensitive to Light Damage (LD) and showed severe RD at an early age after light exposure. Bulk retina RNA-seq analysis from six-week-old control (Ctrl) and RKO mice showed reactive retinal glia in RKO mice. The activated pattern of Müller glia and the interplay between Müller glia and microglia was visualized by immunohistology and 3D reconstruction. In six-week-old RKO mice or light-exposed Ctrl mice, Müller glia were initially activated at the edge of the retina. Moreover, in ten-week-old RKO mice or light-exposed six-week-old RKO mice with severe photoreceptor degeneration, abundant Müller glia were activated across the whole retinas. With the progression of RD, phagocytosis of microglia debris by activated Müller glia were remarkably increased. Altogether, our study establishes a Phb2 photoreceptor-specific knockout mouse model, which is a novel mouse model of RD and can well demonstrate the phenotype of progressive RD. We also report that Müller glia in the peripheral retina is more sensitive to the early damage of photoreceptors. Our study provides more direct evidence for Müller glia engulfing microglia debris in the progression of RD due to photoreceptor Phb2 deficiency.

AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods.

Skin Corrosion/Irritation (Corr./Irrit.) has long been a health hazard in the Globally Harmonized System (GHS). Several in silico models have been built to predict Skin Corr./Irrit. as an alternative to the increasingly restricted animal testing. However, current studies are limited by data amount/quality and model availability. To address these issues, we compiled a traceable consensus GHS data set comprising 731 Corr., 1283 Irrit., and 1205 negative (Neg.) samples from 6 governmental databases and 2 external data sets. Then, a series of binary classifiers were developed with five machine learning (ML) algorithms and six molecular representations. For 10-fold cross-validation, the best Corr. vs Neg. classifier achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 97.1%, while the best Irrit. vs Neg. classifier achieved an AUC of 84.7%. Compared with existing in silico tools on external validation, our Attentive FP classifiers showed the highest metrics on Corr. vs Neg. and the second highest accuracy on Irrit. vs Neg. The SHapley Additive exPlanation approach was further applied to figure out important molecular features, and the attention weights were visualized to perform interpretable prediction. Structural alerts associated with Skin Corr./Irrit. were also identified. The interpretable Attentive FP classifiers were integrated into the software AttentiveSkin at https://github.com/BeeBeeWong/AttentiveSkin. The conventional ML classifiers are also provided on our platform admetSAR at http://lmmd.ecust.edu.cn/admetsar2/. Considering the data deficiency and the limited model availability of Skin Corr./Irrit., we believe that our data set and models could facilitate chemical safety assessment and relevant studies.

In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory.

Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.

In Silico Prediction of Chemical Acute Dermal Toxicity Using Explainable Machine Learning Methods.

The research on acute dermal toxicity has consistently been a crucial component in assessing the potential risks of human exposure to active ingredients in pesticides and related plant protection products. However, it is difficult to directly identify the acute dermal toxicity of potential compounds through animal experiments alone. In our study, we separately integrated 1735 experimental data based on rabbits and 1679 experimental data based on rats to construct acute dermal toxicity prediction models using machine learning and deep learning algorithms. The best models for the two animal species achieved AUC values of 78.0 and 82.0%, respectively, on 10-fold cross-validation. Additionally, we employed SARpy to extract structural alerts, and in conjunction with Shapley additive explanation and attentive FP heatmap, we identified important features and structural fragments associated with acute dermal toxicity. This approach offers valuable insights for the detection of positive compounds. Moreover, a standalone software tool was developed to make acute dermal toxicity prediction easier. In summary, our research would provide an effective tool for acute dermal toxicity evaluation of pesticides, cosmetics, and drug safety assessment.

Directed Self-Assembly by Sparsely Prepatterned Substrates with Self-Responsive Polymer Brushes.

The guiding pattern in the chemoepitaxially directed self-assembly (DSA) of block copolymers is often fabricated by periodically functionalizing homogeneously random copolymer brushes tethered on a substrate. The prepatterned copolymer brushes constitute a soft penetrable surface, and their two components can in principle locally segregate in response to the overlying self-assembly process of block copolymers. To reveal how the self-responsive behavior of the copolymer brushes affects the directing effect, we develop a dissipative particle dynamics model to explicitly include the prepatterned polymer brushes and implement it to simulate the DSA of a cylinder-forming diblock copolymer melt on the sparse pattern of polymer brushes. Through large-scale dynamic simulations, we identify the windows of the content of the random copolymer, the film thickness, and the diameter of the patterned spot, for the formation of perfectly ordered hexagonal patterns composed of perpendicular cylinders. Our dynamic simulations reveal that the random copolymer brushes grafted on the unpatterned area exhibit a remarkable self-responsive ability with respect to the self-assembly of the diblock copolymers overlying them, which may widen the effective window of the content of the random copolymer. Within the processing windows of these key parameters, defect-free patterns are successfully achieved both in simulations and in experiments with sizes as large as a few micrometers for 4-fold density multiplications. This work demonstrates that highly efficient computer simulations based on an effective model can provide helpful guidance for experiments to optimize the critical parameters and even may promote the application of DSA.