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Protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods face challenges: sequence-based approaches struggle with detecting distant homologs, while structure-based approaches are limited by the need for structure generation and often treat proteins as rigid bodies. Protein Language Model-based alignment tools have shown advantages in utilizing sequence information to overcome the challenges of detecting distant homologs without requiring structural input. However, many current Protein Language Model-based alignment methods, which rely on sequence alignment algorithms like the Smith-Waterman algorithm, face significant difficulties when dealing with circular permutation (CP) due to their dependency on linear sequence order. This sequence order dependency makes them unsuitable for accurately detecting CP. Our approach, named plmCP, combines classical genetic principles with modern alignment techniques leveraging Protein Language Models to address these limitations. By integrating genetic knowledge, the plmCP method avoids the sequence order dependency, allowing for effective detection of circular permutations and contributing significantly to protein research and engineering by embracing structural flexibility.

Abstract Noise label learning has attracted considerable attention owing to its ability to leverage large amounts of inexpensive and imprecise data. Sharpness aware minimization (SAM) has shown effective improvements in the generalization performance in the presence of noisy labels by introducing adversarial weight perturbations in the model parameter space. However, our experimental observations have shown that the SAM generalization bottleneck primarily stems from the difficulty of finding the correct adversarial perturbation amidst the noisy data. To address this problem, a theoretical analysis of the mismatch in the direction of the parameter perturbation between noise and clean samples during the training process was conducted. Based on these analyses, a clean aware sharpness aware minimization algorithm known as CA-SAM is proposed. CA-SAM dynamically divides the training data into possible likely clean and noisy datasets based on the historical model output and uses likely clean samples to determine the direction of the parameter perturbation. By searching for flat minima in the loss landscape, the objective was to restrict the gradient perturbation direction of noisy samples to align them while preserving the clean samples. By conducting comprehensive experiments and scrutinizing benchmark datasets containing diverse noise patterns and levels, it is demonstrated that our CA-SAM outperforms certain innovative approaches by a substantial margin.

Abstract Glioblastoma (GBM) is the most common intracranial malignancy, but current treatment options are limited. Super-enhancers (SEs) have been found to drive the expression of key oncogenes in GBM. However, the role of SE-associated long non-coding RNAs (lncRNAs) in GBM remains poorly understood. Here, we screened for an up-regulated lncRNA-SLCO4A1-AS1 expressed in GBM by analyzing data from GSE54791, GSE4536 and TCGA. We systematically analyzed its relationship with clinical characteristics, prognosis, epigenetics, tumor microenvironment (TME), biological functions, and transcription factors. We found that SE-driven SLCO4A1-AS1 was significantly upregulated in GBM and correlated with poor prognosis. Knockdown of SLCO4A1-AS1 decreased glioma cell proliferation, invasive ability, self-renewal ability, and increased apoptosis. Epigenetic analysis revealed that SOX2 and SE could drive SLCO4A1-AS1 expression. In vitro experiments further demonstrated that GBM cells with high SLCO4A1-AS1 expression were more sensitive to VX-11e, and overexpression of SLCO4A1-AS1 could reverse the inhibitory effect of VX-11e on GBM cells. In conclusion, this study revealed that SE-driven SLCO4A1-AS1 may be a potential therapeutic target in GBM.

BackgroundThough droplet digital PCR (ddPCR) has emerged as a promising tool for early pathogen detection in bloodstream infections (BSIs), more studies are needed to support its clinical application widely due to different ddPCR platforms with discrepant diagnostic performance. Additionally, there is still a lack of clinical data to reveal the association between pathogen loads detected by ddPCR and corresponding BSIs.MethodsIn this prospective study, 173 patients with suspected BSIs were enrolled. A multiplex ddPCR assay was used to detect 18 pathogens. The results of ddPCR testing were evaluated in comparison with blood cultures (BCs) and clinical diagnosis. Taking BC as the gold standard, receiver operating characteristic curve and Cohen’s kappa agreement were used to investigate whether the pathogen load could predict a corresponding culture-proven BSI for the top five microorganisms detected by ddPCR.ResultsOf the 173 blood samples collected, BC and ddPCR were positive in 48 (27.7%) and 92 (53.2%) cases, respectively. Compared to BC, the aggregate sensitivity and specificity for ddPCR were 81.3% and 63.2%, respectively. After clinical adjudication, the sensitivity and specificity of ddPCR increased to 88.8% and 86.0%, respectively. There were 143 microorganisms detected by ddPCR. The DNA loads of these microorganisms ranged from 30.0 to 3.2×105 copies/mL (median level: 158.0 copies/mL), 72.7% (104/143) of which were below 1,000 copies/mL. Further, statistical analysis showed the DNA loads of Escherichia coli (AUC: 0.954, 95% CI: 0.898-1.000, κ=0.731, cut-off values: 93.0 copies/mL) and Klebsiella pneumoniae (AUC: 0.994, 95% CI: 0.986-1.000, κ=0.834, cut-off values: 196.5 copies/mL) were excellent predictors for the corresponding BSIs. The DNA loads of Pseudomonas aeruginosa (AUC: 0.816, 95% CI: 0.560-1.000, κ=0.167), Acinetobacter baumannii (AUC: 0.728, 95% CI: 0.195-1.000), and Enterococcus spp. (AUC: 0.282, 95% CI: 0.000-0.778) had little predictive value for the corresponding culture-proven BSIs.ConclusionOur results indicate that the multiplex ddPCR is a promising platform as a complementary add-on to conventional BC. The DNA loads of E. coli and K. pneumoniae present excellent predictive value for the corresponding BSIs. Further research is needed to explore the predictive potential of ddPCR for other microorganisms.