Prediction of membrane protein types and subcellular locations.

Membrane proteins are classified according to two different schemes. In scheme 1, they are discriminated among the following five types: (1) type I single-pass transmembrane, (2) type II single-pass transmembrane, (3) multipass transmembrane, (4) lipid chain-anchored membrane, and (5) GPI-anchored membrane proteins. In scheme 2, they are discriminated among the following nine locations: (1) chloroplast, (2) endoplasmic reticulum, (3) Golgi apparatus, (4) lysosome, (5) mitochondria, (6) nucleus, (7) peroxisome, (8) plasma, and (9) vacuole. An algorithm is formulated for predicting the type or location of a given membrane protein based on its amino acid composition. The overall rates of correct prediction thus obtained by both self-consistency and jackknife tests, as well as by an independent dataset test, were around 76-81% for the classification of five types, and 66-70% for the classification of nine cellular locations. Furthermore, classification and prediction were also conducted between inner and outer membrane proteins; the corresponding rates thus obtained were 88-91%. These results imply that the types of membrane proteins, as well as their cellular locations and other attributes, are closely correlated with their amino acid composition. It is anticipated that the classification schemes and prediction algorithm can expedite the functionality determination of new proteins. The concept and method can be also useful in the prioritization of genes and proteins identified by genomics efforts as potential molecular targets for drug design.

Protein subcellular location prediction.

The function of a protein is closely correlated with its subcellular location. With the rapid increase in new protein sequences entering into data banks, we are confronted with a challenge: is it possible to utilize a bioinformatic approach to help expedite the determination of protein subcellular locations? To explore this problem, proteins were classified, according to their subcellular locations, into the following 12 groups: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracell, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane and (12) vacuole. Based on the classification scheme that has covered almost all the organelles and subcellular compartments in an animal or plant cell, a covariant discriminant algorithm was proposed to predict the subcellular location of a query protein according to its amino acid composition. Results obtained through self-consistency, jackknife and independent dataset tests indicated that the rates of correct prediction by the current algorithm are significantly higher than those by the existing methods. It is anticipated that the classification scheme and concept and also the prediction algorithm can expedite the functionality determination of new proteins, which can also be of use in the prioritization of genes and proteins identified by genomic efforts as potential molecular targets for drug design.

Using discriminant function for prediction of subcellular location of prokaryotic proteins.

The discriminant function algorithm was introduced to predict the subcellular location of proteins in prokaryotic organisms from their amino-acid composition. The rate of correct prediction for the three possible subcellular locations of prokaryotic proteins studied by Reinhardt and Hubbard (Nucleic Acid Research, 1998, 26:2230-2236) was 90% by the self-consistency test, and 87% by the jackknife test. These rates are considerably higher than the results recently reported by them using the neural network method. Furthermore, the test procedure adopted here is also more rigorous. The core of the current algorithm is the covariance matrix, through which the collective interactions among different amino-acid components of a protein can be reflected. It is anticipated that, owing to the intimate correlation of the function of a protein with its subcellular location, the current algorithm will become a useful tool for the systematic analysis of genome data.

Do "antisense proteins" exist?

A DNA double helix consists of two complementary strands antiparallel with each other. One of them is the sense chain, while the other is an antisense chain which does not directly involve the protein-encoding process. The reason that an antisense chain cannot encode for a protein is generally attributed to the lack of certain preconditions such as a promotor and some necessary sequence segments. Suppose it were provided with all these preconditions, could an antisense chain encode for an "antisense protein"? To answer this question, an analysis has been performed based on the existing database. Nine proteins have been found that have a 100% sequence match with the hypothetical antisense proteins derived from the known Escherichia coli antisense chains.

Chemical modification of steffimycin B.

Fifteen 3-substituted analogues of steffimycin B (1) have been synthesized and their activity against P388 murine leukemia has been determined. Three of these were substantially more active than the parent compound.

Structure--activity relationships of nogalamycin analogues.

Nogalamycin (1) has been modified by changes at C-10 and C-7 and in the dimethylamino group to prepare an extensive series of analogues. The chemistry involved in the modifications and structure--activity relationships among these nogalamycin analogues are discussed, as well as comparisons with previously reported compounds 1, 7-con-O-methylnogarol (2), and disnogamycin (11).

Microbial conversion of steffimycin and steffimycin B to 10-dihydrosteffimycin and 10-dihydrosteffimycin B.

It has been shown that steffimycin (1) and steffmycin B (2) are reduced at the C-10 carbonyl by Actinoplanes utahensis, UC-5885 and Chaetomium sp., UC-4634, respectively. Using cell-free extracts of the latter organism, the optimum conversion time, pH, and enzyme concentration have been determined for the conversion of 2 to 4. The biochemical conversion of 2 has been found to be TPNH linked.

Bacterial metabolism of anthracycline antibiotics. Steffimycinone and steffimycinol conversions.

Streptomyces nogalater, UC-2783, and Streptomyces peucetius var. caesius, IMRU-3920/UC-5633, catalyze ketonic carbonyl reduction of steffimycinone (1, Scheme 1). Using cell-free preparations of S. nogalater, the process of ketonic carbonyl reduction has been shown to be TPNH linked. The product, steffimycinol (2), is reduced further by Aeromonas hydrophila, 2C/UC-6303, by the process of microaerophilic conversion of anthracyclinones previously reported1,2) with the result being the formation of 7-deoxysteffimycinol (3). The products (2 and 3) were isolated by extraction from the fermentations followed by chromatographic purification. Identification was by comparison of various physical properties and spectral data with those of authentic materials obtained by chemical means. Catalytic activity of the crude enzyme preparations of S. nogalater was lost by dialysis by restored by addition of TPNH although not by addition of DPNH demonstrating TPNH dependence. The reaction rate increased linearly with added crude enzyme protein up to 4 mg/ml and was highest between pH 6.5 and 7.0.