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Background: The toxicity of alcohols is one of the major roadblocks of biological fermentation for biofuels production. Methylobacterium extorquens AM1, a facultative methylotrophic a-proteobacterium, has been engineered to generate 1-butanol from cheap carbon feedstocks through a synthetic metabolic pathway. However, M. extorquens AM1 is vulnerable to solvent stress, which impedes further development for 1-butanol production. Only a few studies have reported the general stress response of M. extorquens AM1 to solvent stress. Therefore, it is highly desirable to obtain a strain with ameliorated 1-butanol tolerance and elucidate the molecular mechanism of 1-butnaol tolerance in M. extorquens AM1 for future strain improvement. Results: In this work, adaptive laboratory evolution was used as a tool to isolate mutants with 1-butanol tolerance up to 0.5 %. The evolved strains, BHBT3 and BHBT5, demonstrated increased growth rates and higher survival rates with the existence of 1-butanol. Whole genome sequencing revealed a SNP mutation at kefB in BHBT5, which was confirmed to be responsible for increasing 1-butanol tolerance through an allelic exchange experiment. Global metabolomic analysis further discovered that the pools of multiple key metabolites, including fatty acids, amino acids, and disaccharides, were increased in BHBT5 in response to 1-butanol stress. Additionally, the carotenoid synthesis pathway was significantly down-regulated in BHBT5. Conclusions: We successfully screened mutants resistant to 1-butanol and provided insights into the molecular mechanism of 1-butanol tolerance in M. extorquens AM1. This research will be useful for uncovering the mechanism of cellular response of M. extorquens AM1 to solvent stress, and will provide the genetic blueprint for the rational design of a strain of M. extorquens AM1 with increased 1-butanol tolerance in the future. [ABSTRACT FROM AUTHOR]

The ecophysiology of long-chain fatty acid-degrading syntrophic β-oxidizing bacteria has been poorly understood due to a lack of quantitative abundance data. Here, TaqMan quantitative PCR (qPCR) assays targeting the 16S rRNA gene of the known mesophilic syntrophic β-oxidizing bacterial genera Syntrophomonas and Syntrophus were developed and validated. Microbial community dynamics were followed using qPCR and Illumina-based high-throughput amplicon sequencing in triplicate methanogenic bioreactors subjected to five consecutive batch feedings of oleic acid. With repeated oleic acid feeding, the initial specific methane production rate significantly increased along with the relative abundances of Syntrophomonas and methanogenic archaea in the bioreactor communities. The novel qPCR assays showed that Syntrophomonas increased from 7 to 31% of the bacterial community 16S rRNA gene concentration, whereas that of Syntrophus decreased from 0.02 to less than 0.005%. High-throughput amplicon sequencing also revealed that Syntrophomonas became the dominant genus within the bioreactor microbiomes. These results suggest that increased specific mineralization rates of oleic acid were attributed to quantitative shifts within the microbial communities toward higher abundances of syntrophic β-oxidizing bacteria and methanogenic archaea. The novel qPCR assays targeting syntrophic β-oxidizing bacteria may thus serve as monitoring tools to indicate the fatty acid β-oxidization potential of anaerobic digester communities. [ABSTRACT FROM AUTHOR]

We sequenced the genomes of 19 methylotrophic isolates from Lake Washington, which belong to nine genera within eight families of the A lphaproteobacteria, two of the families being the newly proposed families. Comparative genomic analysis with a focus on methylotrophy metabolism classifies these strains into heterotrophic and obligately or facultatively autotrophic methylotrophs. The most persistent metabolic modules enabling methylotrophy within this group are the N-methylglutamate pathway, the two types of methanol dehydrogenase ( MxaFI and XoxF), the tetrahydromethanopterin pathway for formaldehyde oxidation, the serine cycle and the ethylmalonyl- CoA pathway. At the same time, a great potential for metabolic flexibility within this group is uncovered, with different combinations of these modules present. Phylogenetic analysis of key methylotrophy functions reveals that the serine cycle must have evolved independently in at least four lineages of A lphaproteobacteria and that all methylotrophy modules seem to be prone to lateral transfers as well as deletions. [ABSTRACT FROM AUTHOR]

We describe five novel Methylophilaceae ecotypes from a single ecological niche in Lake Washington, USA, and compare them to three previously described ecotypes, in terms of their phenotype and genome sequence divergence. Two of the ecotypes appear to represent novel genera within the Methylophilaceae. Genome-based metabolic reconstruction highlights metabolic versatility of Methylophilaceae with respect to methylotrophy and nitrogen metabolism, different ecotypes possessing different combinations of primary substrate oxidation systems (MxaFI-type methanol dehydrogenase versus XoxF-type methanol dehydrogenase; methylamine dehydrogenase versus N-methylglutamate pathway) and different potentials for denitrification (assimilatory versus respiratory nitrate reduction). By comparing pairs of closely related genomes, we uncover that site-specific recombination is the main means of genomic evolution and strain divergence, including lateral transfers of genes from both closely- and distantly related taxa. The new ecotypes and the new genomes contribute significantly to our understanding of the extent of genomic and metabolic diversity among organisms of the same family inhabiting the same ecological niche. These organisms also provide novel experimental models for studying the complexity and the function of the microbial communities active in methylotrophy. [ABSTRACT FROM AUTHOR]

Methylotrophs, organisms able to gain energy and carbon from compounds containing no carbon–carbon bonds, such as methane, methanol and methylated amines, are widespread in nature. However, knowledge of their nutrient preference and their metabolism is mostly based on experiments with cultures grown in defined laboratory conditions. Here, we use transcriptomics to explore the activity of one methylotroph, Methyotenera mobilis in its natural environment, lake sediment from which it has been previously isolated. Cells encapsulated in incubation cassettes were exposed to sediment conditions, with or without supplementation with a carbon/energy source (methylamine), and gene-expression patterns were compared for those cells to patterns for cells incubated in a defined medium supplemented with methylamine. A few specific trends in gene expression were observed at in situ conditions that may be of environmental significance, as follows. Expression of genes for the linear formaldehyde oxidation pathway linked to tetrahydromethanopterin increased, suggesting an important role for this pathway in situ, in contrast to laboratory condition culture, in which the cyclic ribulose monophosphate pathway seemed to be the major route for formaldehyde oxidation. Along with the ribulose monophosphate cycle that is also a major pathway for assimilating C1 units, the methylcitric acid cycle seemd to be important in situ, suggesting that multicarbon compounds may be the natural carbon and/or energy substrates for M. mobilis, challenging the notion of an obligately methylotrophic lifestyle for this bacterium. We also detected a major switch in expression of genes responsible for the mode of motility between different conditions: from flagellum-enabled motility in defined medium to in situ expression of pili known to be involved in twitching motility and adherence. Overall, this study offers a novel approach for gaining insights into the lifestyle of individual microbes in their native environments. [ABSTRACT FROM AUTHOR]

The correct treatment of van der Waals and electrostatic nonbonded interactions in molecular force fields is essential for performing realistic molecular dynamics (MD) simulations of solvated polypeptides. The most computationally tractable treatment of nonbonded interactions in MD utilizes a spherical distance cutoff (typically, 8-12 A) to reduce the number of pairwise interactions. In this work, we assess three spherical atom-based cutoff approaches for use with all-atom explicit solvent MD: abrupt truncation, a CHARMM-style electrostatic shift truncation, and our own force-shifted truncation. The chosen system for this study is an end-capped 17-residue alanine-based a-helical peptide, selected because of its use in previous computational and experimental studies. We compare the time-averaged helical content calculated from these MD trajectories with experiment. We also examine the effect of varying the cutoff treatment and distance on energy conservation. We find that the abrupt truncation approach is pathological in its inability to conserve energy. The CHARMM-style shift truncation performs quite well but suffers from energetic instability. On the other hand, the force-shifted spherical cutoff method conserves energy, correctly predicts the experimental helical content, and shows convergence in simulation statistics as the cutoff is increased. This work demonstrates that by using proper and rigorous techniques, it is possible to correctly model polypeptide dynamics in solution with a spherical cutoff. The inherent computational advantage of spherical cutoffs over Ewald summation (and related) techniques is essential in accessing longer MD time scales. [ABSTRACT FROM AUTHOR]

An abstract of the article "Simulations of Macromolecules in Protective and Denaturing Osmolytes: Properties of Mixed Solvent Systems and Their Effects on Water and Protein Structure and Dynamics," by David A. C. Beck and colleagues is presented.

Halide perovskites have the potential to disrupt the photovoltaics market based on their high performance and low cost. However, the decomposition of perovskites under moisture, oxygen, and light raises concerns about service lifetime, especially because degradation mechanisms and the corresponding rate laws that fit the observed data have thus far eluded researchers. Here, we report a water-accelerated photooxidation mechanism dominating the degradation kinetics of archetypal perovskite CH 3 NH 3 PbI 3 in air under >1% relative humidity at 25 °C. From this mechanism, we develop a kinetic model that quantitatively predicts the degradation rate as a function of temperature, ambient O 2 and H 2 O levels, and illumination. Because water is a possible product of dry photooxidation, these results highlight the need for encapsulation schemes that rigorously block oxygen ingress, as product water may accumulate beneath the encapsulant and initiate the more rapid water-accelerated photooxidative decomposition.

Standard methods of monitoring the growth kinetics of anaerobic microorganisms are generally impractical when there is a protracted or indeterminate period of active growth, and when high numbers of samples or replications are required. As part of our studies of the adaptive evolution of a simple anaerobic syntrophic mutualism, requiring the characterization of many isolates and alternative syntrophic pairings, we developed a multiplexed growth monitoring system using a combination of commercially available electronics and custom designed circuitry and materials. This system automatically monitors up to 64 sealed, and as needed pressurized, culture tubes and reports the growth data in real-time through integration with a customized relational database. The utility of this system was demonstrated by resolving minor differences in growth kinetics associated with the adaptive evolution of a simple microbial community comprised of a sulfate reducing bacterium, Desulfovibrio vulgaris, grown in syntrophic association with Methanococcus maripaludis, a hydrogenotrophic methanogen., (Copyright © 2021. Published by Elsevier B.V.)

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science-the application tools for molecular discovery and property optimization at the atomic scale. We summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.

Mosaic plasmids, plasmids composed of genetic elements from distinct sources, are associated with the spread of antibiotic resistance genes. Transposons are considered the primary mechanism for mosaic plasmid formation, though other mechanisms have been observed in specific instances. The frequency with which mosaic plasmids have been described suggests they may play an important role in plasmid population dynamics. Our survey of the confirmed plasmid sequences available from complete and draft genomes in the RefSeq database shows that 46% of them fit a strict definition of mosaic. Mosaic plasmids are also not evenly distributed over the taxa represented in the database. Plasmids from some genera, including Piscirickettsia and Yersinia, are almost all mosaic, while plasmids from other genera, including Borrelia, are rarely mosaic. While some mosaic plasmids share identical regions with hundreds of others, the median mosaic plasmid only shares with 8 other plasmids. When considering only plasmids from finished genomes (51.6% of the total), mosaic plasmids have significantly higher proportions of transposase and antibiotic resistance genes. Conversely, only 56.6% of mosaic fragments (DNA fragments shared between mosaic plasmids) contain a recognizable transposase gene, and only 1.2% of mosaic fragments are flanked by inverted repeats. Mosaic fragments associated with the IS26 transposase gene are 3.8-fold more abundant than any other sequence shared between mosaic plasmids in the database, though this is at least partly due to overrepresentation of Enterobacteriaceae plasmids. Mosaic plasmids are a complicated trait of some plasmid populations, only partly explained by transposition. Though antibiotic resistance genes led to the identification of many mosaic plasmids, mosaic plasmids are a broad phenomenon encompassing many more traits than just antibiotic resistance. Further research will be required to determine the influence of ecology, host repair mechanisms, conjugation, and plasmid host range on the formation and influence of mosaic plasmids. AUTHOR SUMMARY: Plasmids are extrachromosomal genetic entities that are found in many prokaryotes. They serve as flexible storage for genes, and individual cells can make substantial changes to their characteristics by acquiring, losing, or modifying a plasmid. In some pathogenic bacteria, such as Escherichia coli, antibiotic resistance genes are known to spread primarily on plasmids. By analyzing a database of 8592 plasmid sequences we determined that many of these plasmids have exchanged genes with each other, becoming mosaics of genes from different sources. We next separated these plasmids into groups based on the organism they were isolated from and found that different groups had different fractions of mosaic plasmids. This result was unexpected and suggests that the mechanisms and selective pressures causing mosaic plasmids do not occur evenly over all species. It also suggests that plasmids may provide different levels of potential variation to different species. This work uncovers a previously unrecognized pattern in plasmids across prokaryotes, that could lead to new insights into the evolutionary role that plasmids play., (Copyright © 2019. Published by Elsevier Inc.)

The bacteria that grow on methane aerobically (methanotrophs) support populations of non-methanotrophs in the natural environment by excreting methane-derived carbon. One group of excreted compounds are short-chain organic acids, generated in highest abundance when cultures are grown under O 2 -starvation. We examined this O 2 -starvation condition in the methanotroph Methylomicrobium buryatense 5GB1. The M. buryatense 5GB1 genome contains homologs for all enzymes necessary for a fermentative metabolism, and we hypothesize that a metabolic switch to fermentation can be induced by low-O 2 conditions. Under prolonged O 2 -starvation in a closed vial, this methanotroph increases the amount of acetate excreted about 10-fold, but the formate, lactate, and succinate excreted do not respond to this culture condition. In bioreactor cultures, the amount of each excreted product is similar across a range of growth rates and limiting substrates, including O 2 -limitation. A set of mutants were generated in genes predicted to be involved in generating or regulating excretion of these compounds and tested for growth defects, and changes in excretion products. The phenotypes and associated metabolic flux modeling suggested that in M. buryatense 5GB1, formate and acetate are excreted in response to redox imbalance. Our results indicate that even under O 2 -starvation conditions, M. buryatense 5GB1 maintains a metabolic state representing a combination of fermentation and respiration metabolism., Competing Interests: Melissa Hendershott is currently an employee of the Allen Institute for Cell Science, Frances Chu is currently an employee of InBios, and Amanda Lee Smith is currently an employee of Zymo Genetics.

This study investigated the impacts of long-chain fatty acid (LCFA) feeding frequencies on microbial community structure, bioconversion kinetics, and process stability during anaerobic codigestion. Parallel laboratory-scale anaerobic codigesters fed with dairy cattle manure were either pulse-fed every two days or continuously-fed daily, respectively, with oleate (C 18:1 ) in incremental step increases over 200 days up to 64% of the influent chemical oxygen demand (COD). The effluent acetate concentration exceeded 3000 mg/L in the continuous-fed codigester at the highest oleate loading rate, but remained below 100 mg/L in the pulse-fed codigester at the end of its 48-hr oleate feed cycle. Maximum substrate conversion rates of oleate (q max, oleate ) and acetate (q max, acetate ) were significantly higher in the pulse-fed codigester compared to the continuous-fed codigester. 16S rRNA gene amplicon sequencing showed that Bacteria and Archaea community profiles diverged based on the codigester LCFA feeding pattern and loading rate. LCFA-degrading Syntrophomonas bacteria were significantly enriched in both LCFA codigesters relative to the control digester. The pulse-fed codigester had the highest community fraction of Syntrophomonas 16S rRNA genes by the end of the experiment with 43% of Bacteria amplicon sequences. q max, oleate and q max, acetate values were both significantly correlated to absolute concentrations of Syntrophomonas and Methanosaeta 16S rRNA genes, respectively. Multiple-linear regression models based on the absolute abundance of Syntrophomonas and Methanosaeta taxa provided improved predictions of oleate and acetate bioconversion kinetics, respectively. These results collectively suggest that pulse feeding rather than continuous feeding LCFA during anaerobic codigestion selected for higher microbial bioconversion kinetics and functional stability, which were related to changes in the physiological diversity and adaptive capacity of syntrophic and methanogenic communities., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Codigesting fats, oils, and greases with municipal wastewater sludge can greatly improve biomethane recovery at wastewater treatment facilities. Process loading rates of fats, oils, and greases have been previously tested with little knowledge of the digester microbial community structure, and high transient fat loadings have led to long chain fatty acid (LCFA) accumulation and digester upsets. This study utilized recently-developed quantitative PCR assays for syntrophic LCFA-degrading bacteria along with 16S amplicon sequencing to relate changes in microbial community structure to LCFA accumulation during transient loading increases to an anaerobic codigester receiving waste restaurant oil and municipal wastewater sludge. The 16S rRNA gene concentration of the syntrophic β-oxidizing genus Syntrophomonas increased to ∼15% of the Bacteria community in the codigester, but stayed below 3% in the control digester that was fed only wastewater sludge. Methanosaeta and Methanospirillum were the dominant methanogenic genera enriched in the codigester, and together comprised over 80% of the Archaea community by the end of the experimental period. Constrained ordination showed that changes in the codigester Bacteria and Archaea community structures were related to measures of digester performance. Notably, the effluent LCFA concentration in the codigester was positively correlated to the specific loading rate of waste oil normalized to the Syntrophomonas 16S rRNA concentration. Specific loading rates of 0-1.5 × 10(-12) g VS oil/16S gene copies-day resulted in LCFA concentrations below 30 mg/g TS, whereas LCFA accumulated up to 104 mg/g TS at higher transient loading rates. Based on the community-dependent loading limitations found, enhanced biomethane production from high loadings of fats, oils and greases can be achieved by promoting a higher biomass of slow-growing syntrophic consortia, such as with longer digester solids retention times. This work also demonstrates the potential for controlling the loading rate of fats, oils, and greases based on the analysis of the codigester community structure, such as with quantitative PCR measurements of syntrophic LCFA-degrading bacteria abundance., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Many methylotrophs, microorganisms that consume carbon compounds lacking carbon-carbon bonds, use two different systems to oxidize methanol for energy production and biomass accumulation. The MxaFI methanol dehydrogenase (MDH) contains calcium in its active site, while the XoxF enzyme contains a lanthanide in its active site. The genes encoding the MDH enzymes are differentially regulated by the presence of lanthanides. In this study, we found that the histidine kinase MxaY controls the lanthanide-mediated switch in Methylomicrobium buryatense 5GB1C. MxaY controls the transcription of genes encoding MxaFI and XoxF at least partially by controlling the transcript levels of the orphan response regulator MxaB. We identify a constitutively active version of MxaY, and identify the mutated residue that may be involved in lanthanide sensing. Lastly, we find evidence to suggest that tight control of active MDH production is required for wild-type growth rates., Competing Interests: The authors declare there no competing interests.

We report observations on the dynamics of bacterial communities in response to methane stimulus in laboratory microcosm incubations prepared with lake sediment samples. We first measured taxonomic compositions of long-term enrichment cultures and determined that, although dominated by Methylococcaceae types, these cultures also contained accompanying types belonging to a limited number of bacterial taxa, methylotrophs and non-methylotrophs. We then followed the short-term community dynamics, in two oxygen tension regimens (150 μM and 15 μM), observing rapid loss of species diversity. In all microcosms, a single type of Methylobacter represented the major methane-oxidizing partner. The accompanying members of the communities revealed different trajectories in response to different oxygen tensions, with Methylotenera species being the early responders to methane stimulus under both conditions. The communities in both conditions were convergent in terms of their assemblage, suggesting selection for specific taxa. Our results support prior observations from metagenomics on distribution of carbon from methane among diverse bacterial populations and further suggest that communities are likely responsible for methane cycling, rather than a single type of microbe.

Methyloversatilis universalis FAM5 utilizes single carbon compounds such as methanol or methylamine as a sole source of carbon and energy. Expression profiling reveals distinct sets of genes altered during growth on methylamine vs methanol. As expected, all genes for the N-methylglutamate pathway were induced during growth on methylamine. Among other functions responding to the aminated source of C₁-carbon, are a heme-containing amine dehydrogenase (Qhp), a distant homologue of formaldehyde activating enzyme (Fae3), molybdenum-containing formate dehydrogenase, ferredoxin reductase, a set of homologues to urea/ammonium transporters and amino-acid permeases. Mutants lacking one of the functional subunits of the amine dehydrogenase (ΔqhpA) or Δfae3 showed no growth defect on C₁-compounds. M. universalis FAM5 strains with a lesion in the H₄-folate pathway were not able to use any C₁-compound, methanol or methylamine. Genes essential for C₁-assimilation (the serine cycle and glyoxylate shunt) and H₄MTP-pathway for formaldehyde oxidation showed similar levels of expression on both C₁-carbon sources. M. universalis FAM5 possesses three homologs of the formaldehyde activating enzyme, a key enzyme of the H₄MTP-pathway. Strains lacking the canonical Fae (fae1) lost the ability to grow on both C₁-compounds. However, upon incubation on methylamine the fae1-mutant produced revertants (Δfae1(R)), which regained the ability to grow on methylamine. Double and triple mutants (Δfae1(R)Δfae3, or Δfae1(R)Δfae2 or Δfae1(R)Δfae2Δfae3) constructed in the revertant strain background showed growth similar to the Δfae1(R) phenotype. The metabolic pathways for utilization of methanol and methylamine in Methyloversatilis universalis FAM5 are reconstructed based on these gene expression and phenotypic data.

Gram-positive methylotrophic bacteria have been known for a long period of time, some serving as model organisms for characterizing the specific details of methylotrophy pathways/enzymes within this group. However, genome-based knowledge of methylotrophy within this group has been so far limited to a single species, Bacillus methanolicus (Firmicutes). The paucity of whole-genome data for Gram-positive methylotrophs limits our global understanding of methylotrophy within this group, including their roles in specific biogeochemical cycles, as well as their biotechnological potential. Here, we describe the isolation of seven novel strains of Gram-positive methylotrophs that include two strains of Bacillus and five representatives of Actinobacteria classified within two genera, Arthrobacter and Mycobacterium. We report whole-genome sequences for these isolates and present comparative analysis of the methylotrophy functional modules within these genomes. The genomic sequences of these seven novel organisms, all capable of growth on methylated amines, present an important reference dataset for understanding the genomic basis of methylotrophy in Gram-positive methylotrophic bacteria. This study is a major contribution to the field of methylotrophy, aimed at closing the gap in the genomic knowledge of methylotrophy within this diverse group of bacteria.

We have previously observed that methane supplied to lake sediment microbial communities as a substrate not only causes a response by bona fide methanotrophic bacteria, but also by non-methane-oxidizing bacteria, especially by members of the family Methylophilaceae. This result suggested that methane oxidation in this environment likely involves communities composed of different functional guilds, rather than a single type of microbe. To obtain further support for this concept and to obtain further insights into the factors that may define such partnerships, we carried out microcosm incubations with sediment samples from Lake Washington at five different oxygen tensions, while methane was supplied at the same concentration in each. Community composition was determined through 16S rRNA gene amplicon sequencing after 10 and 16 weeks of incubation. We demonstrate that, in support of our prior observations, the methane-consuming communities were represented by two major types: the methanotrophs of the family Methylococcaceae and by non-methanotrophic methylotrophs of the family Methylophilaceae. However, different species persisted under different oxygen tensions. At high initial oxygen tensions (150 to 225 µM) the major players were, respectively, species of the genera Methylosarcina and Methylophilus, while at low initial oxygen tensions (15 to 75 µM) the major players were Methylobacter and Methylotenera. These data suggest that oxygen availability is at least one major factor determining specific partnerships in methane oxidation. The data also suggest that speciation within Methylococcaceae and Methylophilaceae may be driven by niche adaptation tailored toward specific placements within the oxygen gradient.

Protein function is intimately linked to protein structure and dynamics yet experimentally determined structures frequently omit regions within a protein due to indeterminate data, which is often due protein dynamics. We propose that atomistic molecular dynamics simulations provide a diverse sampling of biologically relevant structures for these missing segments (and beyond) to improve structural modeling and structure prediction. Here we make use of the Dynameomics data warehouse, which contains simulations of representatives of essentially all known protein folds. We developed novel computational methods to efficiently identify, rank and retrieve small peptide structures, or fragments, from this database. We also created a novel data model to analyze and compare large repositories of structural data, such as contained within the Protein Data Bank and the Dynameomics data warehouse. Our evaluation compares these structural repositories for improving loop predictions and analyzes the utility of our methods and models. Using a standard set of loop structures, containing 510 loops, 30 for each loop length from 4 to 20 residues, we find that the inclusion of Dynameomics structures in fragment-based methods improves the quality of the loop predictions without being dependent on sequence homology. Depending on loop length, ∼ 25-75% of the best predictions came from the Dynameomics set, resulting in lower main chain root-mean-square deviations for all fragment lengths using the combined fragment library. We also provide specific cases where Dynameomics fragments provide better predictions for NMR loop structures than fragments from crystal structures. Online access to these fragment libraries is available at http://www.dynameomics.org/fragments., (© 2014 The Protein Society.)

We carried out whole transcriptome analysis of three species of Methylophilaceae, Methylotenera mobilis, Methylotenera versatilis and Methylovorus glucosotrophus, in order to determine which metabolic pathways are actively transcribed in cultures grown in laboratory on C1 substrates and how metabolism changes under semi-in situ conditions. Comparative analyses of the transcriptomes were used to probe the metabolic strategies utilized by each of the organisms in the environment. Our analysis of transcript abundance data focused on changes in expression of methylotrophy metabolic modules, as well as on identifying any functional modules with pronounced response to in situ conditions compared to a limited set of laboratory conditions, highlighting their potential role in environmental adaptation. We demonstrate that transcriptional responses to environmental conditions involved both methylotrophy and non-methylotrophy metabolic modules as well as modules responsible for functions not directly connected to central metabolism. Our results further highlight the importance of XoxF enzymes that were previously demonstrated to be highly expressed in situ and proposed to be involved in metabolism of methanol by Methylophilaceae. At the same time, it appears that different species employ different homologous Xox systems as major metabolic modules. This study also reinforces prior observations of the apparent importance of the methylcitric acid cycle in the Methylotenera species and its role in environmental adaptation. High transcription from the respective gene clusters and pronounced response to in situ conditions, along with the reverse expression pattern for the ribulose monophosphate pathway that is the major pathway for carbon assimilation in laboratory conditions suggest that a switch in central metabolism of Methylotenera takes place in response to in situ conditions. The nature of the metabolite(s) processed via this pathway still remains unknown. Of the functions not related to central metabolism, flagellum and fimbria synthesis functions appeared to be of significance for environmental adaptation, based on their high abundance and differential expression. Our data demonstrate that, besides shared strategies, the organisms employed in this study also utilize strategies unique to each species, suggesting that the genomic divergence plays a role in environmental adaptation.

We investigated microbial communities active in methane oxidation in lake sediment at different oxygen tensions and their response to the addition of nitrate, via stable isotope probing combined with deep metagenomic sequencing. Communities from a total of four manipulated microcosms were analyzed, supplied with (13)C-methane in, respectively, ambient air, ambient air with the addition of nitrate, nitrogen atmosphere and nitrogen atmosphere with the addition of nitrate, and these were compared to the community from an unamended sediment sample. We found that the major group involved in methane oxidation in both aerobic and microaerobic conditions were members of the family Methylococcaceae, dominated by species of the genus Methylobacter, and these were stimulated by nitrate in aerobic but not microaerobic conditions. In aerobic conditions, we also noted a pronounced response to both methane and nitrate by members of the family Methylophilaceae that are non-methane-oxidizing methylotrophs, and predominantly by the members of the genus Methylotenera. The relevant abundances of the Methylococcaceae and the Methylophilaceae and their coordinated response to methane and nitrate suggest that these species may be engaged in cooperative behavior, the nature of which remains unknown.

A thermostabilized variant (UVF) of the engrailed homeodomain (EnHD) was previously engineered by Mayo and co-workers. The melting temperature of the non-natural, designed protein is 50°C higher than the natural wild-type protein (>99 vs. 52°C), and the two proteins share 22% sequence identity. We have performed extensive (1 μs) all-atom, explicit solvent molecular dynamics simulations of the wild-type and engineered proteins to investigate their structural and dynamic properties at room temperature and at 100°C. Our simulations are in good agreement with nuclear magnetic resonance data available for the two proteins [nuclear Overhauser effect crosspeaks (NOEs), J-coupling constants and order parameters for EnHD; and NOEs for UVF], showing that we reproduce the backbone dynamics and side chain packing in the native state of both proteins. UVF was more dynamic at room temperature than EnHD, with respect to both its backbone and side chain motion. When the temperature was raised, the thermostable protein maintained this mobility while retaining its native conformation. EnHD, on the other hand, was unable to maintain its more rigid native structure at higher temperature and began to unfold. Heightened protein dynamics leading to promiscuous and dynamically interchangeable amino acid contacts makes UVF more tolerant to increasing temperature, providing a molecular explanation for heightened thermostability of this protein.

Background: Molecular dynamics (MD) simulations offer the ability to observe the dynamics and interactions of both whole macromolecules and individual atoms as a function of time. Taken in context with experimental data, atomic interactions from simulation provide insight into the mechanics of protein folding, dynamics, and function. The calculation of atomic interactions or contacts from an MD trajectory is computationally demanding and the work required grows exponentially with the size of the simulation system. We describe the implementation of a spatial indexing algorithm in our multi-terabyte MD simulation database that significantly reduces the run-time required for discovery of contacts. The approach is applied to the Dynameomics project data. Spatial indexing, also known as spatial hashing, is a method that divides the simulation space into regular sized bins and attributes an index to each bin. Since, the calculation of contacts is widely employed in the simulation field, we also use this as the basis for testing compression of data tables. We investigate the effects of compression of the trajectory coordinate tables with different options of data and index compression within MS SQL SERVER 2008., Results: Our implementation of spatial indexing speeds up the calculation of contacts over a 1 nanosecond (ns) simulation window by between 14% and 90% (i.e., 1.2 and 10.3 times faster). For a 'full' simulation trajectory (51 ns) spatial indexing reduces the calculation run-time between 31 and 81% (between 1.4 and 5.3 times faster). Compression resulted in reduced table sizes but resulted in no significant difference in the total execution time for neighbour discovery. The greatest compression (~36%) was achieved using page level compression on both the data and indexes., Conclusions: The spatial indexing scheme significantly decreases the time taken to calculate atomic contacts and could be applied to other multidimensional neighbor discovery problems. The speed up enables on-the-fly calculation and visualization of contacts and rapid cross simulation analysis for knowledge discovery. Using page compression for the atomic coordinate tables and indexes saves ~36% of disk space without any significant decrease in calculation time and should be considered for other non-transactional databases in MS SQL SERVER 2008.

It is widely recognized that most microbes in the biosphere remain uncultured and unknown. In the recent few years, whole genome shotgun (WGS) sequencing of environmental DNA (metagenomics) has revolutionized the field of environmental microbiology by allowing one to tap into the genomic content of microbial communities in specific ecological niches, deducing information on their biochemical potentials. However, ascribing specific functions to specific organisms remains very difficult in most cases, due to low sequence coverage and the lack of sequence assembly that result from metagenomics of complex microbial communities. Therefore, methods that link specific biogeochemical processes to specific members of such complex natural communities are urgently needed. We have developed and implemented a functional metagenomics approach that allows such a connection via substrate-specific stable isotope labeling, followed by WGS sequencing of the labeled DNA to describe bacterial populations involved in metabolism of single-carbon compounds in a freshwater lake. We also developed a pipeline for community transcript analysis based on ultrashort read high-throughput sequencing of messenger RNA, matching these to a specific scaffold. The methodologies described in this chapter can be applied in a wide variety of ecosystems for targeting methylotrophs as well as other functional guilds of microbes., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Biotechnologies for effective conversion of atmospheric greenhouse gases (CO(2) and CH(4)) into valuable compounds, such as chemical and petrochemical feedstocks or alternative fuels, offer promising new strategies for stabilization of global warming. A novel approach in this field involves the use of methanotrophic bacteria as catalysts for CH(4) conversion. In recent years, extremophilic methanotrophic species related to the genus Methylomicrobium have become favorable systems for bioprocess engineering, due to their high growth rates and tolerance of a wide range of environmental conditions and perturbations. While the cultures hold the potential of producing a broader range of chemicals from methane, the biotechnologies are still limited by the lack of reliable genetic approaches for system-level studies and strain engineering. In this chapter, we describe a set of molecular tools for genetic investigation and alteration of the Methylomicrobium spp., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The ultrafast folding pathway of the engrailed homeodomain has been exceptionally well characterized by experiment and simulation. Helices II and III of the three-helix bundle protein form the native helix-turn-helix motif as an on-pathway intermediate within a few microseconds. The slow step is then the proper docking of the helices in approximately 15 mus. However, there is still the unexplained puzzle of why helix docking is relatively slow, which is part of the more general question as to why rearrangements of intermediates occur slowly. To address this problem, we performed 46 all-atom molecular dynamics refolding simulations in explicit water, for a total of 15 micros of simulation time. The simulations started from an intermediate state structure that was generated in an unfolding simulation at 498 K and was then quenched to folding-permissive temperatures. The protein refolded successfully in only one of the 46 simulations, and in that case the refolding pathway mirrored the unfolding pathway at high temperature. In the 45 simulations in which the protein did not fully fold, nonnative salt bridges trapped the protein, which explains why the protein folds relatively slowly from the intermediate state., (Copyright 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

The dynamic behavior of proteins is important for an understanding of their function and folding. We have performed molecular dynamics simulations of the native state and unfolding pathways of over 2000 protein/peptide systems (approximately 11,000 independent simulations) representing the majority of folds in globular proteins. These data are stored and organized using an innovative database approach, which can be mined to obtain both general and specific information about the dynamics and folding/unfolding of proteins, relevant subsets thereof, and individual proteins. Here we describe the project in general terms and the type of information contained in the database. Then we provide examples of mining the database for information relevant to protein folding, structure building, the effect of single-nucleotide polymorphisms, and drug design. The native state simulation data and corresponding analyses for the 100 most populated metafolds, together with related resources, are publicly accessible through http://www.dynameomics.org., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Molecular dynamics simulations of aquaporin-1 embedded in a solvated lipid bilayer were carried out to investigate the mechanism of water permeation. The 2.2 A resolution crystal structure of the bovine protein was used for five independent trajectories. During the equilibration and preparatory steps in which the protein was held fixed, water molecules inside the water channel adopted the same positions as observed in the crystal structure but they did not pass through the channel, suggesting that the dynamic motion of the protein is critical for water permeation. When the protein atoms were allowed to move, the side chains of the two asparagines in the two conserved Asn-Pro-Ala motifs near the center of the channel formed hydrogen bonds with water and helped water molecules move through the channel by actively aligning them for transport. The main-chain oxygen atoms, which were exposed to the pore surface in the crystal structure, also contributed to water transfer. Besides the constriction region observed in the crystal structure (Arg(197), Phe(58), His(182), and Cys(191)), we found that His(76) and Val(155) act as a valve by dynamically blocking water permeation and helping control flow.

The goal of Dynameomics is to perform atomistic molecular dynamics (MD) simulations of representative proteins from all known folds in explicit water in their native state and along their thermal unfolding pathways. Here we present 188-fold representatives and their native state simulations and analyses. These 188 targets represent 67% of all the structures in the Protein Data Bank. The behavior of several specific targets is highlighted to illustrate general properties in the full dataset and to demonstrate the role of MD in understanding protein function and stability. As an example of what can be learned from mining the Dynameomics database, we identified a protein fold with heightened localized dynamics. In one member of this fold family, the motion affects the exposure of its phosphorylation site and acts as an entropy sink to offset another portion of the protein that is relatively immobile in order to present a consistent interface for protein docking. In another member of this family, a polymorphism in the highly mobile region leads to a host of disease phenotypes. We have constructed a web site to provide access to a novel hybrid relational/multidimensional database (described in the succeeding two papers) to view and interrogate simulations of the top 30 targets: http://www.dynameomics.org. The Dynameomics database, currently the largest collection of protein simulations and protein structures in the world, should also be useful for determining the rules governing protein folding and kinetic stability, which should aid in deciphering genomic information and for protein engineering and design.

A properly identified transition state ensemble (TSE) in a molecular dynamics (MD) simulation can reveal a tremendous amount about how a protein folds and offer a point of comparison to experimentally derived Phi(F) values, which reflect the degree of structure in these transient states. In one such method of TSE identification, dubbed P(fold), MD simulations of individual protein structures taken from an unfolding trajectory are used to directly assess an input structure's probability of folding before unfolding, and P(fold) is, by definition, 0.5 for the TSE. Other, less computationally intensive methods, such as multidimensional scaling (MDS) of the pairwise root mean-squared deviation (RMSD) matrix of the conformations sampled in a thermal unfolding trajectory, have also been used to identify the TSE. Identification of the TSE is made from the original MD simulation without the need to run further simulations. Here we present a P(fold)-like study and describe methods for identification of the TSE through the derivation of a high fidelity, bounded, one-dimensional reaction coordinate for protein folding. These methods are applied to the engrailed homeodomain. The TSE identified by this approach is essentially identical to the TSE identified previously by MDS of the pairwise RMSD matrix. However, the cost of performing P(fold), or even our reduced P(fold)-like calculations, is at least 36,000 times greater than the MDS method.

Two independent replica-exchange molecular dynamics (REMD) simulations with an explicit water model were performed of the Trp-cage mini-protein. In the first REMD simulation, the replicas started from the native conformation, while in the second they started from a nonnative conformation. Initially, the first simulation yielded results qualitatively similar to those of two previously published REMD simulations: the protein appeared to be over-stabilized, with the predicted melting temperature 50-150K higher than the experimental value of 315K. However, as the first REMD simulation progressed, the protein unfolded at all temperatures. In our second REMD simulation, which starts from a nonnative conformation, there was no evidence of significant folding. Transitions from the unfolded to the folded state did not occur on the timescale of these simulations, despite the expected improvement in sampling of REMD over conventional molecular dynamics (MD) simulations. The combined 1.42 micros of simulation time was insufficient for REMD simulations with different starting structures to converge. Conventional MD simulations at a range of temperatures were also performed. In contrast to REMD, the conventional MD simulations provide an estimate of Tm in good agreement with experiment. Furthermore, the conventional MD is a fraction of the cost of REMD and continuous, realistic pathways of the unfolding process at atomic resolution are obtained.

All atom molecular dynamics simulations have become a standard method for mapping equilibrium protein dynamics and non-equilibrium events like folding and unfolding. Here, we present detailed methods for performing such simulations. Generic protocols for minimization, solvation, simulation, and analysis derived from previous studies are also presented. As a measure of validation, our water model is compared with experiment. An example of current applications of these methods, simulations of the ultrafast folding protein Engrailed Homeodomain are presented including the experimental evidence used to verify their results. Ultrafast folders are an invaluable tool for studying protein behavior as folding and unfolding events measured by experiment occur on timescales accessible with the high-resolution molecular dynamics methods we describe. Finally, to demonstrate the prospect of these methods for folding proteins, a temperature quench simulation of a thermal unfolding intermediate of the Engrailed Homeodomain is described.

We have determined consensus protein-fold classifications on the basis of three classification methods, SCOP, CATH, and Dali. These classifications make use of different methods of defining and categorizing protein folds that lead to different views of protein-fold space. Pairwise comparisons of domains on the basis of their fold classifications show that much of the disagreement between the classification systems is due to differing domain definitions rather than assigning the same domain to different folds. However, there are significant differences in the fold assignments between the three systems. These remaining differences can be explained primarily in terms of the breadth of the fold classifications. Many structures may be defined as having one fold in one system, whereas far fewer are defined as having the analogous fold in another system. By comparing these folds for a nonredundant set of proteins, the consensus method breaks up broad fold classifications and combines restrictive fold classifications into metafolds, creating, in effect, an averaged view of fold space. This averaged view requires that the structural similarities between proteins having the same metafold be recognized by multiple classification systems. Thus, the consensus map is useful for researchers looking for fold similarities that are relatively independent of the method used to compare proteins. The 30 most populated metafolds, representing the folds of about half of a nonredundant subset of the PDB, are presented here. The full list of metafolds is presented on the Web.

The structure and dynamics of the water hydrating peptides and proteins are examined here at atomic resolution via molecular dynamics simulations. Detailed solvation density and residence time data for all 20 L-amino acids in an end-capped AXA tripeptide motif are presented. In addition, the solvation of the protein chymotrypsin inhibitor 2 is investigated as a point of comparison. Residues on the surface of proteins are not isolated; they interact both locally and non-locally in sequence space, and comparison of the solvation properties of each amino acid in both the peptide and protein allow us to distinguish inherent solvation properties from context-dependent perturbations due to neighboring residues. This work moves beyond traditional radial distribution functions and presents graphical representations of preferential solvation and orientation of water by side chains and the main chain. The combination of 0.3 micros of simulation data improves the statistical sampling over previous studies and reveals the significance of bridging water molecules that stabilize and mediate side chain-side chain, side chain-main chain and main chain-main chain interactions at the solvation interface.