Protein distance constraints predicted by neural networks and probability density functions

Lund, Ole; Frimand, K; Gorodkin, Jan; Bohr, Henrik; Bohr, Jakob; Hansen, Jan Erik; Brunak, Søren

doi:10.1093/protein/10.11.1241

Cited by 141 publications

(72 citation statements)

References 54 publications

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“…In the same way, many researches have been done to predict contacts from the sole knowledge of the sequence [46][47][48][49][50][51][52][53][54]. In spite of steady progresses, contact map prediction remains a largely unsolved challenge.…”

Section: Introductionmentioning

confidence: 99%

Protein contacts, inter-residue interactions and side-chain modelling

Faure¹,

Bornot²,

Brevern³

2008

Biochimie

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To cite this version:Guilhem Faure, Aurélie Bornot, Alexandre De Brevern. Protein contacts, inter-residue interactions and side-chain modelling.: protein contacts. Biochimie, Elsevier, 2008, 90 (4) AbstractThree-dimensional structures of proteins are the support of their biological functions.Their folds are stabilized by contacts between residues. Inner protein contacts are generally described through direct atomic contacts, i.e. interactions between side-chain atoms, while contact prediction methods mainly used inter-C distances. In this paper, we have analyzed the protein contacts on a recent high quality non-redundant databank using different criteria.First, we have studied the average number of contacts depending on the distance threshold to define a contact. Preferential contacts between types of amino acids have been highlighted.Detailed analyses have been done concerning the proximity of contacts in the sequence, the size of the proteins and fold classes. The strongest differences have been extracted, highlighting important residues.Then, we studied the influence of five different side-chain conformation prediction methods (SCWRL, IRECS, SCAP, SCATD and SSCOMP) on the distribution of contacts.The prediction rates of these different methods are quite similar. However, using a distance criterion between side-chains, the results are quite different, e.g. SCAP predicts 50% more contacts than observed, unlike other methods that predict fewer contacts than observed.Contacts deduced are quite distinct from one method to another with at most 75% contacts in common. Moreover, distributions of amino acid preferential contacts present unexpected behaviors distinct from previously observed in the X-ray structures, especially at the surface of proteins. For instance, the interactions involving Tryptophan greatly decrease.

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Section: Introductionmentioning

confidence: 99%

Protein contacts, inter-residue interactions and side-chain modelling

Faure¹,

Bornot²,

Brevern³

2008

Biochimie

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“…The prediction accuracy for the long-range distance intervals estimated here varies between 70 and 90% and the Mathew's correlation coefficient varies between 0.38 and 0.71 for the validation sample, 4 VALID-40, which is much higher when compared to the recent method of Lund et al (1997) who report a prediction accuracy of 57 Á/63% and a maximum Mathew's correlation of 0.42 for the estimation of similar distance intervals.…”

Section: Estimation Of Distance Intervals and Contacts For Long-rangementioning

confidence: 52%

Ab-initio prediction and reliability of protein structural genomics by PROPAINOR algorithm

Joshi

Subramanian

2003

Computational Biology and Chemistry

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We have formulated the ab-initio prediction of the 3D-structure of proteins as a probabilistic programming problem where the inter-residue 3D-distances are treated as random variables. Lower and upper bounds for these random variables and the corresponding probabilities are estimated by nonparametric statistical methods and knowledge-based heuristics. In this paper we focus on the probabilistic computation of the 3D-structure using these distance interval estimates. Validation of the predicted structures shows our method to be more accurate than other computational methods reported so far. Our method is also found to be computationally more efficient than other existing ab-initio structure prediction methods. Moreover, we provide a reliability index for the predicted structures too. Because of its computational simplicity and its applicability to any random sequence, our algorithm called PROPAINOR (PROtein structure Prediction by AI an Nonparametric Regression) has significant scope in computational protein structural genomics.

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“…This indicates that the global folds among all the putidaredoxin-type ferredoxins should be close, the [2Fe-2S] cluster environment being the most similar. In order to spatially locate the 25 amino acids differing in these two ferredoxins, three-dimensional structural models of Fdx1 and Fdx3 were designed by comparative modeling with putidaredoxin structure coordinates as a template, using a fully automated structure prediction web server (20). Fourteen out of these 25 residues are conservatively replaced and can be considered to not drastically modify the structure and therefore the function of the protein.…”

Section: Resultsmentioning

confidence: 99%

“…Prediction of secondary structural elements of Fdx1 and Fdx3 was carried out by using the Jpred 2 resources (a consensus method for protein secondary structure prediction) of the EMBL-European BioInformatics Institute (http://jura.ebi.ac.uk:8888/). A three-dimensional structural model was calculated according to the method of Lund et al (20) by using the CPHmodels resources from the Center for Biological Sequence analysis (http://genome.cbs .dtu.dk/services/CPHmodels) from the amino acid sequences of Fdx1 and Fdx3 with the putidaredoxin three-dimensional structure (28) as a template. The three-dimensional structures were viewed with WebLab ViewerPro 3.0 software (Molecular Simulations, Inc.).…”

Section: Methodsmentioning

confidence: 99%

A Second [2Fe-2S] Ferredoxin from Sphingomonas sp. Strain RW1 Can Function as an Electron Donor for the Dioxin Dioxygenase

Armengaud

Gaillard

Timmis³

2000

J Bacteriol

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The first step in the degradation of dibenzofuran and dibenzo-p-dioxin by Sphingomonas sp. strain RW1 is carried out by dioxin dioxygenase (DxnA1A2), a ring-dihydroxylating enzyme. An open reading frame (fdx3) that could potentially specify a new ferredoxin has been identified downstream of dxnA1A2, a two-cistron gene (J. Armengaud, B. Happe, and K. N. Timmis, J. Bacteriol. 180:3954-3966, 1998). In the present study, we report a biochemical analysis of Fdx3 produced in Escherichia coli. This third ferredoxin thus far identified in Sphingomonas sp. strain RW1 contained a putidaredoxin-type [2Fe-2S] cluster which was characterized by UV-visible absorption spectrophotometry and electron paramagnetic resonance spectroscopy. The midpoint redox potential of this ferredoxin (E 0 ‫؍‬ ؊247 ؎ 10 mV versus normal hydrogen electrode at pH 8.0) is similar to that exhibited by Fdx1 (؊245 mV), a homologous ferredoxin previously characterized in Sphingomonas sp. strain RW1. In in vitro assays, Fdx3 can be reduced by RedA2 (a reductase similar to class I cytochrome P-450 reductases), previously isolated from Sphingomonas sp. strain RW1. RedA2 exhibits a K m value of 3.2 ؎ 0.3 M for Fdx3. In vivo coexpression of fdx3 and redA2 with dxnA1A2 confirmed that Fdx3 can serve as an electron donor for the dioxin dioxygenase.Sphingomonas sp. strain RW1 was isolated from the River Elbe in Germany on the basis of its ability to grow aerobically on dibenzofuran and dibenzo-p-dioxin as the sole sources of carbon and energy (35). The degradative pathways of these two compounds in RW1 have been extensively studied, resulting in the biochemical and genetic characterization of the key enzymes. The initial reaction is an oxygenolytic attack on the carbon atoms adjacent to the ether bridge, and it is carried out by a three-component dioxin dioxygenase system. Unlike the well-known class IIB and class III dioxygenases, which involve electrons provided by a Rieske-type [2Fe-2S] ferredoxin, the dioxin dioxygenase has been shown to be associated with a putidaredoxin-type [2Fe-2S] ferredoxin (8). The gene of this ferredoxin, designated fdx1, has been cloned and characterized (4), as has the gene of its cognate reductase (5). In this type of ferredoxin, the prosthetic group is coordinated with the polypeptide by four cysteine residues arranged in the pattern Cys-X 5 -Cys-X 2 -Cys-X n -Cys and thus differs significantly from the Rieske-type [2Fe-2S] ferredoxins containing a [2Fe-2S] cluster bound to the polypeptide by two cysteine and two histidine residues. The nature and the position of the ligands influence the properties of the Fe-S cluster. The ferredoxins containing a Rieske-type cluster exhibit a high redox potential (Ϫ150 mV for ferredoxins associated with class IIB dioxygenases [19]), whereas putidaredoxin-type [2Fe-2S] ferredoxins exhibit lower redox potentials (4, 12, 13), and ferredoxins containing a plant-type [2Fe-2S] cluster, also bound to the polypeptide by four cysteines, have even lower potential (9,14,33).In a recent report (2), we desc...

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Protein distance constraints predicted by neural networks and probability density functions

Cited by 141 publications

References 54 publications

Protein contacts, inter-residue interactions and side-chain modelling

Protein contacts, inter-residue interactions and side-chain modelling

Ab-initio prediction and reliability of protein structural genomics by PROPAINOR algorithm

A Second [2Fe-2S] Ferredoxin from Sphingomonas sp. Strain RW1 Can Function as an Electron Donor for the Dioxin Dioxygenase

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