Effects of electroacupuncture on mitophagy mediated by SIRT3/PINK1/Parkin pathway in Parkinson's disease mice. / ç”µé’ˆå¯¹å¸•é‡‘æ£®ç— å°é¼ SIRT3/PINK1/Parkin通路介导的线粒体自噬的影响.

OBJECTIVES: To observe the effects of electroacupuncture (EA) at "Fengfu"(GV16), "Taichong"(LR3), and "Zusanli"(ST36) on mitophagy mediated by silencing regulatory protein 3 (SIRT3)/ PTEN induced putative kinase 1 (PINK1)/PARK2 gene coding protein (Parkin) in the midbrain substantia nigra of Parkinson's disease (PD) mice, and to explore the potential mechanisms of EA in treating PD. METHODS: C57BL/6 mice were randomly divided into the control, model, EA, and sham EA groups, with 12 mice in each group. The PD mouse model was established by intraperitoneal injection of 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine (MPTP). The EA group received EA stimulation at GV16, LR3 and ST36, while the sham EA group received shallow needling 1 mm away from the above acupoints without electrical stimulation. The motor ability of mice in each group was evaluated using an open field experiment. Immunohistochemistry was used to detect the expression of tyrosine hydroxylase (TH) and α-synuclein (α-syn) in the substantia nigra of mice. The ultrastructure of neurons in substantia nigra was observed by transmission electron microscope (TEM). Immunofluorescence was used to detect the expression of the autophagy marker autophagy-associated protein light chain 3 (LC3). The expression levels of TH, α-syn, SIRT3, PINK1, Parkin, P62, Beclin-1, LC3Ⅱ mRNA and protein were detected by PCR and Western blot. RESULTS: Compared with the control group, mice in the model group showed a decrease in the total exercise distance, time, movement speed and times of crossing central region (P<0.01)；the positive expressions of TH and LC3 were decreased (P<0.01), while the positive expression of α-syn increased (P<0.01), accompanied by mitochondrial swelling, mitochondrial cristae fragmentation and decrease, and decreased lysosome count；the expression levels of TH, SIRT3, PINK1, Parkin, Beclin-1, and LC3Ⅱ mRNA and protein in the midbrain substantia nigra were decreased (P<0.01), while the expression levels of α-syn and P62 mRNA and protein were increased (P<0.01, P<0.05). Compared with the model group, the mice in EA group showed a significant increase in the total exercise distance, time, movement speed and times of crossing central region (P<0.01, P<0.05)；the positive expressions of TH and LC3 were increased (P<0.01, P<0.05), while the positive expression of α-syn was decreased (P<0.01), accompanied by an increase in mitochondrial count, appearance of autophagic va-cuoles, and a decrease in swelling, the expression levels of TH, SIRT3, PINK1, Parkin, Beclin-1 and LC3Ⅱ mRNA and protein in the midbrain substantia nigra were increased (P<0.01, P<0.05), while the mRNA and protein expression levels of α-syn and P62 were decreased (P<0.01)；the sham EA group showed an increase in the total exercise distance and time(P<0.05), with an increase in the positive expression of TH (P<0.05) and a decrease in the positive expression of α-syn (P<0.05)；some mitochondria exhibited swelling, and no autophagic vacuoles were observed；the protein expression levels of TH, SIRT3, Parkin and LC3Ⅱ were increased (P<0.01, P<0.05), and the expression levels of P62 mRNA, α-syn mRNA and protein were decreased (P<0.01, P<0.05), and LC3Ⅱ mRNA expression was increased (P<0.05). In comparison to the sham EA group, the EA group showed an extension in the total exercise time (P<0.01), the positive expression and mRNA expression levels of α-syn were decreased (P<0.01, P<0.05), while the expression levels of TH, SIRT3, PINK1, Parkin mRNA and SIRT3 protein were increased (P<0.05). CONCLUSIONS: EA at GV16, LR3, and ST36 can exert neuroprotective function and improve the motor ability of PD mice by activating the SIRT3/PINK1/Parkin pathway to enhance the expression of TH and reduce α-syn aggregation in the substantia nigra of PD mice.

Recent Advances and Challenges in Protein Structure Prediction.

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

Determinants of acute and subacute case-fatality in elderly patients with hypertensive intracerebral hemorrhage.

Background: Given that limited reports have described the survival and risk factors for elderly patients with hypertensive intracerebral hemorrhage (HICH), we aimed to develop a valid but simple prediction nomogram for the survival of HICH patients. Methods: All elderly patients ≥65 years old who were diagnosed with HICH between January 2011 and December 2019 were identified. We performed the least absolute shrinkage and selection operator (Lasso) on the Cox regression model with the R package glmnet. A concordance index was performed to calculate the nomogram discrimination; and calibration curves and decision curves were graphically evaluated by depicting the observed rates against the probabilities predicted by the nomogram. Results: A total of 204 eligible patients were analyzed, and over 20 % of the population was above the age of 80 (65-79 years old, n = 161; 80+ years old, n = 43). A hematoma volume ≥13.64 cm3 was associated with higher 7-day mortality (OR = 6.773, 95 % CI = 2.622-19.481; p < 0.001) and higher 90-day mortality (OR = 3.955, 95 % CI = 1.611-10.090, p = 0.003). A GCS score between 13 and 15 at admission was associated with a 7-day favorable outcome (OR = 0.025, 95 % CI = 0.005-0.086; p < 0.001) and a 90-day favorable outcome (OR = 0.033, 95 % CI = 0.010-0.099; p < 0.001). Conclusions: Our nomogram models were visualized and accurate. Neurosurgeons could use them to assess the prognostic factors and provide advice to patients and their relatives.

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.

DeepUMQA3: a web server for accurate assessment of interface residue accuracy in protein complexes.

MOTIVATION: Model quality assessment is a crucial part of protein structure prediction and a gateway to proper usage of models in biomedical applications. Many methods have been proposed for assessing the quality of structural models of protein monomers, but few methods for evaluating protein complex models. As protein complex structure prediction becomes a new challenge, there is an urgent need for model quality assessment methods that can accurately assess the accuracy of interface residues of complex structures. RESULTS: Here, we present DeepUMQA3, a web server for evaluating the accuracy of interface residues of protein complex structures using deep neural networks. For an input complex structure, features are extracted from three levels of overall complex, intra-monomer, and inter-monomer, and an improved deep residual neural network is used to predict per-residue lDDT and interface residue accuracy. DeepUMQA3 ranks first in the blind test of interface residue accuracy estimation in CASP15, with Pearson, Spearman, and AUC of 0.564, 0.535, and 0.755 under the lDDT measurement, which are 17.6%, 23.6%, and 10.9% higher than the second best method, respectively. DeepUMQA3 can also assess the accuracy of all residues in the entire complex and distinguish high- and low-precision residues. AVAILABILITY AND IMPLEMENTATION: The web sever of DeepUMQA3 are freely available at http://zhanglab-bioinf.com/DeepUMQA_server/.

Improving DNA 6mA Site Prediction via Integrating Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Self-Attention Mechanism.

Identifying DNA N6-methyladenine (6mA) sites is significantly important to understanding the function of DNA. Many deep learning-based methods have been developed to improve the performance of 6mA site prediction. In this study, to further improve the performance of 6mA site prediction, we propose a new meta method, called Co6mA, to integrate bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNNs), and self-attention mechanisms (SAM) via assembling two different deep learning-based models. The first model developed in this study is called CBi6mA, which is composed of CNN, BiLSTM, and fully connected modules. The second model is borrowed from LA6mA, which is an existing 6mA prediction method based on BiLSTM and SAM modules. Experimental results on two independent testing sets of different model organisms, i.e., Arabidopsis thaliana and Drosophila melanogaster, demonstrate that Co6mA can achieve an average accuracy of 91.8%, covering 89% of all 6mA samples while achieving an average Matthews correlation coefficient value (0.839), which is higher than the second-best method DeepM6A.

Density and row spacing of short-season cotton suitable for machine picking in the cotton region of Yellow River Basin.

To determine the suitable planting density and row spacing of short-season cotton suitable for machine picking in the Yellow River Basin of China, we conducted a two-year field experiment in Dezhou during 2018-2019. The experiment followed a split-plot design, with planting density (82500 plants·hm-2 and 112500 plants·hm-2) as the main plots and row spacing (equal row spacing of 76 cm, wide-narrow row spacing of 66 cm+10 cm, equal row spacing of 60 cm) as the subplots. We examined the effects of planting density and row spacing on growth and development, canopy structure, seed cotton yield and fiber quality of short-season cotton. The results showed that plant height and LAI under high density treatment were significantly greater than those under low density treatment. The transmittance of the bottom layer was significantly lower than under low density treatment. Plant height under 76 cm equal row spacing was significantly higher than that under 60 cm equal row spacing, while that under wide-narrow row spacing (66 cm +10 cm) was significantly smaller than that under 60 cm equal row spacing in peak bolling stage. The effects of row spacing on LAI varied between the two years, densities, and growth stages. On the whole, the LAI under the wide-narrow row spacing (66 cm+10 cm) was higher, with the curve declining gently after the peak, and it was higher than that in the two cases of equal row spacing in the harvest time. The change in transmittance of the bottom layer presented the opposite trend. Density, row spacing, and their interaction had significant effects on seed cotton yield and its components. In both years, seed cotton yield was the highest (3832 kg·hm-2 in 2018, 3235 kg·hm-2 in 2019) under wide-narrow row spacing (66 cm+10 cm), and it was more stable at high densities. Fiber quality was less affected by density and row spacing. To sum up, the optimal density and row spacing of short-season cotton were as follows: density with 112500 plants·hm-2 and wide-narrow row spacing (66 cm+10 cm).

Improving protein-protein interaction site prediction using deep residual neural network.

Accurate identification of protein-protein interaction (PPI) sites is significantly important for understanding the mechanism of life and developing new drugs. However, it is expensive and time-consuming to identify PPI sites using wet-lab experiments. Developing computational methods is a new road to identify PPI sites, which can accelerate the procedure of PPI-related research. In this study, we propose a novel deep learning-based method (called D-PPIsite) to improve the accuracy of sequence-based PPI site prediction. In D-PPIsite, four discriminative sequence-driven features, i.e., position specific scoring matrix, relative solvent accessibility, position information and physical properties, are employed to feed into a well-designed deep learning module, consisting of convolutional, squeeze and excitation, and fully connected layers, to learn a prediction model. To reduce the risk of a single prediction model getting stuck in local optima, multiple prediction models with different initialization parameters are selected and integrated into one final model using the mean ensemble strategy. Experimental results on five independent testing data sets demonstrate that the proposed D-PPIsite can achieve an average accuracy of 80.2% and precision of 36.9%, covering 53.5% of all PPI sites while achieving the average Matthews correlation coefficient value (0.330) that is significantly higher than most of existing state-of-the-art prediction methods. We implement a new standalone-version predictor for predicting PPI sites, which is freely available at https://github.com/MingDongup/D-PPIsite for academic use.

Surgical management and outcome of primary intracranial Rosai-Dorfman disease: a single-institute experience and pooled analysis of individual patient data.

Primary intracranial Rosai-Dorfman disease (PIRDD) is considered a nonmalignant nonneoplastic entity, and the outcome is unclear due to its rarity. The study aimed to elaborate the clinic-radiological features, treatment strategies, and progression-free survival (PFS) in patients with PIRDD. Patients with pathologically confirmed PIRDD in our institute were reviewed. Literature of PIRDD, updated until December 2019, was systematically searched in 7 databases (Embase, PubMed, Cochrane database, Web of Science, Wanfang Data Knowledge Service Platform, the VIP Chinese Science and Technology Periodical Database (VIP), and the China National Knowledge Infrastructure (CNKI)). These prior publication data were processed and used according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Clinical-radiological characteristics and adverse factors for PFS were evaluated in the pooled cohort. The pooled cohort of 124 cases (81 male and 43 female), with a mean age of 39.7 years, included 11 cases from our cohort and 113 cases from 80 prior studies. Twenty-nine patients (23.4%) had multiple lesions. Seventy-four patients (59.7%) experienced gross total resection (GTR), 50 patients (40.3%) had non-GTR, 15 patients (12.1%) received postoperative adjuvant radiation, and 23 patients (18.5%) received postoperative steroids. A multivariate Cox regression revealed that GTR (HR = 4.52; 95% CI 1.21-16.86; p = 0.025) significantly improved PFS, and multiple lesions (p = 0.060) tended to increase the hazard of recurrence. Neither radiation (p = 0.258) nor steroids (p = 0.386) were associated with PFS. The overall PFS at 3, 5, and 10 years in the pooled cohort was 88.4%, 79.4%, and 70.6%, respectively. The PFS at 5 and 10 years in patients with GTR was 85.4% and 85.4%, respectively, which was 71.5% and 35.8%, respectively, in patients without GTR. Gross total resection significantly improved PFS and was recommended for PIRDD. Radiation and steroids were sometimes empirically administered for residual, multiple, or recurrent PIRDD, but the effectiveness remained arguable and required further investigation.Systematic review registration number: CRD42020151294.

Improving DNA-Binding Protein Prediction Using Three-Part Sequence-Order Feature Extraction and a Deep Neural Network Algorithm.

Identification of the DNA-binding protein (DBP) helps dig out information embedded in the DNA-protein interaction, which is significant to understanding the mechanisms of DNA replication, transcription, and repair. Although existing computational methods for predicting the DBPs based on protein sequences have obtained great success, there is still room for improvement since the sequence-order information is not fully mined in these methods. In this study, a new three-part sequence-order feature extraction (called TPSO) strategy is developed to extract more discriminative information from protein sequences for predicting the DBPs. For each query protein, TPSO first divides its primary sequence features into N- and C-terminal fragments and then extracts the numerical pseudo features of three parts including the full sequence and these two fragments, respectively. Based on TPSO, a novel deep learning-based method, called TPSO-DBP, is proposed, which employs the sequence-based single-view features, the bidirectional long short-term memory (BiLSTM) and fully connected (FC) neural networks to learn the DBP prediction model. Empirical outcomes reveal that TPSO-DBP can achieve an accuracy of 87.01%, covering 85.30% of all DBPs, while achieving a Matthew's correlation coefficient value (0.741) that is significantly higher than most existing state-of-the-art DBP prediction methods. Detailed data analyses have indicated that the advantages of TPSO-DBP lie in the utilization of TPSO, which helps extract more concealed prominent patterns, and the deep neural network framework composed of BiLSTM and FC that learns the nonlinear relationships between input features and DBPs. The standalone package and web server of TPSO-DBP are freely available at https://jun-csbio.github.io/TPSO-DBP/.

Constructing and Validating a Nomogram for Survival in Patients without Hypertension in Hypertensive Intracerebral Hemorrhage-Related Locations.

OBJECTIVE: We aimed to evaluate the risk factors for patients, who had hypertensive intracerebral hemorrhage (ICH)-specific location hemorrhage without hypertensive history, to elucidate a novel and detailed understanding. METHODS: We conducted a retrospective review to identify patients diagnosed with hemorrhage in hypertensive ICH-specific locations without hypertensive history between January 2011 and December 2019 from West China Hospital. A least absolute shrinkage and selector operation (LASSO) algorithm was used to select the optimal prognostic factors, and then we performed a multivariable logistic analysis. To verify the accuracy of the nomogram in predicting patient outcome, we used Harrell's statistics, area under the curve, and a calibration as well as decision curves. RESULTS: The LASSO method, at a tenfold cross-validation for 7-day mortality, 90-day mortality, and 90-day morbidity, was applied to construct the prognosis-predicting models. Both a higher Glasgow Coma Scale (GCS) score at admission and larger hematoma volume ≥13.64 mL were independently associated with better survival at 7 days and 90 days in multivariate analysis. Lactic dehydrogenase >250 IU/L and neutrophilic granulocyte/lymphocyte ratio in 1 increase were significantly associated with poor outcome at 90 days. Only one factor (GCS score at 7 days) influencing 90-day morbidity remained in a LASSO model. CONCLUSIONS: In this study, the GCS score, hematoma volume, and other laboratory factors (Lactic dehydrogenase and neutrophilic granulocyte/lymphocyte ratio) were related to survival. Our current findings of the specific location ICH need to be proven by a large randomized controlled trial study.

DomBpred: Protein Domain Boundary Prediction Based on Domain-Residue Clustering Using Inter-Residue Distance.

Domain boundary prediction is one of the most important problems in the study of protein structure and function, especially for large proteins. At present, most domain boundary prediction methods have low accuracy and limitations in dealing with multi-domain proteins. In this study, we develop a sequence-based protein domain boundary prediction, named DomBpred. In DomBpred, the input sequence is first classified as either a single-domain protein or a multi-domain protein through a designed effective sequence metric based on a constructed single-domain sequence library. For the multi-domain protein, a domain-residue clustering algorithm inspired by Ising model is proposed to cluster the spatially close residues according inter-residue distance. The unclassified residues and the residues at the edge of the cluster are then tuned by the secondary structure to form potential cut points. Finally, a domain boundary scoring function is proposed to recursively evaluate the potential cut points to generate the domain boundary. DomBpred is tested on a large-scale test set of FUpred comprising 2549 proteins. Experimental results show that DomBpred better performs than the state-of-the-art methods in classifying whether protein sequences are composed by single or multiple domains, and the Matthew's correlation coefficient is 0.882. Moreover, on 849 multi-domain proteins, the domain boundary distance and normalised domain overlap scores of DomBpred are 0.523 and 0.824, respectively, which are 5.0% and 4.2% higher than those of the best comparison method, respectively. Comparison with other methods on the given test set shows that DomBpred outperforms most state-of-the-art sequence-based methods and even achieves better results than the top-level template-based method. The executable program is freely available at https://github.com/iobio-zjut/DomBpred and the online server at http://zhanglab-bioinf.com/DomBpred/.

Survival of patients and risk factors for subependymoma: a population-based study.

OBJECTIVE: Given the paucity of data on the subependymoma, we aimed to evaluate its risk factors from the Surveillance, Epidemiology, and End Results (SEER) database. METHODS: We collected survival and clinical information on patients with subependymoma diagnosed between 1975 and 2016 from the SEER database and screened them according to inclusion and exclusion criteria. Then, univariate and multivariate Cox regression analyses were used to identify significant prognostic factors, and nomograms were constructed to visualize the results. The concordance index (C-index), receiver operating characteristic (ROC), and calibration curves were used to assess the predictive ability of the nomogram. We divided the patient scores into two groups according to the high- and low-risk groups and constructed a survival curve using Kaplan-Meier analysis. RESULTS: A total of 731 patients were initially enrolled, including 511 (69.9%) males and 220 (30.1%) females. After screening, a total of 581 patientswere further evaluated by statistical analysis. The 5- and 10-year survival estimates were 92.0% and 81.9%, respectively. Sex (male, p=0.018; HR=2.3547, 95% CI=1.158-4.788) and age (≥56 years, p<0.001; HR=5.640, 95% CI= 3.139-10.133) were identified as independent prognostic factors for overall survival. The nomogram contained 4 prognostic factors. The C-index was 0.741, and the ROC and calibration curves also indicated the good predictability of the nomogram. CONCLUSION: In this large cohort, a significant association was noted between age/sex and outcome, which could serve an important role for patient education. Even though a significant association was not found between the extent of resection and outcome, the effect of surgery on prognosis should be further explored.Abbreviations: AUC: area under the curve; CI: confidence interval; C-index: concordance index; CNS: central nervous system; GTR: gross total resection; HR: hazard ratio; NOS: not specific; OS: overall survival; PTR: partial resection; ROC: receiver operating characteristic; SEER: Surveillance, Epidemiology, and End Results; STR: subtotal resection; WHO: World Health Organization.

Structural analogue-based protein structure domain assembly assisted by deep learning.

MOTIVATION: With the breakthrough of AlphaFold2, the protein structure prediction problem has made remarkable progress through deep learning end-to-end techniques, in which correct folds could be built for nearly all single-domain proteins. However, the full-chain modelling appears to be lower on average accuracy than that for the constituent domains and requires higher demand on computing hardware, indicating the performance of full-chain modelling still needs to be improved. In this study, we investigate whether the predicted accuracy of the full-chain model can be further improved by domain assembly assisted by deep learning. RESULTS: In this article, we developed a structural analogue-based protein structure domain assembly method assisted by deep learning, named SADA. In SADA, a multi-domain protein structure database was constructed for the full-chain analogue detection using individual domain models. Starting from the initial model constructed from the analogue, the domain assembly simulation was performed to generate the full-chain model through a two-stage differential evolution algorithm guided by the energy function with an inter-residue distance potential predicted by deep learning. SADA was compared with the state-of-the-art domain assembly methods on 356 benchmark proteins, and the average TM-score of SADA models is 8.1% and 27.0% higher than that of DEMO and AIDA, respectively. We also assembled 293 human multi-domain proteins, where the average TM-score of the full-chain model after the assembly by SADA is 1.1% higher than that of the model by AlphaFold2. To conclude, we find that the domains often interact in the similar way in the quaternary orientations if the domains have similar tertiary structures. Furthermore, homologous templates and structural analogues are complementary for multi-domain protein full-chain modelling. AVAILABILITY AND IMPLEMENTATION: http://zhanglab-bioinf.com/SADA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting RNA solvent accessibility from multi-scale context feature via multi-shot neural network.

Knowledge of RNA solvent accessibility has recently become attractive due to the increasing awareness of its importance for key biological process. Accurately predicting the solvent accessibility of RNA is crucial for understanding its 3D structure and biological function. In this study, we develop a novel computational method, termed M2pred, for accurately predicting the solvent accessibility of RNA from sequence-based multi-scale context feature. In M2pred, three single-view features, i.e., base-pairing probabilities, position-specific frequency matrix, and a binary one-hot encoding, are first generated as three feature sources, and immediately concatenated to engender a super feature. Secondly, for the super feature, the matrix-format features of each nucleotide are extracted using an initialized sliding window technique, and regularly stacked into a cube-format feature. Then, using multi-scale context feature extraction strategy, a pyramid feature constructed of contextual feature of four scales related to target nucleotides is extracted from the cube-format feature. Finally, a customized multi-shot neural network framework, which is equipped with four different scales of receptive fields mainly integrating several residual attention blocks, is designed to dig discrimination information from the contextual pyramid feature. Experimental results demonstrate that the proposed M2pred achieve a high prediction performance and outperforms existing state-of-the-art prediction methods of RNA solvent accessibility.

Intravenous infusion of the exosomes derived from human umbilical cord mesenchymal stem cells enhance neurological recovery after traumatic brain injury via suppressing the NF-κB pathway.

Traumatic brain injury (TBI) is a predominant cause of death and permanent disability globally. In recent years, much emphasis has been laid on treatments for TBI. Increasing evidence suggests that human umbilical cord mesenchymal stem cells (HUCMSCs) can improve neurological repair after TBI. However, the clinical use of HUCMSCs transplantation in TBI has been limited by immunological rejection, ethical issues, and the risk of tumorigenicity. Many studies have shown that HUCMSCs-derived exosomes may be an alternative approach for HUCMSCs transplantation. We hypothesized that exosomes derived from HUCMSCs could inhibit apoptosis after TBI, reduce neuroinflammation, and promote neurogenesis. A rat model of TBI was established to investigate the efficiency of neurological recovery with exosome therapy. We found that exosomes derived from HUCMSCs significantly ameliorated sensorimotor function and spatial learning in rats after TBI. Moreover, HUCMSCs-derived exosomes significantly reduced proinflammatory cytokine expression by suppressing the NF-κB signaling pathway. Furthermore, we found that HUCMSC-derived exosomes inhibited neuronal apoptosis, reduced inflammation, and promoted neuron regeneration in the injured cortex of rats after TBI. These results indicate that HUCMSCs-derived exosomes may be a promising therapeutic strategy for TBI.

Clinical factors and outcomes of malignant meningioma: a population-based study.

OBJECTIVE: We aimed to investigate prognostic factors and outcomes of malignant meningioma and to construct a nomogram model of survival. METHODS: Patients with malignant meningioma were collected from the Surveillance, Epidemiology, and End Results database. The nomogram was developed for the 3-, 5-, and 8-year prediction of overall survival (OS) and cancer-specific survival (CSS). Harrell's concordance index (C-index) and decision curve analysis (DCA) were used to verify the predicted effect of the nomogram. RESULTS: Between 1998 and 2016, 806 adult patients with histologically confirmed malignant meningioma were included. The mean age at diagnosis was 61.0 years (median 61.0 years), with a range of 19-104 years. Univariate analysis revealed that male gender, distant metastasis, and age ≥ 80 years as significant adverse factors for OS and CSS. These factors remained significance in the multivariate analysis. The nomogram demonstrated satisfactory discrimination, with a C-index value of 0.663 for OS and 0.654 for CSS, respectively. For both OS and CSS, the DCA curves indicated that the nomogram model performed better than other clinical variables. CONCLUSION: Older age, male gender, distant metastasis, and radiotherapy were significantly related to poor prognosis; and extent of resection did not affect survival.

DeepUMQA: ultrafast shape recognition-based protein model quality assessment using deep learning.

MOTIVATION: Protein model quality assessment is a key component of protein structure prediction. In recent research, the voxelization feature was used to characterize the local structural information of residues, but it may be insufficient for describing residue-level topological information. Design features that can further reflect residue-level topology when combined with deep learning methods are therefore crucial to improve the performance of model quality assessment. RESULTS: We developed a deep-learning method, DeepUMQA, based on Ultrafast Shape Recognition (USR) for the residue-level single-model quality assessment. In the framework of the deep residual neural network, the residue-level USR feature was introduced to describe the topological relationship between the residue and overall structure by calculating the first moment of a set of residue distance sets and then combined with 1D, 2D and voxelization features to assess the quality of the model. Experimental results on the CASP13, CASP14 test datasets and CAMEO blind test show that USR could supplement the voxelization features to comprehensively characterize residue structure information and significantly improve model assessment accuracy. The performance of DeepUMQA ranks among the top during the state-of-the-art single-model quality assessment methods, including ProQ2, ProQ3, ProQ3D, Ornate, VoroMQA, ProteinGCN, ResNetQA, QDeep, GraphQA, ModFOLD6, ModFOLD7, ModFOLD8, QMEAN3, QMEANDisCo3 and DeepAccNet. AVAILABILITY AND IMPLEMENTATION: The DeepUMQA server is freely available at http://zhanglab-bioinf.com/DeepUMQA/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate flexible refinement for atomic-level protein structure using cryo-EM density maps and deep learning.

With the rapid progress of deep learning in cryo-electron microscopy and protein structure prediction, improving the accuracy of the protein structure model by using a density map and predicted contact/distance map through deep learning has become an urgent need for robust methods. Thus, designing an effective protein structure optimization strategy based on the density map and predicted contact/distance map is critical to improving the accuracy of structure refinement. In this article, a protein structure optimization method based on the density map and predicted contact/distance map by deep-learning technology was proposed in accordance with the result of matching between the density map and the initial model. Physics- and knowledge-based energy functions, integrated with Cryo-EM density map data and deep-learning data, were used to optimize the protein structure in the simulation. The dynamic confidence score was introduced to the iterative process for choosing whether it is a density map or a contact/distance map to dominate the movement in the simulation to improve the accuracy of refinement. The protocol was tested on a large set of 224 non-homologous membrane proteins and generated 214 structural models with correct folds, where 4.5% of structural models were generated from structural models with incorrect folds. Compared with other state-of-the-art methods, the major advantage of the proposed methods lies in the skills for using density map and contact/distance map in the simulation, as well as the new energy function in the re-assembly simulations. Overall, the results demonstrated that this strategy is a valuable approach and ready to use for atomic-level structure refinement using cryo-EM density map and predicted contact/distance map.

ATPdock: a template-based method for ATP-specific protein-ligand docking.

MOTIVATION: Accurately identifying protein-ATP binding poses is significantly valuable for both basic structure biology and drug discovery. Although many docking methods have been designed, most of them require a user-defined binding site and are difficult to achieve a high-quality protein-ATP docking result. It is critical to develop a protein-ATP-specific blind docking method without user-defined binding sites. RESULTS: Here, we present ATPdock, a template-based method for docking ATP into protein. For each query protein, if no pocket site is given, ATPdock first identifies its most potential pocket using ATPbind, an ATP-binding site predictor; then, the template pocket, which is most similar to the given or identified pocket, is searched from the database of pocket-ligand structures using APoc, a pocket structural alignment tool; thirdly, the rough docking pose of ATP (rdATP) is generated using LS-align, a ligand structural alignment tool, to align the initial ATP pose to the template ligand corresponding to template pocket; finally, the Metropolis Monte Carlo simulation is used to fine-tune the rdATP under the guidance of AutoDock Vina energy function. Benchmark tests show that ATPdock significantly outperforms other state-of-the-art methods in docking accuracy. AVAILABILITY AND IMPLEMENTATION: https://jun-csbio.github.io/atpdock/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.