The Small Interactor of PKD2 protein promotes the assembly and ciliary entry of the Chlamydomonas PKD2-mastigoneme complexes.

In Chlamydomonas, the channel polycystin 2 (PKD2) is primarily present in the distal region of cilia, where it is attached to the axoneme and mastigonemes, extracellular polymers of MST1. In a smaller proximal ciliary region that lacks mastigonemes, PKD2 is more mobile. We show that the PKD2 regions are established early during ciliogenesis and increase proportionally in length as cilia elongate. In chimeric zygotes, tagged PKD2 rapidly entered the proximal region of PKD2-deficient cilia, whereas the assembly of the distal region was hindered, suggesting that axonemal binding of PKD2 requires de novo assembly of cilia. We identified the protein Small Interactor of PKD2 (SIP), a PKD2-related, single-pass transmembrane protein, as part of the PKD2-mastigoneme complex. In sip mutants, stability and proteolytic processing of PKD2 in the cell body were reduced and PKD2-mastigoneme complexes were absent from the cilia. Like the pkd2 and mst1 mutants, sip mutant cells swam with reduced velocity. Cilia of the pkd2 mutant beat with an increased frequency but were less efficient in moving the cells, suggesting a structural role for the PKD2-SIP-mastigoneme complex in increasing the effective surface of Chlamydomonas cilia.

Distinct functional patterns in child and adolescent bipolar and unipolar depression during emotional processing.

Accumulating evidence from functional magnetic resonance imaging studies supported brain dysfunction during emotional processing in bipolar disorder (BD) and major depressive disorder (MDD). However, child and adolescent BD and MDD could display different activation patterns, which have not been fully understood. This study aimed to investigate common and distinct activation patterns of pediatric BD (PBD) and MDD (p-MDD) during emotion processing using meta-analytic approaches. Literature search identified 25 studies, contrasting 252 PBD patients, and 253 healthy controls (HCs) as well as 311 p-MDD patients and 263 HCs. A total of nine meta-analyses were conducted pulling PBD and p-MDD experiments together and separately. The results revealed that PBD and p-MDD showed distinct patterns during negative processing. PBD patients exhibited activity changes in bilateral precuneus, left inferior parietal gyrus, left angular gyrus, and right posterior cingulate cortex while p-MDD patients showed functional disruptions in the left rectus, left triangular part of the inferior frontal gyrus, left orbital frontal cortex, left insula, and left putamen. In conclusion, the activity changes in PBD patients were mainly in regions correlated with emotion perception while the dysfunction among p-MDD patients was in the fronto-limbic circuit and reward-related regions in charge of emotion appraisal and regulation.

Porcine Enteric Alphacoronavirus Entry through Multiple Pathways (Caveolae, Clathrin, and Macropinocytosis) Requires Rab GTPases for Endosomal Transport.

Porcine enteric alphacoronavirus (PEAV) is a new bat HKU2-like porcine coronavirus, and its endemic outbreak has caused severe economic losses to the pig industry. Its broad cellular tropism suggests a potential risk of cross-species transmission. A limited understanding of PEAV entry mechanisms may hinder a rapid response to potential outbreaks. This study analyzed PEAV entry events using chemical inhibitors, RNA interference, and dominant-negative mutants. PEAV entry into Vero cells depended on three endocytic pathways: caveolae, clathrin, and macropinocytosis. Endocytosis requires dynamin, cholesterol, and a low pH. Rab5, Rab7, and Rab9 GTPases (but not Rab11) regulate PEAV endocytosis. PEAV particles colocalize with EEA1, Rab5, Rab7, Rab9, and Lamp-1, suggesting that PEAV translocates into early endosomes after internalization, and Rab5, Rab7, and Rab9 regulate trafficking to lysosomes before viral genome release. PEAV enters porcine intestinal cells (IPI-2I) through the same endocytic pathway, suggesting that PEAV may enter various cells through multiple endocytic pathways. This study provides new insights into the PEAV life cycle. IMPORTANCE Emerging and reemerging coronaviruses cause severe human and animal epidemics worldwide. PEAV is the first bat-like coronavirus to cause infection in domestic animals. However, the PEAV entry mechanism into host cells remains unknown. This study demonstrates that PEAV enters into Vero or IPI-2I cells through caveola/clathrin-mediated endocytosis and macropinocytosis, which does not require a specific receptor. Subsequently, Rab5, Rab7, and Rab9 regulate PEAV trafficking from early endosomes to lysosomes, which is pH dependent. The results advance our understanding of the disease and help to develop potential new drug targets against PEAV.

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/.

Molecular characterization, expression analysis and function identification of Pf_IL-12p35, Pf_IL-23p19 and Pf_IL-12p40 genes in yellow catfish (Pelteobagrus fulvidraco).

The interleukin-12 (IL-12) family is a class of heterodimeric cytokines that play crucial roles in pro-inflammatory and pro-stimulatory responses. Although some IL-12 and IL-23 paralogues have been found in fish, their functional activity in fish remains poorly understood. In this study, Pf_IL-12p35a/b, Pf_IL-23p19 and Pf_IL-12p40a/b/c genes were cloned from yellow catfish (Pelteobagrus fulvidraco), four α-helices were found in Pf_IL-12p35a/b and Pf_IL-23p19. The transcripts of these six genes were relatively high in mucus and immune tissues of healthy individuals, and in gill leukocytes. Following Edwardsiella ictaluri infection, Pf_IL-12p35a/b and Pf_IL-23p19 mRNAs were induced in brain and kidney (or head kidney), Pf_IL-12p40a mRNA was induced in gill, and Pf_IL-12p40b/c mRNAs were induced in brain and liver (or skin). The mRNA expression of these genes in PBLs was induced by phytohaemagglutinin (PHA) and polyinosinic-polycytidylic acid (poly I:C), while lipopolysaccharides (LPS) induced the mRNA expression of Pf_IL-12p35a and Pf_IL-12p40b/c in PBLs. After stimulation with recombinant (r) Pf_IL-12 and rPf_IL-23 subunit proteins, either alone or in combination, mRNA expression patterns of genes related to T helper cell development exhibited distinct differences. The results suggest that Pf_IL-12 and Pf_IL-23 subunits may play important roles in regulating immune responses to pathogens and T helper cell development.

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.

Pharmacologically significant constituents collectively responsible for anti-sepsis action of XueBiJing, a Chinese herb-based intravenous formulation.

Sepsis, a life-threatening health issue, lacks effective medicine targeting the septic response. In China, treatment combining the intravenous herbal medicine XueBiJing with conventional procedures reduces the 28-day mortality of critically ill patients by modulating septic response. In this study, we identified the combined active constituents that are responsible for the XueBiJing's anti-sepsis action. Sepsis was induced in rats by cecal ligation and puncture (CLP). The compounds were identified based on their systemic exposure levels and anti-sepsis activities in CLP rats that were given an intravenous bolus dose of XueBiJing. Furthermore, the identified compounds in combination were assessed, by comparing with XueBiJing, for levels of primary therapeutic outcome, pharmacokinetic equivalence, and pharmacokinetic compatibility. We showed that a total of 12 XueBiJing compounds, unchanged or metabolized, circulated with significant systemic exposure in CLP rats that received XueBiJing. Among these compounds, hydroxysafflor yellow A, paeoniflorin, oxypaeoniflorin, albiflorin, senkyunolide I, and tanshinol displayed significant anti-sepsis activities, which involved regulating immune responses, inhibiting excessive inflammation, modulating hemostasis, and improving organ function. A combination of the six compounds, with the same respective doses as in XueBiJing, displayed percentage survival and systemic exposure in CLP rats similar to those by XueBiJing. Both the combination and XueBiJing showed high degrees of pharmacokinetic compatibility regarding interactions among the six active compounds and influences of other circulating XueBiJing compounds. The identification of XueBiJing's pharmacologically significant constituents supports the medicine's anti-sepsis use and provides insights into a polypharmacology-based approach to develop medicines for effective sepsis management.

Solibacillus ferritrahens sp. nov., a novel siderophore-producing bacterium isolated from Wumeng Mountain National Nature Reserve in Yunnan Province.

A gram-stain-positive, aerobic, rod-shaped bacterial strain capable of producing siderophores, named YIM B08730T, was isolated from a soil sample collected from Wumeng Mountain National Nature Reserve, Zhaotong City, Yunnan Province. Growth occurred at 10-45 °C (optimum, 35-40 â„ƒ), pH 7.0-9.0 (optimum, 7.0) and in the presence of 0-5 % (w/v) NaCl (optimum, 0-1 %, w/v). A comparative analysis of the 16S rRNA gene sequence (1558 bp) of strain YIM B08730T showed the highest similarity to Solibacillus isronensis JCM 13838T (96.2 %), followed by Solibacillus silvestris DSM 12223T (96.0 %) and Solibacillus kalamii ISSFR-015T (95.4 %). The main polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, phosphatidylserine and one unidentified lipid. The main respiratory quinone of strain YIM B08730T was menaquinone 7 (MK-7). The major fatty acids were iso-C15:0 and C16:1ω7c alcohol. The digital DNA-DNA hybridization and average nucleotide identity values between strain YIM B08730T and the reference strain S. isronensis JCM 13838T were 24.8 % and 81.2 %, respectively. The G + C content of the genomic DNA was 37.1 mol%. The genome of the novel strain contained genes associated with the production of siderophores, and it also revealed other functional gene clusters involved in plant growth promotion and soil bioremediation. Based on these phenotypic, chemotaxonomic and phylogenetic analyses, strain YIM B08730T is considered to be a novel species of the genus Solibacillus, for which the name Solibacillus ferritrahens sp. nov. is proposed. The type strain is YIM B08730T (= NBRC 116268T = CGMCC 1.60169T).

Association of serum uric acid with hypertriglyceridemia in children and adolescents: a cross-sectional study.

BACKGROUND: Uric acid (UA), a liver-derived metabolite, is intimately tied to metabolic disorders. Although ample research underscores its connection with hypertriglyceridemia (HTG), studies focusing on adolescents remain limited. To fill the gaps in epidemiology,this study focused on analyzing the relationship between the levels of uric acid and HTG in a demographic sample comprising adolescents from the United States. METHODS: In this study, a total of 4,435 participants through the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2020. The exposure variable was serum uric acid (SUA), the effect variable was HTG, and the covariates included demographic, questionnaire, physical examination and laboratory indicators. We utilized weighted logistic regression and meticulous subgroup evaluations to discern the intrinsic link between SUA and HTG. Stratified analyses augmented the validation of this association, while smooth curve fitting probed for potential nonlinear correlations. RESULTS: The study included 4,435 participants. Male adolescents exhibit elevated SUA levels. After adjusting for all variables, the weighted multiple logistic regression model revealed that SUA was positively correlated with HTG risk (OR = 1.006, 95% CI: 1.005-1.007). This relationship was consistent across the three tertiles group of SUA (T1: OR = 1.006 [95% CI: 1.005-1.007]; T2: OR = 1.006 [95% CI: 1.005-1.007]; T3: OR = 1.004 [95% CI: 1.003-1.006]; P for trend < 0.001). Stratified analyses confirmed that the positive correlation between SUA and HTG risk was significant, irrespective of sex, age or race. CONCLUSIONS: In American children and adolescents aged 12 to 18 years, there was a pronounced association between SUA and HTG. SUA could serve as a risk indicator for HTG. It is recommended that children diagnosed with HTG should be regularly tested for SUA levels. In addition, it is recommended that SUA be included in the comprehensive care of children diagnosed with HTG.

Teadenol B as a Component of Microorganism-Fermented Tea Extract Inhibited Breast Cancers by Promoting Autophagy.

Breast cancer is a significant threat to life and health, which needs more safe and effective drugs to be explored. Teadenol B is a characteristic chemical component of microbial fermented tea. This study discovered that teadenol B could exhibit obvious inhibitory effects on all four different clinical subtype characteristics of breast cancer cells. Proteomic studies show that deoxycytidine triphosphate deaminase (DCTD), which could block DNA synthesis and repair DNA damage, had the most significant and consistent reduction in all four types of breast cancer cells with the treatment of teadenol B. Considering MDA-MB-231 cells exhibit poor clinical prognosis and displayed substantial statistical differences in KEGG pathway enrichment analysis results, we investigated its impact on the size and growth of MDA-MB-231 triple-negative breast tumors transplanted into nude mice and demonstrated that teadenol B significantly suppressed tumor growth without affecting body weight significantly. Finally, we found that the conversion of LC3-I to LC3-II in MDA-MB-231 increased significantly with teadenol B treatment. This proved that teadenol B could be a strong autophagy promotor, which explained the down-regulation of DCTD to some extent and may be the potential mechanism underlying teadenol B's anti-breast cancer effects. This finding provides new evidence for drinking fermented tea to prevent breast cancer and highlights the potential of teadenol B as a novel therapeutic option for breast cancer prevention and treatment, necessitating further investigations to clarify its exact target and the details involved.

Impact of perioperative enhanced recovery after surgery on wound infection in patients undergoing orthopaedic surgery: A meta-analysis.

The aim of this study was to evaluate the effects of perioperative application of enhanced recovery after surgery (ERAS) concepts on wound infections and post-operative complications in patients receiving orthopaedic surgery, to provide a theoretical basis for post-operative care. Randomised controlled trials (RCTs) on the application of ERAS to patients receiving orthopaedic surgery, published up to October 2023, were identified in PubMed, Web of Science, Cochrane Library, Embase, Wanfang, China Biomedical Literature Database and China National Knowledge Infrastructure databases. Literature was screened and evaluated by two reviewers based on the inclusion and exclusion criteria, and data were extracted from the final included articles. Data were analysed using RevMan 5.4 software. A total of 20 RCTs were included in the analysis, which included 1875 patients undergoing orthopaedic surgery, of whom 938 and 937 were in the ERAS and control groups, respectively. The analysis revealed that in patients undergoing orthopaedic surgery, implementation of ERAS in the perioperative period was associated with a significantly reduced the rate of wound infections (1.6% vs. 6.19%, risk ratio [RR]: 0.30, 95% confidence interval [CI]: 0.18-0.50, p < 0.001) and complication (5.12% vs. 21.88%, RR: 0.23, 95% CI: 0.17-0.32, p < 0.001) and can effectively shorten the hospital length of stay (standardised mean difference [SMD]: -2.50 days, 95% CI: -3.17 to -1.83 days, p < 0.001) compared with that of conventional care. The available evidence suggests that the implementation of ERAS in the perioperative period of patients undergoing orthopaedic surgery could effectively reduce the rate of wound infections and complications, shorten the hospital length of stay and promote the early recovery of patients.

Effect of humanised care on the surgical site wound infection after caesarean: A meta-analysis.

Herein, a meta-analysis was conducted to systematically evaluate the effect of humanised care on maternal postoperative wound infections in patients who underwent caesarean section. A computerised search of Embase, Cochrane Library, PubMed, Web of Science, China National Knowledge Infrastructure, Chinese Biomedical Literature Database and Wanfang database was performed, supplemented by a manual search from database inception to September 2023, to collate randomised controlled trials (RCTs) regarding the application of humanised care during the perioperative period of caesarean section. Two researchers screened and selected studies identified according to inclusion and exclusion criteria, and the included literature was evaluated for quality, extracted information and required data. Data analysis was performed using RevMan 5.4 software. Twenty RCTs comprising 2408 patients were included. The results revealed the humanised care group had a lower incidence of postoperative wound infections (0.83% vs. 4.32%, odds ratio [OR]: 0.26, 95% confidence interval [CI]: 0.15-0.46, p < 0.00001) and fewer postoperative complications than the conventional care group (4.32% vs. 16.35%, OR: 0.23, 95% CI: 0.16-0.31, p < 0.00001), with lower anxiety scores (standardised mean difference [SMD]: -3.15, 95% CI: -3.90 to -2.40, p < 0.00001) and depression scores (SMD: -3.68, 95% CI: -4.49 to -2.88, p < 0.00001). The application of humanised care during the perioperative period of caesarean section can prevent postoperative wound infection, reduce postoperative complications and help alleviate maternal anxiety and depression, which is worthy of clinical promotion and application.

Evaluation of combined epiretinal membrane removal with intravitreal triamcinolone injection utilizing ectopic inner foveal layer staging scheme.

OBJECTIVE: To investigate the macular morphological and visual outcomes of combined idiopathic epiretinal membrane (iERM) removal with triamcinolone acetonide (TA) injection based on consideration of the ectopic inner foveal layer (EIFL) staging scheme. METHODS: Retrospective case-control study. The clinical data of 84 eyes of 84 patients who underwent vitrectomy for iERM between 2018 and 2022 were reviewed. The enrolled subjects were divided into the TA and non-TA groups. Fifty-one eyes received intravitreal TA injection following vitrectomy and ERM peeling (TA group), and 33 were only treated by standard vitrectomy and ERM peeling (non-TA group). Preoperative and postoperative EIFL stages, central foveal thickness (CFT), and best-corrected visual acuity (BCVA) were compared between both groups. RESULTS: After a mean follow-up of 7.69 ± 3.68 months, both groups exhibited significant improvement in EIFL stages (P < 0.01), with no discernible advantage observed in the TA group. The TA and non-TA groups demonstrated improvement in the EIFL stages in 56.86 and 63.64% of eyes, respectively (P = 0.43). The CFT and BCVA significantly improved in both groups at the final visit (P < 0.01). However, CFT in the non-TA group displayed a more significant reduction during the follow-up (P < 0.03). Subgroup analysis revealed no significant differences in postoperative CFT and BCVA between the two groups in cases with or without continuous EIFL (P > 0.10). CONCLUSION: Our findings indicate that combined intravitreal TA injection following ERM removal conferred no significant benefits in alleviating macular thickening or improving visual acuity in iERM.

[Identification and functional analysis of jasmonic acid signaling repressor McJAZ8 gene in Mentha canadensis].

Mentha canadensis, as a plant with medicinal and culinary uses, holds significant economic value. Jasmonic acid signaling repressor JAZ protein has a crucial role in regulating plant response to adversity stresses. The M. canadensis McJAZ8 gene is cloned and analyzed for protein characterization, protein interactions, and expression patterns, so as to provide genetic resources for molecular breeding of M. canadensis for stress tolerance. This experiment will analyze the protein structural characteristics, subcellular localization, protein interactions, and gene expression of McJAZ8 using bioinformatics, yeast two-hybrid(Y2H), transient expression in tobacco leaves, qRT-PCR, and other technologies. The results show that:(1)The full length of the McJAZ8 gene is 543 bp, encoding 180 amino acids. The McJAZ8 protein contains conserved TIFY and Jas domains and exhibits high homology with Arabidopsis thaliana AtJAZ1 and AtJAZ2.(2)The McJAZ8 protein is localized in the nucleus and cytoplasm.(3)The Y2H results show that McJAZ8 interacts with itself or McJAZ1/3/4/5 proteins to form homologous or heterologous dimers.(4)McJAZ8 is expressed in different tissue, with the highest expression level in young leaves. In terms of leaf sequence, McJAZ8 shows the highest expression level in the fourth leaf and the lowest expression level in the second leaf.(5) In leaves and roots, the expression of McJAZ8 is upregulated to varying degrees under methyl jasmonate(MeJA), drought, and NaCl treatments. The expression of McJAZ8 shows an initial upregulation followed by a downregulation pattern under CdCl_2 treatment. In leaves, the expression of McJAZ8 tends to gradually decrease under CuCl_2 treatment, while in roots, it initially decreases and then increases before decreasing again. In both leaves and roots, the expression of McJAZ8 is downregulated to varying degrees under AlCl_(3 )treatment. This study has enriched the research on jasmonic acid signaling repressor JAZ genes in M. canadensis and provided genetic resources for the molecular breeding of M. canadensis.

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.

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.

A Novel Mechanism Underlying Inflammatory Smooth Muscle Phenotype in Abdominal Aortic Aneurysm.

[Figure: see text].