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Structural Annotation of the Proteome

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  • Authors: Nagasuma Chandra1, Sankaran Sandhya2, Praveen Anand3
  • Editors: Graham F. Hatfull4, William R. Jacobs Jr.5
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India; 2: Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India; 3: Department of Biochemistry, Indian Institute of Science, Bangalore, Karnataka 560012, India; 4: University of Pittsburgh, Pittsburgh, PA; 5: Howard Hughes Medical Institute, Albert Einstein College of Medicine, Bronx, NY
  • Source: microbiolspec March 2014 vol. 2 no. 2 doi:10.1128/microbiolspec.MGM2-0027-2013
  • Received 06 August 2013 Accepted 03 October 2013 Published 28 March 2014
  • Nagasuma Chandra, nchandra@biochem.iisc.ernet.in
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  • Abstract:

    Efforts from the TB Structural Genomics Consortium together with those of tuberculosis structural biologists worldwide have led to the determination of about 350 structures, making up nearly a tenth of the pathogen's proteome. Given that knowledge of protein structures is essential to obtaining a high-resolution understanding of the underlying biology, it is desirable to have a structural view of the entire proteome. Indeed, structure prediction methods have advanced sufficiently to allow structural models of many more proteins to be built based on homology modeling and fold recognition strategies. By means of these approaches, structural models for about 2,877 proteins, making up nearly 70% of the proteome, are available. Knowledge from bioinformatics has made significant inroads into an improved annotation of the genome and in the prediction of key protein players that interact in vital pathways, some of which are unique to the organism. Functional inferences have been made for a large number of proteins based on fold-function associations. More importantly, ligand-binding pockets of the proteins are identified and scanned against a large database, leading to binding site–based ligand associations and hence structure-based function annotation. Near proteome-wide structural models provide a global perspective of the fold distribution in the genome. New insights about the folds that predominate in the genome, as well as the fold combinations that make up multidomain proteins, are also obtained. This chapter describes the structural proteome, functional inferences drawn from it, and its applications in drug discovery.

  • Citation: Chandra N, Sandhya S, Anand P. 2014. Structural Annotation of the Proteome. Microbiol Spectrum 2(2):MGM2-0027-2013. doi:10.1128/microbiolspec.MGM2-0027-2013.

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Amino Acid Synthesis
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2014-03-28
2017-11-19

Abstract:

Efforts from the TB Structural Genomics Consortium together with those of tuberculosis structural biologists worldwide have led to the determination of about 350 structures, making up nearly a tenth of the pathogen's proteome. Given that knowledge of protein structures is essential to obtaining a high-resolution understanding of the underlying biology, it is desirable to have a structural view of the entire proteome. Indeed, structure prediction methods have advanced sufficiently to allow structural models of many more proteins to be built based on homology modeling and fold recognition strategies. By means of these approaches, structural models for about 2,877 proteins, making up nearly 70% of the proteome, are available. Knowledge from bioinformatics has made significant inroads into an improved annotation of the genome and in the prediction of key protein players that interact in vital pathways, some of which are unique to the organism. Functional inferences have been made for a large number of proteins based on fold-function associations. More importantly, ligand-binding pockets of the proteins are identified and scanned against a large database, leading to binding site–based ligand associations and hence structure-based function annotation. Near proteome-wide structural models provide a global perspective of the fold distribution in the genome. New insights about the folds that predominate in the genome, as well as the fold combinations that make up multidomain proteins, are also obtained. This chapter describes the structural proteome, functional inferences drawn from it, and its applications in drug discovery.

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FIGURE 1

Schematic diagram of proteome structural annotation. The figure depicts the circular map of the H37Rv structural proteome, corresponding to the first genome view reported with its complete genome sequence ( 1 ). The outer circle represents the model coverage in terms of the percentage of the polypeptide chain, whereas the inner circle represents the percentage sequence identity shared by each model with its corresponding template. On both the outer and inner circles, radiating lines are drawn to indicate the parameters of the structural model for the corresponding protein in the genome view. The length of the lines in both cases is proportional to their values in percentages. The 100% mark is also shown for both the circles. In the outer circle, those models that had greater than 40% length coverage are drawn outside the circle, whereas those with coverage of less than that are drawn inside the circle (for clarity). Length coverage is divided into five classes and color coded as indicated, while the levels of sequence identity are divided into four classes and color coded as indicated. Predominantly occurring folds in the proteome are shown surrounding the outer circle, ordered clockwise by frequency of occurrence (indicated in parentheses). doi:10.1128/microbiolspec.MGM2-0027-2013.f1

Source: microbiolspec March 2014 vol. 2 no. 2 doi:10.1128/microbiolspec.MGM2-0027-2013
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FIGURE 2

Aspects of annotation. ERRAT output of Rv1485 showing an overall quality factor of ∼70% by evaluating the nonbonded interactions between different atom types. Superposition of Rv1485 with its template 1HRK:A. Multiple sequence alignment with selected sequence neighbors, highlighting conserved catalytic site residues (in triangles). Binding site prediction using LigsiteCSC and PocketDepth. Predicted ligand-binding pockets in red surface. The expected ligand-binding site as determined by superposing the template is shown as sticks. Association of the heme ligand to the predicted binding site (residues in red) based on high similarity to a known heme binding site by searching against PDB pockets (blue). doi:10.1128/microbiolspec.MGM2-0027-2013.f2

Source: microbiolspec March 2014 vol. 2 no. 2 doi:10.1128/microbiolspec.MGM2-0027-2013
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FIGURE 3

Coverage of a structural proteome. Distribution of different structural classes as described through folds, superfamilies, and families occurring in SCOP and correspondingly in the proteome adjacent to it. Distribution of structural information according to TubercuList functional categories. The inner circle represents the total number of genes in a particular functional category, and the outer circle represents the genes with structural information in the corresponding functional category. doi:10.1128/microbiolspec.MGM2-0027-2013.f3

Source: microbiolspec March 2014 vol. 2 no. 2 doi:10.1128/microbiolspec.MGM2-0027-2013
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FIGURE 4

Fold combination and higher-order assemblies. Network of fold combination observed in modeled multidomain proteins. Each node represents a fold, and an edge represents two folds occurring together within a polypeptide. The topmost occurring fold combination is the tetracycline-like repressor C-terminal (a.121) domain with DNA 3-helical bundle (a.4). An example protein, Rv3557c, is shown with both folds highlighted. Examples of higher-order assemblies. The predicted assembly of methylmalonyl CoA mutase derived from the structural template 1REQ is shown. The assembly consists of Rv1492, MutA (cyan), and Rv1493, MutB (green). The conserved residues that could be involved in the interaction at the interface are shown in stick representation below. Similarly, the complex of fumarate reductase generated from 1KF6 is shown below, with residues involved in quinol binding that are conserved highlighted as spheres. doi:10.1128/microbiolspec.MGM2-0027-2013.f4

Source: microbiolspec March 2014 vol. 2 no. 2 doi:10.1128/microbiolspec.MGM2-0027-2013
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FIGURE 5

Example annotations. Example annotation of a conserved hypothetical protein, Rv3402c. The superposition consists of a model of Rv3402c (shown in green), and the template 1MDO is shown as cyan. The conserved residues predicted to be involved in the interaction with PLP are represented as sticks. The structure-based sequence alignment of Rv3402c with the template 1MDO. Functionally important residues are marked with (*). The amino acids are colored based on their chemical properties. Superposition of Rv0469 (green) with the template 1KPG. The residues involved in cofactor recognition are shown in blue, and residues determining the substrate specificities are highlighted in red. The predicted pocket is shown in a surface representation. Superposition of Rv2503c (green) with the template 2CTZ. The pocket predicted using PocketDepth and SURFnet enclosing the active site is shown in a mesh. doi:10.1128/microbiolspec.MGM2-0027-2013.f5

Source: microbiolspec March 2014 vol. 2 no. 2 doi:10.1128/microbiolspec.MGM2-0027-2013
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