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Nearest-neighbor classifier as a tool for classification of protein families.

ABSTRACT: Knowledge about protein function is essential in understanding the biological processes. A specific class or family of protein shares common structural and chemical properties amongst its member sequences. The set of properties that display its unique characteristics for clearly classifying a protein sequence into its corresponding protein family needs to be studied. Our study of these important properties conducted on four major classes of proteins namely Globins, Homeoboxes, Heat Shock proteins (HSP) and Kinase have shown that frequency of twenty naturally occurring amino acids, hydrophobic content of protein, molecular weight of protein, isoelectric point of protein, secondary structure composition of amino acid residues as helices, coils and sheets and the composition of helices, coils and sheets in the secondary structure topology plays a significant role in correctly classifying the protein into its corresponding class or family as indicated by the overall efficiency of Nearest Neighbor Classifier as 84.92%.

SUBMITTER: Chaurasiya M

PROVIDER: S-EPMC2951634 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Nearest-neighbor classifier as a tool for classification of protein families.

Chaurasiya Mona M Chandulah Gohel Bakul GB Misra Krishna K Chaurasiya Vivek Kumar VK

Bioinformation 20100331 9

Knowledge about protein function is essential in understanding the biological processes. A specific class or family of protein shares common structural and chemical properties amongst its member sequences. The set of properties that display its unique characteristics for clearly classifying a protein sequence into its corresponding protein family needs to be studied. Our study of these important properties conducted on four major classes of proteins namely Globins, Homeoboxes, Heat Shock protein ...[more]

PMID: 20975888

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Project description:BackgroundMetagenomics, or the sequencing and analysis of collective genomes (metagenomes) of microorganisms isolated from an environment, promises direct access to the "unculturable majority". This emerging field offers the potential to lay solid basis on our understanding of the entire living world. However, the taxonomic classification is an essential task in the analysis of metagenomics data sets that it is still far from being solved. We present a novel strategy to predict the taxonomic origin of environmental genomic fragments. The proposed classifier combines the idea of the k-nearest neighbor with strategies from kernel-based learning.ResultsOur novel strategy was extensively evaluated using the leave-one-out cross validation strategy on fragments of variable length (800 bp - 50 Kbp) from 373 completely sequenced genomes. TACOA is able to classify genomic fragments of length 800 bp and 1 Kbp with high accuracy until rank class. For longer fragments > or = 3 Kbp accurate predictions are made at even deeper taxonomic ranks (order and genus). Remarkably, TACOA also produces reliable results when the taxonomic origin of a fragment is not represented in the reference set, thus classifying such fragments to its known broader taxonomic class or simply as "unknown". We compared the classification accuracy of TACOA with the latest intrinsic classifier PhyloPythia using 63 recently published complete genomes. For fragments of length 800 bp and 1 Kbp the overall accuracy of TACOA is higher than that obtained by PhyloPythia at all taxonomic ranks. For all fragment lengths, both methods achieved comparable high specificity results up to rank class and low false negative rates are also obtained.ConclusionAn accurate multi-class taxonomic classifier was developed for environmental genomic fragments. TACOA can predict with high reliability the taxonomic origin of genomic fragments as short as 800 bp. The proposed method is transparent, fast, accurate and the reference set can be easily updated as newly sequenced genomes become available. Moreover, the method demonstrated to be competitive when compared to the most current classifier PhyloPythia and has the advantage that it can be locally installed and the reference set can be kept up-to-date.

Project description:Escherichia coli single-strand (ss) DNA-binding protein (SSB) is an essential protein that binds ssDNA intermediates formed during genome maintenance. SSB homotetramers bind ssDNA in several modes differing in occluded site size and cooperativity. The 35-site-size ((SSB)35) mode favored at low [NaCl] and high SSB/DNA ratios displays high "unlimited" nearest-neighbor cooperativity (?35), forming long protein clusters, whereas the 65-site-size ((SSB)65) mode in which ssDNA wraps completely around the tetramer is favored at higher [NaCl] (>200 mM) and displays "limited" cooperativity (?65), forming only dimers of tetramers. In addition, a non-nearest-neighbor high cooperativity can also occur in the (SSB)65 mode on long ssDNA even at physiological salt concentrations in the presence of glutamate and requires its intrinsically disordered C-terminal linker (IDL) region. However, whether cooperativity exists between the different modes and the role of the IDL in nearest-neighbor cooperativity has not been probed. Here, we combine sedimentation velocity and fluorescence titration studies to examine nearest-neighbor cooperativity in each binding mode and between binding modes using (dT)70 and (dT)140. We find that the (SSB)35 mode always shows extremely high "unlimited" cooperativity that requires the IDL. At high salt, wild-type SSB and a variant without the IDL, SSB-?L, bind in the (SSB)65 mode but show little cooperativity, although cooperativity increases at lower [NaCl] for wild-type SSB. We also find significant intermode nearest-neighbor cooperativity (?65/35), with ?65 ? ?65/35 <?35. The intrinsically disordered region of SSB is required for all cooperative interactions; however, in contrast to the non-nearest-neighbor cooperativity observed on longer ssDNA, glutamate does not enhance these nearest-neighbor cooperativities. Therefore, we show that SSB possesses four types of cooperative interactions, with clear differences in the forces stabilizing nearest-neighbor versus non-nearest-neighbor cooperativity.

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Nearest-neighbor classifier as a tool for classification of protein families.

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Nearest-neighbor classifier as a tool for classification of protein families.

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