Improved prediction of DNA and RNA binding proteins with deep learning models.

Nucleic acid-binding proteins (NABPs), including DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs), play important roles in essential biological processes. To facilitate functional annotation and accurate prediction of different types of NABPs, many machine learning-based computational approaches have been developed. However, the datasets used for training and testing as well as the prediction scopes in these studies have limited their applications. In this paper, we developed new strategies to overcome these limitations by generating more accurate and robust datasets and developing deep learning-based methods including both hierarchical and multi-class approaches to predict the types of NABPs for any given protein. The deep learning models employ two layers of convolutional neural network and one layer of long short-term memory. Our approaches outperform existing DBP and RBP predictors with a balanced prediction between DBPs and RBPs, and are more practically useful in identifying novel NABPs. The multi-class approach greatly improves the prediction accuracy of DBPs and RBPs, especially for the DBPs with ~12% improvement. Moreover, we explored the prediction accuracy of single-stranded DNA binding proteins and their effect on the overall prediction accuracy of NABP predictions.

LEP inhibits intramuscular adipogenesis through the AMPK signaling pathway in vitro.

Leptin can indirectly regulate fatty-acid metabolism and synthesis in muscle in vivo and directly in incubated muscle ex vivo. In addition, non-synonymous mutations in the bovine leptin gene (LEP) are associated with carcass intramuscular fat (IMF) content. However, the effects of LEP on lipid synthesis of adipocytes have not been clearly studied at the cellular level. Therefore, this study focused on bovine primary intramuscular preadipocytes to investigate the effects of LEP on the proliferation and differentiation of intramuscular preadipocytes, as well as its regulatory mechanism in lipid synthesis. The results showed that both the LEP and leptin receptor gene (LEPR) were highly expressed in IMF tissues, and their mRNA expression levels were positively correlated at different developmental stages of intramuscular preadipocytes. The overexpression of LEP inhibited the proliferation and differentiation of intramuscular preadipocytes, while interference with LEP had the opposite effect. Additionally, LEP significantly promoted the phosphorylation level of AMPKα by promoting the protein expression of CAMKK2. Meanwhile, rescue experiments showed that the increasing effect of AMPK inhibitors on the number of intramuscular preadipocytes was significantly weakened by the overexpression of LEP. Furthermore, the overexpression of LEP could weaken the promoting effect of AMPK inhibitor on triglyceride content and droplet accumulation, and prevent the upregulation of adipogenic protein expression (SREBF1, FABP4, FASN, and ACCα) caused by AMPK inhibitor. Taken together, LEP acted on the AMPK signaling pathway by regulating the protein expression of CAMKK2, thereby downregulating the expression of proliferation-related and adipogenic-related genes and proteins, ultimately reducing intramuscular adipogenesis.

Molecular cloning of TPM3 gene in qinchuan cattle and its effect on myoblast proliferation and differentiation.

Tropomyosin 3 (TPM3) plays a significant role as a regulatory protein in muscle contraction, affecting the growth and development of skeletal muscles. Despite its importance, limited research has been conducted to investigate the influence of TPM3 on bovine skeletal muscle development. Therefore, this study revealed the role of TPM3 in bovine myoblast growth and development. This research involved conducting a thorough examination of the Qinchuan cattle TPM3 gene using bioinformatics tools to examine its sequence and structural characteristics. Furthermore, TPM3 expression was evaluated in various bovine tissues and cells using quantitative real-time polymerase chain reaction (qRT-PCR). The results showed that the coding region of TPM3 spans 855 bp, with the 161st base being the T base, encoding a protein with 284 amino acids and 19 phosphorylation sites. This protein demonstrated high conservation across species while displaying a predominant α-helix secondary structure despite being an unstable acidic protein. Notably, a noticeable increase in TPM3 expression was observed in the longissimus dorsi muscle and myocardium of calves and adult cattle. Expression patterns varied during different stages of myoblast differentiation. Functional studies that involved interference with TPM3 in Qinchuan cattle myoblasts revealed a very significantly decrease in S-phase cell numbers and EdU-positive staining (P < 0.01), and disrupted myotube morphology. Moreover, interference with TPM3 resulted in significantly (P < 0.05) or highly significantly (P < 0.01) decreased mRNA and protein levels of key proliferation and differentiation markers, indicating its role in the modulation of myoblast behavior. These findings suggest that TPM3 plays an essential role in bovine skeletal muscle growth by influencing myoblast proliferation and differentiation. This study provides a foundation for further exploration into the mechanisms underlying TPM3-mediated regulation of bovine muscle development and provides valuable insights that could guide future research directions as well as potential applications for livestock breeding and addressing muscle-related disorders.

ComparePD: Improving protein-DNA complex model comparison with hydrogen bond energy-based metrics.

Computational modeling of protein-DNA complex structures has important implications in biomedical applications such as structure-based, computer aided drug design. A key step in developing methods for accurate modeling of protein-DNA complexes is similarity assessment between models and their reference complex structures. Existing methods primarily rely on distance-based metrics and generally do not consider important functional features of the complexes, such as interface hydrogen bonds that are critical to specific protein-DNA interactions. Here, we present a new scoring function, ComparePD, which takes interface hydrogen bond energy and strength into account besides the distance-based metrics for accurate similarity measure of protein-DNA complexes. ComparePD was tested on two datasets of computational models of protein-DNA complexes generated using docking (classified as easy, intermediate, and difficult cases) and homology modeling methods. The results were compared with PDDockQ, a modified version of DockQ tailored for protein-DNA complexes, as well as the metrics employed by the community-wide experiment CAPRI (Critical Assessment of PRedicted Interactions). We demonstrated that ComparePD provides an improved similarity measure over PDDockQ and the CAPRI classification method by considering both conformational similarity and functional importance of the complex interface. ComparePD identified more meaningful models as compared to PDDockQ for all the cases having different top models between ComparePD and PDDockQ except for one intermediate docking case.

Single-cell sequencing reveals the new existence form of dermal papilla cells in the hair follicle regeneration of cashmere goats.

The problem of human hair loss has caused widespread concern, however, such research is difficult because the periodicity is not obvious and the deeper levels knowledge of dermal papilla (DP) stem cells' differentiation are limited. Here, cashmere goats which have obvious periodicity of hair follicles were used, based on unbiased scRNA sequencing, we constructed DP cell lineage differentiation trajectory and revealed the key genes, signals and functions involved in cell fate decisions. And then we revealed the molecular landscape of hair follicle on regeneration. Revealed that DP cells differentiate into four intermediate cell states at different periodicity: Intermediate-cell-10 showed important functions in the growth and maintenance of cashmere; intermediate-cell-1 acting on apoptosis and cashmere shedding; intermediate-cell-0 initiated new follicular cycles, the migration of hair follicles and the occurrence of cashmere; and intermediate-cell-15 are suggested to be DP progenitor cells. In general, we provide new insights for hair regrowth. At the same time, it provides a new research ideas, directions and molecular landscape for the mechanism of dermal papilla cells.

Molecular Oxygen Activation by Citric Acid Boosted Pyrite-Photo-Fenton Process for Degradation of PPCPs in Water.

Pyrite has been used in photo-Fenton reactions for the degradation of pollutants, but the application of photo-Fenton processes with extra H2O2 in real water/wastewater treatment has still been limited by the economic cost of H2O2 and artificial light sources. Herein, citric acid (CA) and simulated/natural sunlight are used to develop a pyrite-based photo-Fenton system (pyrite-CA-light) in situ generating H2O2 through the enhanced activation of molecular oxygen. The degradation of pharmaceuticals and personal care products (PPCPs), especially acetaminophen (APAP) as the main target pollutant, in the pyrite-CA-light system was investigated. The effects of influencing factors such as various organic acids, APAP concentration, pH, pyrite dosage, CA concentration and co-existing anions (HCO3-, Cl-, NO3-, SO42- and H2PO4-) were examined. At a pyrite dosage of 0.1 g L-1, CA concentration of 0.6 mM and an initial pH of 6.0, the degradation efficiency of APAP (30 µM) was 99.1% within 30 min under the irradiation of xenon lamp (70 W, λ ≥ 350 nm). Almost the same high efficiency of APAP degradation (93.9%) in the system was achieved under natural sunlight irradiation (ca. 650 W m-2). The scavenging experiments revealed that the dominant active species for degrading APAP was hydroxyl radical (HO•). Moreover, a quantitative structural-activity relationship (QSAR) model for pseudo-first-order rate constants (kobs) was established with a high significance (R2 = 0.932, p = 0.001) by using three descriptors: octanol-water partition coefficient (logKow), dissociation constant (pKa) and highest occupied molecular orbital (HOMO). This work provides an innovative strategy of the photo-Fenton process for the degradation of PPCPs using natural minerals and ordinary carboxylic acid under sunlight.

Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses.

Hydrogen bonds play important roles in protein folding and protein-ligand interactions, particularly in specific protein-DNA recognition. However, the distributions of hydrogen bonds, especially hydrogen bond energy (HBE) in different types of protein-ligand complexes, is unknown. Here we performed a comparative analysis of hydrogen bonds among three non-redundant datasets of protein-protein, protein-peptide, and protein-DNA complexes. Besides comparing the number of hydrogen bonds in terms of types and locations, we investigated the distributions of HBE. Our results indicate that while there is no significant difference of hydrogen bonds within protein chains among the three types of complexes, interfacial hydrogen bonds are significantly more prevalent in protein-DNA complexes. More importantly, the interfacial hydrogen bonds in protein-DNA complexes displayed a unique energy distribution of strong and weak hydrogen bonds whereas majority of the interfacial hydrogen bonds in protein-protein and protein-peptide complexes are of predominantly high strength with low energy. Moreover, there is a significant difference in the energy distributions of minor groove hydrogen bonds between protein-DNA complexes with different binding specificity. Highly specific protein-DNA complexes contain more strong hydrogen bonds in the minor groove than multi-specific complexes, suggesting important role of minor groove in specific protein-DNA recognition. These results can help better understand protein-DNA interactions and have important implications in improving quality assessments of protein-DNA complex models.

Bone mesenchymal stem cell-derived extracellular vesicles inhibit DAPK1-mediated inflammation by delivering miR-191 to macrophages.

Alveolar macrophage activation and apoptosis are vital contributors to sepsis-associated acute lung injury (ALI). However, the mechanisms of alveolar macrophage activation are yet to be clarified. Death-associated protein kinase 1 (DAPK1) is one of the potential candidates that play crucial roles in regulating alveolar macrophage inflammation. Herein, we found that primary human bone mesenchymal stem cell (BMSC)-derived extracellular vesicles (EVs) antagonize LPS-induced inflammation in the THP-1 human macrophage-like cell line. Mechanistically, LPS stimulation elevates the expression of DAPK1 and the inflammation markers in THP-1 cells, while BMSC-derived EVs inhibit the expression of DAPK1 and inflammation through delivering miR-191, which can target the 3'-UTR of the DAPK1 mRNA and therefore suppress its translation. The importance of DAPK1 in the activation of THP-1 is also stressed in this study. Our findings provide evidence that BMSC-derived EVs regulate the alveolar macrophage inflammation and highlight BMSC-derived EVs as a potential vehicle to deliver biomacromolecules to macrophages.

Effect of Co-Existing Cations and Anions on the Adsorption of Antibiotics on Iron-Containing Minerals.

The adsorption of antibiotics on minerals is an important process in their environment behavior. The adsorption behavior of antibiotics on iron-containing minerals and the effect of co-existing cations and anions were studied in this work. Magnetite, hematite, goethite and kaolin were selected as the representative minerals and characterized by SEM, XRD and BET. A total of eight antibiotics, including three quinolones, three sulfonamides and two mycins were chosen as the research targets. Results showed a higher adsorption amount of quinolones than that of sulfonamides and mycins on the surface of iron-containing minerals in most mineral systems. The adsorption isotherms of quinolones can be well fitted using the Freundlich models. The effects of five cations and five anions on the adsorption of quinolones were investigated, among which Mg2+, Ca2+, HCO3- and H2PO4- mainly showed significant inhibition on the adsorption, while the effects of K+, Na+, NH4+, Cl-, NO3- and SO42- showed less. Natural surface water samples were also collected and used as media to investigate the adsorption behavior of quinolones on iron-containing minerals. The buffering capacity of the natural water kept the reaction solution at circumneutral conditions, and the adsorption amount was mostly promoted in the goethite system (from 0.56~0.78 µmol/g to 0.52~1.43 µmol/g), but was inhibited in the other systems (magnetite: from 1.13~1.33 µmol/g to 0.45~0.76 µmol/g; hematite: from 0.52~0.65 µmol/g to 0.02~0.18 µmol/g; kaolin: from 1.98~1.99 µmol/g to 0.90~1.40 µmol/g). The results in this work help to further understand the transportation and fate of antibiotics in an aqueous environment.

New insights into protein-DNA binding specificity from hydrogen bond based comparative study.

Knowledge of protein-DNA binding specificity has important implications in understanding DNA metabolism, transcriptional regulation and developing therapeutic drugs. Previous studies demonstrated hydrogen bonds between amino acid side chains and DNA bases play major roles in specific protein-DNA interactions. In this paper, we investigated the roles of individual DNA strands and protein secondary structure types in specific protein-DNA recognition based on side chain-base hydrogen bonds. By comparing the contribution of each DNA strand to the overall binding specificity between DNA-binding proteins with different degrees of binding specificity, we found that highly specific DNA-binding proteins show balanced hydrogen bonding with each of the two DNA strands while multi-specific DNA binding proteins are generally biased towards one strand. Protein-base pair hydrogen bonds, in which both bases of a base pair are involved in forming hydrogen bonds with amino acid side chains, are more prevalent in the highly specific protein-DNA complexes than those in the multi-specific group. Amino acids involved in side chain-base hydrogen bonds favor strand and coil secondary structure types in highly specific DNA-binding proteins while multi-specific DNA-binding proteins prefer helices.

Mechanisms of enhanced hexavalent chromium removal from groundwater by sodium carboxymethyl cellulose stabilized zerovalent iron nanoparticles.

Chromium (Cr) contamination poses serious threats to the environment and human health. Thus, batch and column experiments were performed to investigate hexavalent chromium [Cr (VI)] removal from solution and porous media using nanoscale zerovalent iron nanoparticles (NZVI) stabilized by sodium carboxymethyl cellulose (CMC). Batch experiments indicated that the mass ratio of Fe/CMC = 1, the presence of 150-200 mg L-1 CMC and lower ionic strength led to optimum Cr (VI) removal in aqueous solution. Column experiments demonstrated that Cr (VI) removal was enhanced with decreasing solution pH and increasing CMC-NZVI concentration. The presence of CMC can increase Cr (VI) removal by NZVI in both aqueous solution and porous media by complexation precipitation of Cr (VI) compounds and better dispersion of NZVI. X-ray photoelectron spectroscopy (XPS) analysis revealed that an appropriate amount of CMC supported the redox reaction of Cr (VI) and NZVI. The removal of Cr (VI) through columns was 20.8% and 88.5% under no additional CMC and optimized CMC content, respectively. However, Cr (VI) removal decreased to 64.6% under excessive CMC content. The CMC modified NZVI nanoparticles were characterized by XRD, XPS and TEM techniques. These findings imply that CMC can be used as an effective stabilizer on NZVI which can in turn be applied for the efficient removal of Cr (VI) from industrial wastewater and groundwater.

High incidence and low case detection rate among contacts of tuberculosis cases in Shanghai, China.

BACKGROUND: To assess the effect of a contact investigation strategy by assessing the incidence of tuberculosis and the case detection rate among contacts of tuberculosis patients. METHODS: The pulmonary tuberculosis incidence among contacts was determined retrospectively from a tuberculosis information management system. For each detection method (symptom examination only, symptom examination plus chest radiography or other alternatives), the detection rate of pulmonary tuberculosis patients among contacts was derived from contact investigation form records. RESULTS: Sixty-nine cases of pulmonary tuberculosis were identified among a total of 8137 contacts after an average follow-up of 2.6 years (range: 0.25-5.25) during the period from 2010 to 2014. The incidence density was 329/100,000 person-years (PYs), and the 95% confidence interval (CI) was 256-419/100,000 PYs, which was significantly higher than the notification rate during the same period in the general population (29-30/100,000 PYs). The incidence density was higher (p < 0.0001) among male contacts (462/100,000 PYs) than among female contacts (236/100,000 PYs). The incidence density did not differ (p > 0.05) between contacts whose index case was sputum smear positive and those whose index case was sputum smear negative. Contacts who were biologically related family of the index cases exhibited a higher (p < 0.05) incidence density (475/100,000 PYs) than other contacts (281/100,000 PYs). Fifteen of the 69 incident cases were found through contact investigation, corresponding to a case detection rate via contact investigation of 22% (95% CI: 13-33%). The relevance ratio was 288/100,000 (12/4163) by both chest radiography and symptom survey, which was significantly higher than the rate detected by symptom survey alone, of 57/100,000 (2/3486), p = 0.028. The cumulative incidence in the contacts was 761/100,000 (62/8137) within 3 years from the time that the index cases were diagnosed with pulmonary tuberculosis, which was higher than the incidence rate of 210/100,000 (7/3328) recorded after 3 years (p < 0.001). CONCLUSIONS: The contacts were at higher risk of pulmonary tuberculosis than the general population; however, only approximately 22% of the incident cases could be detected through contact investigation. Therefore, the contact investigation strategy must be improved for better detection of potential pulmonary tuberculosis cases.

An SVM-based method for assessment of transcription factor-DNA complex models.

BACKGROUND: Atomic details of protein-DNA complexes can provide insightful information for better understanding of the function and binding specificity of DNA binding proteins. In addition to experimental methods for solving protein-DNA complex structures, protein-DNA docking can be used to predict native or near-native complex models. A docking program typically generates a large number of complex conformations and predicts the complex model(s) based on interaction energies between protein and DNA. However, the prediction accuracy is hampered by current approaches to model assessment, especially when docking simulations fail to produce any near-native models. RESULTS: We present here a Support Vector Machine (SVM)-based approach for quality assessment of the predicted transcription factor (TF)-DNA complex models. Besides a knowledge-based protein-DNA interaction potential DDNA3, we applied several structural features that have been shown to play important roles in binding specificity between transcription factors and DNA molecules to quality assessment of complex models. To address the issue of unbalanced positive and negative cases in the training dataset, we applied hard-negative mining, an iterative training process that selects an initial training dataset by combining all of the positive cases and a random sample from the negative cases. Results show that the SVM model greatly improves prediction accuracy (84.2%) over two knowledge-based protein-DNA interaction potentials, orientation potential (60.8%) and DDNA3 (68.4%). The improvement is achieved through reducing the number of false positive predictions, especially for the hard docking cases, in which a docking algorithm fails to produce any near-native complex models. CONCLUSIONS: A learning-based SVM scoring model with structural features for specific protein-DNA binding and an atomic-level protein-DNA interaction potential DDNA3 significantly improves prediction accuracy of complex models by successfully identifying cases without near-native structural models.

An efficient algorithm for improving structure-based prediction of transcription factor binding sites.

BACKGROUND: Gene expression is regulated by transcription factors binding to specific target DNA sites. Understanding how and where transcription factors bind at genome scale represents an essential step toward our understanding of gene regulation networks. Previously we developed a structure-based method for prediction of transcription factor binding sites using an integrative energy function that combines a knowledge-based multibody potential and two atomic energy terms. While the method performs well, it is not computationally efficient due to the exponential increase in the number of binding sequences to be evaluated for longer binding sites. In this paper, we present an efficient pentamer algorithm by splitting DNA binding sequences into overlapping fragments along with a simplified integrative energy function for transcription factor binding site prediction. RESULTS: A DNA binding sequence is split into overlapping pentamers (5 base pairs) for calculating transcription factor-pentamer interaction energy. To combine the results from overlapping pentamer scores, we developed two methods, Kmer-Sum and PWM (Position Weight Matrix) stacking, for full-length binding motif prediction. Our results show that both Kmer-Sum and PWM stacking in the new pentamer approach along with a simplified integrative energy function improved transcription factor binding site prediction accuracy and dramatically reduced computation time, especially for longer binding sites. CONCLUSION: Our new fragment-based pentamer algorithm and simplified energy function improve both efficiency and accuracy. To our knowledge, this is the first fragment-based method for structure-based transcription factor binding sites prediction.

Structure-based prediction of transcription factor binding specificity using an integrative energy function.

UNLABELLED: Transcription factors (TFs) regulate gene expression through binding to specific target DNA sites. Accurate annotation of transcription factor binding sites (TFBSs) at genome scale represents an essential step toward our understanding of gene regulation networks. In this article, we present a structure-based method for computational prediction of TFBSs using a novel, integrative energy (IE) function. The new energy function combines a multibody (MB) knowledge-based potential and two atomic energy terms (hydrogen bond and π interaction) that might not be accurately captured by the knowledge-based potential owing to the mean force nature and low count problem. We applied the new energy function to the TFBS prediction using a non-redundant dataset that consists of TFs from 12 different families. Our results show that the new IE function improves the prediction accuracy over the knowledge-based, statistical potentials, especially for homeodomain TFs, the second largest TF family in mammals. CONTACT: jguo4@uncc.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular evolution of Zika virus as it crossed the Pacific to the Americas.

Zika virus was previously considered to cause only a benign infection in humans. Studies of recent outbreaks of Zika virus in the Pacific, South America, Mexico and the Caribbean have associated the virus with severe neuropathology. Viral evolution may be one factor contributing to an apparent change in Zika disease as it spread from Southeast Asia across the Pacific to the Americas. To address this possibility, we have employed computational tools to compare the phylogeny, geography, immunology and RNA structure of Zika virus isolates from Africa, Asia, the Pacific and the Americas. In doing so, we compare and contrast methods and results for tree search and rooting of Zika virus phylogenies. In some phylogenetic analyses we find support for the hypothesis that there is a deep common ancestor between African and Asian clades (the "Asia/Africa" hypothesis). In other phylogenetic analyses, we find that Asian lineages are descendent from African lineages (the "out of Africa" hypothesis). In addition, we identify and evaluate key mutations in viral envelope protein coding and untranslated terminal RNA regions. We find stepwise mutations that have altered both immunological motif sets and regulatory sequence elements. Both of these sets of changes distinguish viruses found in Africa from those in the emergent Asia-Pacific-Americas lineage. These findings support the working hypothesis that mutations acquired by Zika virus in the Pacific and Americas contribute to changes in pathology. These results can inform experiments required to elucidate the role of viral genetic evolution in changes in neuropathology, including microcephaly and other neurological and skeletomuscular issues in infants, and Guillain-Barré syndrome in adults.

Chlorophyll-Catalyzed Visible-Light-Mediated Synthesis of Tetrahydroquinolines from N,N-Dimethylanilines and Maleimides.

Natural pigment chlorophyll was used as a green photosensitizer for the first time in a visible-light photoredox catalysis for the efficient synthesis of tetrahydroquinolines from N,N-dimethylanilines and maleimides in an air atmosphere. The reaction involves direct cyclization via an sp3 C-H bond functionalization process to afford products in moderate to high yields (61-98%) from a wide range of substrates with a low loading of chlorophyll under mild conditions. This work demonstrates the potential benefits of chlorophyll as photosensitizer in visible light catalysis.

Statistical analysis of structural determinants for protein-DNA-binding specificity.

DNA-binding proteins play critical roles in biological processes including gene expression, DNA packaging and DNA repair. They bind to DNA target sequences with different degrees of binding specificity, ranging from highly specific (HS) to nonspecific (NS). Alterations of DNA-binding specificity, due to either genetic variation or somatic mutations, can lead to various diseases. In this study, a comparative analysis of protein-DNA complex structures was carried out to investigate the structural features that contribute to binding specificity. Protein-DNA complexes were grouped into three general classes based on degrees of binding specificity: HS, multispecific (MS), and NS. Our results show a clear trend of structural features among the three classes, including amino acid binding propensities, simple and complex hydrogen bonds, major/minor groove and base contacts, and DNA shape. We found that aspartate is enriched in HS DNA binding proteins and predominately binds to a cytosine through a single hydrogen bond or two consecutive cytosines through bidentate hydrogen bonds. Aromatic residues, histidine and tyrosine, are highly enriched in the HS and MS groups and may contribute to specific binding through different mechanisms. To further investigate the role of protein flexibility in specific protein-DNA recognition, we analyzed the conformational changes between the bound and unbound states of DNA-binding proteins and structural variations. The results indicate that HS and MS DNA-binding domains have larger conformational changes upon DNA-binding and larger degree of flexibility in both bound and unbound states. Proteins 2016; 84:1147-1161. © 2016 Wiley Periodicals, Inc.

Direct activation of human and mouse Oct4 genes using engineered TALE and Cas9 transcription factors.

The newly developed transcription activator-like effector protein (TALE) and clustered regularly interspaced short palindromic repeats/Cas9 transcription factors (TF) offered a powerful and precise approach for modulating gene expression. In this article, we systematically investigated the potential of these new tools in activating the stringently silenced pluripotency gene Oct4 (Pou5f1) in mouse and human somatic cells. First, with a number of TALEs and sgRNAs targeting various regions in the mouse and human Oct4 promoters, we found that the most efficient TALE-VP64s bound around -120 to -80 bp, while highly effective sgRNAs targeted from -147 to -89-bp upstream of the transcription start sites to induce high activity of luciferase reporters. In addition, we observed significant transcriptional synergy when multiple TFs were applied simultaneously. Although individual TFs exhibited marginal activity to up-regulate endogenous gene expression, optimized combinations of TALE-VP64s could enhance endogenous Oct4 transcription up to 30-fold in mouse NIH3T3 cells and 20-fold in human HEK293T cells. More importantly, the enhancement of OCT4 transcription ultimately generated OCT4 proteins. Furthermore, examination of different epigenetic modifiers showed that histone acetyltransferase p300 could enhance both TALE-VP64 and sgRNA/dCas9-VP64 induced transcription of endogenous OCT4. Taken together, our study suggested that engineered TALE-TF and dCas9-TF are useful tools for modulating gene expression in mammalian cells.

A knowledge-based orientation potential for transcription factor-DNA docking.

MOTIVATION: Computational modeling of protein-DNA complexes remains a challenging problem in structural bioinformatics. One of the key factors for a successful protein-DNA docking is a potential function that can accurately discriminate the near-native structures from decoy complexes and at the same time make conformational sampling more efficient. Here, we developed a novel orientation-dependent, knowledge-based, residue-level potential for improving transcription factor (TF)-DNA docking. RESULTS: We demonstrated the performance of this new potential in TF-DNA binding affinity prediction, discrimination of native protein-DNA complex from decoy structures, and most importantly in rigid TF-DNA docking. The rigid TF-DNA docking with the new orientation potential, on a benchmark of 38 complexes, successfully predicts 42% of the cases with root mean square deviations lower than 1 Å and 55% of the cases with root mean square deviations lower than 3 Å. The results suggest that docking with this new orientation-dependent, coarse-grained statistical potential can achieve high-docking accuracy and can serve as a crucial first step in multi-stage flexible protein-DNA docking. AVAILABILITY AND IMPLEMENTATION: The new potential is available at http://bioinfozen.uncc.edu/Protein_DNA_orientation_potential.tar.