New Unstable Variants of Green Fluorescent Protein for Studies of Transient Gene Expression in Bacteria

Use of the green fluorescent protein (Gfp) from the jellyfish Aequorea victoria is a powerful method for nondestructive in situ monitoring, since expression of green fluorescence does not require any substrate addition. To expand the use of Gfp as a reporter protein, new variants have been constructed by the addition of short peptide sequences to the C-terminal end of intact Gfp. This rendered the Gfp susceptible to the action of indigenous housekeeping proteases, resulting in protein variants with half-lives ranging from 40 min to a few hours when synthesized in Escherichia coli and Pseudomonas putida. The new Gfp variants should be useful for in situ studies of temporal gene expression.     

Fitting semi-Markov models to interval-censored data with unknown initiation times

In a semi-Markov model, the hazard of making a transition between stages depends on the time spent in the current stage but is independent of time spent in other stages. If the initiation time (time of entry into the network) is not known for some persons and if transition time data are interval censored (i.e., if transition times are not known exactly but are known only to have occurred in some interval), then the length of time these persons spent in any stage is not known. We show how a semi-Markov model can still be fit to interval-censored data with missing initiation times. For the special case of models in which all persons enter the network at the same initial stage and proceed through the same succession of stages to a unique absorbing stage, we present discrete-time nonparametric maximum likelihood estimators of the waiting-time distributions for this type of data.     

Discrete-time nonparametric estimation for semi-Markov models of chain-of-events data subject to interval censoring and truncation

Chain-of-events data are longitudinal observations on a succession of events that can only occur in a prescribed order. One goal in an analysis of this type of data is to determine the distribution of times between the successive events. This is difficult when individuals are observed periodically rather than continuously because the event times are then interval censored. Chain-of-events data may also be subject to truncation when individuals can only be observed if a certain event in the chain (e.g., the final event) has occurred. We provide a nonparametric approach to estimate the distributions of times between successive events in discrete time for data such as these under the semi-Markov assumption that the times between events are independent. This method uses a self-consistency algorithm that extends Turnbull's algorithm (1976, Journal of the Royal Statistical Society, Series B 38, 290-295). The quantities required to carry out the algorithm can be calculated recursively for improved computational efficiency. Two examples using data from studies involving HIV disease are used to illustrate our methods.     

Use of pair potentials across protein interfaces in screening predicted docked complexes.

Empirical residue-residue pair potentials are used to screen possible complexes for protein-protein dockings. A correct docking is defined as a complex with not more than 2.5 A root-mean-square distance from the known experimental structure. The complexes were generated by "ftdock" (Gabb et al. J Mol Biol 1997;272:106-120) that ranks using shape complementarity. The complexes studied were 5 enzyme-inhibitors and 2 antibody-antigens, starting from the unbound crystallographic coordinates, with a further 2 antibody-antigens where the antibody was from the bound crystallographic complex. The pair potential functions tested were derived both from observed intramolecular pairings in a database of nonhomologous protein domains, and from observed intermolecular pairings across the interfaces in sets of nonhomologous heterodimers and homodimers. Out of various alternate strategies, we found the optimal method used a mole-fraction calculated random model from the intramolecular pairings. For all the systems, a correct docking was placed within the top 12% of the pair potential score ranked complexes. A combined strategy was developed that incorporated "multidock," a side-chain refinement algorithm (Jackson et al. J Mol Biol 1998;276:265-285). This placed a correct docking within the top 5 complexes for enzyme-inhibitor systems, and within the top 40 complexes for antibody-antigen systems.     

Multiple positive and negative regulators of signaling by the EGF-receptor

Signaling via the epidermal growth factor (EGF)-receptor family is subject to regulation and modulation by multiple ligands, effectors and negative regulators, as well as regulation by heterodimerization between family members and crosstalk between heterologous signaling pathways. Besides serving as a paradigm for receptor tyrosine kinases in general, this family is crucial for development and is often mutated or amplified in human tumors.     

A Free-living Panagrolaimus sp. from Armenia Can Survive in Anhydrobiosis for 8.7 Years

Although the ability of plant-parasitic nematodes to survive in a dehydrated or anhydrobiotic state for long periods of time has been well documented, the ability of free-living nematodes has not. Here we report on the survival of a free-living nematode, Panagrolaimus sp., from Armenia in the anhydrobiotic state for 8.7 years. This Panagrolaimus sp. can be cultured and maintained readily and may provide a good system for studying anhydrobiosis in nematodes.     

Prediction of the three-dimensional structures of the nerve growth factor and epidermal growth factor binding proteins (kallikreins) and an hypothetical structure of the high molecular weight complex of epidermal growth factor with its binding protein.

We have predicted the three-dimensional structures of the serine protease subunits (gamma-NGF, alpha-NGF, and EGF-BP) of the high molecular weight complexes of nerve growth factor (7S NGF) and epidermal growth factor (HMW-EGF) from the mouse submandibular gland (from the X-ray crystal structures of two related glandular kallikreins). The conformations of three of the six loops surrounding the active site are relatively well defined in the models of gamma-NGF and EGF-BP, but three other loops are likely to have flexible conformations. Although the amino acid sequence of alpha-NGF is closely related to those of gamma-NGF and EGF-BP, it is catalytically inactive. Model-building studies on alpha-NGF suggested that mutations (in alpha-NGF) just prior to the active site serine (195) and an unusual N-terminal sequence are consistent with alpha-NGF having a zymogen-like conformation (similar to that in chymotrypsinogen). An hypothetical model of the quaternary structure of HMW-EGF has been constructed using this model of EGF-BP and the NMR structure of murine EGF. The C-terminal arm of EGF was modeled into the active site of EGF-BP based on data indicating that the C-terminal arginine of EGF occupies the S1 subsite of EGF-BP. Data suggesting one of the surface loops of EGF-BP is buried in the HMW-EGF complex and symmetry constraints were important in deriving a schematic model. A molecular docking program was used to fit EGF to EGF-BP.     

Prediction of beta-turns in proteins using neural networks

The use of neural networks to improve empirical secondary structure prediction is explored with regard to the identification of the position and conformational class of beta-turns, a four-residue chain reversal. Recently an algorithm was developed for beta-turn predictions based on the empirical approach of Chou and Fasman using different parameters for three classes (I, II and non-specific) of beta-turns. In this paper, using the same data, an alternative approach to derive an empirical prediction method is used based on neural networks which is a general learning algorithm extensively used in artificial intelligence. Thus the results of the two approaches can be compared. The most severe test of prediction accuracy is the percentage of turn predictions that are correct and the neural network gives an overall improvement from 20.6% to 26.0%. The proportion of correctly predicted residues is 71%, compared to a chance level of about 58%. Thus neural networks provide a method of obtaining more accurate predictions from empirical data than a simpler method of deriving propensities.