Abstract: Detection of phenols using engineered bacteria. A biosensor can be created by placing a reporter gene under control of an inducible promoter. The reporter gene produces a signal when a cognate transcriptional activator senses the inducing chemical. Creation of bacterial biosensors is currently restricted by limited knowledge of the genetic systems of bacteria that catabolize xenobiotics. By using mutagenic PCR to change the chemical specificity of the Pseudomonas species CF600 DmpR protein, the potential for engineering novel biosensors for detection of phenols has been demonstrated. DmpR, a well-characterized transcriptional activator of the P. CF600's dmp operon mediates growth on simple phenols. Transcription from Po, the promoter heading the dmp operon, is activated when the sensor domain of DmpR interacts with phenol and mono-substituted phenols.

Abstract: A computer implemented method modifies an experimental electron density map. A set of selected known experimental and model electron density maps is provided and standard templates of electron density are created from the selected experimental and model electron density maps by clustering and averaging values of electron density in a spherical region about each point in a grid that defines each selected known experimental and model electron density maps. Histograms are also created from the selected experimental and model electron density maps that relate the value of electron density at the center of each of the spherical regions to a correlation coefficient of a density surrounding each corresponding grid point in each one of the standard templates. The standard templates and the histograms are applied to grid points on the experimental electron density map to form new estimates of electron density at each grid point in the experimental electron density map.

Abstract: Structure factor bias in an electron density map for an unknown crystallographic structure is minimized by using information in a first electron density map to elicit expected structure factor information. Observed structure factor amplitudes are combined with a starting set of crystallographic phases to form a first set of structure factors. A first electron density map is then derived and features of the first electron density map are identified to obtain expected distributions of electron density. Crystallographic phase probability distributions are established for possible crystallographic phases of reflection k, and the process is repeated as k is indexed through all of the plurality of reflections. An updated electron density map is derived from the crystallographic phase probability distributions for each one of the reflections. The entire process is then iterated to obtain a final set of crystallographic phases with minimum bias from known electron density maps.

Abstract: A maximum-likelihood method for improves an electron density map of an experimental crystal structure. A likelihood of a set of structure factors {Fh} is formed for the experimental crystal structure as (1) the likelihood of having obtained an observed set of structure factors {FhOBS} if structure factor set {Fh} was correct, and (2) the likelihood that an electron density map resulting from {Fh} is consistent with selected prior knowledge about the experimental crystal structure. The set of structure factors {Fh} is then adjusted to maximize the likelihood of {Fh} for the experimental crystal structure. An improved electron density map is constructed with the maximized structure factors.

Abstract: Detection of phenols using engineered bacteria. A biosensor can be created by placing a reporter gene under control of an inducible promoter. The reporter gene produces a signal when a cognate transcriptional activator senses the inducing chemical. Creation of bacterial biosensors is currently restricted by limited knowledge of the genetic systems of bacteria that catabolize xenobiotics. By using mutagenic PCR to change the chemical specificity of the Pseudomonas species CF600 DmpR protein, the potential for engineering novel biosensors for detection of phenols has been demonstrated. DmpR, a well-characterized transcriptional activator of the P. CF600's dmp operon mediates growth on simple phenols. Transcription from Po, the promoter heading the dmp operon, is activated when the sensor domain of DmpR interacts with phenol and mono-substituted phenols.

Abstract: An electron density for a crystallographic structure having protein regions and solvent regions is improved by maximizing the log likelihood of a set of structures factors {Fh} using a local log-likelihood function: LL(&rgr;(x, {Fh}))=ln[p(&rgr;(x)|PROT)pPROT(x)+p(&rgr;(x)|SOLV)pSOLV(x)+p(&rgr;(x)|H)pH(x)], where pPROT(x) is the probability that x is in the protein region, p(&rgr;(x)|PROT) is the conditional probability for &rgr;(x) given that x is in the protein region, and pSOLV(x) and p(&rgr;(x)|SOLV) are the corresponding quantities for the solvent region, pH(x) refers to the probability that there is a structural motif at a known location, with a known orientation, in the vicinity of the point x; and p(&rgr;(x)|H) is the probability distribution for electron density at this point given that the structural motif actually is present.

Abstract: Detection of phenols using engineered bacteria. A biosensor can be created by placing a reporter gene under control of an inducible promoter. The reporter gene produces a signal when a cognate transcriptional activator senses the inducing chemical. Creation of bacterial biosensors is currently restricted by limited knowledge of the genetic systems of bacteria that catabolize xenobiotics. By using mutagenic PCR to change the chemical specificity of the Pseudomonas species CF600 DmpR protein, the potential for engineering novel biosensors for detection of phenols has been demonstrated. DmpR, a well-characterized transcriptional activator of the P. CF600's dmp operon mediates growth on simple phenols. Transcription from Po, the promoter heading the dmp operon, is activated when the sensor domain of DmpR interacts with phenol and mono-substituted phenols.

Abstract: Structure factor bias in an electron density map for an unknown crystallographic structure is minimized by using information in a first electron density map to elicit expected structure factor information. Observed structure factor amplitudes are combined with a starting set of crystallographic phases to form a first set of structure factors. A first electron density map is then derived and features of the first electron density map are identified to obtain expected distributions of electron density. Crystallographic phase probability distributions are established for possible crystallographic phases of reflection k, and the process is repeated as k is indexed through all of the plurality of reflections. An updated electron density map is derived from the crystallographic phase probability distributions for each one of the reflections. The entire process is then iterated to obtain a final set of crystallographic phases with minimum bias from known electron density maps.