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Hartmut Oschkinat - One of the best experts on this subject based on the ideXlab platform.
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quantification of pdz Domain Specificity prediction of ligand affinity and rational design of super binding peptides
Journal of Molecular Biology, 2004Co-Authors: Urs Wiedemann, Prisca Boisguerin, Rainer Leben, Dietmar Leitner, Gerd Krause, Karin Moelling, Rudolf Volkmerengert, Hartmut OschkinatAbstract:Transient macromolecular complexes are often formed by protein-protein interaction Domains (e.g. PDZ, SH2, SH3, WW) which recognize linear sequence motifs with in vitro affinities typically in the micromolar range. The analysis of the resulting interaction networks requires a quantification of Domain Specificity and selectivity towards all possible ligands with physiologically relevant affinity. As representative examples, we determined Specificity as a function of ligand sequence-dependent affinity contributions by statistical analysis of peptide library screens for the AF6, ERBIN and SNA1 (alpha-1-syntrophin) PDZ Domains. For this purpose, the three PDZ Domains were first screened for binding with a peptide library comprising 6223 human C termini created by SPOT synthesis. Based on the detected ligand preferences, we designed focused peptide libraries (profile libraries). These libraries were used to quantify the affinity contributions of the four C-terminal ligand residues by means of ANOVA models (analysis of variance) relating the C-terminal ligand sequences to the corresponding dissociation constants. Our models agreed well with experimentally determined dissociation constants and allowed us to design super binding peptides. The latter were shown experimentally to bind to their cognate PDZ Domains with the highest affinity. In addition, we determined structure-activity relationships and thereby rationalized the position-specific affinity contributions. Furthermore, we used the statistical models to predict the dissociation constants for the complete ligand sequence space and thus determined the Specificity overlap for the three investigated PDZ Domains (). Altogether, we present an efficient method for profiling protein-protein interaction Domains that provides a biophysical picture of Specificity and selectivity. This approach allows the rational design of functional experiments and provides a basis for simulating interaction networks in the field of systems biology.
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quantification of pdz Domain Specificity prediction of ligand affinity and rational design of super binding peptides
Journal of Molecular Biology, 2004Co-Authors: Urs Wiedemann, Prisca Boisguerin, Rainer Leben, Dietmar Leitner, Gerd Krause, Karin Moelling, Rudolf Volkmerengert, Hartmut OschkinatAbstract:Transient macromolecular complexes are often formed by protein–protein interaction Domains (e.g. PDZ, SH2, SH3, WW) which recognize linear sequence motifs with in vitro affinities typically in the micromolar range. The analysis of the resulting interaction networks requires a quantification of Domain Specificity and selectivity towards all possible ligands with physiologically relevant affinity. As representative examples, we determined Specificity as a function of ligand sequence-dependent affinity contributions by statistical analysis of peptide library screens for the AF6, ERBIN and SNA1 (α-1-syntrophin) PDZ Domains. For this purpose, the three PDZ Domains were first screened for binding with a peptide library comprising 6223 human C termini created by SPOT synthesis. Based on the detected ligand preferences, we designed focused peptide libraries (profile libraries). These libraries were used to quantify the affinity contributions of the four C-terminal ligand residues by means of ANOVA models (analysis of variance) relating the C-terminal ligand sequences to the corresponding dissociation constants. Our models agreed well with experimentally determined dissociation constants and allowed us to design super binding peptides. The latter were shown experimentally to bind to their cognate PDZ Domains with the highest affinity. In addition, we determined structure–activity relationships and thereby rationalized the position-specific affinity contributions. Furthermore, we used the statistical models to predict the dissociation constants for the complete ligand sequence space and thus determined the Specificity overlap for the three investigated PDZ Domains ( http://www.fmp-berlin.de/nmr/pdz ). Altogether, we present an efficient method for profiling protein–protein interaction Domains that provides a biophysical picture of Specificity and selectivity. This approach allows the rational design of functional experiments and provides a basis for simulating interaction networks in the field of systems biology.
Keith E. Stanovich - One of the best experts on this subject based on the ideXlab platform.
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the Domain Specificity and generality of disjunctive reasoning searching for a generalizable critical thinking skill
Journal of Educational Psychology, 2002Co-Authors: Maggie E Toplak, Keith E. StanovichAbstract:The Domain Specificity and generality of an important critical thinking skill was examined by administering 9 reasoning and decision-making tasks to 125 adults. Optimal performance on all of the tasks required that disjunctive processing strategies—strategies requiring the exhaustive consideration of all of the possible states of the world—be adopted. Performance across these disjunctive reasoning tasks displayed considerable Domain Specificity, but 5 of the tasks displayed moderate convergence. Cognitive ability was associated with performance on only 3 of 9 tasks. Six of the 9 tasks displayed associations with 1 of 2 cognitive styles that were examined in the multivariate task battery (need for cognition and reflectivity). Performance on the 5 tasks that displayed some Domain generality was also more associated with thinking styles than with cognitive ability in several regression analyses. In an important article on the cognitive processes that underlie performance on many thinking and reasoning tasks, Shafir (1994) emphasized the importance of a fully disjunctive approach to decision-making and problem-solving situations. Shafir defined this disjunctive reasoning skill as the tendency to consider all possible states of the world when deciding among options or when choosing a problem solution in a reasoning task. Most decisionmaking situations can be thought of as disjunctions of possible states of the world. Thus, choosing optimally entails combining the probabilities of the states with the desirabilities of the outcomes under each of the decision options (Jeffrey, 1983; Savage, 1954). Many problem-solving situations can likewise be optimally evaluated by constructing all of the mental models that are consistent with the premises as presented (Johnson-Laird, 1983, 1999; Johnson-Laird & Byrne, 1991). Despite the seeming obviousness of disjunctive reasoning as a general thinking strategy, Shafir (1994) demonstrated how, if one looks across the wide Domain of reasoning tasks used in cognitive science, it is easy to find tasks in which people perform suboptimally because they do not use the strategy. Consider one of the simplest applications of the disjunctive reasoning strategy—that embodied in the sure-thing principle in Savage’s (1954) seminal derivation of the axioms of expected utility theory. Imagine you are choosing between two possible outcomes, A and B, and event X is an event that may or may not occur in the future. If you prefer prospect A to prospect B if X happens and you also prefer prospect A to prospect B if X does not happen, then you definitely prefer A to B. A disjunctive consideration of the alternative states of the world (either X will occur or not) should lead to the conclusion that uncertainty about whether X will occur or not should have no bearing on your preference. Because your preference is in no way changed by knowledge of event X, you should prefer A to B whether you know anything about event X or not.
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the Domain Specificity and generality of mental contamination accuracy and projection in judgments of mental content
British Journal of Psychology, 2001Co-Authors: Keith E. StanovichAbstract:In this study we examined individual differences across two tasks requiring people to judge the mental contents of the minds of other people - an opinion prediction task and a knowledge prediction task. The tendency to overproject one's own mental contents in both of these tasks has been interpreted as an instance of mental contamination by Wilson and Brekke (1994). Results demonstrate no Domain generality in the process of projecting one's own internal states onto predictions about the internal states of others. Furthermore, projection was efficacious in the opinion prediction task but not in the knowledge prediction task. The differing consequences of mental contamination across these tasks was moderated by the presence of other diagnostic cues that were negatively correlated with the diagnosticity of one's own mental contents in the knowledge prediction task but not in the opinion prediction task. Mental contamination was largely unrelated to cognitive ability or to styles of epistemic regulation. However, predictive accuracy (and its primary determinant - use of other diagnostic cues) was correlated across the two tasks, and was also related to cognitive ability and styles of epistemic regulation. The results are interpreted within the context of two-process models of cognitive functioning (e.g. Evans & Over, 1996; Sloman, 1996).
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the Domain Specificity and generality of belief bias searching for a generalizable critical thinking skill
Journal of Educational Psychology, 1999Co-Authors: Walter C Sa, Richard F. West, Keith E. StanovichAbstract:The Domain Specificity and generality of belief-biased reasoning was examined across a height judgment task and a syllogistic reasoning task that differed greatly in cognitive requirements. Moderate correlations between belief-bias indices on these 2 tasks falsified an extreme form of the Domain Specificity view of critical thinking skills. Two measures of cognitive ability and 2 measures of cognitive decontextualization skill were positively correlated with belief bias in a height judgment task where prior knowledge accurately reflected an aspect of the environment and negatively correlated with belief bias in a height judgment task where prior knowledge was incongruent with the environment. Likewise, cognitive ability was associated with skill at resisting the influence of prior knowledge in the syllogistic reasoning task. Participants high in cognitive ability were able to flexibly use prior knowledge, depending upon its efficacy in a particular environment. They were more likely to project a relationship when it reflected a useful cue, but they were also less likely to project a prior belief when the belief was inefficacious.
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The Domain Specificity and generality of overconfidence: Individual differences in performance estimation bias
Psychonomic Bulletin & Review, 1997Co-Authors: Richard F. West, Keith E. StanovichAbstract:One hundred twenty-three college students performed a knowledge assessment task and a game of motor skill in which they had to predict their performance before each block of trials. There was a bias in the direction of overconfidence on both tasks, even though the latter involved the motor Domain, did not require the use of numeric probabilities, and allowed predictions to be made by using an aggregate judgment made in a frequentist mode. An analysis of individual differences indicated that there was considerable Domain Specificity in confidence judgments. However, participants who persevered in showing overconfidence in the motor task—despite previous feedback revealing their overconfident performance predictions—were significantly more overconfident in the knowledge calibration task than were participants who moderated their motor performance predictions so as to remove their bias toward overconfidence. The latter finding is consistent with explanations of overconfidence effects that implicate mechanisms with some degree of Domain generality.
Prisca Boisguerin - One of the best experts on this subject based on the ideXlab platform.
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quantification of pdz Domain Specificity prediction of ligand affinity and rational design of super binding peptides
Journal of Molecular Biology, 2004Co-Authors: Urs Wiedemann, Prisca Boisguerin, Rainer Leben, Dietmar Leitner, Gerd Krause, Karin Moelling, Rudolf Volkmerengert, Hartmut OschkinatAbstract:Transient macromolecular complexes are often formed by protein-protein interaction Domains (e.g. PDZ, SH2, SH3, WW) which recognize linear sequence motifs with in vitro affinities typically in the micromolar range. The analysis of the resulting interaction networks requires a quantification of Domain Specificity and selectivity towards all possible ligands with physiologically relevant affinity. As representative examples, we determined Specificity as a function of ligand sequence-dependent affinity contributions by statistical analysis of peptide library screens for the AF6, ERBIN and SNA1 (alpha-1-syntrophin) PDZ Domains. For this purpose, the three PDZ Domains were first screened for binding with a peptide library comprising 6223 human C termini created by SPOT synthesis. Based on the detected ligand preferences, we designed focused peptide libraries (profile libraries). These libraries were used to quantify the affinity contributions of the four C-terminal ligand residues by means of ANOVA models (analysis of variance) relating the C-terminal ligand sequences to the corresponding dissociation constants. Our models agreed well with experimentally determined dissociation constants and allowed us to design super binding peptides. The latter were shown experimentally to bind to their cognate PDZ Domains with the highest affinity. In addition, we determined structure-activity relationships and thereby rationalized the position-specific affinity contributions. Furthermore, we used the statistical models to predict the dissociation constants for the complete ligand sequence space and thus determined the Specificity overlap for the three investigated PDZ Domains (). Altogether, we present an efficient method for profiling protein-protein interaction Domains that provides a biophysical picture of Specificity and selectivity. This approach allows the rational design of functional experiments and provides a basis for simulating interaction networks in the field of systems biology.
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quantification of pdz Domain Specificity prediction of ligand affinity and rational design of super binding peptides
Journal of Molecular Biology, 2004Co-Authors: Urs Wiedemann, Prisca Boisguerin, Rainer Leben, Dietmar Leitner, Gerd Krause, Karin Moelling, Rudolf Volkmerengert, Hartmut OschkinatAbstract:Transient macromolecular complexes are often formed by protein–protein interaction Domains (e.g. PDZ, SH2, SH3, WW) which recognize linear sequence motifs with in vitro affinities typically in the micromolar range. The analysis of the resulting interaction networks requires a quantification of Domain Specificity and selectivity towards all possible ligands with physiologically relevant affinity. As representative examples, we determined Specificity as a function of ligand sequence-dependent affinity contributions by statistical analysis of peptide library screens for the AF6, ERBIN and SNA1 (α-1-syntrophin) PDZ Domains. For this purpose, the three PDZ Domains were first screened for binding with a peptide library comprising 6223 human C termini created by SPOT synthesis. Based on the detected ligand preferences, we designed focused peptide libraries (profile libraries). These libraries were used to quantify the affinity contributions of the four C-terminal ligand residues by means of ANOVA models (analysis of variance) relating the C-terminal ligand sequences to the corresponding dissociation constants. Our models agreed well with experimentally determined dissociation constants and allowed us to design super binding peptides. The latter were shown experimentally to bind to their cognate PDZ Domains with the highest affinity. In addition, we determined structure–activity relationships and thereby rationalized the position-specific affinity contributions. Furthermore, we used the statistical models to predict the dissociation constants for the complete ligand sequence space and thus determined the Specificity overlap for the three investigated PDZ Domains ( http://www.fmp-berlin.de/nmr/pdz ). Altogether, we present an efficient method for profiling protein–protein interaction Domains that provides a biophysical picture of Specificity and selectivity. This approach allows the rational design of functional experiments and provides a basis for simulating interaction networks in the field of systems biology.
Urs Wiedemann - One of the best experts on this subject based on the ideXlab platform.
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quantification of pdz Domain Specificity prediction of ligand affinity and rational design of super binding peptides
Journal of Molecular Biology, 2004Co-Authors: Urs Wiedemann, Prisca Boisguerin, Rainer Leben, Dietmar Leitner, Gerd Krause, Karin Moelling, Rudolf Volkmerengert, Hartmut OschkinatAbstract:Transient macromolecular complexes are often formed by protein-protein interaction Domains (e.g. PDZ, SH2, SH3, WW) which recognize linear sequence motifs with in vitro affinities typically in the micromolar range. The analysis of the resulting interaction networks requires a quantification of Domain Specificity and selectivity towards all possible ligands with physiologically relevant affinity. As representative examples, we determined Specificity as a function of ligand sequence-dependent affinity contributions by statistical analysis of peptide library screens for the AF6, ERBIN and SNA1 (alpha-1-syntrophin) PDZ Domains. For this purpose, the three PDZ Domains were first screened for binding with a peptide library comprising 6223 human C termini created by SPOT synthesis. Based on the detected ligand preferences, we designed focused peptide libraries (profile libraries). These libraries were used to quantify the affinity contributions of the four C-terminal ligand residues by means of ANOVA models (analysis of variance) relating the C-terminal ligand sequences to the corresponding dissociation constants. Our models agreed well with experimentally determined dissociation constants and allowed us to design super binding peptides. The latter were shown experimentally to bind to their cognate PDZ Domains with the highest affinity. In addition, we determined structure-activity relationships and thereby rationalized the position-specific affinity contributions. Furthermore, we used the statistical models to predict the dissociation constants for the complete ligand sequence space and thus determined the Specificity overlap for the three investigated PDZ Domains (). Altogether, we present an efficient method for profiling protein-protein interaction Domains that provides a biophysical picture of Specificity and selectivity. This approach allows the rational design of functional experiments and provides a basis for simulating interaction networks in the field of systems biology.
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quantification of pdz Domain Specificity prediction of ligand affinity and rational design of super binding peptides
Journal of Molecular Biology, 2004Co-Authors: Urs Wiedemann, Prisca Boisguerin, Rainer Leben, Dietmar Leitner, Gerd Krause, Karin Moelling, Rudolf Volkmerengert, Hartmut OschkinatAbstract:Transient macromolecular complexes are often formed by protein–protein interaction Domains (e.g. PDZ, SH2, SH3, WW) which recognize linear sequence motifs with in vitro affinities typically in the micromolar range. The analysis of the resulting interaction networks requires a quantification of Domain Specificity and selectivity towards all possible ligands with physiologically relevant affinity. As representative examples, we determined Specificity as a function of ligand sequence-dependent affinity contributions by statistical analysis of peptide library screens for the AF6, ERBIN and SNA1 (α-1-syntrophin) PDZ Domains. For this purpose, the three PDZ Domains were first screened for binding with a peptide library comprising 6223 human C termini created by SPOT synthesis. Based on the detected ligand preferences, we designed focused peptide libraries (profile libraries). These libraries were used to quantify the affinity contributions of the four C-terminal ligand residues by means of ANOVA models (analysis of variance) relating the C-terminal ligand sequences to the corresponding dissociation constants. Our models agreed well with experimentally determined dissociation constants and allowed us to design super binding peptides. The latter were shown experimentally to bind to their cognate PDZ Domains with the highest affinity. In addition, we determined structure–activity relationships and thereby rationalized the position-specific affinity contributions. Furthermore, we used the statistical models to predict the dissociation constants for the complete ligand sequence space and thus determined the Specificity overlap for the three investigated PDZ Domains ( http://www.fmp-berlin.de/nmr/pdz ). Altogether, we present an efficient method for profiling protein–protein interaction Domains that provides a biophysical picture of Specificity and selectivity. This approach allows the rational design of functional experiments and provides a basis for simulating interaction networks in the field of systems biology.
Craig A Townsend - One of the best experts on this subject based on the ideXlab platform.
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non ribosomal propeptide precursor in nocardicin a biosynthesis predicted from adenylation Domain Specificity dependent on the mbth family protein noci
Journal of the American Chemical Society, 2013Co-Authors: Jeanne M Davidsen, David M Bartley, Craig A TownsendAbstract:Nocardicin A is a monocyclic β-lactam isolated from the actinomycete Nocardia uniformis that shows moderate antibiotic activity against a broad spectrum of Gram-negative bacteria. The monobactams are of renewed interest due to emerging Gram-negative strains resistant to clinically available penicillins and cephalosporins. Like isopenicillin N, nocardicin A has a tripeptide core of non-ribosomal origin. Paradoxically, the nocardicin A gene cluster encodes two non-ribosomal peptide synthetases (NRPSs), NocA and NocB, predicted to encode five modules pointing to a pentapeptide precursor in nocardicin A biosynthesis, unless module skipping or other nonlinear reactions are occurring. Previous radiochemical incorporation experiments and bioinformatic analyses predict the incorporation of p-hydroxy-l-phenylglycine (l-pHPG) into positions 1, 3, and 5 and l-serine into position 4. No prediction could be made for position 2. MultiDomain constructs of each module were heterologous expressed in Escherichia coli for d...
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Non-ribosomal Propeptide Precursor in Nocardicin A Biosynthesis Predicted from Adenylation Domain Specificity Dependent on the MbtH Family Protein NocI
2013Co-Authors: Jeanne M Davidsen, David M Bartley, Craig A TownsendAbstract:Nocardicin A is a monocyclic β-lactam isolated from the actinomycete Nocardia uniformis that shows moderate antibiotic activity against a broad spectrum of Gram-negative bacteria. The monobactams are of renewed interest due to emerging Gram-negative strains resistant to clinically available penicillins and cephalosporins. Like isopenicillin N, nocardicin A has a tripeptide core of non-ribosomal origin. Paradoxically, the nocardicin A gene cluster encodes two non-ribosomal peptide synthetases (NRPSs), NocA and NocB, predicted to encode five modules pointing to a pentapeptide precursor in nocardicin A biosynthesis, unless module skipping or other nonlinear reactions are occurring. Previous radiochemical incorporation experiments and bioinformatic analyses predict the incorporation of p-hydroxy-l-phenylglycine (l-pHPG) into positions 1, 3, and 5 and l-serine into position 4. No prediction could be made for position 2. MultiDomain constructs of each module were heterologous expressed in Escherichia coli for determination of the adenylation Domain (A-Domain) substrate Specificity using the ATP/PPi exchange assay. Three of the five A-Domains, from modules 1, 2, and 4, required the addition of stoichiometric amounts of MbtH family protein NocI to detect exchange activity. On the basis of these analyses, the predicted product of the NocA and NocB NRPSs is l-pHPG–l-Arg–d-pHPG–l-Ser–l-pHPG, a pentapeptide. Despite being flanked by non-proteinogenic amino acids, proteolysis of this pentapeptide by trypsin yields two fragments from cleavage at the C terminus of the l-Arg residue. Thus, a proteolytic step is likely involved in the biosynthesis of nocardicin A, a rare but precedented editing event in the formation of non-ribosomal natural products that is supported by the identification of trypsin-encoding genes in N. uniformis