Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation ...of missense variants but have limited performance. Here, we describe MVP (Missense Variant Pathogenicity prediction), a new prediction method that uses deep residual network to leverage large training data sets and many correlated predictors. We train the model separately in genes that are intolerant of loss of function variants and the ones that are tolerant in order to take account of potentially different genetic effect size and mode of action. We compile cancer mutation hotspots and de novo variants from developmental disorders for benchmarking. Overall, MVP achieves better performance in prioritizing pathogenic missense variants than previous methods, especially in genes tolerant of loss of function variants. Finally, using MVP, we estimate that de novo coding variants contribute to 7.8% of isolated congenital heart disease, nearly doubling previous estimates.
Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a ...popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available.
We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13's TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively.
These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.
Emergence of multidrug-resistant bacteria causes an urgent need for new generation of antibiotics, which may have a different mechanism of inhibition or killing action from the existing. Here, we ...report on the design, synthesis, and biological evaluation of thirty-nine coumarin derivatives in order to solve the antibacterial resistance by targeting at the inhibition of biosynthesis pathway of fatty acids. Their antibacterial activities against Escherichia coli, Staphylococcus aureus, Streptococcus agalactiae, and Flavobacterium cloumnare are tested and action mechanism against the key enzyme in bacterial fatty acid synthesis pathway are studied. The results show that compounds 13 and 18 have potent and broad spectrum antimicrobial activity. In addition, 9, 14 and 19 show eminent antimicrobial efficacy toward S. aureus, S. agalactiae, and F. cloumnare. Mechanistically, coumarin derivatives display the antibacterial activity via the control of FabI and FabK function. The structure-activity relationship analysis indicate that the length of linker and imidazole substitute group could significantly influence the antimicrobial activity, as well as the inhibitory activity against FabI and FabK. The structural optimization analysis of coumarin suggest that derivatives 9, 13, 14, 18 and 19 could be a viable way of preventing and controlling bacteria and considered as promising lead compounds for the development of commercial drugs.
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•Novel coumarin-imidazoles with antibacterial properties were synthesized.•Coumarin derivatives showed the antibacterial activity by inhibiting the FabI and FabK.•Structure-activity relationship showed the importance of the alkyl linker and imidazole.
Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are ...selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.
Tissue-resident memory T cells (TRMs) in mice mediate optimal protective immunity to infection and vaccination, while in humans, the existence and properties of TRMs remain unclear. Here, we use a ...unique human tissue resource to determine whether human tissue memory T cells constitute a distinct subset in diverse mucosal and lymphoid tissues. We identify a core transcriptional profile within the CD69+ subset of memory CD4+ and CD8+ T cells in lung and spleen that is distinct from that of CD69− TEM cells in tissues and circulation and defines human TRMs based on homology to the transcriptional profile of mouse CD8+ TRMs. Human TRMs in diverse sites exhibit increased expression of adhesion and inhibitory molecules, produce both pro-inflammatory and regulatory cytokines, and have reduced turnover compared with circulating TEM, suggesting unique adaptations for in situ immunity. Together, our results provide a unifying signature for human TRM and a blueprint for designing tissue-targeted immunotherapies.
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•CD69+ memory T cells predominate in multiple tissues throughout the human body•A core signature defining human TRMs is enriched within CD69+ tissue memory T cells•Human TRMs have unique adhesion and migratory abilities and functional capacities•Human TRMs exhibiting the core profile populate multiple lymphoid and mucosal sites
Kumar et al. identify a core transcriptional and phenotypic signature that defines human TRMs for both CD4+ and CD8+ T cells that is preserved across diverse individuals and in mucosal and lymphoid sites.
To further understand the molecular pathogenesis of pulmonary sarcomatoid carcinoma (PSC) and develop new therapeutic strategies in this treatment-refractory disease.
Whole-exome sequencing in a ...discovery set (n = 10) as well as targeted MET mutation screening in an independent validation set (n = 26) of PSC were performed. Reverse transcriptase polymerase chain reaction and Western blotting were performed to validate MET exon 14 skipping. Functional studies for validation of the oncogenic roles of MET exon 14 skipping were conducted in lung adenosquamous cell line H596 (MET exon 14 skipped and PIK3CA mutated) and gastric adenocarcinoma cell line Hs746T (MET exon 14 skipped). Response to MET inhibitor therapy with crizotinib in a patient with advanced PSC and MET exon 14 skipping was evaluated to assess clinical translatability.
In addition to confirming mutations in known cancer-associated genes (TP53, KRAS, PIK3CA, MET, NOTCH, STK11, and RB1), several novel mutations in additional genes, including RASA1, CDH4, CDH7, LAMB4, SCAF1, and LMTK2, were identified and validated. MET mutations leading to exon 14 skipping were identified in eight (22%) of 36 patient cases; one of these tumors also harbored a concurrent PIK3CA mutation. Short interfering RNA silencing of MET and MET inhibition with crizotinib showed marked effects on cell viability and decrease in downstream AKT and mitogen-activated protein kinase activation in Hs746T and H596 cells. Concurrent PIK3CA mutation required addition of a second agent for successful pathway suppression and cell viability effect. Dramatic response to crizotinib was noted in a patient with advanced chemotherapy-refractory PSC carrying a MET exon 14 skipping mutation.
Mutational events of MET leading to exon 14 skipping are frequent and potentially targetable events in PSC.
Nanoscale or single-cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of ...cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (nanodroplet processing in one pot for trace samples) platform for small cell population proteomics analysis. NanoPOTS enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive liquid chromatography-MS, nanoPOTS allows identification of ~1500 to ~3000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins are consistently identified from as few as 10 cells. Furthermore, we demonstrate quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues.
•An insect image dataset was established by an Online Insect Trapping Device.•An optimized deep neural network was developed based on the Faster R-CNN.•This neural network extracted multi-scale ...feature maps of insects to detect them.•The insects is mixed with fines, foreign materials, dockages and broken grains.
A detection and identification method for stored-grain insects was developed by applying deep neural network. Adults of following six species of common stored-grain insects mixed with grain and dockage were artificially added into the developed insect-trapping device: Cryptoleste Pusillus(S.), Sitophilus Oryzae(L.), Oryzaephilus Surinamensis(L.), Tribolium Confusum(Jaquelin Du Val), Rhizopertha Dominica(F.). Database of Red Green and Blue (RGB) images of these live insects was established. We used Faster R-CNN to extract areas which might contain the insects in these images and classify the insects in these areas. An improved inception network was developed to extract feature maps. Excellent results for the detection and classification of these insects were achieved. The test results showed that the developed method could detect and identify insects under stored grain condition, and its mean Average Precision (mAP) reached 88.
We report on the quantitative proteomic analysis of single mammalian cells. Fluorescence‐activated cell sorting was employed to deposit cells into a newly developed nanodroplet sample processing ...chip, after which samples were analyzed by ultrasensitive nanoLC‐MS. An average of circa 670 protein groups were confidently identified from single HeLa cells, which is a far greater level of proteome coverage for single cells than has been previously reported. We demonstrate that the single‐cell proteomics platform can be used to differentiate cell types from enzyme‐dissociated human lung primary cells and identify specific protein markers for epithelial and mesenchymal cells.
Single‐cell proteomics: A microfluidic platform coupled to nanoLC‐MS was developed to enable quantitative proteomic analysis of single mammalian cells containing only 0.1–0.2 ng of total protein. Label‐free cell differentiation was enabled by quantifying protein expression in individual cells.
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