3Dee: a database of protein structural domains

Siddiqui, Asim; Dengler, Uwe; Barton, Geoffrey J.

doi:10.1093/bioinformatics/17.2.200

Cited by 56 publications

(35 citation statements)

References 8 publications

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“…After manual editing and elimination of potentially chimeric sequences (7% of total number), the 16S rRNA gene sequences were grouped into OTUs based on 99%, 98%, 97%, 96%, 95%, 90%, 80%, 70%, 60%, and 50% sequence similarity cut-off values. This grouping was achieved by first making all possible pair-wise sequence alignments by using CLUSTALW at default settings, and calculating % sequence identities, followed by clustering the sequences into OTUs by using the unweighted pair group method with arithmetic mean as implemented in the OC clustering program (20). The OTU grouping was checked manually to verify that all OTUs were assembled at the cut-off level desired.…”

Section: Methodsmentioning

confidence: 99%

Predicting microbial species richness

Hong

Bunge

Jeon

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

191

165

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Microorganisms are spectacularly diverse phylogenetically, but available estimates of their species richness are vague and problematic. For example, for comparable environments, the estimated numbers of species range from a few dozen or hundreds to tens of thousands and even half a million. Such estimates provide no baseline information on either local or global microbial species richness. We argue that this uncertainty is due in large part to the way statistical tools are used, if not indeed misused, in biodiversity research. Here we develop a powerful synthetic statistical approach to quantify biodiversity. It provides statistically sound estimates of microbial richness at any level of taxonomic hierarchy. We apply this approach to a large original 16S rRNA dataset on marine bacterial diversity and show that the number of bacterial species in a sample from marine sediments is (2.4 ؎ 0.5 SE) ؋ 10 3 . We argue that our methodology provides estimates of microbial richness that are reliable and general, have biologically meaningful SEs, and meet other fundamental statistical standards. This approach can be an essential tool in biodiversity research, and the estimates of microbial richness presented here can serve as a baseline in microbial diversity studies.global biodiversity ͉ microorganisms ͉ number of species

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Section: Methodsmentioning

confidence: 99%

Predicting microbial species richness

Hong

Bunge

Jeon

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

191

165

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“…Hence, methods for phylogenetic analyses and protein modelling usually perform better for single domains [58]. Automatic domain parsing generally makes the assumption that interdomain interaction (under a correct domain assignment) is weaker than the intradomain interaction (PUU [59], DOMAK [60] and 3Dee [61,62], DETECTIVE [63], DALI [64], STRUDL [65], DomainParser [66,67], Protein Domain Parser [68] and DDOMAIN [69]). These approaches maximize the number of contacts within a domain.…”

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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“…No two proteins of sequence sizes greater than 80 residues in the set share more than 25% homology. CB396, composed by 396 sequences, was taken from 3Dee structural database [21] and the homology of any of two sequences is smaller than 25%. The same multiple sequence alignment of the reference [20] is used in our experiment to compare our prediction methods with other methods fairly.…”

Section: Data Setmentioning

confidence: 99%