Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.

Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the challenge, machine learning approaches have been proposed to automate this process. Currently, the majority of the experimental maps in the database lack atomic resolution features, making it challenging for machine learning-based methods to precisely determine protein structures from cryogenic electron microscopy density maps. On the other hand, protein structure prediction methods, such as AlphaFold2, leverage evolutionary information from protein sequences and have recently achieved groundbreaking accuracy. However, these methods often require manual refinement, which is labor intensive and time consuming. In this study, we present DeepTracer-Refine, an automated method that refines AlphaFold predicted structures by aligning them to DeepTracers modeled structure. Our method was evaluated on 39 multi-domain proteins and we improved the average residue coverage from 78.2 to 90.0% and average local Distance Difference Test score from 0.67 to 0.71. We also compared DeepTracer-Refine with Phenixs AlphaFold refinement and demonstrated that our method not only performs better when the initial AlphaFold model is less precise but also surpasses Phenix in run-time performance.

Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps.

Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.

ComplexQA: a deep graph learning approach for protein complex structure assessment.

MOTIVATION: In recent years, the end-to-end deep learning method for single-chain protein structure prediction has achieved high accuracy. For example, the state-of-the-art method AlphaFold, developed by Google, has largely increased the accuracy of protein structure predictions to near experimental accuracy in some of the cases. At the same time, there are few methods that can evaluate the quality of protein complexes at the residue level. In particular, evaluating the quality of residues at the interface of protein complexes can lead to a wide range of applications, such as protein function analysis and drug design. In this paper, we introduce a new deep graph neural network-based method ComplexQA, to evaluate the local quality of interfaces for protein complexes by utilizing the residue-level structural information in 3D space and the sequence-level constraints. RESULTS: We benchmark our method to other state-of-the-art quality assessment approaches on the HAF2 and DBM55-AF2 datasets (high-quality structural models predicted by AlphaFold-Multimer), and the BM5 docking dataset. The experimental results show that our proposed method achieves better or similar performance compared with other state-of-the-art methods, especially on difficult targets which only contain a few acceptable models. Our method is able to suggest a score for each interfac e residue, which demonstrates a powerful assessment tool for the ever-increasing number of protein complexes. AVAILABILITY: https://github.com/Cao-Labs/ComplexQA.git. Contact: caora@plu.edu.

Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge.

This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.

ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.

MOTIVATION: The Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. As of CASP14, there are 79 global QA methods, and a minority of 39 residue-level QA methods with very few of them working on protein complexes. Here, we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure/complex prediction at residue level, which have many applications such as drug discovery. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius $r$ of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grade their placement within the protein as a whole. Moreover, we have shown the potential of ZoomQA to identify problematic regions of the SARS-CoV-2 protein complex. RESULTS: We benchmark ZoomQA on CASP14, and it outperforms other state-of-the-art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features and shows that our method is able to match the performance of other state-of-the-art methods without the use of homology searching against databases or PSSM matrices. AVAILABILITY: http://zoomQA.renzhitech.com.

Predominantly inverse modulation of gene expression in genomically unbalanced disomic haploid maize.

The phenotypic consequences of the addition or subtraction of part of a chromosome is more severe than changing the dosage of the whole genome. By crossing diploid trisomies to a haploid inducer, we identified 17 distal segmental haploid disomies that cover ∼80% of the maize genome. Disomic haploids provide a level of genomic imbalance that is not ordinarily achievable in multicellular eukaryotes, allowing the impact to be stronger and more easily studied. Transcriptome size estimates revealed that a few disomies inversely modulate most of the transcriptome. Based on RNA sequencing, the expression levels of genes located on the varied chromosome arms (cis) in disomies ranged from being proportional to chromosomal dosage (dosage effect) to showing dosage compensation with no expression change with dosage. For genes not located on the varied chromosome arm (trans), an obvious trans-acting effect can be observed, with the majority showing a decreased modulation (inverse effect). The extent of dosage compensation of varied cis genes correlates with the extent of trans inverse effects across the 17 genomic regions studied. The results also have implications for the role of stoichiometry in gene expression, the control of quantitative traits, and the evolution of dosage-sensitive genes.

Genomic imbalance determines positive and negative modulation of gene expression in diploid maize.

Genomic imbalance caused by changing the dosage of individual chromosomes (aneuploidy) has a more detrimental effect than varying the dosage of complete sets of chromosomes (ploidy). We examined the impact of both increased and decreased dosage of 15 distal and 1 interstitial chromosomal regions via RNA-seq of maize (Zea mays) mature leaf tissue to reveal new aspects of genomic imbalance. The results indicate that significant changes in gene expression in aneuploids occur both on the varied chromosome (cis) and the remainder of the genome (trans), with a wider spread of modulation compared with the whole-ploidy series of haploid to tetraploid. In general, cis genes in aneuploids range from a gene-dosage effect to dosage compensation, whereas for trans genes the most common effect is an inverse correlation in that expression is modulated toward the opposite direction of the varied chromosomal dosage, although positive modulations also occur. Furthermore, this analysis revealed the existence of increased and decreased effects in which the expression of many genes under genome imbalance are modulated toward the same direction regardless of increased or decreased chromosomal dosage, which is predicted from kinetic considerations of multicomponent molecular interactions. The findings provide novel insights into understanding mechanistic aspects of gene regulation.

Dual vector millimeter-wave signal generation based on optical carrier suppression modulation and direct detection with one photodetector.

We propose a novel, to the best of our knowledge, scheme for dual vector millimeter-wave (mm-wave) signal generation and transmission, based on optical carrier suppression (OCS) modulation, precoding, and direct detection by a single-ended photodiode (PD). At the transmitter side, two independent vector radio frequency (RF) signals with precoding, generated via digital signal processing (DSP), are used to drive an in-phase/quadrature (I/Q) modulator operating at the optical OCS modulation mode to simultaneously generate two independent frequency-doubling optical vector mm-wave signals, which can reduce the bandwidth requirement of transmitter's components and enhance spectral efficiency. With the aid of the single-ended PD and subsequent DSP at the receiver side, two independent frequency-doubling vector mm-wave signals can be separated and demodulated without data error. Based on our proposed scheme, we experimentally demonstrate the generation, transmission, and detection of 2-Gbaud 30-GHz quadrature-phase-shift-keying (QPSK) and 2-Gbaud 46-GHz QPSK signals over 10-km single-mode fiber-28 (SMF-28) and 1-m wireless transmission. The results indicate that the bit-error ratio (BER) of the dual vector mm-wave signals can each reach the hard-decision forward-error-correction (HD-FEC) threshold of 3.8 × 10-3.

Electron Donor-Acceptor Complex Driven Photocatalyst-Free Trifluoromethylation of Heterocycles.

Heterocyclic trifluoromethylation is efficiently initiated through a photochemical reaction utilizing an electron donor-acceptor (EDA) complex, proceeding smoothly without the use of photocatalysts, transition-metal catalysts, or additional oxidants. This method has been optimized through extensive experimentation, demonstrating its versatility and efficacy across various substrates, including quinoxalinones, coumarins, and indolones. Notably, this approach enables the practical synthesis of trifluoromethylated quinoxalinones on a gram scale. Mechanistic investigations that incorporate radical trapping and ultraviolet/visible spectroscopy, confirmed the formation of the an EDA complex and elucidated the reaction pathways. This study highlights the crucial role of EDA photoactivation in trifluoromethylation, significantly expanding the application scope of EDA complexes in chemical synthesis.

Phosphine-Catalyzed Stereospecific and Enantioselective Desymmetrizative [3+2] Cycloaddition of MBH Carbonates and N-(2-tert-Butylphenyl)maleimides.

Herein, we report an l-valine-derived amide phosphine-catalyzed [3+2] cyclization of MBH carbonates and N-(2-tert-butylphenyl)maleimides via asymmetric desymmetrization. Bicyclic N-aryl succinimide derivatives bearing three continuous chiral centers with a remote C-N atropisomeric chirality were constructed stereospecifically and enantioselectively. A wide variety of MBH carbonates could be employed in this process to deliver highly optically pure succinimide derivatives in moderate to excellent yields.

Design, Synthesis, and Biological Evaluation of Chroman Derivatives as PD-1/PD-L1 Antagonists.

Programmed death-ligand 1 (PD-L1) has emerged as a promising therapeutic target for various cancers due to its crucial role in promoting tumor immune evasion. Here, we report a novel class of chroman-like small-molecule PD-L1 inhibitors exhibiting significant activity in inhibiting the PD-1/PD-L1 interaction. Employing a "ring-close" strategy for conformational restriction, we have achieved compound C27, which demonstrates superior PD-1/PD-L1 inhibitory activity compared to the positive control. Molecular dynamics simulation and binding free energy calculation predict that (R)-C27 with inhibitory activity surpassed (S)-C27. The experimental results from bioassay and X-ray structural analysis corroborate these findings. All these results collectively indicate that (R)-C27 is a promising lead compound deserving further exploration.

Ferric Oxide Nanomaterials and Plant-Rhizobacteria Symbionts Cogenerate Iron Plaque for Removing Highly Chlorinated Contaminants in Dryland Soils.

Rhizosphere iron plaques derived from Fe-based nanomaterials (NMs) are a promising tool for sustainable agriculture. However, the requirement for flooded conditions to generate iron plaque limits the scope of the NM application. In this study, we achieved in situ Fenton oxidation of a highly chlorinated persistent organic pollutant (2,2',4,5,5'-pentachlorobiphenyl, PCB101) through iron plaque mediated by the interaction between α-Fe2O3 NMs and plant-rhizobacteria symbionts under dryland conditions. Mechanistically, the coexistence of α-Fe2O3 NMs and Pseudomonas chlororaphis JD37 stimulated alfalfa roots to secrete acidic and reductive agents as well as H2O2, which together mediated the rhizosphere Fenton reaction and converted α-Fe2O3 NMs into iron plaque rich in Fe(II)-silicate. Further verifications reproduced the Fenton reaction in vitro using α-Fe2O3 NMs and rhizosphere compounds, confirming the critical role of •OH in the oxidative degradation of PCB101. Significant reductions in PCB101 content by 18.6%, 42.9%, and 23.2% were respectively found in stem, leaf, and soil after a 120-d treatment, proving the effectiveness of this NMs-plant-rhizobacteria technique for simultaneously safe crop production and soil remediation. These findings can help expand the potential applications of nanobio interaction and its mediated iron plaque generation for both agricultural practice and soil remediation.

Mobile genetic elements affect the dissemination of antibiotic resistance genes (ARGs) of clinical importance in the environment.

The prevalence of antibiotic resistance genes (ARGs) in the environment is a quintessential One Health issue that threats both human and ecosystem health; however, the source and transmission of ARGs, especially clinically important ARGs (CLIARGs), in the environment have not yet been well studied. In the present study, shotgun metagenomic approaches were used to characterize the microbiome, resistome, and mobilome composition in human feces and six different environment sample types in South China. Overall, the resistome harbored 157 CLIARGs, with specific ARG hotspots (e.g., human feces, wastewater treatment plants, livestock manure and wastewater) excreting significantly higher abundance of CLIARGs compared with the natural environment. A redundancy analysis (RDA) was performed and revealed that the bacterial community compositions and mobile genetic elements (MGEs) explained 55.08% and 34.68% of the variations in ARG abundance, respectively, indicating that both bacterial community and MGEs are key contributors to the maintenance and dissemination of CLIARGs in the environment. The network analysis revealed non-random co-occurrence patterns between 200 bacterial genera and 147 CLIARGs, as well as between 135 MGEs and 123 CLIARGs. In addition to numerous co-shared CLIARGs among different sample types, the source tracking program based on the FEAST probabilistic model was used to estimate the relative contributions of the CLIARGs from potential sources to the natural environment. The source tracking analysis results delineated that mobilome, more than microbiome, contributed CLIARG transmission from those ARG hotspots into natural environment, and the MGEs in WWTPs seem to play the most significant role in the spread of CLIARGs to the natural environment (average contribution 32.9%-46.4%). Overall, this study demonstrated the distribution and dissemination of CLIARGs in the environment, and aimed to better inform strategies to control the spread of CLIARGs into the natural environment.

Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue.

BACKGROUND: Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide the resection range of tumor. This study aimed to examine the effectiveness of a nomogram created by combining radiomics signatures from both intratumoral and derived tissues with clinical characteristics for predicting pCR after NACT. METHODS: The clinical data of 133 BCa patients were analyzed retrospectively and divided into training and validation sets. The radiomics features for Intratumoral, peritumoral, and background parenchymal enhancement (BPE) in the training set were dimensionalized. Logistic regression analysis was used to select the optimal feature set, and a radiomics signature was constructed using a decision tree. The signature was combined with clinical features to build joint models and generate nomograms. The area under curve (AUC) value of receiver operating characteristic (ROC) curve was then used to assess the performance of the nomogram and independent predictors. RESULTS: Among single region, intratumoral had the best predictive value. The diagnostic performance of the intratumoral improved after adding the BPE features. The AUC values of the radiomics signature were 0.822 and 0.82 in the training and validation sets. Multivariate logistic regression analysis revealed that age, ER, PR, Ki-67, and radiomics signature were independent predictors of pCR in constructing a nomogram. The AUC of the nomogram in the training and validation sets were 0.947 and 0.933. The DeLong test showed that the nomogram had statistically significant differences compared to other independent predictors in both the training and validation sets (P < 0.05). CONCLUSION: BPE has value in predicting the efficacy of neoadjuvant chemotherapy, thereby revealing the potential impact of tumor growth environment on the efficacy of neoadjuvant chemotherapy.

Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study.

BACKGROUND: Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. METHODS: This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. RESULTS: The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. CONCLUSION: The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

Epithelial thickness remodeling after small incision lenticule intrastromal keratoplasty in correcting hyperopia measured by RTVue OCT.

PURPOSE: To characterize the in vivo corneal epithelial thickness (CET) remodeling profile in a population of eyes after small incision lenticule intrastromal keratoplasty (SMI-LIKE) for hyperopia. METHODS: The CET profile was measured by RTVue-100 Fourier-domain OCT system across the central 6-mm diameter of the cornea of 17 eyes from 12 subjects (five males and seven females) who accepted corneal stromal lens implantation surgery for correcting hyperopia. The CET were measured at positions with a radius of 0-1.0 mm, 1.0-2.5 mm (divided into eight quadrants) and 2.5-3.0 mm (divided into eight quadrants) from the corneal center. Corneal maximum simulated keratometry (Km) was measured by Pentacam HR anterior segment analyzer to analyze CET changes. The examination data of subjects were collected in four time periods, which were preoperative, short-term postoperative (one week after surgery), mid-term postoperative (the last review within 3-6 months after surgery), and long-term postoperative (the last review over 1-2.5 years after surgery). The changes of CET were compared and analyzed in the four time periods. RESULTS: Mean CET in 0-1.0 mm, 1.0-2.5 mm and 2.5-3.0 mm of the cornea decreased in one week after surgery, respectively, as compared to CET in the preoperative period, which turned from 55.06 ± 0.82 µm、54.42 ± 0.75 µm、53.46 ± 0.60 µm to 51.18 ± 1.05 µm (P = 0.005), 49.38 ± 0.70 µm (P = 0.000), 51.29 ± 0.59 µm (P = 0.025). In the mid-term postoperative period, mean CET in 0-1.0 mm and 1.0-2.5 mm areas kept thinner than mean CET in the preoperative period, CET in 0-1.0 mm is 50.59 ± 0.76 µm (P = 0.000),CET in 1.0-2.5 mm is 50.23 ± 0.57 µm (P = 0.000), while mean CET in 2.5-3.0 mm area recovered to the same thickness as the preoperative level, which is 54.36 ± 0.66 µm (P = 1.000), until the long-term period, CET stabilized in the above doughnut pattern. CONCLUSIONS: After stromal lenticule implantation for hyperopia, CET showed a remodeled form of thinning in the 0-2.5 mm area and thickening in the 2.5-3.0 mm area, and remained stable within one year after surgery.

Sequence of the supernumerary B chromosome of maize provides insight into its drive mechanism and evolution.

B chromosomes are enigmatic elements in thousands of plant and animal genomes that persist in populations despite being nonessential. They circumvent the laws of Mendelian inheritance but the molecular mechanisms underlying this behavior remain unknown. Here we present the sequence, annotation, and analysis of the maize B chromosome providing insight into its drive mechanism. The sequence assembly reveals detailed locations of the elements involved with the cis and trans functions of its drive mechanism, consisting of nondisjunction at the second pollen mitosis and preferential fertilization of the egg by the B-containing sperm. We identified 758 protein-coding genes in 125.9 Mb of B chromosome sequence, of which at least 88 are expressed. Our results demonstrate that transposable elements in the B chromosome are shared with the standard A chromosome set but multiple lines of evidence fail to detect a syntenic genic region in the A chromosomes, suggesting a distant origin. The current gene content is a result of continuous transfer from the A chromosomal complement over an extended evolutionary time with subsequent degradation but with selection for maintenance of this nonvital chromosome.

Sodium aescinate ameliorates chronic neuropathic pain in male mice via suppressing JNK/p38-mediated microglia activation.

OBJECTIVE: A study confirmed that sodium aescinate (SA) can effectively relieve bone cancer pain, but its role in neuropathic pain (NP) remains confused. METHODS: Eighty male mice were randomly divided into four groups: sham+vehicle, sham+SA (40 µg/L, intrathecal injection), chronic contraction injury (CCI)+vehicle, CCI+SA. Behavioral assessments were used to evaluate the locomotor activity and paw withdrawal threshold (PWT) of mice. At the end of the study, spinal cord tissues were collected for histopathological analysis. The JNK/p38 signaling activation, Iba-1 expression, pro-inflammatory cytokines levels, and microglia subtype were assessed by western blotting, immunohistochemical staining, enzyme-linked immunosorbent assay, and flow cytometry with CD86/CD206, respectively. RESULTS: Early treatment with SA delayed the development of mechanical allodynia in CCI mice. Repeated SA treatment could prominently increase the reduction of PWT induced by CCI, and improve the locomotor activity of CCI mice. Mechanically, CCI surgery induced significant up-regulation of p-JNK and p-p38 protein levels, increased number and M1/M2 ratio of microglia, as well as pro-inflammatory factors in the spinal cords of mice, which could be blocked after SA administration. CONCLUSIONS: SA might suppress the activation of microglia and neuroinflammation by selectively inhibiting the JNK/p38 signaling pathway, thereby alleviating CCI-induced NP in male mice.

FPIRST: Fatigue Driving Recognition Method Based on Feature Parameter Images and a Residual Swin Transformer.

Fatigue driving is a serious threat to road safety, which is why accurately identifying fatigue driving behavior and warning drivers in time are of great significance in improving traffic safety. However, accurately recognizing fatigue driving is still challenging due to large intra-class variations in facial expression, continuity of behaviors, and illumination conditions. A fatigue driving recognition method based on feature parameter images and a residual Swin Transformer is proposed in this paper. First, the face region is detected through spatial pyramid pooling and a multi-scale feature output module. Then, a multi-scale facial landmark detector is used to locate 23 key points on the face. The aspect ratios of the eyes and mouth are calculated based on the coordinates of these key points, and a feature parameter matrix for fatigue driving recognition is obtained. Finally, the feature parameter matrix is converted into an image, and the residual Swin Transformer network is presented to recognize fatigue driving. Experimental results on the HNUFD dataset show that the proposed method achieves an accuracy of 96.512%, thus outperforming state-of-the-art methods.