Mitophagy bridges DNA sensing with metabolic adaption to expand lung cancer stem-like cells.

While previous studies have identified cancer stem-like cells (CSCs) as a crucial driver for chemoresistance and tumor recurrence, the underlying mechanisms for populating the CSC pool remain unclear. Here, we identify hypermitophagy as a feature of human lung CSCs, promoting metabolic adaption via the Notch1-AMPK axis to drive CSC expansion. Specifically, mitophagy is highly active in CSCs, resulting in increased mitochondrial DNA (mtDNA) content in the lysosome. Lysosomal mtDNA acts as an endogenous ligand for Toll-like receptor 9 (TLR9) that promotes Notch1 activity. Notch1 interacts with AMPK to drive lysosomal AMPK activation by inducing metabolic stress and LKB1 phosphorylation. This TLR9-Notch1-AMPK axis supports mitochondrial metabolism to fuel CSC expansion. In patient-derived xenograft chimeras, targeting mitophagy and TLR9-dependent Notch1-AMPK pathway restricts tumor growth and CSC expansion. Taken together, mitochondrial hemostasis is interlinked with innate immune sensing and Notch1-AMPK activity to increase the CSC pool of human lung cancer.

Krüppel-like factor 12 regulates aging ovarian granulosa cell apoptosis by repressing SPHK1 transcription and sphingosine-1-phosphate (S1P) production.

Oxidative stress triggered by aging, radiation, or inflammation impairs ovarian function by inducing granulosa cell (GC) apoptosis. However, the mechanism inducing GC apoptosis has not been characterized. Here, we found that ovarian GCs from aging patients showed increased oxidative stress, enhanced reactive oxygen species activity, and significantly decreased expression of the known antiapoptotic factor sphingosine-1-phosphate/sphingosine kinase 1 (SPHK1) in GCs. Interestingly, the expression of Krüppel-like factor 12 (KLF12) was significantly increased in the ovarian GCs of aging patients. Furthermore, we determined that KLF12 was significantly upregulated in hydrogen peroxide-treated GCs and a 3-nitropropionic acid-induced in vivo model of ovarian oxidative stress. This phenotype was further confirmed to result from inhibition of SPHK1 by KLF12. Interestingly, when endogenous KLF12 was knocked down, it rescued oxidative stress-induced apoptosis. Meanwhile, supplementation with SPHK1 partially reversed oxidative stress-induced apoptosis. However, this function was lost in SPHK1 with deletion of the binding region to the KLF12 promoter. SPHK1 reversed apoptosis caused by hydrogen peroxide-KLF12 overexpression, a result further confirmed in an in vitro ovarian culture model and an in vivo 3-nitropropionic acid-induced ovarian oxidative stress model. Overall, our study reveals that KLF12 is involved in regulating apoptosis induced by oxidative stress in aging ovarian GCs and that sphingosine-1-phosphate/SPHK1 can rescue GC apoptosis by interacting with KLF12 in negative feedback.

Neutrino mass and mixing with modular symmetry.

This is a review article about neutrino mass and mixing and flavour model building strategies based on modular symmetry. After a brief survey of neutrino mass and lepton mixing, and various Majorana seesaw mechanisms, we construct and parameterise the lepton mixing matrix and summarise the latest global fits, before discussing the flavour problem of the Standard Model. We then introduce some simple patterns of lepton mixing, introduce family (or flavour) symmetries, and show how they may be applied to direct, semi-direct and tri-direct CP models, where the simple patterns of lepton mixing, or corrected versions of them, may be enforced by the full family symmetry or a part of it, leading to mixing sum rules. We then turn to the main subject of this review, namely a pedagogical introduction to modular symmetry as a candidate for family symmetry, from the bottom-up point of view. After an informal introduction to modular symmetry, we introduce the modular group, and discuss its fixed points and residual symmetry, assuming supersymmetry throughout. We then introduce finite modular groups of levelNand modular forms with integer or rational modular weights, corresponding to simple geometric groups or their double or metaplectic covers, including the most general finite modular groups and vector-valued modular forms, with detailed results forN=2,3,4,5. The interplay between modular symmetry and generalized CP symmetry is discussed, deriving CP transformations on matter multiplets and modular forms, highlighting the CP fixed points and their implications. In general, compactification of extra dimensions generally leads to a number of moduli, and modular invariance with factorizable and non-factorizable multiple moduli based on symplectic modular invariance and automorphic forms is reviewed. Modular strategies for understanding fermion mass hierarchies are discussed, including the weighton mechanism, small deviations from fixed points, and texture zeroes. Then examples of modular models are discussed based on single modulusA4models, a minimalS4'model of leptons (and quarks), and a multiple moduli model based on threeS4groups capable of reproducing the Littlest Seesaw model. We then extend the discussion to include Grand Unified Theories based on modular (flipped)SU(5) andSO(10). Finally we briefly mention some issues related to top-down approaches based on string theory, including eclectic flavour symmetry and moduli stabilisation, before concluding.

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.

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/.

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.

Gonadal white adipose tissue is important for gametogenesis in mice through maintenance of local metabolic and immune niches.

Gonadal white adipose tissue (gWAT) can regulate gametogenesis via modulation of neuroendocrine signaling. However, the effect of gWAT on the local microenvironment of the gonad was largely unknown. Herein, we ruled out that gWAT had a neuroendocrine effect on gonad function through a unilateral lipectomy strategy, in which cutting off epididymal white adipose tissue could reduce seminiferous tubule thickness and decrease sperm counts only in the adjacent testis and epididymis of the affected gonad. Consistent with the results in males, in females, ovary mass was similarly decreased by lipectomy. We determined that the defects in spermatogenesis were mainly caused by augmented apoptosis and decreased proliferation of germ cells. Transcriptome analysis suggested that lipectomy could disrupt immune privilege and activate immune responses in both the testis and ovary on the side of the lipectomy. In addition, lipidomics analysis in the testis showed that the levels of lipid metabolites such as free carnitine were elevated, whereas the levels of glycerophospholipids such as phosphatidylcholines and phosphatidylethanolamines were decreased, which indicated that the metabolic niche was also altered. Finally, we show that supplementation of phosphatidylcholine and phosphatidylethanolamine could partially rescue the observed phenotype. Collectively, our findings suggest that gWAT is important for gonad function by not only affecting whole-body homeostasis but also via maintaining local metabolic and immune niches.

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.

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.

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.

Depalmitoylation rewires FLT3-ITD signaling and exacerbates leukemia progression.

Internal tandem duplication within FLT3 (FLT3-ITD) is one of the most frequent mutations in acute myeloid leukemia (AML) and correlates with a poor prognosis. Whereas the FLT3 receptor tyrosine kinase is activated at the plasma membrane to transduce PI3K/AKT and RAS/MAPK signaling, FLT3-ITD resides in the endoplasmic reticulum and triggers constitutive STAT5 phosphorylation. Mechanisms underlying this aberrant FLT3-ITD subcellular localization or its impact on leukemogenesis remain poorly established. In this study, we discovered that FLT3-ITD is S-palmitoylated by the palmitoyl acyltransferase ZDHHC6. Disruption of palmitoylation redirected FLT3-ITD to the plasma membrane and rewired its downstream signaling by activating AKT and extracellular signal-regulated kinase pathways in addition to STAT5. Consequently, abrogation of palmitoylation increased FLT3-ITD-mediated progression of leukemia in xenotransplant-recipient mouse models. We further demonstrate that FLT3 proteins were palmitoylated in primary human AML cells. ZDHHC6-mediated palmitoylation restrained FLT3-ITD surface expression, signaling, and colonogenic growth of primary FLT3-ITD+ AML. More important, pharmacological inhibition of FLT3-ITD depalmitoylation synergized with the US Food and Drug Administration-approved FLT3 kinase inhibitor gilteritinib in abrogating the growth of primary FLT3-ITD+ AML cells. These findings provide novel insights into lipid-dependent compartmentalization of FLT3-ITD signaling in AML and suggest targeting depalmitoylation as a new therapeutic strategy to treat FLT3-ITD+ leukemias.

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.

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.

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.

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/.

Whole fresh fruit intake and risk of incident diabetes in different glycemic stages: a nationwide prospective cohort investigation.

PURPOSE: Fruit intake is beneficial to several chronic diseases, but controversial in diabetes. We aimed to investigate prospectively the associations of whole fresh fruit intake with risk of incident type 2 diabetes (T2D) in subjects with different glucose regulation capacities. METHODS: The present study included 79,922 non-diabetic participants aged ≥ 40 years from an ongoing nationwide prospective cohort in China. Baseline fruit intake information was collected by a validated food frequency questionnaire. Plasma HbA1c, fasting and 2 h post-loading glucose levels were measured at both baseline and follow-up examinations. Cox proportional hazards models were used to calculate hazard ratio (HR) and 95% confidence intervals (CI) for incident diabetes among participants with normal glucose tolerance (NGT) and prediabetes, after adjusted for multiple confounders. Restricted cubic spline analysis was applied for dose-response relation. RESULTS: During a median 3.8-year follow-up, 5886 (7.36%) participants developed diabetes. Overall, we identified a linear and dose-dependent inverse association between dietary whole fresh fruit intake and risk of incident T2D. Each 100 g/d higher fruit intake was associated with 2.8% lower risk of diabetes (HR 0.972, 95%CI [0.949-0.996], P = 0.0217), majorly benefiting NGT subjects with 15.2% lower risk (HR 0.848, 95%CI [0.766-0.940], P = 0.0017), while not significant in prediabetes (HR 0.981, 95%CI 0.957-4.005, P = 0.1268). Similarly, the inverse association was present in normoglycemia individuals with a 48.6% lower risk of diabetes when consuming fruits > 7 times/week comparing to those < 1 time/week (HR 0.514, 95% CI [0.368-0.948]), but not in prediabetes (HR 0.883, 95% CI [0.762-1.023]). CONCLUSION: These findings suggest that higher frequency and amount of fresh fruit intake may protect against incident T2D, especially in NGT, but not in prediabetes, highlighting the dietary recommendation of higher fresh fruit consumption to prevent T2D in normoglycemia population.

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.

Genome-wide identification and expression pattern analysis of novel chemosensory genes in the German cockroach Blattella germanica.

The German cockroach Blattella germanica is an important urban insect pest worldwide. In many insects, chemosensation is essential for guiding their behaviors for survival. Although a large number of chemosensory-related genes have been identified in B. germanica, little information on tissue-specific and developmental expression patterns has not been uncovered yet. In this study, we performed transcriptome analysis of different B. germanica tissues to reveal novel chemosensory proteins (CSPs) and sensory neuron membrane proteins (SNMPs). In addition, a phylogenetic tree and gender-specific expression of multiple chemosensory gene families have been analyzed. We identified three CSPs genes (BgerCSP11, BgerCSP12, and BgerCSP13) and five SNMP genes in B. germanica. Tissue-specific expression profiling showed that CSP1, 8, and 9 exhibited significant expression levels in both adult and 5th instar nymph antennae. The results have paved the way for further functional study of the chemosensory mechanism in B. germanica and provided potential insecticide targets.

A sequential niche multimodal conformational sampling algorithm for protein structure prediction.

MOTIVATION: Massive local minima on the protein energy landscape often cause traditional conformational sampling algorithms to be easily trapped in local basin regions, because they find it difficult to overcome high-energy barriers. Also, the lowest energy conformation may not correspond to the native structure due to the inaccuracy of energy models. This study investigates whether these two problems can be alleviated by a sequential niche technique without loss of accuracy. RESULTS: A sequential niche multimodal conformational sampling algorithm for protein structure prediction (SNfold) is proposed in this study. In SNfold, a derating function is designed based on the knowledge learned from the previous sampling and used to construct a series of sampling-guided energy functions. These functions then help the sampling algorithm overcome high-energy barriers and avoid the re-sampling of the explored regions. In inaccurate protein energy models, the high-energy conformation that may correspond to the native structure can be sampled with successively updated sampling-guided energy functions. The proposed SNfold is tested on 300 benchmark proteins, 24 CASP13 and 19 CASP14 FM targets. Results show that SNfold correctly folds (TM-score ≥ 0.5) 231 out of 300 proteins. In particular, compared with Rosetta restrained by distance (Rosetta-dist), SNfold achieves higher average TM-score and improves the sampling efficiency by more than 100 times. On several CASP FM targets, SNfold also shows good performance compared with four state-of-the-art servers in CASP. As a plug-in conformational sampling algorithm, SNfold can be extended to other protein structure prediction methods. AVAILABILITY AND IMPLEMENTATION: The source code and executable versions are freely available at https://github.com/iobio-zjut/SNfold. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A de novo protein structure prediction by iterative partition sampling, topology adjustment and residue-level distance deviation optimization.

MOTIVATION: With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achieved. Designing an effective closed-loop continuous dihedral angle optimization strategy that complements the discrete fragment assembly is crucial to improve the performance of the distance-assisted fragment assembly method. RESULTS: In this article, we proposed a de novo protein structure prediction method called IPTDFold based on closed-loop iterative partition sampling, topology adjustment and residue-level distance deviation optimization. First, local dihedral angle crossover and mutation operators are designed to explore the conformational space extensively and achieve information exchange between the conformations in the population. Then, the dihedral angle rotation model of loop region with partial inter-residue distance constraints is constructed, and the rotation angle satisfying the constraints is obtained by differential evolution algorithm, so as to adjust the spatial position relationship between the secondary structures. Finally, the residue distance deviation is evaluated according to the difference between the conformation and the predicted distance, and the dihedral angle of the residue is optimized with biased probability. The final model is generated by iterating the above three steps. IPTDFold is tested on 462 benchmark proteins, 24 FM targets of CASP13 and 20 FM targets of CASP14. Results show that IPTDFold is significantly superior to the distance-assisted fragment assembly method Rosetta_D (Rosetta with distance). In particular, the prediction accuracy of IPTDFold does not decrease as the length of the protein increases. When using the same FastRelax protocol, the prediction accuracy of IPTDFold is significantly superior to that of trRosetta without orientation constraints, and is equivalent to that of the full version of trRosetta. AVAILABILITYAND IMPLEMENTATION: The source code and executable are freely available at https://github.com/iobio-zjut/IPTDFold. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.