Ab initio modeling of small proteins by iterative TASSER simulations

Background: Predicting 3-dimensional protein structures from amino-acid sequences is an important unsolved problem in computational structural biology. The problem becomes relatively easier if close homologous proteins have been solved, as high-resolution models can be built by aligning target sequences to the solved homologous structures. However, for sequences without similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch. Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER (threading/assembly/refinement) method for the ab initio modeling and examine systemically its ability to fold small single-domain proteins. Results: We developed I-TASSER by iteratively implementing the TASSER method, which is used in the folding test of three benchmarks of small proteins. First, data on 16 small proteins (< 90 residues) were used to generate I-TASSER models, which had an average Cα-root mean square deviation (RMSD) of 3.8Å, with 6 of them having a Cα-RMSD < 2.5Å. The overall result was comparable with the all-atomic ROSETTA simulation, but the central processing unit (CPU) time by I-TASSER was much shorter (150 CPU days vs. 5 CPU hours). Second, data on 20 small proteins (< 120 residues) were used. I-TASSER folded four of them with a Cα-RMSD < 2.5Å. The average Cα-RMSD of the I-TASSER models was 3.9Å, whereas it was 5.9Å using TOUCHSTONE-II software. Finally, 20 non-homologous small proteins (< 120 residues) were taken from the PDB library. An average Cα-RMSD of 3.9Å was obtained for the third benchmark, with seven cases having a Cα-RMSD < 2.5Å. Conclusion: Our simulation results show that I-TASSER can consistently predict the correct folds and sometimes high-resolution models for small single-domain proteins. Compared with other ab initio modeling methods such as ROSETTA and TOUCHSTONE II, the average performance of I-TASSER is either much better or is similar within a lower computational time. These data, together with the significant performance of automated I-TASSER server (the Zhang-Server) in the 'free modeling' section of the recent Critical Assessment of Structure Prediction (CASP)7 experiment, demonstrate new progresses in automated ab initio model generation. The I-TASSER server is freely available for academic users http://zhang.bioinformatics.ku.edu/I-TASSER webcite

Dichloramine Hydrolysis in Membrane Desalination Permeate: Mechanistic Insights and Implications for Oxidative Capacity in Potable Reuse Applications.

Dichloramine (NHCl2) naturally exists in reverse osmosis (RO) permeate due to its application as an antifouling chemical in membrane-based potable reuse treatment. This study investigated mechanisms of background NHCl2 hydrolysis associated with the generation of oxidative radical species in RO permeate, established a kinetic model to predict the oxidative capacity, and examined its removal efficiency on trace organic contaminants in potable reuse. Results showed that NHCl2 hydrolysis generated transient peroxynitrite (ONOO-) and subsequently dissociated into hydroxyl radical (HO•). The maximal HO• exposure was observed at an RO permeate pH of 8.4, higher than that from typical ultraviolet (UV)-based advanced oxidation processes. The HO• exposure during NHCl2 hydrolysis also peaked at a NH2Cl-to-NHCl2 molar ratio of 1:1. The oxidative capacity rapidly degraded 1,4-dioxane, carbamazepine, atenolol, and sulfamethoxazole in RO permeate. Furthermore, background elevated carbonate in fresh RO permeate can convert HO• to carbonate radical (CO3•-). Aeration of the RO permeate removed total carbonate, significantly increased HO• exposure, and enhanced the degradation kinetics of trace organic contaminants. The kinetic model of NHCl2 hydrolysis predicted well the degradation of contaminants in RO permeate. This study provides new mechanistic insights into NHCl2 hydrolysis that contributes to the oxidative degradation of trace organic contaminants in potable reuse systems

Alternative Splicing Regulated by Butyrate in Bovine Epithelial Cells

As a signaling molecule and an inhibitor of histone deacetylases (HDACs), butyrate exerts its impact on a broad range of biological processes, such as apoptosis and cell proliferation, in addition to its critical role in energy metabolism in ruminants. This study examined the effect of butyrate on alternative splicing in bovine epithelial cells using RNA-seq technology. Junction reads account for 11.28 and 12.32% of total mapped reads between the butyrate-treated (BT) and control (CT) groups. 201,326 potential splicing junctions detected were supported by ≥3 junction reads. Approximately 94% of these junctions conformed to the consensus sequence (GT/AG) while ∼3% were GC/AG junctions. No AT/AC junctions were observed. A total of 2,834 exon skipping events, supported by a minimum of 3 junction reads, were detected. At least 7 genes, their mRNA expression significantly affected by butyrate, also had exon skipping events differentially regulated by butyrate. Furthermore, COL5A3, which was induced 310-fold by butyrate (FDR <0.001) at the gene level, had a significantly higher number of junction reads mapped to Exon#8 (Donor) and Exon#11 (Acceptor) in BT. This event had the potential to result in the formation of a COL5A3 mRNA isoform with 2 of the 69 exons missing. In addition, 216 differentially expressed transcript isoforms regulated by butyrate were detected. For example, Isoform 1 of ORC1 was strongly repressed by butyrate while Isoform 2 remained unchanged. Butyrate physically binds to and inhibits all zinc-dependent HDACs except HDAC6 and HDAC10. Our results provided evidence that butyrate also regulated deacetylase activities of classical HDACs via its transcriptional control. Moreover, thirteen gene fusion events differentially affected by butyrate were identified. Our results provided a snapshot into complex transcriptome dynamics regulated by butyrate, which will facilitate our understanding of the biological effects of butyrate and other HDAC inhibitors

Metagenome Plasticity of the Bovine Abomasal Microbiota in Immune Animals in Response to Ostertagia Ostertagi Infection

Infections in cattle by the abomasal nematode Ostertagia ostertagi result in impaired gastrointestinal function. Six partially immune animals were developed using multiple drug-attenuated infections, and these animals displayed reduced worm burdens and a slightly elevated abomasal pH upon reinfection. In this study, we characterized the abomasal microbiota in response to reinfection using metagenomic tools. Compared to uninfected controls, infection did not induce a significant change in the microbial community composition in immune animals. 16S rRNA gene-based phylogenetic analysis identified 15 phyla in the bovine abomasal microbiota with Bacteroidetes (60.5%), Firmicutes (27.1%), Proteobacteria (7.2%), Spirochates (2.9%), and Fibrobacteres (1.5%) being the most predominant. The number of prokaryotic genera and operational taxonomic units (OTU) identified in the abomasal microbial community was 70.8±19.8 (mean ± SD) and 90.3±2.9, respectively. However, the core microbiome comprised of 32 genera and 72 OTU. Infection seemingly had a minimal impact on the abomasal microbial diversity at a genus level in immune animals. Proteins predicted from whole genome shotgun (WGS) DNA sequences were assigned to 5,408 Pfam and 3,381 COG families, demonstrating dazzling arrays of functional diversity in bovine abomasal microbial communities. However, none of COG functional classes were significantly impacted by infection. Our results demonstrate that immune animals may develop abilities to maintain proper stability of their abomasal microbial ecosystem. A minimal disruption in the bovine abomasal microbiota by reinfection may contribute equally to the restoration of gastric function in immune animals

Perturbation Dynamics of the Rumen Microbiota in Response to Exogenous Butyrate

The capacity of the rumen microbiota to produce volatile fatty acids (VFAs) has important implications in animal well-being and production. We investigated temporal changes of the rumen microbiota in response to butyrate infusion using pyrosequencing of the 16S rRNA gene. Twenty one phyla were identified in the rumen microbiota of dairy cows. The rumen microbiota harbored 54.5±6.1 genera (mean ± SD) and 127.3±4.4 operational taxonomic units (OTUs), respectively. However, the core microbiome comprised of 26 genera and 82 OTUs. Butyrate infusion altered molar percentages of 3 major VFAs. Butyrate perturbation had a profound impact on the rumen microbial composition. A 72 h-infusion led to a significant change in the numbers of sequence reads derived from 4 phyla, including 2 most abundant phyla, Bacteroidetes and Firmicutes. As many as 19 genera and 43 OTUs were significantly impacted by butyrate infusion. Elevated butyrate levels in the rumen seemingly had a stimulating effect on butyrate-producing bacteria populations. The resilience of the rumen microbial ecosystem was evident as the abundance of the microorganisms returned to their pre-disturbed status after infusion withdrawal. Our findings provide insight into perturbation dynamics of the rumen microbial ecosystem and should guide efforts in formulating optimal uses of probiotic bacteria treating human diseases

Quantification of Transcriptome Responses of the Rumen Epithelium to Butyrate Infusion using RNA-seq Technology

Short-chain fatty acids (SCFAs), such as butyrate, produced by gut microorganisms, play a critical role in energy metabolism and physiology of ruminants as well as in human health. In this study, the temporal effect of elevated butyrate concentrations on the transcriptome of the rumen epithelium was quantified via serial biopsy sampling using RNA-seq technology. The mean number of genes transcribed in the rumen epithelial transcriptome was 17,323.63 ± 277.20 (±SD; N = 24) while the core transcriptome consisted of 15,025 genes. Collectively, 80 genes were identified as being significantly impacted by butyrate infusion across all time points sampled. Maximal transcriptional effect of butyrate on the rumen epithelium was observed at the 72-h infusion when the abundance of 58 genes was altered. The initial reaction of the rumen epithelium to elevated exogenous butyrate may represent a stress response as Gene Ontology (GO) terms identified were predominantly related to responses to bacteria and biotic stimuli. An algorithm for the reconstruction of accurate cellular networks (ARACNE) inferred regulatory gene networks with 113,738 direct interactions in the butyrate-epithelium interactome using a combined cutoff of an error tolerance (ɛ = 0.10) and a stringent P-value threshold of mutual information (5.0 × 10−11). Several regulatory networks were controlled by transcription factors, such as CREBBP and TTF2, which were regulated by butyrate. Our findings provide insight into the regulation of butyrate transport and metabolism in the rumen epithelium, which will guide our future efforts in exploiting potential beneficial effect of butyrate in animal well-being and human health

LOMETS: A local meta-threading-server for protein structure prediction

We developed LOMETS, a local threading meta-server, for quick and automated predictions of protein tertiary structures and spatial constraints. Nine state-of-the-art threading programs are installed and run in a local computer cluster, which ensure the quick generation of initial threading alignments compared with traditional remote-server-based meta-servers. Consensus models are generated from the top predictions of the component-threading servers, which are at least 7% more accurate than the best individual servers based on TM-score at a t-test significance level of 0.1%. Moreover, side-chain and C-alpha (Cα) contacts of 42 and 61% accuracy respectively, as well as long- and short-range distant maps, are automatically constructed from the threading alignments. These data can be easily used as constraints to guide the ab initio procedures such as TASSER for further protein tertiary structure modeling. The LOMETS server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/LOMETS

Metagenomic Insights into the RDX-Degrading Potential of the Ovine Rumen Microbiome

The manufacturing processes of royal demolition explosive (RDX), or hexahydro-1,3,5-trinitro-1,3,5-triazine, have resulted in serious water contamination. As a potential carcinogen, RDX can cause a broad range of harmful effects to humans and animals. The ovine rumen is capable of rapid degradation of nitroaromatic compounds, including RDX. While ruminal RDX-degrading bacteria have been identified, the genes and pathways responsible for RDX degradation in the rumen have yet to be characterized. In this study, we characterized the metabolic potential of the ovine rumen using metagenomic approaches. Sequences homologous to at least five RDX-degrading genes cloned from environmental samples (diaA, xenA, xenB, xplA, and xplB) were present in the ovine rumen microbiome. Among them, diaA was the most abundant, likely reflective of the predominance of the genus Clostridium in the ovine rumen. At least ten genera known to harbor RDX-degrading microorganisms were detectable. Metagenomic sequences were also annotated using public databases, such as Pfam, COG, and KEGG. Five of the six Pfam protein families known to be responsible for RDX degradation in environmental samples were identified in the ovine rumen. However, increased substrate availability did not appear to enhance the proliferation of RDX-degrading bacteria and alter the microbial composition of the ovine rumen. This implies that the RDX-degrading capacity of the ovine rumen microbiome is likely regulated at the transcription level. Our results provide metagenomic insights into the RDX-degrading potential of the ovine rumen, and they will facilitate the development of novel and economic bioremediation strategies

ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction

We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28°/46°, which is ∼10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value <1.0×10−300 (or <1.0×10−148) by Wilcoxon signed rank test. For some residues (ILE, LEU, PRO and VAL) and especially the residues in helix and buried regions, the MAE of phi angles is much smaller (10–20°) than that in other environments. Thus, although the average accuracy of the ANGLOR prediction is still low, the portion of the accurately predicted dihedral angles may be useful in assisting protein fold recognition and ab initio 3D structure modeling