PROTINFO: secondary and tertiary protein structure prediction

Hung, Ling‐Hong; Samudrala, Ram

doi:10.1093/nar/gkg541

Cited by 39 publications

(34 citation statements)

References 29 publications

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“…Structural modeling of AMO was performed with the comparative modeling protocol on the PROTINFO structure prediction server (http://protinfo.compbio.washington.edu) using the particulate methane monooxygenase crystal structure as a template (28). This comparative modeling protocol has been shown to work well in the CASP protein structure prediction experiments (18,19). Initial models were constructed using a minimum perturbation approach that aims to preserve as much information as possible from the template structure solved by X-ray crystallography.…”

Section: Methodsmentioning

confidence: 99%

Transcription of All amoC Copies Is Associated with Recovery of Nitrosomonas europaea from Ammonia Starvation

2007

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The chemolithotrophic ammonia-oxidizing bacterium Nitrosomonas europaea is known to be highly resistant to starvation conditions. The transcriptional response of N. europaea to ammonia addition following short-and long-term starvation was examined by primer extension and S1 nuclease protection analyses of genes encoding enzymes for ammonia oxidation (amoCAB operons) and CO 2 fixation (cbbLS), a third, lone copy of amoC (amoC 3 ), and two representative housekeeping genes (glyA and rpsJ). Primer extension analysis of RNA isolated from growing, starved, and recovering cells revealed two differentially regulated promoters upstream of the two amoCAB operons. The distal 70 type amoCAB promoter was constitutively active in the presence of ammonia, but the proximal promoter was only active when cells were recovering from ammonia starvation. The lone, divergent copy of amoC (amoC 3 ) was expressed only during recovery. Both the proximal amoC 1,2 promoter and the amoC 3 promoter are similar to gram-negative E promoters, thus implicating E in the regulation of the recovery response. Although modeling of subunit interactions suggested that a nonconservative proline substitution in AmoC 3 may modify the activity of the holoenzyme, characterization of a ⌬amoC 3 strain showed no significant difference in starvation recovery under conditions evaluated. In contrast to the amo transcripts, a delayed appearance of transcripts for a gene required for CO 2 fixation (cbbL) suggested that its transcription is retarded until sufficient energy is available. Overall, these data revealed a programmed exit from starvation likely involving regulation by E and the coordinated regulation of catabolic and anabolic genes.Ammonia-oxidizing bacteria fulfill an important biological role because they carry out the first reaction in the oxidative pathway of the nitrogen cycle. These bacteria obtain energy for growth from the oxidation of ammonia and acquire the majority of their carbon through the fixation of CO 2 via the Calvin cycle. Since these microorganisms must compete with plants and other microorganisms that assimilate ammonia for biosynthesis (47, 48), it is likely that they have developed adaptive strategies to cope with periods of ammonia starvation. Both Nitrosomonas cryotolerans and Nitrosomonas europaea have been shown to be particularly resilient to energy source deprivation. Following 10 weeks of starvation, N. cryotolerans retained 85% of initial viability, showed no apparent degradation of DNA, protein, or RNA, and maintained an active electron transport system (21). Studies of N. europaea demonstrated persistence of the AmoB protein and immediate oxidation of added ammonia following 1 year of starvation for ammonia (38,51). This remarkable resistance to starvation likely reflects unique physiological facets of these highly specialized microorganisms. In this study, we examined transcription of genes for key energy-generating (ammonia oxidation) and energyconsuming (CO 2 fixation) reactions during recovery of N. europaea from ammo...

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Section: Methodsmentioning

confidence: 99%

Transcription of All amoC Copies Is Associated with Recovery of Nitrosomonas europaea from Ammonia Starvation

2007

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“…We suggest that the region including the majority of FlgG* mutations (residues 52-66, drawn in a green oval) are located near the top of the FlgG subunit where it interacts with residues G183 and S197, located at the bottom of the structure during FlgG polymerization. A three-dimensional model of FlgG was constructed using the comparative modeling module of the Protinfo server (http://protinfo.compbio.washington.edu), which has been shown to work well in the CASP protein structure prediction experiments (Hung and Samudrala 2003;Hung et al 2005). The structure of FlgE was used as a template for the modeling the FlgG sequence excluding the N-and C-terminal insertions (Samatey et al 2004).…”

Section: Rod Length Control Is Required For Normal P-and L-ring Formamentioning

confidence: 99%

“…A three-dimensional model of the flgG was constructed using the comparative modeling module of the Protinfo server (http:// protinfo.compbio.washington.edu), which has been shown to work well in the CASP protein structure prediction experiments (Hung and Samudrala 2003;Hung et al 2005). The structure of FlgE was used as a template for the modeling the flgG sequence excluding the N-and C-terminal insertions (Samatey et al 2004).…”

Section: Flgg Structure Modelingmentioning

confidence: 99%

The mechanism of outer membrane penetration by the eubacterial flagellum and implications for spirochete evolution

et al. 2007

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The rod component of the bacterial flagellum polymerizes from the inner membrane across the periplasmic space and stops at a length of 25 nm at the outer membrane. Bushing structures, the P-and L-rings, polymerize around the distal rod and form a pore in the outer membrane. The flagellar hook structure is then added to the distal rod growing outside the cell. Hook polymerization stops after the rod-hook structure reaches ∼80 nm in length. This study describes mutants in the distal rod protein FlgG that fail to terminate rod growth. The mutant FlgG subunits continue to polymerize close to the length of the normal rod-hook structure of 80 nm. These filamentous rod structures have multiple P-rings and fail to form the L-ring pore at the outer membrane. The flagella grow within the periplasm similar to spirochete flagella. This provides a simple method to evolve intracellular flagella as in spirochetes. The mechanism that couples rod growth termination to the ring assembly and outer membrane penetration exemplifies the importance of stopping points in the construction of a complex macromolecular machine that facilitate efficient coupling to the next step in the assembly pathway.

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“…In the first approach, for each protein sequence in the database of the solved protein structures, we use an ab initio conformational space sampling protocol to generate 10 decoy structures, as a result yielding a total of 3150 · 10 = 31 500 decoy structures (hereafter denoted as the database of decoy structures). The ab initio conformational space sampling protocol consists of a Monte-Carlo method with simulated annealing procedure, with move set based on the standard fragment replacement scheme, namely, the existing conformation of three consecutive residues at a random position is replaced by the torsion values of three residues with identical sequence from an experimentally determined structure (Simons et al, 1997;Hung and Samudrala, 2003). The energy function used to generate the decoys is a combination of the all-atom distance-dependent function, a hydrophobic compactness function and a bad contacts function (Samudrala et al, 1999;Samudrala and Levitt, 2002).…”

Section: Theoretical Background and Methodsmentioning

confidence: 99%

A knowledge-based scoring function based on residue triplets for protein structure prediction

Ngan

Inouye

Samudrala

2006

Protein Engineering, Design and Selection

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One of the general paradigms for ab initio protein structure prediction involves sampling the conformational space such that a large set of decoy (candidate) structures are generated and then selecting native-like conformations from those decoys using various scoring functions. In this study, based on a physical/geometric approach first suggested by Banavar and colleagues, we formulate a knowledge-based scoring function, which uses the radii of curvature formed among triplets of residues in a protein conformation. By analyzing its performance on various decoy sets, we determine a good set of parameters-the distance cutoff and the number of distance bins-to use for configuring such a function. Furthermore, we investigate the effect of using various approaches for compiling the prior distribution on the performance of the knowledge-based function. Possible extensions to the current form of the residue triplet scoring function are discussed.

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PROTINFO: secondary and tertiary protein structure prediction

Cited by 39 publications

References 29 publications

Transcription of All amoC Copies Is Associated with Recovery of Nitrosomonas europaea from Ammonia Starvation

Transcription of All amoC Copies Is Associated with Recovery of Nitrosomonas europaea from Ammonia Starvation

The mechanism of outer membrane penetration by the eubacterial flagellum and implications for spirochete evolution

A knowledge-based scoring function based on residue triplets for protein structure prediction

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