Search

Background: The Escherichia coli MutT (NudA) protein catalyzes the hydrolysis of an oxidized form of dGTP, 8-oxo-7,8-dihydro-dGTP (8-hydroxy-dGTP), and the spontaneous mutation frequency is elevated in E. coli cells deficient in the mutT gene. Results: A split MutT, comprising the N-terminal (residues 1–95) and C-terminal (residues 96–129) peptides, was designed based on the known tertiary structure and linker insertion mutagenesis experiments. The mutator phenotype was complemented when the two peptides were separately expressed in mutT E. coli cells. Conclusions: These results indicated that this split MutT functions as a nucleotide pool sanitization enzyme in vivo. [ABSTRACT FROM AUTHOR]

To date, there have been no reports on tertiary lymphoid structures (TLS) in primary adenocarcinoma of jejunum and ileum. In this study, we employed digital pathology image analysis software to classify and quantify TLS, and evaluated the maturity of TLS using immunohistochemistry. Molecular genetics and immunotherapy biomarker detection were performed using next-generation sequencing technology, such as tumor mutational burden (TMB) and microsatellite instability (MSI). The aim of this study was to investigate the presence, location, maturity, association with immunotherapy biomarkers, and prognostic value of TLS in primary adenocarcinoma of jejunum and ileum. Compared to secondary follicle-like TLS (SFL-TLS), intra-tumoral TLS (IT-TLS) were more likely to manifest as early TLS (E-TLS) (P = 0.007). Compared to IT-TLS, SFL-TLS had a higher propensity to occur at the invasive margin (IM) (P = 0.032) and showed a trend towards being more prevalent at the tumor periphery (P = 0.057). In terms of immunotherapy biomarkers, there was a higher trend of IM-TLS density in PD-L1(22C3) score CPS < 1 group compared to PD-L1(22C3) score CPS ≥ 1 group (P = 0.071). TMB-H was significantly associated with MSI-H (P = 0.040). Univariate survival analysis demonstrated a correlation between high SFL-TLS group and prolonged disease free survival (DFS) (P = 0.047). There was also a trend towards prolonged DFS in the E-TLS-high group compared to the E-TLS-low group (P = 0.069). The peri-tumoral TLS (PT-TLS)-high group showed a trend of prolonged overall survival (OS) compared to the PT-TLS-low group (P = 0.090). In conclusion, the majority of TLS were located at the invasive margin and tumor periphery, predominantly consisting of mature TLS, while IT-TLS were mainly immature. Notably, TMB was closely associated with MSI and PD-L1, indicating potential predictive value for immunotherapy in primary adenocarcinoma of jejunum and ileum. [ABSTRACT FROM AUTHOR]

Soft tissue sarcomas (STS) have long been a formidable challenge in oncology, partly because of their rarity and diversity, which complicates large-scale studies and slows the advent of new treatments. Traditionally anchored by anthracycline-based chemotherapy, the landscape of STS treatment hasn't shifted dramatically in the past twenty years. However, recent strides in research are starting to paint a more hopeful picture. Leveraging advanced molecular profiling, researchers are now tailoring treatments to the unique genetic makeup of tumors, with targeted therapies showing promise. Innovations such as NTRK inhibitors for NTRK-rearranged sarcomas and gamma-secretase inhibitors for desmoid tumors are changing clinical practices. The rise of immunotherapy, including novel agents like LAG-3 inhibitors and bifunctional proteins that target both TGF-β and PD-L1, offers new avenues for treatment, particularly when combined with traditional therapies like chemotherapy. Meanwhile, the approval of epigenetic treatments for specific sarcoma subtypes heralds a new wave of strategy based on histological specificity, which could lead to more personalized and effective care. While challenges remain, the field of STS treatment is evolving, driven by a deeper understanding of the disease's biological underpinnings and a commitment to innovative research approaches. [ABSTRACT FROM AUTHOR]

Background: We investigated the feasibility of the tertiary lymphoid structure (TLS) as a prognostic marker for penile squamous cell carcinoma(SCC). Methods: We retrospectively collected data from 83 patients with penile squamous cell carcinoma. H&E-stained slides were reviewed for TLS density. In addition, clinical parameters were analyzed, the prognostic value of these parameters on overall survival (OS) was evaluated using ‒ Kaplan–Meier survival curves, and the prognostic value of influencing factors was evaluated using Cox multifactor design nomogram analysis. Result: BMI, T, N, and M are significant in the survival curve with or without tertiary lymphoid structure. BMI, T, N, M and TLS were used to construct a prognostic model for penile squamous cell carcinoma, and the prediction accuracy reached a consensus of 0.884(0.835–0.932), and the decision consensus reached 0.581(0.508–0.655). Conclusion: TLS may be a positive prognostic factor for penile squamous cell carcinoma, and the combination of BMI, T, N and M can better evaluate the prognosis of patients. [ABSTRACT FROM AUTHOR]

Background: The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application. Methods: We established an integrative prognostic neural network for intrahepatic cholangiocarcinoma (iCCA), towards a comprehensive evaluation of both architectural and fine-grained information from whole-slide images. Then, leveraging on multi-modal data, we conducted extensive interrogative approaches to the models, to extract and visualize the morphological features that most correlated with clinical outcome and underlying molecular alterations. Results: The models were developed and optimized on 373 iCCA patients from our center and demonstrated consistent accuracy and robustness on both internal (n = 213) and external (n = 168) cohorts. The occlusion sensitivity map revealed that the distribution of tertiary lymphoid structures, the geometric traits of the invasive margin, the relative composition of tumor parenchyma and stroma, the extent of necrosis, the presence of the disseminated foci, and the tumor-adjacent micro-vessels were the determining architectural features that impacted on prognosis. Quantifiable morphological vector extracted by CellProfiler demonstrated that tumor nuclei from high-risk patients exhibited significant larger size, more distorted shape, with less prominent nuclear envelope and textural contrast. The multi-omics data (n = 187) further revealed key molecular alterations left morphological imprints that could be attended by the network, including glycolysis, hypoxia, apical junction, mTORC1 signaling, and immune infiltration. Conclusions: We proposed an interpretable deep-learning framework to gain insights into the biological behavior of iCCA. Most of the significant morphological prognosticators perceived by the network are comprehensible to human minds. [ABSTRACT FROM AUTHOR]

Background: Colon cancer (CC) is a malignancy associated with significant morbidity and mortality within the gastrointestinal tract. Recurrence and metastasis are the main factors affecting the prognosis of CC patients undergoing radical surgery; consequently, we attempted to determine the impact of immunity-related genes. Result: We constructed a CC risk model based on ZG16, MPC1, RBM47, SMOX, CPM and DNASE1L3. Consistently, we found that a significant association was found between the expression of most characteristic genes and tumor mutation burden (TMB), microsatellite instability (MSI) and neoantigen (NEO). Additionally, a notable decrease in RBM47 expression was observed in CC tissues compared with that in normal tissues. Moreover, RBM47 expression was correlated with clinicopathological characteristics and improved disease-free survival (DFS) and overall survival (OS) among patients with CC. Lastly, immunohistochemistry and co-immunofluorescence staining revealed a clear positive correlation between RBM47 and CXCL13 in mature tertiary lymphoid structures (TLS) region. Conclusion: We conclude that RBM47 was identified as a prognostic-related gene, which was of great significance to the prognosis evaluation of patients with CC and was correlated with CXCL13 in the TLS region. [ABSTRACT FROM AUTHOR]

Background: Oral squamous cell carcinoma (OSCC) is a malignant tumor with a poor prognosis. Traditional treatments have limited effectiveness. Regulation of the immune response represents a promising new approach for OSCC treatment. B cells are among the most abundant immune cells in OSCC. However, the role of B cells in OSCC treatment has not been fully elucidated. Methods: Single-cell RNA sequencing analysis of 13 tissues and 8 adjacent normal tissues from OSCC patients was performed to explore differences in B-cell gene expression between OSCC tissues and normal tissues. We further investigated the relationship between differentially expressed genes and the immune response to OSCC. We utilized tissue microarray data for 146 OSCC clinical samples and RNA sequencing data of 359 OSCC samples from The Cancer Genome Atlas (TCGA) to investigate the role of T-cell leukemia 1 A (TCL1A) in OSCC prognosis. Multiplex immunohistochemistry (mIHC) was employed to investigate the spatial distribution of TCL1A in OSCC tissues. We then investigated the effect of TCL1A on B-cell proliferation and trogocytosis. Finally, lentiviral transduction was performed to induce TCL1A overexpression in B lymphoblastoid cell lines (BLCLs) to verify the function of TCL1A. Results: Our findings revealed that TCL1A was predominantly expressed in B cells and was associated with a better prognosis in OSCC patients. Additionally, we found that TCL1A-expressing B cells are located at the periphery of lymphatic follicles and are associated with tertiary lymphoid structures (TLS) formation in OSCC. Mechanistically, upregulation of TCL1A promoted the trogocytosis of B cells on dendritic cells by mediating the upregulation of CR2, thereby improving antigen-presenting ability. Moreover, the upregulation of TCL1A expression promoted the proliferation of B cells. Conclusion: This study revealed the role of B-cell TCL1A expression in TLS formation and its effect on OSCC prognosis. These findings highlight TCL1A as a novel target for OSCC immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Due to antibiotic resistance, the Klebsiella genus is linked to morbidity and death, necessitating the development of a universally protective vaccine against Klebsiella pathogens. Methods: Core sequence analysis prioritized non-redundant host molecules and expected lipid bilayer peptides from fully sequenced Klebsiella genomes. These proteins were refined to identify epitopes, examining their immunogenicity, toxicity, solubility, and interaction with MHC alleles. Epitopes were linked to CPG ODN C274 via EAAAK, HEYGAEALERAG, and GGGS linkers to enhance immunological responses. The vaccine's tertiary structure was modelled and docked with MHC-I and MHC-II. Results: Fifty-five proteins were recognized in the Vaxign collection as having remarkable features. Twenty-three proteins with potential pathogenicity were then identified. Eight options for vaccines emerged after the immunogenicity of proteins was examined. The best antigens were three proteins: MrkD, Iron-regulated lipid membrane polypeptides, and RmpA. These compounds were selected for their sensitivity. The structural protein sequences of K. pneumoniae were utilized to identify seven CTL epitopes, seven HTL epitopes, and seven LBL epitopes, respectively. The produced immunization displayed a stable contact with the receptors, based on molecular dynamic simulations lasting 250 nanoseconds. Intermolecular binding free energies also indicated the dominance of the van der Waals and electrostatic energies. Conclusion: In summary, the results of this study might help scientists develop a novel vaccine to prevent K. pneumoniae infections. [ABSTRACT FROM AUTHOR]

Background: Primary cardiac angiosarcoma(PCA) has a low incidence rate and poor prognosis. Currently, no unified clinical treatment standards are available. Case presentation: We report the case of a 48-year-old man presenting chest tightness, breathlessness, and dyspnea. Imaging and postoperative histopathologic studies confirmed PCA and that the tumor had invaded the entire right atrium. The patient developed progressive disease (PD) during postoperative radiotherapy. We used immunotherapy combined with targeted therapy based on the results of molecular profile and evaluation of tertiary lymphoid structures (TLSs) and programmed cell death-ligand 1 (PD-L1). After treatment, the metastatic lymph nodes of the patient were reduced to a certain extent, indicating that combination therapy was effective. Conclusion: To the best of our knowledge, this is the first report of radiotherapy combined with anti-PD-1 and tyrosine kinase inhibitors(TKI) for PCA. In addition, this is the first report on immunotherapy for PCA based on new evaluation methods, including TLSs, PD-L1, and genomic profile. [ABSTRACT FROM AUTHOR]

Tertiary lymphoid structures (TLS) are clusters of immune cells that resemble and function similarly to secondary lymphoid organs (SLOs). While TLS is generally associated with an anti-tumour immune response in most cancer types, it has also been observed to act as a pro-tumour immune response. The heterogeneity of TLS function is largely determined by the composition of tumour-infiltrating lymphocytes (TILs) and the balance of cell subsets within the tumour-associated TLS (TA-TLS). TA-TLS of varying maturity, density, and location may have opposing effects on tumour immunity. Higher maturity and/or higher density TLS are often associated with favorable clinical outcomes and immunotherapeutic response, mainly due to crosstalk between different proportions of immune cell subpopulations in TA-TLS. Therefore, TLS can be used as a marker to predict the efficacy of immunotherapy in immune checkpoint blockade (ICB). Developing efficient imaging and induction methods to study TA-TLS is crucial for enhancing anti-tumour immunity. The integration of imaging techniques with biological materials, including nanoprobes and hydrogels, alongside artificial intelligence (AI), enables non-invasive in vivo visualization of TLS. In this review, we explore the dynamic interactions among T and B cell subpopulations of varying phenotypes that contribute to the structural and functional diversity of TLS, examining both existing and emerging techniques for TLS imaging and induction, focusing on cancer immunotherapies and biomaterials. We also highlight novel therapeutic approaches of TLS that are being explored with the aim of increasing ICB treatment efficacy and predicting prognosis. [ABSTRACT FROM AUTHOR]

Background: Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) recommended for the patients with subsolid nodule in early lung cancer stage is not routinely. The clinical value and impact in patients with EGFR mutation on survival outcomes is further needed to be elucidated to decide whether the application of EGFR-TKIs was appropriate in early lung adenocarcinoma (LUAD) stage appearing as subsolid nodules. Materials and methods: The inclusion of patients exhibiting clinical staging of IA-IIB subsolid nodules. Clinical information, computed tomography (CT) features before surgical resection and pathological characteristics including tertiary lymphoid structures of the tumors were recorded for further exploration of correlation with EGFR mutation and prognosis. Results: Finally, 325 patients were enrolled into this study, with an average age of 56.8 ± 9.8 years. There are 173 patients (53.2%) harboring EGFR mutation. Logistic regression model analysis showed that female (OR = 1.944, p = 0.015), mix ground glass nodule (OR = 2.071, p = 0.003, bubble-like lucency (OR = 1.991, p = 0.003) were significant risk factors of EGFR mutations. Additionally, EGFR mutations were negatively correlated with TLS presence and density. Prognosis analysis showed that the presence of TLS was associated with better recurrence-free survival (RFS)(p = 0.03) while EGFR mutations were associated with worse RFS(p = 0.01). The RFS in patients with TLS was considerably excel those without TLS within EGFR wild type group(p = 0.018). Multivariate analyses confirmed that EGFR mutation was an independent prognostic predictor for RFS (HR = 3.205, p = 0.037). Conclusions: In early-phase LUADs, subsolid nodules with EGFR mutation had specific clinical and radiological signatures. EGFR mutation was associated with worse survival outcomes and negatively correlated with TLS, which might weaken the positive impact of TLS on prognosis. Highly attention should be paid to the use of EGFR-TKI for further treatment as agents in early LUAD patients who carrying EGFR mutation. [ABSTRACT FROM AUTHOR]

Background & aims: Tertiary lymphoid structures (TLSs) are predictive biomarkers of favorable clinical outcomes and immunotherapy response in several solid malignancies, including non-small cell lung cancer (NSCLC). However, the relationship between TLSs and NSCLC prognosis has not been eludicated from the aspects of location, density, and maturity. This study aimed to investigate the clinicopathological and prognostic significance of TLSs in NSCLC. Methods: A collection of 151 resected pulmonary nodules in patients with NSCLC was retrospectively analyzed. Two experienced pathologists reviewed hematoxylin-eosin (H&E) slides and assessed TLS scores at different anatomic subregions. Then, we analyzed their correlation with clinicopathologic parameters and CD8 staining intensity and assessed multiple clinicopathological factors affecting patient prognosis. Results: CD8 expression was correlated with total (TLS-CT) (P = 0.000), aggregates (Agg) (TLS-CT) (P = 0.001), follicles (FOL)-I (TLS-CT) (P = 0.025), and TLS(overall) (P = 0.013). TLS scores in the central tumor (CT) and invasion margin (IM) areas were negatively correlated with distant metastasis and Union for International Cancer Control (UICC) stage in NSCLC patients, while TLS score in the CT area was positively correlated with CD8 expression. TLS (overall), Agg (TLS-CT), and FOL-I (TLS-CT) were positively correlated with distant metastasis, UICC stage, and CD8 expression in NSCLC patients. Agg (TLS-IM) was positively correlated with distant metastasis and UICC stage. FOL-I (TLS-IM) was positively correlated with UICC stage. FOL-II (TLS-IM) was positively correlated with distant metastasis (P < 0.05). Multivariate Cox regression analysis showed that unfavorable independent prognostic factors were associated with metastasis status and UICC stage. Independent prognostic factors with protective effects included Agg (TLS-CT), FOL-I (TLS-CT), total (TLS-CT), and overall TLS (P < 0.05). Conclusion: Histological score assessment of H&E sections of Agg (TLS-CT), FOL-I (TLS-CT), total (TLS-CT), and overall TLS levels in NSCLC has prognostic value. [ABSTRACT FROM AUTHOR]

Background: Among the polyhydroxyalkanoate (PHA), poly[(R)-3-hydroxybutyrate-co-(R)-3-hydroxyhexanoate] [P(3HB-co-3HHx)] is reported to closely resemble polypropylene and low-density polyethylene. Studies have shown that PHA synthase (PhaC) from mangrove soil (PhaCBP-M-CPF4) is an efficient PhaC for P(3HB-co-3HHx) production and N-termini of PhaCs influence its substrate specificity, dimerization, granule morphology, and molecular weights of PHA produced. This study aims to further improve PhaCBP-M-CPF4 through N-terminal truncation. Results: The N-terminal truncated mutants of PhaCBP-M-CPF4 were constructed based on the information of the predicted secondary and tertiary structures using PSIPRED server and AlphaFold2 program, respectively. The N-terminal truncated PhaCBP-M-CPF4 mutants were evaluated in C. necator mutant PHB−4 based on the cell dry weight, PHA content, 3HHx molar composition, molecular weights, and granule morphology of the PHA granules. The results showed that most transformants harbouring the N-terminal truncated PhaCBP-M-CPF4 showed a reduction in PHA content and cell dry weight except for PhaCBP-M-CPF4 G8. PhaCBP-M-CPF4 G8 and A27 showed an improved weight-average molecular weight (Mw) of PHA produced due to lower expression of the truncated PhaCBP-M-CPF4. Transformants harbouring PhaCBP-M-CPF4 G8, A27, and T74 showed a reduction in the number of granules. PhaCBP-M-CPF4 G8 produced higher Mw PHA in mostly single larger PHA granules with comparable production as the full-length PhaCBP-M-CPF4. Conclusion: This research showed that N-terminal truncation had effects on PHA accumulation, substrate specificity, Mw, and granule morphology. This study also showed that N-terminal truncation of the amino acids that did not adopt any secondary structure can be an alternative to improve PhaCs for the production of PHA with higher Mw in mostly single larger granules. [ABSTRACT FROM AUTHOR]

Background: Muscle-invasive bladder cancer (MIBC) is a highly aggressive disease with a poor prognosis. B cells are crucial factors in tumor suppression, and tertiary lymphoid structures (TLSs) facilitate immune cell recruitment to the tumor microenvironment (TME). However, the function and mechanisms of tumor-infiltrating B cells and TLSs in MIBC need to be explored further. Methods: We performed single-cell RNA sequencing analysis of 11,612 B cells and 55,392 T cells from 12 bladder cancer patients and found naïve B cells, proliferating B cells, plasma cells, interferon-stimulated B cells and germinal center-associated B cells, and described the phenotype, gene enrichment, cell–cell communication, biological processes. We utilized immunohistochemistry (IHC) and immunofluorescence (IF) to describe TLSs morphology in MIBC. Results: The interferon-stimulated B-cell subtype (B-ISG15) and germinal center-associated B-cell subtypes (B-LMO2, B-STMN1) were significantly enriched in MIBC. TLSs in MIBC exhibited a distinct follicular structure characterized by a central region of B cells resembling a germinal center surrounded by T cells. CellChat analysis showed that CXCL13 + T cells play a pivotal role in recruiting CXCR5 + B cells. Cell migration experiments demonstrated the chemoattraction of CXCL13 toward CXCR5 + B cells. Importantly, the infiltration of the interferon-stimulated B-cell subtype and the presence of TLSs correlated with a more favorable prognosis in MIBC. Conclusions: The study revealed the heterogeneity of B-cell subtypes in MIBC and suggests a pivotal role of TLSs in MIBC outcomes. Our study provides novel insights that contribute to the precision treatment of MIBC. Highlights: Single-cell RNA sequencing analysis identified 12 B-cell subtypes and characterized the transcriptomic heterogeneity of B cells in muscle-invasive bladder cancer (MIBC). The interferon-stimulated B-cell subtype (B-ISG15) and germinal center-associated B-cell subtypes (B-LMO2, B-STMN1) were significantly enriched in muscle-invasive bladder cancer. TLSs in MIBC are distinguished by the presence of a central region of CD19+ B cells forming a follicular structure characteristic of a germinal center and surrounded by T cells. Interferon-stimulated B-cell subtype and tertiary lymphoid structures are associated with favorable survival in MIBC patients. [ABSTRACT FROM AUTHOR]

The genomes of species belonging to the genus Colletotrichum harbor a substantial number of cytochrome P450 monooxygenases (CYPs) encoded by a broad diversity of gene families. However, the biological role of their CYP complement (CYPome) has not been elucidated. Here, we investigated the putative evolutionary scenarios that occurred during the evolution of the CYPome belonging to the Colletotrichum Graminicola species complex (s.c.) and their biological implications. The study revealed that most of the CYPome gene families belonging to the Graminicola s.c. experienced gene contractions. The reductive evolution resulted in species restricted CYPs are predominant in each CYPome of members from the Graminicola s.c., whereas only 18 families are absolutely conserved among these species. However, members of CYP families displayed a notably different phylogenetic relationship at the tertiary structure level, suggesting a putative convergent evolution scenario. Most of the CYP enzymes of the Graminicola s.c. share redundant functions in secondary metabolite biosynthesis and xenobiotic metabolism. Hence, this current work suggests that the presence of a broad CYPome in the genus Colletotrichum plays a critical role in the optimization of the colonization capability and virulence. [ABSTRACT FROM AUTHOR]

Background: Existing literature suggests that tertiary lymphatic structure (TLS) is associated with the progression of cancer. However, the prognostic role of TLS in digestive system cancers remains controversial. This meta-analysis aims to synthesize currently available evidence in the association between TLS and the survival of digestive system cancers. Methods: We systematically searched three digital databases (PubMed, Embase, Web of Science) for articles published from database inception to December 23, 2022. Study selection criteria are based on PECO framework: P (population: patients with digestive system cancers), E (exposure: presence of TLS), C (comparator: absence of TLS), O (outcome: overall survival, OS; recurrence-free survival, RFS; disease-free survival, DFS). The Quality in Prognostic Studies (QUIPS) tool was used to assess risk of bias for included studies. The study protocol was registered with PROSPERO (CRD42023416307). Results: A total of 25 studies with 6910 patients were included into the final meta-analysis. Random-effects models revealed that the absence of TLS was associated with compromised OS, RFS, and DFS of digestive system cancers, with pooled hazard ratios (HRs) of 1.74 (95% CI: 1.50–2.03), 1.96 (95% CI: 1.58–2.44), and 1.81 (95% CI: 1.49–2.19), respectively. Subgroup analyses disclosed a stronger TLS-survival association for pancreatic cancer, compared with other digestive system cancers. Conclusion: TLS may be of prognostic significance for digestive system cancers. More original studies are needed to further corroborate this finding. [ABSTRACT FROM AUTHOR]

Background: Aptamers, which are biomaterials comprised of single-stranded DNA/RNA that form tertiary structures, have significant potential as next-generation materials, particularly for drug discovery. The systematic evolution of ligands by exponential enrichment (SELEX) method is a critical in vitro technique employed to identify aptamers that bind specifically to target proteins. While advanced SELEX-based methods such as Cell- and HT-SELEX are available, they often encounter issues such as extended time consumption and suboptimal accuracy. Several In silico aptamer discovery methods have been proposed to address these challenges. These methods are specifically designed to predict aptamer-protein interaction (API) using benchmark datasets. However, these methods often fail to consider the physicochemical interactions between aptamers and proteins within tertiary structures. Results: In this study, we propose AptaTrans, a pipeline for predicting API using deep learning techniques. AptaTrans uses transformer-based encoders to handle aptamer and protein sequences at the monomer level. Furthermore, pretrained encoders are utilized for the structural representation. After validation with a benchmark dataset, AptaTrans has been integrated into a comprehensive toolset. This pipeline synergistically combines with Apta-MCTS, a generative algorithm for recommending aptamer candidates. Conclusion: The results show that AptaTrans outperforms existing models for predicting API, and the efficacy of the AptaTrans pipeline has been confirmed through various experimental tools. We expect AptaTrans will enhance the cost-effectiveness and efficiency of SELEX in drug discovery. The source code and benchmark dataset for AptaTrans are available at https://github.com/pnumlb/AptaTrans. [ABSTRACT FROM AUTHOR]

Background: The Gibberellic Acid-Stimulated Arabidopsis (GASA) gene family is widely involved in the regulation of plant growth, development, and stress response. However, information on the GASA gene family has not been reported in Chinese cabbage (Brassica rapa L. ssp. pekinensis). Results: Here, we conducted genome-wide identification and analysis of the GASA genes in Chinese cabbage. In total, 15 GASA genes were identified in the Chinese cabbage genome, and the physicochemical property, subcellular location, and tertiary structure of the corresponding GASA proteins were elucidated. Phylogenetic analysis, conserved motif, and gene structure showed that the GASA proteins were divided into three well-conserved subfamilies. Synteny analysis proposed that the expansion of the GASA genes was influenced mainly by whole-genome duplication (WGD) and transposed duplication (TRD) and that duplication gene pairs were under negative selection. Cis-acting elements of the GASA promoters were involved in plant development, hormonal and stress responses. Expression profile analysis showed that the GASA genes were widely expressed in different tissues of Chinese cabbage, but their expression patterns appeared to diverse. The qRT-PCR analysis of nine GASA genes confirmed that they responded to salt stress, heat stress, and hormonal triggers. Conclusions: Overall, this study provides a theoretical basis for further exploring the important role of the GASA gene family in the functional genome of Chinese cabbage. [ABSTRACT FROM AUTHOR]

Background: Enterovirus 71(EV71)-associated hand, foot and mouth disease (HFMD) decreased dramatically in Beijing from 2009 to 2019. This study was to investigate the epidemiological characteristics, evolutionary dynamics, geographic diffusion pathway, and other features of EV71 in Beijing, China. Methods: We conducted a retrospective study of EV71-associated HFMD and its causative agent in Beijing, China, from 2009 to 2019. Phylogenetic and phylogeographic methods based on the EV71 genome were used to determine the evolution features, origin, and spatiotemporal dynamics. Positive selection sites in the VP1 gene were identified and exhibited in the tertiary structure. Bayesian birth-death skyline model was used to estimate the effective reproductive number (Re). Results: EV71-associated HFMD decreased greatly in Beijing. From 2009 to 2019, EV71 strains prevalent in Beijing shared high homology in each gene segment and evolved with a rate of 4.99*10− 3 substitutions per site per year. The genetic diversity of EV71 first increased and peaked in 2012 and then decreased with fluctuations. The time to the most recent common ancestor (TMRCA) of EV71 in Beijing was estimated around 2003 when the EV71 strains were transmitted to Beijing from east China. Beijing played a crucial role in seeding EV71 to central China as well. Two residues (E145Q/G, A293S) under positive selection were detected from both the VP1 dataset and the P1 dataset. They were embedded within the loop of the VP1 capsid and were exposed externally. Mean Re estimate of EV71 in Beijing was about 1.007. Conclusion: In recent years, EV71 was not the primary causative agent of HFMD in Beijing. The low Re estimate of EV71 in Beijing implied that strategies for preventing and controlling HFMD were performed effectively. Beijing and east China played a crucial role in disseminating EV71 to other regions in China. [ABSTRACT FROM AUTHOR]

Background: The relationship between the sequence of a protein, its structure, and the resulting connection between its structure and function, is a foundational principle in biological science. Only recently has the computational prediction of protein structure based only on protein sequence been addressed effectively by AlphaFold, a neural network approach that can predict the majority of protein structures with X-ray crystallographic accuracy. A question that is now of acute relevance is the "inverse protein folding problem": predicting the sequence of a protein that folds into a specified structure. This will be of immense value in protein engineering and biotechnology, and will allow the design and expression of recombinant proteins that can, for instance, fold into specified structures as a scaffold for the attachment of recombinant antigens, or enzymes with modified or novel catalytic activities. Here we describe the development of SeqPredNN, a feed-forward neural network trained with X-ray crystallographic structures from the RCSB Protein Data Bank to predict the identity of amino acids in a protein structure using only the relative positions, orientations, and backbone dihedral angles of nearby residues. Results: We predict the sequence of a protein expected to fold into a specified structure and assess the accuracy of the prediction using both AlphaFold and RoseTTAFold to computationally generate the fold of the derived sequence. We show that the sequences predicted by SeqPredNN fold into a structure with a median TM-score of 0.638 when compared to the crystal structure according to AlphaFold predictions, yet these sequences are unique and only 28.4% identical to the sequence of the crystallized protein. Conclusions: We propose that SeqPredNN will be a valuable tool to generate proteins of defined structure for the design of novel biomaterials, pharmaceuticals, catalysts, and reporter systems. The low sequence identity of its predictions compared to the native sequence could prove useful for developing proteins with modified physical properties, such as water solubility and thermal stability. The speed and ease of use of SeqPredNN offers a significant advantage over physics-based protein design methods. [ABSTRACT FROM AUTHOR]

Background: Furofuran lignans, the main insecticidal ingredient in Phryma leptostachya, exhibit excellent controlling efficacy against a variety of pests. During the biosynthesis of furofuran lignans, Dirigent proteins (DIRs) are thought to be dominant in the stereoselective coupling of coniferyl alcohol to form (±)-pinoresinol. There are DIR family members in almost every vascular plant, but members of DIRs in P. leptostachya are unknown. To identify the PlDIR genes and elucidate their functions in lignan biosynthesis, this study performed transcriptome-wide analysis and characterized the catalytic activity of the PlDIR1 protein. Results: Fifteen full-length unique PlDIR genes were identified in P. leptostachya. A phylogenetic analysis of the PlDIRs classified them into four subfamilies (DIR-a, DIR-b/d, DIR-e, and DIR-g), and 12 conserved motifs were found among them. In tissue-specific expression analysis, except for PlDIR7, which displayed the highest transcript abundance in seeds, the other PlDIRs showed preferential expression in roots, leaves, and stems. Furthermore, the treatments with signaling molecules demonstrated that PlDIRs could be significantly induced by methyl jasmonate (MeJA), salicylic acid (SA), and ethylene (ETH), both in the roots and leaves of P. leptostachya. In examining the tertiary structure of the protein and the critical amino acids, it was found that PlDIR1, one of the DIR-a subfamily members, might be involved in the region- and stereo-selectivity of the phenoxy radical. Accordingly, LC–MS/MS analysis demonstrated the catalytic activity of recombinant PlDIR1 protein from Escherichia coli to direct coniferyl alcohol coupling into (+)-pinoresinol. The active sites and hydrogen bonds of the interaction between PlDIR1 and bis-quinone methide (bisQM), the intermediate in (+)-pinoresinol formation, were analyzed by molecular docking. As a result, 18 active sites and 4 hydrogen bonds (Asp-42, Ala-113, Leu-138, Arg-143) were discovered in the PlDIR1-bisQM complex. Moreover, correlation analysis indicated that the expression profile of PlDIR1 was closely connected with lignan accumulations after SA treatment. Conclusions: The results of this study will provide useful clues for uncovering P. leptostachya's lignan biosynthesis pathway as well as facilitate further studies on the DIR family. [ABSTRACT FROM AUTHOR]

Background: Porcine Teschovirus (PTV), also named Teschovirus A, is prevalent in pig populations, mainly causing neurological symptoms, diarrhea, pneumonia, and reproductive failure, however the morbidity and mortality are usually low in pig farms. Case presentation: In this study, we reported a PTV outbreak investigation in one large-scale pig farm in China with severe symptoms including diarrhea, lethargy, locomotor ataxia, nystagmus, paralysis of the hind limbs, and coma in piglets. More importantly, the mortality reached 38% in suckling pigs, which is remarkably high in PTV history. A novel PTV strain, named HeNZ1, was isolated from cerebral samples of one suckling pig and the genome sequence was obtained by NGS sequencing. Phylogenetic and evolutionary divergence analyses revealed that HeNZ1 belongs to PTV genotype 2. Surprisingly, the VP1 coding region of HeNZ1 shares the highest sequence similarity with European PTV-2 strains, instead of China domestic PTV-2 strains, implying it may not derive from China local PTV-2 strains. Multiple sequence alignment and B cell epitope prediction of PTV VP1 and VP2 protein revealed 10 B cell epitopes, 5 mutant clusters and 36 unique mutation sites, of which 19 unique mutation sites are located in B cell epitopes and exposed on the surface of VP1 or VP2, implying significant antigenic drift potential of HeNZ1. Conclusion: These results indicate that HeNZ1 is a highly virulent PTV-2 strain, which capable of causing severe neurological symptoms and high mortality in piglets. Bioinformatic analysis suggest that HeNZ1 is genetically and antigenically different from other Chinese PTV-2 strains. Overall, current case expanded our understanding of PTV-2 clinical spectrum and revealed the emergence of a highly virulent PTV-2 strain with substantial genetic diversity and antigenic drift potential in VP1 and VP2. [ABSTRACT FROM AUTHOR]

Background: Compound–protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound–protein interaction. For protein inputs, how to make use of protein primary sequence and tertiary structure information has impact on prediction results. Results: In this study, we propose a deep learning model based on a multi-objective neural network, which involves a multi-objective neural network for compound–protein interaction site and binding affinity prediction. We used several kinds of self-supervised protein embeddings to enrich our protein inputs and used convolutional neural networks to extract features from them. Our results demonstrate that our model had improvements in terms of interaction site prediction and affinity prediction compared to previous models. In a case study, our model could better predict binding sites, which also showed its effectiveness. Conclusion: These results suggest that our model could be a helpful tool for compound–protein related predictions. [ABSTRACT FROM AUTHOR]

Background: Glutathione S-transferase (GST) in eukaryotic organisms has multiple functions such as detoxifying endogenous/exogenous harmful substances to protect cells from oxidative damage, participating in sterol synthesis and metabolism, and regulating signaling pathways. Our previous work identified an important GST protein in Toxoplasma that contributes to vesicle trafficking called TgGST2, the deletion of which significantly reduces the virulence of the parasite. Meanwhile, we considered that TgGST2 may also play a role in other pathways of parasite life activities. Methods: The tertiary structures of TgGST2 as well as estradiol (E2) and progesterone (P4) were predicted by trRosetta and Autodock Vina software, the binding sites were analyzed by PyMol's GetBox Plugin, and the binding capacity was evaluated using Discovery Studio plots software. We examined the influence of E2 and P4 on TgGST2 via glutathione S-transferase enzyme activity and indirect immunofluorescence assay (IFA) and through the localization observation of TgGST2 to evaluate its response ability in different drugs. Results: TgGST2 could bind to exogenous E2 and P4, and that enzymatic activity was inhibited by the hormones in a concentration-dependent manner. Upon P4 treatment, the localization of TgGST2 changed from Golgi and vesicles to hollow circles, leading to abnormal localization of the molecular transporter Sortilin (VPS10) and microneme proteins (M2AP and MIC2), which ultimately affect the parasite life activities, but E2 had no significant effect. Moreover, diverse types of drugs had divergent effects on TgGST2, among which treatment with antifungal agents (voriconazole and clarithromycin), anticarcinogens (KU-60019, WYE-132 and SH5-07) and coccidiostats (dinitolmide and diclazuril) made the localization of TgGST2 appear in different forms, including dots, circles and rod shaped. Conclusions: Our study shows that TgGST2 plays a role in sterol treatment and can be affected by P4, which leads to deficient parasite motility. TgGST2 exerts divergent effects in response to the different properties of the drugs themselves. Its responsiveness to diverse drugs implies a viable target for the development of drugs directed against Toxoplasma and related pathogenic parasites. [ABSTRACT FROM AUTHOR]

Inspired by nature, green chemistry uses various biomolecules, such as proteins, as reducing agents to synthesize metallic nanostructures. This methodology provides an alternative route to conventional harsh synthetic processes, which include polluting chemicals. Tuning the resulting nanostructure properties, such as their size and shape, is challenging as the exact mechanism involved in their formation is still not well understood. This work reports a well-controlled method to program gold nanostructures' shape, size, and aggregation state using only one protein type, mucin, as a reduction and capping material in a one-pot bio-assisted reaction. Using mucin as a gold reduction template while varying its tertiary structure via the pH of the synthesis, we demonstrate that spherical, coral-shaped, and hexagonal gold crystals can be obtained and that the size can be tuned over three orders of magnitude. This is achieved by leveraging the protein's intrinsic reducing properties and pH-induced conformational changes. The systematic study of the reaction kinetics and growth steps developed here provides an understanding of the mechanism behind this phenomenon. We further show that the prepared gold nanostructures exhibit tunable photothermal properties that can be optimized for various hyperthermia-induced antibacterial applications. [ABSTRACT FROM AUTHOR]

Background: The clinical significance of tertiary lymphoid structures (TLSs) is not well-documented in early oral tongue squamous cell carcinoma (OTSCC).Methods: A total of 310 cases of early (cT1-2N0) OTSCC were included in this multicenter study. Assessment of TLSs was conducted on hematoxylin and eosin-stained sections. TLSs were assessed both in the central part of the tumor and at the invasive front area.Results: The presence of TLSs associated with improved survival of early OTSCC as presented by Kaplan-Meier survival analyses for disease-specific survival (P = 0.01) and overall survival (P = 0.006). In multivariable analyses, which included conventional prognostic factors, the absence of TLSs associated with worse disease-specific survival with a hazard ratio (HR) of 1.96 (95% CI 1.09-3.54; P = 0.025) and poor overall survival (HR 1.66, 95% CI 1.11-2.48; P = 0.014).Conclusion: Histological evaluation of TLSs predicts survival in early OTSCC. TLSs showed superior prognostic power independent of routine WHO grading and TNM staging system. [ABSTRACT FROM AUTHOR]

To explore the damage mechanisms of freeze-thaw cycles on cooked crayfish in frozen storage, changes in the physicochemical properties and structure of cooked crayfish during the freeze-thaw cycles were investigated. The physicochemical properties of cooked crayfish changed significantly after five freeze-thaw cycles. The moisture content, water holding capacity, pH, and textural properties were decreased, while the total color difference, drip loss, and protein and lipid oxidation were significantly increased (P < 0.05). LF-NMR and MRI verified the water loss, and SDS-PAGE showed denaturation/degradation of myofibrillar proteins (MPs). Multiple freeze-thaw cycles promoted the transition from α-helix to β-turn in the secondary structure, the unfolding of tertiary structure, and a significant change in the chemical forces of MPs. SEM results revealed a disruption in the microstructure of muscle fibers. Repeated freeze-thaw cycles reduced the moisture content and distorted the structure of MPs in cooked crayfish, resulting in the disruption of physicochemical properties and its structure. [ABSTRACT FROM AUTHOR]

Background: Sheep pulmonary adenocarcinoma (OPA) is a contagious lung cancer of sheep caused by the Jaagsiekte retrovirus (JSRV). OPA typically has a serious economic impact worldwide. A vaccine has yet to be developed, even though the disease has been globally spread, along with its complications. This study aimed to construct an effective multi-epitopes vaccine against JSRV eliciting B and T lymphocytes using immunoinformatics tools. Results: The designed vaccine was composed of 499 amino acids. Before the vaccine was computationally validated, all critical parameters were taken into consideration; including antigenicity, allergenicity, toxicity, and stability. The physiochemical properties of the vaccine displayed an isoelectric point of 9.88. According to the Instability Index (II), the vaccine was stable at 28.28. The vaccine scored 56.51 on the aliphatic index and -0.731 on the GRAVY, indicating that the vaccine was hydrophilic. The RaptorX server was used to predict the vaccine's tertiary structure, the GalaxyWEB server refined the structure, and the Ramachandran plot and the ProSA-web server validated the vaccine's tertiary structure. Protein-sol and the SOLPro servers showed the solubility of the vaccine. Moreover, the high mobile regions in the vaccine's structure were reduced and the vaccine's stability was improved by disulfide engineering. Also, the vaccine construct was docked with an ovine MHC-1 allele and showed efficient binding energy. Immune simulation remarkably showed high levels of immunoglobulins, T lymphocytes, and INF-γ secretions. The molecular dynamic simulation provided the stability of the constructed vaccine. Finally, the vaccine was back-transcribed into a DNA sequence and cloned into a pET-30a (+) vector to affirm the potency of translation and microbial expression. Conclusion: A novel multi-epitopes vaccine construct against JSRV, was formed from B and T lymphocytes epitopes, and was produced with potential protection. This study might help in controlling and eradicating OPA. [ABSTRACT FROM AUTHOR]

Background: Native-like secondary structures and biological activity have been described for proteins in inclusion bodies (IBs). Tertiary structure analysis, however, is hampered due to the necessity of mild solubilization conditions. Denaturing reagents used for IBs solubilization generally lead to the loss of these structures and to consequent reaggregation due to intermolecular interactions among exposed hydrophobic domains after removal of the solubilization reagent. The use of mild, non-denaturing solubilization processes that maintain existing structures could allow tertiary structure analysis and increase the efficiency of refolding. Results: In this study we use a variety of biophysical methods to analyze protein structure in human growth hormone IBs (hGH-IBs). hGH-IBs present native-like secondary and tertiary structures, as shown by far and near-UV CD analysis. hGH-IBs present similar λmax intrinsic Trp fluorescence to the native protein (334 nm), indicative of a native-like tertiary structure. Similar fluorescence behavior was also obtained for hGH solubilized from IBs and native hGH at pH 10.0 and 2.5 kbar and after decompression. hGH-IBs expressed in E. coli were extracted to high yield and purity (95%) and solubilized using non-denaturing conditions [2.4 kbar, 0.25 M arginine (pH 10), 10 mM DTT]. After decompression, the protein was incubated at pH 7.4 in the presence of the glutathione-oxidized glutathione (GSH-GSSG) pair which led to intramolecular disulfide bond formation and refolded hGH (81% yield). Conclusions: We have shown that hGH-IBs present native-like secondary and tertiary structures and that non-denaturing methods that aim to preserve them can lead to high yields of refolded protein. It is likely that the refolding process described can be extended to different proteins and may be particularly useful to reduce the pH required for alkaline solubilization. [ABSTRACT FROM AUTHOR]

Background: DIRs are mysterious protein that have the ability to scavenge free radicals, which, are highly reactive with molecules in their vicinity. What is even more fascinating is that they carry out from these highly unstable species, a selective reaction (i.e., stereoenantioselective) from a well-defined substrate to give a very precise product. Unfortunately, to date, only three products have been demonstrated following studies on DIRs from the plant world, which until now was the kingdom where these proteins had been demonstrated. Within this kingdom, each DIR protein has its own type of substrate. The products identified to date, have on the other hand, a strong economic impact: in agriculture for example, the biosynthesis of (+)-gossypol could be highlighted (a repellent antifood produced by the cotton plant) by the DIRs of cotton. In forsythia plant species, it is the biosynthesis of (−)-pinoresinol, an intermediate leading to the synthesis of podophyllotoxine (a powerful anicancerous agent) which has been revealed. Recently, a clear path of study, potentially with strong impact, appeared by the hypothesis of the potential existence of protein DIR within the genomes of prokaryotes. The possibility of working with this type of organism is an undeniable advantage: since many sequenced genomes are available and the molecular tools are already developed. Even easier to implement and working on microbes, of less complex composition, offers many opportunities for laboratory studies. On the other hand, the diversity of their environment (e.g., soil, aquatic environments, extreme environmental conditions (pH, temperature, pressure) make them very diverse and varied subjects of study. Identifying new DIR proteins from bacteria means identifying new substrate or product molecules from these organisms. It is the promise of going further in understanding the mechanism of action of these proteins and this will most likely have a strong impact in the fields of agricultural, pharmaceutical and/or food chemistry. Results: Our goal is to obtain as much information as possible about these proteins to unlock the secrets of their exceptional functioning. Analyzes of structural and functional genomic data led to the identification of the Pfam PF03018 domain as characteristic of DIR proteins. This domain has been further identified in the sequence of bacterial proteins therefore named as DIR-like (DIRL). We have chosen a multidisciplinary bioinformatic approach centered on bacterial genome identification, gene expression and regulation signals, protein structures, and their molecular information content. The objective of this study was to perform a thorough bioinformatic analysis on these DIRLs to highlight any information leading to the selection of candidate bacteria for further cloning, purification, and characterization of bacterial DIRs. Conclusions: From studies of DIRL genes identification, primary structures, predictions of their secondary and tertiary structures, prediction of DIRL signals sequences, analysis of their gene organization and potential regulation, a list of primary bacterial candidates is proposed. [ABSTRACT FROM AUTHOR]

Background: Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data. Results: To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods. Conclusion: The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites. [ABSTRACT FROM AUTHOR]

Sucrose synthase (SUS) is a common sugar-base transfer enzyme in plants, and sucrose phosphate synthase (SPS) is one of the major enzymes in higher plants that regulates sucrose synthesis. However, information of the SPS and SUS gene families in Actinidia, as well as their evolutionary and functional properties, is limited. According to the SPS and SUS proteins conserved domain of Arabidopsis thaliana, we found 6 SPS genes and 6 SUS genes from A. chinensis (cultivar: 'Hongyang'), and 3 SPS genes and 6 SUS genes from A. eriantha (cultivar: 'White'). The novel CDC50 conserved domains were discovered on AcSUS2, and all members of the gene family contain similar distinctive conserved domains. The majority of SUS and SPS proteins were hydrophilic, lipid-soluble enzymes that were expected to be found in the cytoplasm. The tertiary structure of SPS and SUS protein indicated that there were many tertiary structures in SPS, and there were windmill-type and spider-type tertiary structures in SUS. The phylogenetic tree was created using the neighbor-joining method, and members of the SPS and SUS gene families are grouped into three subgroups. Genes with comparable intron counts, conserved motifs, and phosphorylation sites were clustered together first. SPS and SUS were formed through replication among their own family members. AcSPS1, AcSPS2, AcSPS4, AcSPS5, AcSUS5, AcSUS6, AeSPS3, AeSUS3 and AeSUS4 were the important genes in regulating the synthesis and accumulation of sucrose for Actinidia during the fruit growth stages. [ABSTRACT FROM AUTHOR]

Background: Terpene synthases are versatile catalysts in all domains of life, catalyzing the formation of an enormous variety of different terpenoid secondary metabolites. Due to their diverse bioactive properties, terpenoids are of great interest as innovative ingredients in pharmaceutical and cosmetic applications. Recent advances in genome sequencing have led to the discovery of numerous terpene synthases, in particular in Basidiomycota like the wood rotting fungus Coniophora puteana, which further enhances the scope for the manufacture of terpenes for industrial purposes. Results: In this study we describe the identification of two novel (+)-δ-cadinol synthases from C. puteana, Copu5 and Copu9. The sesquiterpene (+)-δ-cadinol was previously shown to exhibit cytotoxic activity therefore having an application as possible, new, and sustainably sourced anti-tumor agent. In an Escherichia coli strain, optimized for sesquiterpene production, titers of 225 mg l−1 and 395 mg l−1, respectively, could be achieved. Remarkably, both enzymes share the same product profile thereby representing the first two terpene synthases from Basidiomycota with identical product profiles. We solved the crystal structure of Copu9 in its closed conformation, for the first time providing molecular details of sesquiterpene synthase from Basidiomycota. Based on the Copu9 structure, we conducted structure-based mutagenesis of amino acid residues lining the active site, thereby altering the product profile. Interestingly, the mutagenesis study also revealed that despite the conserved product profiles of Copu5 and Copu9 different conformational changes may accompany the catalytic cycle of the two enzymes. This observation suggests that the involvement of tertiary structure elements in the reaction mechanism(s) employed by terpene synthases may be more complex than commonly expected. Conclusion: The presented product selectivity and titers of Copu5 and Copu9 may pave the way towards a sustainable, biotechnological production of the potentially new bioactive (+)-δ-cadinol. Furthermore, Copu5 and Copu9 may serve as model systems for further mechanistic studies of terpenoid catalysis. [ABSTRACT FROM AUTHOR]

Background: The prediction of protein secondary structures is a crucial and significant step for ab initio tertiary structure prediction which delivers the information about proteins activity and functions. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. Currently, most of the top methods use evolutionary-based input features produced by PSSM and HHblits software, although quite recently the embeddings—the new description of protein sequences generated by language models (LM) have appeared that could be leveraged as input features. Apart from input features calculation, the top models usually need extensive computational resources for training and prediction and are barely possible to run on a regular PC. SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Moreover, as the benchmark datasets are not random samples, the classical statistical null hypothesis testing based on the Neyman–Pearson approach is not appropriate. Results: We present a lightweight deep network ProteinUnet2 for SS prediction which is based on U-Net convolutional architecture and evolutionary-based input features (from PSSM and HHblits) as well as SPOT-Contact features. Through an extensive evaluation study, we report the performance of ProteinUnet2 in comparison with top SS prediction methods based on evolutionary information (SAINT and SPOT-1D). We also propose a new statistical methodology for prediction performance assessment based on the significance from Fisher–Pitman permutation tests accompanied by practical significance measured by Cohen's effect size. Conclusions: Our results suggest that ProteinUnet2 architecture has much shorter training and inference times while maintaining results similar to SAINT and SPOT-1D predictors. Taking into account the relatively long times of calculating evolutionary-based features (from PSSM in particular), it would be worth conducting the predictive ability tests on embeddings as input features in the future. We strongly believe that our proposed here statistical methodology for the evaluation of SS prediction results will be adopted and used (and even expanded) by the research community. [ABSTRACT FROM AUTHOR]

Background: The Alphapapillomavirus 9 (α-9 HPV) is a member of the Alphapapillomavirus genus and Papillomaviridae family. These viruses are almost all carcinogenic HPV, which is closely related to 75% of invasive cervical cancer worldwide, and has a high prevalence in Sichuan. The carcinogenic function is mainly realized by its E6 oncoprotein. Methods: Cell samples were collected by cervical scraped for HPV detecting and typing. HPV-16, HPV-31, HPV-33, HPV-52, HPV-58 5 α-9 genus HPV subtype positive samples were selected, their E6 gene was sequenced and analyzed. The positive selection sites of HPV E6 genes were estimated by PAML 4.8 server. The secondary and tertiary structure of E6 protein were predicted by PSIPred and Swiss-model. The T-cell antigen epitopes of E6 protein were predicted by IEDB. Results: α-9 HPV has a high prevalence in Sichuan, China. From 2012 to 2017, 18,067 cell cervical samples were collected, and 3135 were detected with α-9 HPV infection. Among which, 250 cases HPV-16 E6, 96 cases HPV-31 E6, 216 cases HPV-33 E6, 288 cases HPV-52 E6 and 405 cases HPV-58 E6 were successfully amplified, 17, 6, 6, 13, and 4 non-synonymous nucleotide mutations were respectively detected in HPV-16, 31, 33, 52, and 58 E6, 7 positive selection sites of α-9 HPV E6 were selected out (D32E of HPV-16 E6, K35N, K93N and R145I of HPV-33 E6, K93R of HPV-52 E6, K93N and R145K of HPV-58 E6). The structure and antigen epitopes of E6 protein with amino acid substitution differ from those of wild-type E6 protein, especially for the mutation located in the E6 positive selection site. Conclusions: HPV E6 nucleotide non-synonymous mutation in the positive selection site influence the protein structure and decrease the antigen epitopes affinity of the E6 protein overall, making it more difficult for the HPV-infected cells to be detected by the immune system, and enhancing the HPV adaptability to the environment. Mutations influence the validity of HPV clinical diagnostic probes, the polymorphism analysis of α-9 HPV E6 enrich the data of HR-risk HPV in Sichuan China, and the detection probes designed with the polymorphism data in mind can improve the efficiency of clinical detection; Mutations influence epitopes affinity, the association of E6 polymorphism and epitope affinity can improve the design of therapeutic vaccine with good immunity and high generality antigen epitope; The above study all provide a good theoretical basis for the prevention and treatment of HPV-related diseases. [ABSTRACT FROM AUTHOR]

Background: Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, docking simulation is generally applied because it only requires a tertiary structure of the target protein. However, the conformation search and the evaluation of binding energy of docking simulation are computationally heavy and thus docking simulation needs huge computational resources. Thus, if we can apply a machine learning-based activity prediction method for a novel target protein, such methods would be highly useful. Recently, Tsubaki et al. proposed an end-to-end learning method to predict the activity of compounds for novel target proteins. However, the prediction accuracy of the method was still insufficient because it only used amino acid sequence information of a protein as the input. Results: In this research, we proposed an end-to-end learning-based compound activity prediction using structure information of a binding pocket of a target protein. The proposed method learns the important features by end-to-end learning using a graph neural network both for a compound structure and a protein binding pocket structure. As a result of the evaluation experiments, the proposed method has shown higher accuracy than an existing method using amino acid sequence information. Conclusions: The proposed method achieved equivalent accuracy to docking simulation using AutoDock Vina with much shorter computing time. This indicated that a machine learning-based approach would be promising even for novel target proteins in activity prediction. [ABSTRACT FROM AUTHOR]

Background: Tertiary lymphoid structures (TLSs), organizationally resemble lymph nodes, are frequently present in breast cancer (BCa). It is usually, but not always, associated with a positive prognosis or immunotherapy response in cancer patients. This meta-analysis was performed to assess the prognostic and clinical impact of TLSs in BCa. Methods: We conducted a systematic search in PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, and WanFang Database to obtain eligible research data up to May 30, 2021. This meta-analysis is focusing on the studies evaluated the prognostic value of TLSs and the associated clinicopathologic indicators, related gene expression and survival. STATA software 16.0 software was used to assess the prognostic significance and clinical impact of TLSs. Results: Nine studies involved with 2281 cases were incorporated in this meta-analysis, in which four of them evaluated the prognostic value of TLSs. There are 6 studies assessed the relationship of TLSs and 4 studies investigated the clinicopathologic parameters as well as the key gene expression, respectively. The results showed the presence of TLSs were predicting a better OS (HR = 0.61, 95% CI: 0.51–0.73, p < 0.001) and DFS (HR = 0.40, 95% CI: 0.17–0.93, p < 0.001) of BCa patients. It also revealed that the presence of TLSs was significantly correlated with tumor differentiation (p < 0.001), pTNM stage (p < 0.001), lymph node metastasis (p < 0.001), and TILs density (p < 0.001) of BCa, and the expression of Her2 (p < 0.001), ER (p < 0.001), PR (p < 0.001) and Ki67 (p = 0.009) of the tumor cell. Conclusion: Our results indicated that high levels of TLSs could predict a favorable prognosis for BCa. Moreover, the TLSs were significantly correlated with the clinicopathological indicators and the critical gene expression of BCa, indicating its potential clinical impact on BCa patients. [ABSTRACT FROM AUTHOR]

Background: The design of protein structures from scratch requires special attention to the combination of the types and lengths of the secondary structures and the loops required to build highly designable backbone structure models. However, it is difficult to predict the combinations that result in globular and protein-like conformations without simulations. In this study, we used single-chain three-helix bundles as simple models of protein tertiary structures and sought to thoroughly investigate the conditions required to construct them, starting from the identification of the typical αα-hairpin motifs. Results: First, by statistical analysis of naturally occurring protein structures, we identified three αα-hairpins motifs that were specifically related to the left- and right-handedness of helix-helix packing. Second, specifying these αα-hairpins motifs as junctions, we performed sequence-independent backbone-building simulations to comparatively build single-chain three-helix bundle structures and identified the promising combinations of the length of the α-helix and αα-hairpins types that results in tight packing between the first and third α-helices. Third, using those single-chain three-helix bundle backbone structures as template structures, we designed amino acid sequences that were predicted to fold into the target topologies, which supports that the compact single-chain three-helix bundles structures that we sampled show sufficient quality to allow amino-acid sequence design. Conclusion: The enumeration of the dominant subsets of possible backbone structures for small single-chain three-helical bundle topologies revealed that the compact foldable structures are discontinuously and sparsely distributed in the conformational space. Additionally, although the designs have not been experimentally validated in the present research, the comprehensive set of computational structural models generated also offers protein designers the opportunity to skip building similar structures by themselves and enables them to quickly focus on building specialized designs using the prebuilt structure models. The backbone and best design models in this study are publicly accessible from the following URL: https://doi.org/10.5281/zenodo.4321632. [ABSTRACT FROM AUTHOR]

Background: Accurate prediction of protein tertiary structures is highly desired as the knowledge of protein structures provides invaluable insights into protein functions. We have designed two approaches to protein structure prediction, including a template-based modeling approach (called ProALIGN) and an ab initio prediction approach (called ProFOLD). Briefly speaking, ProALIGN aligns a target protein with templates through exploiting the patterns of context-specific alignment motifs and then builds the final structure with reference to the homologous templates. In contrast, ProFOLD uses an end-to-end neural network to estimate inter-residue distances of target proteins and builds structures that satisfy these distance constraints. These two approaches emphasize different characteristics of target proteins: ProALIGN exploits structure information of homologous templates of target proteins while ProFOLD exploits the co-evolutionary information carried by homologous protein sequences. Recent progress has shown that the combination of template-based modeling and ab initio approaches is promising. Results: In the study, we present FALCON2, a web server that integrates ProALIGN and ProFOLD to provide high-quality protein structure prediction service. For a target protein, FALCON2 executes ProALIGN and ProFOLD simultaneously to predict possible structures and selects the most likely one as the final prediction result. We evaluated FALCON2 on widely-used benchmarks, including 104 CASP13 (the 13th Critical Assessment of protein Structure Prediction) targets and 91 CASP14 targets. In-depth examination suggests that when high-quality templates are available, ProALIGN is superior to ProFOLD and in other cases, ProFOLD shows better performance. By integrating these two approaches with different emphasis, FALCON2 server outperforms the two individual approaches and also achieves state-of-the-art performance compared with existing approaches. Conclusions: By integrating template-based modeling and ab initio approaches, FALCON2 provides an easy-to-use and high-quality protein structure prediction service for the community and we expect it to enable insights into a deep understanding of protein functions. [ABSTRACT FROM AUTHOR]

Background: RNA regulates a variety of biological functions by interacting with other molecules. The ligand often binds in the RNA pocket to trigger structural changes or functions. Thus, it is essential to explore and visualize the RNA pocket to elucidate the structural and recognition mechanism for the RNA-ligand complex formation. Results: In this work, we developed one user-friendly bioinformatics tool, RPocket. This database provides geometrical size, centroid, shape, secondary structure element for RNA pocket, RNA-ligand interaction information, and functional sites. We extracted 240 RNA pockets from 94 non-redundant RNA-ligand complex structures. We developed RPDescriptor to calculate the pocket geometrical property quantitatively. The geometrical information was then subjected to RNA-ligand binding analysis by incorporating the sequence, secondary structure, and geometrical combinations. This new approach takes advantage of both the atom-level precision of the structure and the nucleotide-level tertiary interactions. The results show that the higher-level topological pattern indeed improves the tertiary structure prediction. We also proposed a potential mechanism for RNA-ligand complex formation. The electrostatic interactions are responsible for long-range recognition, while the Van der Waals and hydrophobic contacts for short-range binding and optimization. These interaction pairs can be considered as distance constraints to guide complex structural modeling and drug design. Conclusion: RPocket database would facilitate RNA-ligand engineering to regulate the complex formation for biological or medical applications. RPocket is available at http://zhaoserver.com.cn/RPocket/RPocket.html. [ABSTRACT FROM AUTHOR]

Background: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA–protein interactions are time-consuming and labor-intensive. Therefore, there is an increasing demand for computational methods to accurately and efficiently predict ncRNA–protein interactions. Results: In this work, we presented an ensemble deep learning-based method, EDLMFC, to predict ncRNA–protein interactions using the combination of multi-scale features, including primary sequence features, secondary structure sequence features, and tertiary structure features. Conjoint k-mer was used to extract protein/ncRNA sequence features, integrating tertiary structure features, then fed into an ensemble deep learning model, which combined convolutional neural network (CNN) to learn dominating biological information with bi-directional long short-term memory network (BLSTM) to capture long-range dependencies among the features identified by the CNN. Compared with other state-of-the-art methods under five-fold cross-validation, EDLMFC shows the best performance with accuracy of 93.8%, 89.7%, and 86.1% on RPI1807, NPInter v2.0, and RPI488 datasets, respectively. The results of the independent test demonstrated that EDLMFC can effectively predict potential ncRNA–protein interactions from different organisms. Furtherly, EDLMFC is also shown to predict hub ncRNAs and proteins presented in ncRNA–protein networks of Mus musculus successfully. Conclusions: In general, our proposed method EDLMFC improved the accuracy of ncRNA–protein interaction predictions and anticipated providing some helpful guidance on ncRNA functions research. The source code of EDLMFC and the datasets used in this work are available at https://github.com/JingjingWang-87/EDLMFC. [ABSTRACT FROM AUTHOR]

Background: Driven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction. Results: To explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist's real-value distance prediction is 0.896 Å2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å2 of DeepDist's multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist's multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment. Conclusions: DeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone. [ABSTRACT FROM AUTHOR]

Background: Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available. Results: We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13's TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively. Conclusions: These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins. [ABSTRACT FROM AUTHOR]

Background: Coiled-coils are described as stable structural motifs, where two or more helices wind around each other. However, coiled-coils are associated with local mobility and intrinsic disorder. Intrinsically disordered regions in proteins are characterized by lack of stable secondary and tertiary structure under physiological conditions in vitro. They are increasingly recognized as important for protein function. However, characterizing their behaviour in solution and determining precisely the extent of disorder of a protein region remains challenging, both experimentally and computationally. Results: In this work, we propose a computational framework to quantify the extent of disorder within a coiled-coil in solution and to help design substitutions modulating such disorder. Our method relies on the analysis of conformational ensembles generated by relatively short all-atom Molecular Dynamics (MD) simulations. We apply it to the phosphoprotein multimerisation domains (PMD) of Measles virus (MeV) and Nipah virus (NiV), both forming tetrameric left-handed coiled-coils. We show that our method can help quantify the extent of disorder of the C-terminus region of MeV and NiV PMDs from MD simulations of a few tens of nanoseconds, and without requiring an extensive exploration of the conformational space. Moreover, this study provided a conceptual framework for the rational design of substitutions aimed at modulating the stability of the coiled-coils. By assessing the impact of four substitutions known to destabilize coiled-coils, we derive a set of rules to control MeV PMD structural stability and cohesiveness. We therefore design two contrasting substitutions, one increasing the stability of the tetramer and the other increasing its flexibility. Conclusions: Our method can be considered as a platform to reason about how to design substitutions aimed at regulating flexibility and stability. [ABSTRACT FROM AUTHOR]

Background: Numerous data obtained by different research laboratories indicate that specific IgE production is triggered independently of specific IgG or IgA ones and so it is not linked to fully matured germinal centers formation in the secondary lymphoid organs. The aim of this study was to clarify whether specific IgE production is triggered by low antigen doses administrated in tertiary tissues enriched by lymphoid structures. Methods: Ovalbumin (OVA) in different doses (100 ng to 10 μg) was administrated three times a week for 4–5 weeks intraperitoneally (i.p.) or subcutaneously (s.c.) to female BALB/c mice in the wither region which is enriched in fat-associated lymphoid clusters or in the foot pad region not containing them. Results: OVA-specific IgE was predominantly induced by low but not high antigen doses and only after immunization into the withers. IgE isotype switching was triggered exclusively in the withers adipose tissue but not in the regional lymph nodes while mature IgE expressing cells were observed both in the withers and lymph nodes. Anti-proliferative genotoxic stress inducing drugs shifted the balance from IgG1 towards IgE production. Conclusions: Tertiary lymphoid structures possess unique environment where B-cell antibody isotype switching to IgE predominantly occurs. This phenomenon is partially explained by hampered proliferation of B-cells in these structures. [ABSTRACT FROM AUTHOR]

Background: Traditional sandwich enzyme-linked immunosorbent assay (ELISA) using polyclonal and monoclonal antibodies as reagents presents several drawbacks, including limited amounts, difficulty in permanent storage, and required use of a secondary antibody. Nanobodies can be easily expressed with different systems and fused with several tags in their tertiary structure by recombinant technology, thus offering an effective detection method for diagnostic purposes. Recently, the fenobody (ferritin-fused nanobody) and RANbody (nanobody-fused reporter) have been designed and derived from the nanobody for developing the diagnostic immunoassays. However, there was no report about developing the sandwich ELISA using the fenobody and RANbody as pairing reagents. Results: A platform for developing a sandwich ELISA utilizing fenobody as the capture antibody and RANbody as the detection antibody was firstly designed in the study. Newcastle disease virus (NDV) was selected as the antigen, from which 13 NDV-specific nanobodies were screened from an immunized Bactrian camel. Then, 5 nanobodies were selected to produce fenobodies and RANbodies. The best pairing of fenobodies (NDV-fenobody-4, 800 ng/well) and RANbodies (NDV-RANbody-49, 1:10) was determined to develop the sandwich ELISA for detecting NDV. The detection limits of the assay were determined to be 22 of hemagglutination (HA) titers and 10 ng of purified NDV particles. Compared with two commercial assays, the developed assay shows higher sensitivity and specificity. Meanwhile, it exhibits 98.7% agreement with the HA test and can detect the reference NDV strains belonging to Class II but not Class I. Conclusions: In the presented study, the 13 anti-NDV nanobodies binding the NDV particles were first produced. Then, for the first time, the sandwich ELISA to detect the NDV in the different samples has been developed using the fenobody and RANbody as reagents derived from the nanobodies. Considering the rapidly increasing generation of nanobodies, the platform can reduce the cost of production for the sandwich ELISA and be universally used to develop assays for detecting other antigens. [ABSTRACT FROM AUTHOR]

Background: The maintenance of protein structural stability requires the cooperativity among spatially neighboring residues. Previous studies have shown that conserved residues tend to occur clustered together within enzyme active sites and protein-protein/DNA interfaces. It is possible that conserved residues form one or more local clusters in protein tertiary structures as it can facilitate the formation of functional motifs. In this work, we systematically investigate the spatial distributions of conserved residues as well as hot spot ones within protein-RNA interfaces. Results: The analysis of 191 polypeptide chains from 160 complexes shows the polypeptides interacting with tRNAs evolve relatively rapidly. A statistical analysis of residues in different regions shows that the interface residues are often more conserved, while the most conserved ones are those occurring at protein interiors which maintain the stability of folded polypeptide chains. Additionally, we found that 77.8% of the interfaces have the conserved residues clustered within the entire interface regions. Appling the clustering characteristics to the identification of the real interface, there are 31.1% of cases where the real interfaces are ranked in top 10% of 1000 randomly generated surface patches. In the conserved clusters, the preferred residues are the hydrophobic (Leu, Ile, Met), aromatic (Tyr, Phe, Trp) and interestingly only one positively charged Arg residues. For the hot spot residues, 51.5% of them are situated in the conserved residue clusters, and they are largely consistent with the preferred residue types in the conserved clusters. Conclusions: The protein-RNA interface residues are often more conserved than non-interface surface ones. The conserved interface residues occur more spatially clustered relative to the entire interface residues. The high consistence of hot spot residue types and the preferred residue types in the conserved clusters has important implications for the experimental alanine scanning mutagenesis study. This work deepens the understanding of the residual organization at protein-RNA interface and is of potential applications in the identification of binding site and hot spot residues. [ABSTRACT FROM AUTHOR]

Background: Quadruplexes are specific structure motifs occurring, e.g., in telomeres and transcriptional regulatory regions. Recent discoveries confirmed their importance in biomedicine and led to an intensified examination of their properties. So far, the study of these motifs has focused mainly on the sequence and the tertiary structure, and concerned canonical structures only. Whereas, more and more non-canonical quadruplex motifs are being discovered. Results: Here, we present ElTetrado, a software that identifies quadruplexes (composed of guanine- and other nucleobase-containing tetrads) in nucleic acid structures and classifies them according to the recently introduced ONZ taxonomy. The categorization is based on the secondary structure topology of quadruplexes and their component tetrads. It supports the analysis of canonical and non-canonical motifs. Besides the class recognition, ElTetrado prepares a dot-bracket and graphical representations of the secondary structure, which reflect the specificity of the quadruplex's structure topology. It is implemented as a freely available, standalone application, available at https://github.com/tzok/eltetrado. Conclusions: The proposed software tool allows to identify and classify tetrads and quadruplexes based on the topology of their secondary structures. It complements existing approaches focusing on the sequence and 3D structure. [ABSTRACT FROM AUTHOR]

Background: Microtubule proteins are able to produce electromagnetic fields and have an important role in memory formation, and learning. Therefore, microtubules have the potential to be affected by exogenous electromagnetic fields. This study aimed to examine the comparison of microtubule polymerization and its structural behavior in brain and sperm affected by 50 Hz extremely low-frequency electromagnetic field (ELEF). Results: Twenties adult male rats were randomly and equally divided into control and experimental groups, to evaluate the effect of 50 Hz ELEF on the sperm and brain functions. Plus-maze, serum testosterone and corticosterone, and sperm evaluation were performed. Next, the semen and brain samples were obtained, and they were divided into four experimental groups for investigation of microtubule polymerization. There was no significant difference in testosterone and, corticosterone levels, anxiety behaviors, and sperm morphology between control and ELEF-exposure groups. The sperm viability, total and progressive motility were significantly higher in the ELEF-exposed group than that of the control group. The microtubule polymerization in sperm ELEF was significantly higher than in other groups. The secondary and tertiary structures of tubulins were significantly affected in the brain, and sperm ELEF groups. Conclusion: It seems that the polymerization of microtubules and conformational changes of tubulin dimers are improved by ELEF application. [ABSTRACT FROM AUTHOR]

Background: Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging. Results: In this article, we present a novel method (named RaptorX-Angle) to predict real-valued angles by combining clustering and deep learning. Tested on a subset of PDB25 and the targets in the latest two Critical Assessment of protein Structure Prediction (CASP), our method outperforms the existing state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE). Our result also shows approximately linear relationship between the real prediction errors and our estimated bounds. That is, the real prediction error can be well approximated by our estimated bounds. Conclusions: Our study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study. [ABSTRACT FROM AUTHOR]