Biology subjects

Hou, J.

Publications and source records attributed to Hou, J..

119 records · Page 7Linked to original sources

bioRxiv2019

Cascaded-CNN: Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

Cryo-electron microscopy (cryo-EM) has become a leading technology for determining protein structures. Recent advances in this field have allowed for atomic resolution. However, predicting the backbone trace of a protein has remained a challenge on all but the most pristine density maps (< 2.5[A] resolution). Here we introduce a deep learning model that uses a set of cascaded convolutional neural networks (CNNs) to predict C atoms along a proteins backbone structure. The cascaded-CNN (C-CNN) is a novel deep learning architecture comprised of multiple CNNs, each predicting a specific aspect of a proteins structure. This model predicts secondary structure elements (SSEs), backbone structure, and C atoms, combining the results of each to produce a complete prediction map. The cascaded-CNN is a semantic segmentation image classifier and was trained using thousands of simulated density maps. This method is largely automatic and only requires a recommended threshold value for each evaluated protein. A specialized tabu-search path walking algorithm was used to produce an initial backbone trace with C placements. A helix-refinement algorithm made further improvements to the -helix SSEs of the backbone trace. Finally, a novel quality assessment-based combinatorial algorithm was used to effectively map C traces to obtain full-atom protein structures. This method was tested on 50 experimental maps between 2.6[A] and 4.4[A] resolution. It outperformed several state-of-the-art prediction methods including RosettaES, MAINMAST, and a Phenix based method by producing the most complete prediction models, as measured by percentage of found C atoms. This method accurately predicted 88.5% (mean) of the C atoms within 3[A] of a proteins backbone structure surpassing the 66.8% mark achieved by the leading alternate method (Phenix based fully automatic method) on the same set of density maps. The C-CNN also achieved an average RMSD of 1.23[A] for all 50 experimental density maps which is similar to the Phenix based fully automatic method. The source code and demo of this research has been published at https://github.com/DrDongSi/Ca-Backbone-Prediction.

molecular biology↗

bioRxiv2019

Effects of topical PaiTeLing in nude mice implanted with human condyloma acuminatum tissue infected with HPV 6, 31, and 81: comparison with imiquimod and interferon-α-2b

BACKGROUNDThe standard treatment for condyloma acuminatum is topical imiquimod. In the current study, we used a mouse model to compare the effects of an herbal medication PaiTeLing. METHODSLesion tissue was obtained from a woman with genital condyloma acuminatum. DNA genotyping revealed HPV6, 31, and 81. Tissue prism (0.5 cm3) was implanted to BALB/C nude mice, 22 days after the implantation, mice began to receive topical treatment with imiquimod, interferon--2b gel, or PaiTeLing over the site of implantation for 2 consecutive weeks. Mice receiving tissue implantation but no other intervention was included as a control. Skin tissue was collected for H&E staining and anti-CD207 immunohistochemistry. Blood was collected to determine a panel of cytokines. RESULTSH&E staining showed lower number of koilocytes and higher number of Langerhans cells in the treatment groups, particularly in mice receiving imiquimod or PaiTeLing. Blood levels of TNF, IL-2, INF-{gamma} and IL-12p70 were increased in the treatment groups, particularly in mice receiving imiquimod or PaiTeLing. CONCLUSIONImmune response in nude mice infected with HPV6, 31, and 81 is enhanced by treatment with imiquimod, interferon--2b and PaiTeLing. Effects of imiquimod and PaiTeLing seems to be stronger than interferon--2b.

immunology↗

bioRxiv2019

SAXSDom: Modeling multi-domain protein structures using small-angle X-ray scattering data

Many proteins are composed of several domains that pack together into a complex tertiary structure. Some multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for the domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the assembled protein. Small-angle X-ray scattering (SAXS) reports on the solution structural properties of proteins and has the potential for guiding homology modeling of multidomain proteins. In this work, we describe a novel multi-domain protein assembly modeling method, SAXSDom, that integrates experimental knowledge from SAXS profiles with probabilistic Input-Output Hidden Markov model (IOHMM). Four scoring functions to account for the energetic contribution of SAXS restraints for domain assembly were developed and tested. The method was evaluated on multi-domain proteins from two public datasets. Based on the results, the accuracy of domain assembly was improved for 40 out of 46 CASP multi-domain proteins in terms of RMSD and TM-score when SAXS information was used. Our method also achieved higher accuracy for at least 45 out of 73 multi-domain proteins according to RMSD and TM-score metrics in the AIDA dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at http://github.com/multicom-toolbox/SAXSDom.

bioinformatics↗

bioRxiv2019

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

Prediction of residue-residue distance relationships (e.g. contacts) has become the key direction to advance protein tertiary structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, contact distance-driven template-free (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction, in addition to an update of other components such as template library, sequence database, and alignment tools. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template-free and template-based protein structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue-residue features such as co-evolution scores to substantially improve inter-residue contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template-based modeling targets from scratch. Deep learning also successfully integrated 1D structural features, 2D contact information, and 3D structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system in the CASP13 experiment clearly shows that protein contact distance prediction and model selection driven by powerful deep learning holds the key of solving protein structure prediction problem. However, there are still major challenges in accurately predicting protein contact distance when there are few homologous sequences to generate co-evolutionary signals, folding proteins from noisy contact distances, and ranking models of hard targets.

bioinformatics↗

bioRxiv2018

The dynamic proteome of influenza A virus infection identifies M segment splicing as a host range determinant

A century ago, influenza A virus (IAV) infection caused the 1918 flu pandemic and killed an estimated 20-40 million people. Pandemic IAV outbreaks occur when strains from animal reservoirs acquire the ability to infect and spread among humans. The molecular details of this species barrier are incompletely understood. We combined metabolic pulse labeling and quantitative shotgun proteomics to globally monitor protein synthesis upon infection of human cells with a human-and a bird-adapted IAV strain. While production of host proteins was remarkably similar, we observed striking differences in the kinetics of viral protein synthesis over the course of infection. Most importantly, the matrix protein M1 was inefficiently produced by the bird-adapted strain at later stages. We show that impaired production of M1 from bird-adapted strains is caused by increased splicing of the M segment RNA to alternative isoforms. Experiments with reporter constructs and recombinant influenza viruses revealed that strain-specific M segment splicing is controlled by the 3 splice site and functionally important for permissive infection. Independent in silico evidence shows that avian-adapted M segments have evolved different conserved RNA structure features than human-adapted sequences. Thus, our data identifies M segment RNA splicing as a viral determinant of host range.

systems biology↗

bioRxiv2018

Activated Protein C blocks the inhibitory effect on neurite outgrowth by extracellular histones that mediates its inhibition through a retrograde YB-1 signal

Axonal regeneration in the mature CNS is limited by extracellular inhibitory factors. Triple knockout mice lacking the major myelin-associated inhibitors do not display spontaneous regeneration after injury, indicating the presence of other inhibitors. Searching for such inhibitors we have detected elevated levels of histone H3 in human cerebrospinal fluid (CSF) 24 hours after spinal cord injury. Following dorsal column lesions in mice and optic nerve crushes in rats, elevated levels of extracellular histone H3 were detected at the injury site. Similar to myelin-associated inhibitors, these extracellular histones induced growth cone collapse and inhibited neurite outgrowth. Histones mediate inhibition through the transcription factor YB-1 and Toll-like receptor 2, and these effects are independent of the Nogo receptor. Histone-mediated inhibition can be reversed by the addition of activated protein C (APC) in vitro, and APC treatment promotes axonal regeneration in the crushed optic nerve in vivo. These findings identify extracellular histones as a new class of nerve regeneration-inhibiting molecules within the injured CNS. One sentence summaryProteins typically associated with chromatin structure play an unexpected role in limiting axonal regeneration after injury.

neuroscience↗

bioRxiv2018

Protein Deacetylase CobB Interplays with c-di-GMP

As a ubiquitous bacterial secondary messenger, c-di-GMP plays key regulatory roles in processes such as bacterial motility and transcription regulation. CobB is the Sir2 family protein deacetylase that controls energy metabolism, chemotaxis and DNA supercoiling in many bacteria. Using an E.coli proteome microarray, we found that c-di-GMP strongly binds to CobB. Protein deacetylation assays showed that c-di-GMP inhibits CobB activity and thereby modulates the biogenesis of acetyl-CoA. Through mutagenesis studies, residues R8, R17 and E21 of CobB were shown to be required for c-di-GMP binding. Next, we found that CobB is an effective deacetylase of YdeH, a major diguanylate cyclase (DGC) of E.coli that is endogenously acetylated. Mass spectrometry analysis identified YdeH K4 as the major site of acetylation, and it could be deacetylated by CobB. Interestingly, deacetylation of YdeH enhances its stability and cyclase activity in c-di-GMP production. Thus, our work establishes a novel negative feedback loop linking c-di-GMP biogenesis and CobB-mediated protein deacetylation.

microbiology↗

bioRxiv2018

Claudin-4 Reconstituted in Unilamellar Vesicles is Sufficient to Form Tight Interfaces that Partition Membrane Proteins

Tight junctions have been hypothesized to act as molecular fences in the plasma membrane of epithelial cells, helping to form differentiated apical and basolateral domains. While this fence function is believed to arise from the interaction of four-pass transmembrane claudins, the complexity of tight junctions has made direct evidence of their role as a putative diffusion barrier difficult to obtain. Here we address this challenge by reconstituting claudin-4 into giant unilamellar vesicles using microfluidic jetting. We find that reconstituted claudin-4 is sufficient to form adhesive interfaces between unilamellar vesicles without accessory proteins present in vivo. By controlling the molecular composition of the inner and outer leaflets of jetted membranes, we show that claudin-4-mediated interfaces can drive partitioning of extracellular membrane proteins but not of inner or outer leaflet lipids. Our findings indicate that homotypic interactions of claudins and their small size can contribute to the polarization of epithelial cells.

cell biology↗

bioRxiv2017

DNCON2: Improved protein contact prediction using two-level deep convolutional neural networks

MotivationSignificant improvements in the prediction of protein residue-residue contacts are observed in the recent years. These contacts, predicted using a variety of coevolution-based and machine learning methods, are the key contributors to the recent progress in ab initio protein structure prediction, as demonstrated in the recent CASP experiments. Continuing the development of new methods to reliably predict contact maps is essential to further improve ab initio structure prediction.\n\nResultsIn this paper we discuss DNCON2, an improved protein contact map predictor based on two-level deep convolutional neural networks. It consists of six convolutional neural networks - the first five predict contacts at 6, 7.5, 8, 8.5, and 10 [A] distance thresholds, and the last one uses these five predictions as additional features to predict final contact maps. On the free-modeling datasets in CASP10, 11, and 12 experiments, DNCON2 achieves mean precisions of 35%, 50%, and 53.4%, respectively, higher than 30.6% by MetaPSICOV on CASP10 dataset, 34% by MetaPSICOV on CASP11 dataset, and 46.3% by Raptor-X on CASP12 dataset, when top L/5 long-range contacts are evaluated. We attribute the improved performance of DNCON2 to the inclusion of short- and medium-range contacts into training, two-level approach to prediction, use of the state-of-the-art optimization and activation functions, and a novel deep learning architecture that allows each filter in a convolutional layer to access all the input features of a protein of arbitrary length.\n\nAvailabilityThe web server of DNCON2 is at http://sysbio.rnet.missouri.edu/dncon2/ where training and testing datasets as well as the predictions for CASP10, 11, and 12 free-modeling datasets can also be downloaded. Its source code is available at https://github.com/multicom-toolbox/DNCON2/.\n\nContactchengji@missouri.edu\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2017

Targeting posttranslational modifications of RioK1 inhibits the progression of colorectal and gastric cancers

RioK1 has recently been shown to play important roles in cancers, but its posttranslational regulation is largely unknown. Here we report that RioK1 is methylated at K411 by SETD7 methyltransferase, and that lysine-specific demethylase 1 (LSD1) reverses its methylation. The mutated RioK1 (K411R) that cannot be methylated exhibits a longer half-life than does the methylated RioK1. FBXO6 specifically interacts with K411-methylated RioK1 through its FBA domain to induce RioK1 ubiquitination. Casein kinase 2 (CK2) phosphorylates RioK1 at T410, which stabilizes RioK1 by antagonizing K411 methylation and impeding the recruitment of FBXO6 to RioK1. Functional experiments demonstrate the RioK1 methylation reduces the tumor growth and metastasis in CRC and GC. Importantly, the protein levels of CK2 and LSD1 show an inverse correlation with FBXO6 and SETD7 expression in human CRC tissues. Therefore, this study highlights the importance of a RioK1 methylation-phosphorylation switch in determining CRC and GC development.

cancer biology↗

bioRxiv2017

Variation Of The Meiotic Recombination Landscape And Properties Over A Broad Evolutionary Distance In Yeasts

Meiotic recombination is a major factor of genome evolution, deeply characterized in only a few model species, notably the yeast Saccharomyces cerevisiae. Consequently, little is known about variations of its properties across species. In this respect, we explored the recombination landscape of Lachancea kluyveri, a protoploid yeast species that diverged from the Saccharomyces genus more than 100 million years ago and we found striking differences with S. cerevisiae. These variations include a lower recombination rate, a higher frequency of chromosomes segregating without any crossover and the absence of recombination on the chromosome arm containing the sex locus. In addition, although well conserved within the Saccharomyces clade, the S. cerevisiae recombination hotspots are not conserved over a broader evolutionary distance. Finally and strikingly, we found evidence of frequent reversion of meiotic commitment to mitotic growth allowing allele shuffling without meiosis completion. Identification of this major but underestimated evolutionary phenomenon illustrates the relevance of exploring non-model species.\n\nAuthor summaryMeiotic recombination promotes accurate chromosome segregation and genetic diversity. To date, the mechanisms and rules lying behind recombination were dissected using model organisms such as the budding yeast Saccharomyces cerevisiae. To assess the conservation and variation of this process over a broad evolutionary distance, we explored the meiotic recombination landscape in Lachancea kluyveri, a budding yeast species that diverged from S. cerevisiae more than 100 million years ago. The meiotic recombination map we generated revealed that the meiotic recombination landscape and properties significantly vary across distantly related yeast species, supporting that recombination hotspots conservation across yeast species is likely associated to the conservation of synteny. Finally, the frequent meiotic reversions we observed led us to re-evaluate their evolutionary importance.

genomics↗