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Mona Singh - One of the best experts on this subject based on the ideXlab platform.
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systematic Domain based aggregation of protein structures highlights dna rna and other ligand binding positions
Nucleic Acids Research, 2019Co-Authors: Shilpa Nadimpalli Kobren, Mona SinghAbstract:: Domains are fundamental subunits of proteins, and while they play major roles in facilitating protein-DNA, protein-RNA and other protein-ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within Domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of Domain Functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here, we introduce an approach to identify per-Domain-position interaction 'frequencies' by aggregating protein co-complex structures by Domain and ascertaining how often residues mapping to each Domain position interact with ligands. We perform this Domain-based analysis on ∼91000 co-complex structures, and infer positions involved in binding DNA, RNA, peptides, ions or small molecules across 4128 Domains, which we refer to collectively as the InteracDome. Cross-validation testing reveals that ligand-binding positions for 2152 Domains are highly consistent and can be used to identify residues facilitating interactions in ∼63-69% of human genes. Our resource of Domain-inferred ligand-binding sites should be a great aid in understanding disease etiology: whereas these sites are enriched in Mendelian-associated and cancer somatic mutations, they are depleted in polymorphisms observed across healthy populations. The InteracDome is available at http://interacdome.princeton.edu.
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systematic Domain based aggregation of protein structures highlights dna rna and other ligand binding positions
bioRxiv, 2018Co-Authors: Shilpa Nadimpalli Kobren, Mona SinghAbstract:Domains are fundamental subunits of proteins, and while they play major roles in facilitating protein-DNA, protein-RNA and other protein-ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within Domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of Domain Functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here we introduce an approach to identify per-Domain-position interaction "propensities" by aggregating protein co-complex structures by Domain and ascertaining how frequently residues mapping to each Domain position interact with ligands. We perform this Domain-based analysis on ~82,000 co-complex structures, and infer positions involved in binding DNA, RNA, peptides, ions, or small molecules across 4,120 Domains, which we refer to collectively as the InteracDome. Cross-validation testing reveals that ligand-binding positions for 1,327 Domains can be confidently modeled and used to identify residues facilitating interactions in ~60-69% of human genes. Our resource of Domain-inferred ligand-binding sites should be a great aid in understanding disease etiology: whereas these sites are enriched in Mendelian-associated and cancer somatic mutations, they are depleted in polymorphisms observed across healthy populations. The InteracDome is available at http://interacdome.princeton.edu.
Shilpa Nadimpalli Kobren - One of the best experts on this subject based on the ideXlab platform.
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systematic Domain based aggregation of protein structures highlights dna rna and other ligand binding positions
Nucleic Acids Research, 2019Co-Authors: Shilpa Nadimpalli Kobren, Mona SinghAbstract:: Domains are fundamental subunits of proteins, and while they play major roles in facilitating protein-DNA, protein-RNA and other protein-ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within Domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of Domain Functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here, we introduce an approach to identify per-Domain-position interaction 'frequencies' by aggregating protein co-complex structures by Domain and ascertaining how often residues mapping to each Domain position interact with ligands. We perform this Domain-based analysis on ∼91000 co-complex structures, and infer positions involved in binding DNA, RNA, peptides, ions or small molecules across 4128 Domains, which we refer to collectively as the InteracDome. Cross-validation testing reveals that ligand-binding positions for 2152 Domains are highly consistent and can be used to identify residues facilitating interactions in ∼63-69% of human genes. Our resource of Domain-inferred ligand-binding sites should be a great aid in understanding disease etiology: whereas these sites are enriched in Mendelian-associated and cancer somatic mutations, they are depleted in polymorphisms observed across healthy populations. The InteracDome is available at http://interacdome.princeton.edu.
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systematic Domain based aggregation of protein structures highlights dna rna and other ligand binding positions
bioRxiv, 2018Co-Authors: Shilpa Nadimpalli Kobren, Mona SinghAbstract:Domains are fundamental subunits of proteins, and while they play major roles in facilitating protein-DNA, protein-RNA and other protein-ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within Domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of Domain Functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here we introduce an approach to identify per-Domain-position interaction "propensities" by aggregating protein co-complex structures by Domain and ascertaining how frequently residues mapping to each Domain position interact with ligands. We perform this Domain-based analysis on ~82,000 co-complex structures, and infer positions involved in binding DNA, RNA, peptides, ions, or small molecules across 4,120 Domains, which we refer to collectively as the InteracDome. Cross-validation testing reveals that ligand-binding positions for 1,327 Domains can be confidently modeled and used to identify residues facilitating interactions in ~60-69% of human genes. Our resource of Domain-inferred ligand-binding sites should be a great aid in understanding disease etiology: whereas these sites are enriched in Mendelian-associated and cancer somatic mutations, they are depleted in polymorphisms observed across healthy populations. The InteracDome is available at http://interacdome.princeton.edu.
Sergey N. Fedosov - One of the best experts on this subject based on the ideXlab platform.
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Kinetic and structural characterization of a two-Domain streptokinase: dissection of Domain Functionality.
Biochemistry, 2000Co-Authors: Laust B. Johnsen, Lone K. Rasmussen, Torben E. Petersen, Michael Etzerodt, Sergey N. FedosovAbstract:The mammalian protease plasminogen can be activated by bacterial activators, the three- Domain (R, ‚, A) streptokinases and the one-Domain (R) staphylokinases. These activators act as plasmin- (ogen) cofactors, and the resulting complexes initiate proteolytic activity of host plasminogen which facilitates bacterial colonization of the host organism. We have investigated the kinetic mechanism of the plasminogen activation mediated by a novel two-Domain (R, ‚) streptokinase isolated from Streptococcus uberis (Sk U ) with specificity toward bovine plasminogen. The interaction between Sk U and plasminogen occurred in two steps: (1) rapid association of the proteins and (2) slow transition to the active complex Sk U -PgA. The complex Sk U -PgA converted plasminogen to plasmin with the following parameters: Km e 1.5 IM and kcat ) 0.55 s -1 . The ability of proteolytic fragments of Sk U to activate plasminogen was investigated. Only two C-terminal segments (97-261 and 123-261), which both contain the ‚-Domain (126-261), were shown to be active. They initiated plasminogen activation in complex with plasmin, but not with plasminogen, and thereby exhibited functional similarity to the staphylokinase. The fusion protein His6-Sk U (i.e., Sk U with a small N-terminal tag) acted exclusively in complex with plasmin as well. These observations demonstrate that (1) the N-terminal R-Domain, including a native N-terminus, was necessary for "virgin" activation of the associated plasminogen in the Sk U -PgA complex and (2) the C-terminal ‚-Domain of Sk U is important for recognition of the substrate in the Sk U -PgA complex.
Laust B. Johnsen - One of the best experts on this subject based on the ideXlab platform.
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Kinetic and structural characterization of a two-Domain streptokinase: dissection of Domain Functionality.
Biochemistry, 2000Co-Authors: Laust B. Johnsen, Lone K. Rasmussen, Torben E. Petersen, Michael Etzerodt, Sergey N. FedosovAbstract:The mammalian protease plasminogen can be activated by bacterial activators, the three- Domain (R, ‚, A) streptokinases and the one-Domain (R) staphylokinases. These activators act as plasmin- (ogen) cofactors, and the resulting complexes initiate proteolytic activity of host plasminogen which facilitates bacterial colonization of the host organism. We have investigated the kinetic mechanism of the plasminogen activation mediated by a novel two-Domain (R, ‚) streptokinase isolated from Streptococcus uberis (Sk U ) with specificity toward bovine plasminogen. The interaction between Sk U and plasminogen occurred in two steps: (1) rapid association of the proteins and (2) slow transition to the active complex Sk U -PgA. The complex Sk U -PgA converted plasminogen to plasmin with the following parameters: Km e 1.5 IM and kcat ) 0.55 s -1 . The ability of proteolytic fragments of Sk U to activate plasminogen was investigated. Only two C-terminal segments (97-261 and 123-261), which both contain the ‚-Domain (126-261), were shown to be active. They initiated plasminogen activation in complex with plasmin, but not with plasminogen, and thereby exhibited functional similarity to the staphylokinase. The fusion protein His6-Sk U (i.e., Sk U with a small N-terminal tag) acted exclusively in complex with plasmin as well. These observations demonstrate that (1) the N-terminal R-Domain, including a native N-terminus, was necessary for "virgin" activation of the associated plasminogen in the Sk U -PgA complex and (2) the C-terminal ‚-Domain of Sk U is important for recognition of the substrate in the Sk U -PgA complex.
R. J. Epstein - One of the best experts on this subject based on the ideXlab platform.
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Nonrandom Intragenic Variations in Patterns of Codon Bias Implicate a Sequential Interplay Between Transitional Genetic Drift and Functional Amino Acid Selection
Journal of Molecular Evolution, 2003Co-Authors: K. Lin, S. B. Tan, P. R. Kolatkar, R. J. EpsteinAbstract:Although most codon third bases appear to be functionless, the synonymous codons so defined exhibit a strikingly nonrandom distribution (codon bias) within human and other genes. To examine this phenomenon further, we generated a database of DNA sequences encoding human transmembrane cell-surface receptor proteins. Using this database we show here that the guanine and cytosine content of codon third bases (GC3) varies intragenically with the nature of the specified receptor Domains (transmembrane > extracellular > intracellular Domains; p < 0.001), the phenotype of the encoded amino acids (hydrophobic > hydrophilic > neutral amino acids; p < 0.001), and the receptor affiliation of the transmembrane Domain superfamily (G-protein- coupled receptors > receptor tyrosine kinases; p < 0.001). Within gene regions specifying transmembrane Domains, GC3 declines as Domain Functionality becomes redundant with increasing hydrophobicity (p < 0.001). Codons containing the second-base cytosine ( X C Z , which encodes neutral amino acids) are selectively depleted of third-base adenine content (A3: X CA codons) when encoding transmembrane Domain residues, consistent with positive selection for transitional mutation of X CG to X TG (which encodes hydrophobic amino acids) rather than to the synonymous X CA. Supporting this X CG → X TG mechanism of codon bias, the G3:A3 ratio of codons specifying the transmembrane amino acid glycine (GG Z ) is intermediate between that of its functional homolog alanine (GC Z ) and that of hydrophobic valine (GT Z ), even though the C3:T3 ratios are similar. Conversely, nearest-neighbor analysis of third bases 5′ to codons specifying valine and leucine (CT Z ) confirms a significant difference in C3:T3 but not G3:A3 ratios (i.e., C3/G1 → T3/G1 > C3/A1; p < 0.001), consistent with the functionally advantageous retention of hydrophobic residues. These data raise the possibility that patterns of intragenic codon bias reflect a balance between negative and positive selection, suggesting in turn that analysis of codon third-base usage may help to predict the functional significance of encoded products.