JPred4: a protein secondary structure prediction server

Drozdetskiy, Alexey; Callebaut, Christian; Procter, James B.; Barton, Geoffrey J.

doi:10.1093/nar/gkv332

Cited by 1,572 publications

(1,395 citation statements)

References 37 publications

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“…It is highly conserved and consists of one or two alpha helices and a transmembrane *Secondary structure predictions were made with the Jnet algorithm (Cuff & Barton, 2000) on the Jpred4 server ( (Drozdetskiy et al, 2015) Geobacter uraniireducens…”

Section: Resultsmentioning

confidence: 99%

“…Alpha helices and beta strands were predicted with the Jnet algorithm (Cuff & Barton, 2000) on the JPred4 server (Drozdetskiy et al, 2015) and transmembrane helices were predicted with TMpred (Hofmann & Stoffel, 1993), TMHMM (Krogh et al, 2001), and HMMTOP (Tusnády & Simon, 2001). Signal peptides were identified with PSORTb v. 3.0.2 (Yu et al, 2010) and Signal P v. 4.1 (Petersen et al, 2011).…”

Section: Impact Statementmentioning

confidence: 99%

“…4, Table S1) that has been verified to reduce Fe(III) (Lovley et al, 1993) and Mn(IV) (Lovley & Phillips, 1994). Other Not present *Alpha helices and beta strands were identified with the Jnet algorithm (Cuff & Barton, 2000) on the Jpred 4 server (Drozdetskiy, 2015). †Signal peptides were predicted with PSORTb v. 3.0 (Wagner, 2010) and SignalP 4.1 (Nielsen,1997).…”

Section: Horizontal Transfer Of E-pilin Outside the Desulfuromondalesmentioning

confidence: 99%

“…These alternative strategies for long-range electron transport may be most favored when rates of microbial metabolism are slow. For example, species of the 4, 1 *Beta strands were identified with the Jnet algorithm (Cuff & Barton, 2000) on the Jpred 4 server (Drozdetskiy, 2015).…”

Section: Most Fe(iii)-reducing Bacteria Lack E-pilimentioning

confidence: 99%

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The electrically conductive pili of Geobacter species are a recently evolved feature for extracellular electron transfer

et al. 2016

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The electrically conductive pili (e-pili) of Geobactersulfurreducens have environmental and practical significance because they can facilitate electron transfer to insoluble Fe(III) oxides; to other microbial species; and through electrically conductive biofilms. E-pili conductivity has been attributed to the truncated PilA monomer, which permits tight packing of aromatic amino acids to form a conductive path along the length of e-pili. In order to better understand the evolution and distribution of e-pili in the microbial world, type IVa PilA proteins from various Gram-negative and Gram-positive bacteria were examined with a particular emphasis on Fe(III)-respiring bacteria. E-pilin genes are primarily restricted to a tight phylogenetic group in the order Desulfuromonadales. The downstream gene in all but one of the Desulfuromonadales that possess an e-pilin gene is a gene previously annotated as 'pilA-C' that has characteristics suggesting that it may encode an outermembrane protein. Other genes associated with pilin function are clustered with e-pilin and 'pilA-C' genes in the Desulfuromonadales. In contrast, in the few bacteria outside the Desulfuromonadales that contain e-pilin genes, the other genes required for pilin function may have been acquired through horizontal gene transfer. Of the 95 known Fe(III)-reducing micro-organisms for which genomes are available, 80 % lack e-pilin genes, suggesting that e-pili are just one of several mechanisms involved in extracellular electron transport. These studies provide insight into where and when e-pili are likely to contribute to extracellular electron transport processes that are biogeochemically important and involved in bioenergy conversions.

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Section: Resultsmentioning

confidence: 99%

Section: Impact Statementmentioning

confidence: 99%

Section: Horizontal Transfer Of E-pilin Outside the Desulfuromondalesmentioning

confidence: 99%

Section: Most Fe(iii)-reducing Bacteria Lack E-pilimentioning

confidence: 99%

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The electrically conductive pili of Geobacter species are a recently evolved feature for extracellular electron transfer

et al. 2016

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“…1). Secondary structure prediction of lips221 by JPRED4 (Drozdetskiy et al 2015) suggested all SFK3309 mutations fell in loop regions and are less probable to severely disrupt overall protein structure.…”

Section: Discussionmentioning

confidence: 99%

Identification and kinetics characterization of a wax ester hydrolase from a feather-degrading actinomycete

Barcus

Mizrachi

Lei

2017

Preprint

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Streptomyces fradiae var. k11 is a Gram-positive soil microorganism capable of degrading chicken feathers. Apart from being mostly protein, chicken feathers have a considerable level of lipids, with wax esters being the largest lipid class. The waxes may pose a challenge while rendering the feathers into coproducts, such as feather meal, and so the identification of a wax-ester hydrolase is warranted. A draft genome sequence of S. fradiae var. k11 was used to identify 14 gene sequences of potential lipid-degrading enzymes. The genes were expressed in E. coli BL21(DE3) cells on a pET vector and screened for activity. Four of the 14 enzymes had detectable activity, with two of the enzymes, SFK3309 and SFK3087, active against p-nitrophenyl palmitate, a representative water-insoluble substrate.A modified enzymatic assay was designed to measure activity against three model wax substrates: jojoba oil, beeswax, and cetyl-palmitate. SFK3309 was characterized to hydrolyze all three wax substrates. Kinetic experiments for SFK3309 were performed with cetyl-palmitate at 37 o C, pH 8.0. The K m was determined to be 850 µM and the K cat was 11.63 s -1 . Through the characterization of SFK3309 as a wax-ester hydrolase, biotechnological implications of wax ester hydrolases in the rendering of many industrial wastes can be substantiated for further studies.

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Computational Toxicology

Wijeyesakere

Richardson

2023

Patty's Toxicology

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This chapter briefly explores the principles and application of predictive computational approaches (cheminformatics) to the field of toxicology. In recent years, regulatory and consumer pressures have promoted the use of non‐animal alternatives (including cheminformatics approaches) to assess hazards associated with novel and existing chemicals. In general, these in silico approaches enable us to do two important things: ( 1 ) Rapidly assess likely molecular mechanisms of toxicity; and ( 2 ) Suggest mechanistic hypotheses for experimental validation or refutation. To this end, we provide an overview of current two‐dimensional and three‐dimensional computational approaches including mechanistic machine learning, structural scaffolding, docking, and molecular dynamics simulations. Together with techniques such as inverse docking and pharmacophore/toxicophore mapping, it is possible to use these techniques to align compounds with their potential macromolecular targets, including off‐targets of toxicity. While the approaches discussed in this chapter could potentially be of value when assessing hazards associated with biological xenobiotics and protein–DNA interactions, our focus is on small‐molecule toxicants and their in vivo [protein] targets.

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JPred4: a protein secondary structure prediction server

Cited by 1,572 publications

References 37 publications

The electrically conductive pili of Geobacter species are a recently evolved feature for extracellular electron transfer

The electrically conductive pili of Geobacter species are a recently evolved feature for extracellular electron transfer

Identification and kinetics characterization of a wax ester hydrolase from a feather-degrading actinomycete

Computational Toxicology

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