A molecular piston mechanism of pumping protons by bacteriorhodopsin

In this review the proton-pumping mechanism proposed recently for bacteriorhodopsin [Chou, K. C. (1993) Journal of Protein Chemistry, 12: 337-350] is illustrated in terms of a phenomenological model. According to the model, theβ-ionone of the retinal chromophore in bacteriorhodopsin can be phenomenologically imagined as a molecular "piston". The photon capture by bacteriorhodopsin would "pull" it up while the spontaneous decrease in potential energy would "push" it down so that it would be up and down alternately during the photocycle process. When it is pulled up, the gate of pore is open and the water channel for the proton translocation is through; when it is pushed down, the gate of pore is closed and the water channel is shut up. Such a model not only is quite consistent with experimental observations, but also provides useful insights and a different view to elucidate the protonpumping mechanism of bacteriorhodopsin. The essence of the model might be useful in investigating the mechanism of ion-channels of other membrane proteins.

Vibrational Properties of CO at the Pt(111)−Solution Interface: the Anomalous Stark-Tuning Slope

Vibrational properties of CO have been studied on Pt(111) in acid and alkaline electrolytes by synchronous measurements of CO oxidation current (0.5 mV/s) and IRAS spectra (one spectrum for every 1 mV). We found that in acid solutions the frequency-tuning rate (dnu(CO)/dE) as well as the potential-dependent bandwidth (dDeltanu1/2/dE) deviates from expected linear relationships. This unusual potential-dependent behavior is interpreted in terms of compression/dissipation of CO islands during the CO oxidation, engendered by competitive adsorption between inactive anions from a supporting electrolyte and the reactive OH species.

Effect of human dietary exposure levels of genistein during gestation and lactation on long-term reproductive development and sperm quality in mice

The objective of the present study was to determine the long-term reproductive effects of gestational and lactational exposure (0, 0.1, 0.5, 2.5 and 10 mg/kg/day) to genistein on male mice at levels comparable to or greater than human dietary exposures. Testicular growth, sperm count and motility, and sperm fertilizing ability in vitro was assessed in male offspring on postnatal days (PND) 105 and 315. Selected genes were also examined by real-time PCR to determine whether genistein caused changes in gene expression similar to those previously observed with diethylstilbestrol (DES). No significant treatment-related effects on male offspring body weight, anogenital distance, seminal vesicle weight or testis weight were observed. There were also no significant effects on sperm count, the percent of motile sperm or the number of motile sperm at any age. The in vitro fertilizing ability of epididymal sperm was increased significantly in the high-dose group approximately 17% (P < 0.001) on PND 105 and 315. The results indicate that developmental exposure of mice to genistein at human exposure levels does not induce adverse effects on sperm quality or changes in testicular gene expression similar to DES.

Prediction of signal peptides using scaled window

Cells use a ZIP code system to sort newly synthesized proteins and deliver them wherever they are needed: into different internal compartments called organelles or even out of the cell altogether. One of the most essential features of the ZIP code system is the signal sequence or "address tag," which is originally present in the N-terminal part of the protein and is trimmed away by the time it is secreted. Owing to the importance of signal peptides for understanding the molecular mechanisms of genetic diseases, reprogramming cells for gene therapy, and constructing new drugs for correcting a specific defect, it is highly desirable to develop a fast and accurate method to identify the signal peptides. In this paper, a scaled window model is proposed. Based on such a model as well as Markov chain theory, a new algorithm is formulated for predicting the signal peptides. Test results for the 1939 secretory proteins and 1440 non-secretary proteins have indicated that the new algorithm is particularly successful in the overall success rate, and hence can serve as a complementary tool to the existing algorithms for signal peptide prediction.

[W206R]-procaspase 3: an inactivatable substrate for caspase 8

We report here the cloning and high-level expression of a soluble proform of human caspase 3 (Ser(24)-H(277)) engineered to contain a short stretch of N-terminal sequence (MTISDSPREQD) from the prosegment of procaspase 8 and a C-terminal heptahistidine tag. The precursor protein isolated from extracts of recombinant Escherichia coli by immobilized metal-ion affinity chromatography was predominantly unprocessed and migrated as a 32-kDa polypeptide on sodium dodecyl sulfate-polyacrylamide gels. Incubation of this protein with recombinant human caspase 8 produced fragments characteristic of the properly processed caspase 3, but the product was inactive. Amino-terminal sequence analysis of the caspase 3 polypeptides proved that caspase 8 had specifically cleaved the Asp(175)-Ser(176) bond to yield the expected p18 and p12 subunits, with partial cleavage at the Asp(28)-Ser(29) bond to release the prosegment. The lack of caspase 3 activity was found to be the result of a fortuitous mutation in which Trp(206) in the S4 subsite was replaced by arginine (W206R). This mutant procaspase 3, which we call m-pro3, serves as a useful reagent with which to test the efficacy of caspase 8 inhibitors in blocking processing of the natural polypeptide substrate of this enzyme and may be valuable as a source of "proenzyme" for crystallographic analysis.

Prediction of protein cellular attributes using pseudo-amino acid composition

The cellular attributes of a protein, such as which compartment of a cell it belongs to and how it is associated with the lipid bilayer of an organelle, are closely correlated with its biological functions. The success of human genome project and the rapid increase in the number of protein sequences entering into data bank have stimulated a challenging frontier: How to develop a fast and accurate method to predict the cellular attributes of a protein based on its amino acid sequence? The existing algorithms for predicting these attributes were all based on the amino acid composition in which no sequence order effect was taken into account. To improve the prediction quality, it is necessary to incorporate such an effect. However, the number of possible patterns for protein sequences is extremely large, which has posed a formidable difficulty for realizing this goal. To deal with such a difficulty, the pseudo-amino acid composition is introduced. It is a combination of a set of discrete sequence correlation factors and the 20 components of the conventional amino acid composition. A remarkable improvement in prediction quality has been observed by using the pseudo-amino acid composition. The success rates of prediction thus obtained are so far the highest for the same classification schemes and same data sets. It has not escaped from our notice that the concept of pseudo-amino acid composition as well as its mathematical framework and biochemical implication may also have a notable impact on improving the prediction quality of other protein features.

Using subsite coupling to predict signal peptides

Given a nascent protein sequence, how can one predict its signal peptide or "Zipcode" sequence? This is a first important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy. Based on a model that takes into account the coupling effect among some key subsites, the so-called [-3, -1, +1] coupling model, a new prediction algorithm is developed. The overall rate of correct prediction for 1939 secretory proteins and 1440 non-secretary proteins was over 92%. It has not escaped our attention that the new method may also serve as a useful tool for helping investigate further many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells.

Artificial neural network model for predicting membrane protein types

Membrane proteins can be classified among the following five types: (1) type I membrane protein. (2) type II membrane protein. (3) multipass transmembrane proteins. (4) lipid chain-anchored membrane proteins, and (5) GPI-anchored membrane proteins. T. Kohonen's self-organization model which is a typical neural network is applied for predicting the type of a given membrane protein based on its amino acid composition. As a result, the high rates of self-consistency (94.80%) and cross-validation (77.76%), and stronger fault-tolerant ability were obtained.

Prediction of protein signal sequences and their cleavage sites

Protein signal sequences play a central role in the targeting and translocation of nearly all secreted proteins and many integral membrane proteins in both prokaryotes and eukaryotes. The knowledge of signal sequences has become a crucial tool for pharmaceutical scientists who genetically modify bacteria, plants, and animals to produce effective drugs. However, to effectively use such a tool, the first important thing is to find a fast and effective method to identify the "zipcode" entity; this is also evoked by both the huge amount of unprocessed data available and the industrial need to find more effective vehicles for the production of proteins in recombinant systems. In view of this, a sequence-encoded algorithm was developed to identify the signal sequences and predict their cleavage sites. The rate of correct prediction for 1,939 secretory proteins and 1,440 nonsecretory proteins by self-consistency test is 90.14% and that by jackknife test is 90.13%. The encouraging results indicate that the signal sequences share some common features although they lack similarity in sequence, length, and even composition and that they are predictable to a considerably accurate extent.

Prediction of protein subcellular locations by incorporating quasi-sequence-order effect

How to incorporate the sequence order effect is a key and logical step for improving the prediction quality of protein subcellular location, but meanwhile it is a very difficult problem as well. This is because the number of possible sequence order patterns in proteins is extremely large, which has posed a formidable barrier to construct an effective training data set for statistical treatment based on the current knowledge. That is why most of the existing prediction algorithms are operated based on the amino-acid composition alone. In this paper, based on the physicochemical distance between amino acids, a set of sequence-order-coupling numbers was introduced to reflect the sequence order effect, or in a rigorous term, the quasi-sequence-order effect. Furthermore, the covariant discriminant algorithm by Chou and Elrod (Protein Eng. 12, 107-118, 1999) developed recently was augmented to allow the prediction performed by using the input of both the sequence-order-coupling numbers and amino-acid composition. A remarkable improvement was observed in the prediction quality using the augmented covariant discriminant algorithm. The approach described here represents one promising step forward in the efforts of incorporating sequence order effect in protein subcellular location prediction. It is anticipated that the current approach may also have a series of impacts on the prediction of other protein features by statistical approaches.

Prediction of tight turns and their types in proteins

A tight turn in protein structure is defined as a site where (i) a polypeptide chain reverses its overall direction, i.e., leads the chain to fold back on itself by nearly 180 degrees, and (ii) the amino acid residues directly involved in forming the turn are no more than six. Tight turns are generally categorized as delta-turn, gamma-turn, beta-turn, alpha-turn, and pi-turn, which are formed by two-, three-, four-, five-, and six-amino-acid residues, respectively. According to the folding mode, each of such tight turns can be further classified into several different types. Tight turns play an important role in globular proteins from both the structural and functional points of view. In view of this, various efforts have been made to predict tight turns and their types. This Review summarizes the development in this area, with an emphasis focused on the most recent work concerned that is featured by the sequence-coupled model. Meanwhile, the future challenge in this area has also been briefly addressed.

Support vector machines for prediction of protein subcellular location

Support Vector Machine (SVM), which is one kind of learning machines, was applied to predict the subcellular location of proteins from their amino acid composition. In this research, the proteins are classified into the following 12 groups: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracall, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane, and (12) vacuole, which have covered almost all the organelles and subcellular compartments in an animal or plant cell. The examination for the self-consistency and the jackknife test of the SVMs method was tested for the three sets: 2022 proteins, 2161 proteins, and 2319 proteins. As a result, the correct rate of self-consistency and jackknife test reaches 91 and 82% for 2022 proteins, 89 and 75% for 2161 proteins, and 85 and 73% for 2319 proteins, respectively. Furthermore, the predicting rate was tested by the three independent testing datasets containing 2240 proteins, 2513 proteins, and 2591 proteins. The correct prediction rates reach 82, 75, and 73% for 2240 proteins, 2513 proteins, and 2591 proteins, respectively.

A phase II study of single-agent docetaxel chemotherapy for non-small cell lung cancer

BACKGROUND

Docetaxel is an active agent used in the treatment of non-small cell lung cancer (NSCLC).

METHODS

A phase II trial of single-agent docetaxel chemotherapy was conducted in Chinese patients with NSCLC, as either a first- or second-line treatment, to assess response and toxicity. The treatment scheme was docetaxel 75 mg/m2 intravenous infusion for 1 h every 3 weeks for up to nine cycles. Dexamethasone was routinely given for 3 days, beginning 1 day before chemotherapy.

RESULTS

From August 1996 to December 1997, 48 patients were enrolled, including 34 chemo-naive patients and 14 patients previously treated with one chemotherapeutic regimen. All patients were evaluable for toxicity profiles and 47 patients were evaluable for response rate. As expected, the major toxicity was myelosuppression. Grade 3 or 4 neutropenia occurred in 41 of 48 (85.5%) patients during treatment. Twenty patients (41.7%) experienced febrile neutropenia and accounted for two toxic deaths. Only one patient suffered from grade 3 thrombocytopenia and two patients from grade 3 anemia. Moderate or severe asthenia occurred in 30 patients (62.5%). Moderate fluid retention (peripheral edema) was observed in five patients (10.4%) and severe fluid retention in three; all were reversible. No grade 3 or 4 neurosensory toxicity was observed. After two cycles of treatment, 14 of 47 evaluable patients attained a partial response (29.8%, 95% CI 16.7-42.9%), including 30.3% (95% CI 14.6-46%) of those in first-line treatment and 28.6% (95% CI 4.9-52.3%) of those in second-line treatment. The median time to disease progression was 13 weeks in first-line patients and 19 weeks in second-line patients. Median survival time was 7.1 and 11.7 months in first- and second-line patients, respectively.

CONCLUSION

Docetaxel is active and has an acceptable toxicity profile, in both first- and second-line treatments, in Chinese patients with inoperable NSCLC.

Using neural networks for prediction of subcellular location of prokaryotic and eukaryotic proteins

T. Kohonen's self-organization model, a typical neural network model, was applied to predict the subcellular location of proteins from their amino acid composition. The Reinhardt and Hubbard database was used to examine the performance of the neural network method. The rates of correct prediction for the three possible subcellular location of prokaryotic proteins were 96.1% by the self-consistency test and 84.4% by the jackknife test. The rates of correct prediction for the four possible subcellular location of eukaryotic proteins were 95.6% by the self-consistency test and 70.6% by the jackknife test.

Prediction of protein structural classes and subcellular locations

The structural class and subcellular location are the two important features of proteins that are closely related to their biological functions. With the rapid increase in new protein sequences entering into data banks, it is highly desirable to develop a fast and accurate method for predicting the attributes of these features for them. This can expedite the functionality determination of new proteins and the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design. Various prediction methods have been developed during the last two decades. This review is devoted to presenting a systematic introduction and comparison of the existing methods in respect to the prediction algorithm and classification scheme. The attention is focused on the state-of-the-art, which is featured by the covarient-discriminant algorithm developed very recently, as well as some new classification schemes for protein structural classes and subcellular locations. Particularly, addressed are also the physical chemistry foundation of the existing prediction methods, and the essence why the covariant-discriminant algorithm is so powerful.

Prediction of the tertiary structure of a caspase-9/inhibitor complex

Apoptosis, or programmed cell death, plays a central role in the development and homeostasis of an organism. The breakdown of cellular proteins in apoptosis is mediated by caspases, which comprise a highly conserved family of cysteine proteases with specificity for aspartic acid residues at the P1 positions of their substrates. Multiple lines of evidence show that caspase-9 is critical for an apoptosis pathway mediated via the mitochondria. In this study, the three-dimensional structure of the catalytic domain of caspase-9 and its interaction with the inhibitor acetyl-Asp-Val-Ala-Asp fluoromethyl ketone (Ac-DVAD-fmk) have been predicted by a segment matching modeling procedure. As expected, the predicted caspase-9 structure shows both a high similarity in the overall folding topology and remarkable differences in the surface loop regions as compared to other caspase family members such as caspase-1, -3 and -8, for which crystal structures have been determined. This kind of comparative analysis reflects the convergence-divergence duality among the caspases. Moreover, some subtle differences have been observed between caspase-9 and caspase-3 in the subsite contacts with the covalently linked inhibitor Ac-DVAD-fmk. Based on the X-ray structural analysis of caspase-8, a main chain carbonyl oxygen appears to be involved in a catalytic triad with the active site Cys and His residues. The corresponding carbonyl oxygen in caspase-9, together with other expected features of the catalytic apparatus, appears in our model. The predicted structure of caspase-9 can serve as a reference for subsite analysis relative to rational design of highly selective caspase inhibitors for therapeutic application.

A phase II trial of vinorelbine and cisplatin in previously untreated inoperable non--small-cell lung cancer

Weekly vinorelbine injection with cisplatin had been used in treatment of non-small-cell lung cancer. We performed a phase II trial to evaluate the efficacy and toxicity of a new schedule of vinorelbine and cisplatin in patients with previously untreated, inoperable (stage IIIB or stage IV) non-small-cell lung cancer. From April 1996 to May 1997, 52 patients were enrolled for study, and 50 patients were eligible and evaluable for both response and toxicity assessment. Therapy consisted of vinorelbine, 30 mg/m2, intravenously on days 1 and 5 of a 21-day cycle, and cisplatin 100 mg/m2 (reduced to 80 mg/m2 after the first seven patients) given on day 1. A total of 211 treatment courses were administered; the median number of cycles administered per patient was 4.5 (range: 1-6), the median dose intensity for vinorelbine was 16.9 mg/m2/week (84.4%), whereas that of cisplatin was 22.8 mg/m2/week (84.7%). Twenty-five patients responded to therapy for an overall response rate of 50%; one patient attained a complete response (2%). The main toxicities were vomiting, myelosuppression, and diarrhea, which included World Health Organization grade 3 or 4 nausea/vomiting (58% patients), anemia (41% patients), neutropenia (12% patients), and diarrhea (14%). The median duration of responses was 9 months. The median time to disease progression was 6.8 months (range 0.4-18.1 months). Median survival was 13 months, and 54% of patients were alive at 1 year. We conclude that this new schedule of vinorelbine and cisplatin achieves a high response with acceptable toxicity profile in patients with advanced non-small-cell lung cancer.

Prediction of protein secondary structure content

All existing algorithms for predicting the content of protein secondary structure elements have been based on the conventional amino-acid-composition, where no sequence coupling effects are taken into account. In this article, an algorithm was developed for predicting the content of protein secondary structure elements that was based on a new amino-acid-composition, in which the sequence coupling effects are explicitly included through a series of conditional probability elements. The prediction was examined by a self-consistency test and an independent dataset test. Both indicated a remarkable improvement obtained when using the current algorithm to predict the contents of alpha-helix, beta-sheet, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend and random coil. Examples of the improved accuracy by introducing the new amino-acid-composition, as well as its impact on the study of protein structural class and biologically function, are discussed.

A key driving force in determination of protein structural classes

The three-dimensional structure of a protein is uniquely dictated by its primary sequence. However, owing to the very high degenerative nature of the sequence-structure relationship, proteins are generally folded into one of only a few structural classes that are closely correlated with the amino-acid composition. This suggests that the interaction among the components of amino acid composition may play a considerable role in determining the structural class of a protein. To quantitatively test such a hypothesis at a deeper level, three potential functions, U((0)), U((1)), and U((2)), were formulated that respectively represent the 0th-order, 1st-order, and 2nd-order approximations for the interaction among the components of the amino acid composition in a protein. It was observed that the correct rates in recognizing protein structural classes by U((2)) are significantly higher than those by U((0)) and U((1)), indicating that an algorithm that can more completely incorporate the interaction contributions will yield better recognition quality, and hence further demonstrate that the interaction among the components of amino acid composition is an important driving force in determining the structural class of a protein during the sequence folding process.

A model of the complex between cyclin-dependent kinase 5 and the activation domain of neuronal Cdk5 activator

Tau protein kinase II (TPKII) is a heterodimer comprising a catalytic cyclin-dependent kinase subunit (Cdk5) and a regulatory protein called neuronal Cdk5 activator (Nck5a). TPKII is somewhat reminiscent, therefore, of the Cdk2-cyclin complex important in cell cycle regulation. In fact, although the amino acid sequence of Nck5a has little similarity to those of cyclins, recent experimental results obtained by site-directed mutagenesis studies have indicated that its activation domain, Nck5a*, may adopt a conformation of the cyclin-fold structure. Based on this structural inference, a 3-dimensional model of the Cdk5-Nck5a*-ATP complex was derived from the X-ray structure of Cdk2-cyclinA-ATP complex. The computed structure for TPKII is fully compatible with experimental data derived from studies of the Cdk5-Nck5a system, and also predicts which amino acid residues might be involved in formation of the Cdk5-Nck5a* interface and ATP binding pocket in TPKII. The computational structure also shows the interactive region of Nck5a* and the T-loop of Cdk5, a critical region in TPKII which functions as a gate-control-lever of the catalytic cleft. Furthermore, a physical mechanism is put forth to explain why the activation of TPKII is not dependent upon phosphorylation of the Cdk5 subunit, a puzzle long-standing in this area. These findings provide a model with which to consider design of compounds which might serve as inhibitors of TPKII.

Using pair-coupled amino acid composition to predict protein secondary structure content

The pair-coupled amino acid composition is introduced to predict the secondary structure contents of a protein. Compared with the existing methods all based on singlewise amino acid composition as defined in a 20D (dimensional) space, this represents a step forward to the consideration of the sequence coupling effect. The test results indicate that the introduction of the pair-coupled amino acid composition can significantly improve the prediction quality. It is anticipated that the concept of the pair-coupled amino acid composition can be used to simplify the formulation of sequence coupling (or sequence order) effects and to study many other features of proteins as well.

Body position, membrane diffusing capacity and pulmonary capillary blood volume in chronic bronchitis and pulmonary emphysema

BACKGROUND

The effect of body position on diffusing capacity and its components, membrane diffusing capacity (Dm) and pulmonary capillary blood volume (Vc), in patients with chronic obstructive pulmonary disease (COPD) has remained elusive. This study was designed to evaluate the effect of body position on diffusing capacity for carbon monoxide (DLco), Dm and Vc in male patients with chronic bronchitis and pulmonary emphysema.

METHODS

Pulmonary function tests including spirometry and lung volume were assessed in the erect position, and DLco, Dm and Vc were measured in the erect and supine positions in a random order in 17 men with chronic bronchitis and 19 men with pulmonary emphysema.

RESULTS

Spirometry results and lung volumes were comparable between both groups of patients; however, significantly lower values of DLco and Kco (DLco corrected by alveolar volume, VA) were observed in the emphysema than in the bronchitis group. In the bronchitis group, Kco and Vc were significantly higher in the supine than in the erect position, but Dm was significantly lower in the supine position. Alternation of body position did not significantly affect DLco and its components in the emphysema group. DLco, Kco and Vc in both the erect and supine positions were significantly higher in the bronchitis than in the emphysema group. Vc-SE (SE, the data in the supine minus those in the erect position) was also significantly higher in the bronchitis group. In the bronchitis group, DLco-SE was significantly correlated with Dm-SE and Vc-SE. However, Kco-SE was highly correlated with Dm-SE. In the emphysema group, DLco-SE and Kco-SE were highly correlated with Vc-SE only.

CONCLUSIONS

An increase in Vc in the supine position may account for the postural effect on Kco in bronchitis patients. In patients with pulmonary emphysema, decreased DLco and an absence of postural effect on DLco and its components may be due to a widespread abnormality of the pulmonary capillary bed. These findings may be of value in elucidating the difference in mechanisms of impaired gas exchange between patients with chronic bronchitis and pulmonary emphysema.

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.

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.

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.

Singular points of protein beta-sheets

Protein beta-sheets can be regarded as surfaces. Two surfaces can be connected along a common edge to form a larger surface, or two edges of a surface can coalesce to form a closed sheet such as a beta-barrel. Singular points are locations where these connections are not perfect. In protein beta-sheets, a singular point is characterized by a residue separating two beta-ladders. In this paper, we study the singular points of protein beta-sheets from the surface topologic viewpoint, summarize our search results from the protein structural data in the Protein Data Bank, and present examples where singular points are near the active sites and may contribute to forming the proper relative positions of catalytic residues.

Artificial neural network method for predicting HIV protease cleavage sites in protein

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity, and the information thus acquired will be useful for designing specific and efficient HIV protease inhibitors. The search for inhibitors of HIV protease will be greatly expedited if one can find an accurate, robust, and rapid method for predicting the cleavage sites in proteins by HIV protease. In this paper, Kohonen's self-organization model, which uses typical artificial neural networks, is applied to predict the cleavability of oligopeptides by proteases with multiple and extended specificity subsites. We selected HIV-1 protease as the subject of study. We chose 299 oligopeptides for the training set, and another 63 oligopeptides for the test set. Because of its high rate of correct prediction (58/63 = 92.06%) and stronger fault-tolerant ability, the neural network method should be a useful technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the artificial neural network method can also be applied to analyzing the specificity of any multisubsite enzyme.

Domain structural class prediction

The structural class of a protein domain can be approximately predicted according to its amino acid composition. However, can the prediction quality be improved by taking into account the coupling effect among different amino acid components? This question has evoked much controversy because completely different conclusions have been obtained by different investigators. To resolve such a perplexing problem, predictions by means of various algorithms were performed based on the SCOP database (Murzin et aL, 1995), which is more natural and reliable for the study of structural classes because it is based on evolutionary relationships and on the principles that govern their three-dimensional structure. The results obtained using both resubstitution and jackknife tests indicated that the overall rates of correct prediction by an algorithm incorporating the coupling effect among different amino acid components were significantly higher than those by the algorithms that did not include such an effect. A completely consistent conclusion was also obtained when tests were performed on two large independent testing datasets classified into four and seven structural classes, respectively. It is revealed through an analysis that the reasons for reaching the opposite conclusion are mainly due to (1) misclassifying structural classes according to a conceptually incorrect rule, (2) misapplying the component-coupled algorithm by ignoring some important factors and (3) misrepresenting structural classes with statistically insignificant training subsets. Clarification of these problems would be instructive for effectively using the prediction algorithm and correctly interpreting the results.

Prediction of beta-turns

Kohonen's self-organization model, a neural network model, is applied to predict the beta-turns in proteins. There are 455 beta-turn tetrapeptides and 3807 non-beta-turn tetrapeptides in the training database. The rates of correct prediction for the 110 beta-turn tetrapeptides and 30,229 non-beta-turn tetrapeptides in the testing database are 81.8% and 90.7%, respectively. The high quality of prediction of neural network model implies that the residue-coupled effect along a polypeptide chain is important for the formation of reversal turns, such as beta-turns, during the process of protein folding.

Prediction of protein structural classes by modified mahalanobis discriminant algorithm

We first discuss quantitative rules for determining the protein structural classes based on their secondary structures. Then we propose a modification of the least Mahalanobis distance method for prediction of protein classes. It is a generalization of a quadratic discriminant function to the case of degenerate covariance matrices. The resubstitution tests and leave-one-out tests are carried out to compare several methods. When the class sample sizes or the covariance matrices of different classes are significantly different, the modified method should be used to replace the least Mahalanobis distance method. Two lemmas for the derivation of our new algorithm are proved in an appendix.

Prediction and classification of domain structural classes

Can the coupling effect among different amino acid components be used to improve the prediction of protein structural classes? The answer is yes according to the study by Chou and Zhang (Crit. Rev. Biochem. Mol. Biol. 30:275-349, 1995), but a completely opposite conclusion was drawn by Eisenhaber et al. when using a different dataset constructed by themselves (Proteins 25:169-179, 1996). To resolve such a perplexing problem, predictions were performed by various approaches for the datasets from an objective database, the SCOP database (Murzin, Brenner, Hubbard, and Chothia. J. Mol. Biol. 247:536-540, 1995). According to SCOP, the classification of structural classes for protein domains is based on the evolutionary relationship and on the principles that govern the 3D structure of proteins, and hence is more natural and reliable. The results from both resubstitution tests and jackknife tests indicate that the overall rates of correct prediction by the algorithm incorporated with the coupling effect among different amino acid components are significantly higher than those by the algorithms without using such an effect. It is elucidated through an analysis that the main reasons for Eisenhaber et al. to have reached an opposite conclusion are the result of (1) misusing the component-coupled algorithm, and (2) using a conceptually incorrect rule to classify protein structural classes. The formulation and analysis presented in this article are conducive to clarify these problems, helping correctly to apply the prediction algorithm and interpret the results.

Phase II trial of intrapleural paclitaxel injection for non-small-cell lung cancer patients with malignant pleural effusions

A phase II clinical trial of intrapleural paclitaxel injection for malignant effusions of non-small-cell lung cancer (NSCLC) was conducted in order to evaluate the efficacy and toxicity profile of paclitaxel pleurodesis in patients with malignant effusions. From February to May of 1996, 15 NSCLC patients with malignant pleural effusions were enrolled on study. After adequate drainage and assurance of lung re-expansion, paclitaxel 125 mg m-2 diluted in normal saline was infused through a preinserted pig-tail catheter which was removed 2 h later. Chest radiography and sonography were scheduled 4 days later; depending on whether there remained a significant amount of pleural effusion, further drainage by needle thoracentesis or by a pig-tail catheter was performed. All patients were assessable for toxicity. Ipsilateral chest and/or shoulder pain, fever, facial flushing and nausea were the most frequent side-effects. Grade 4 neutropenia, grade 3 anaemia, and grade 3 renal impairment occurred in one patient each. Fourteen patients were evaluable for response at the end of the fourth week. Overall response rate of pleural effusion in evaluable patients was 92.9%, with a complete response rate of 28.6%. There was one out of 14 evaluable patients whose measurable tumour lesion decreased by more than 50% (partial response). No disease progression was noted among evaluable patients at the end of the fourth week. It is concluded that paclitaxel is a useful agent for the treatment of malignant pleural effusions. Because of its relatively low systemic toxicity, intrapleural paclitaxel injection in combination with systemic chemotherapy or radiotherapy can be considered in treating NSCLC patients with malignant pleural effusions.

A model for structure-dependent binding of Congo red to Alzheimer beta-amyloid fibrils

The cytotoxic A beta fibril is a logical candidate for the entity causing the initiating damage to neurons in Alzheimer's disease and Down's syndrome. We have derived a model of binding for the dye molecule, Congo red (CR), to a beta-sheet structure of beta-amyloid (1-42). This model is based on the crystal coordinates of CR binding to porcine insulin fibrils from Turnell and Finch. Intact insulin is composed of protein dimers and X-ray diffraction studies show that CR intercalates between two insulin monomers at an interface formed by a pair of antiparallel beta-strands. The intercalation of CR has disrupted the four main-chain hydrogen bonds between the two beta-strands, but they are still tethered with each other through new hydrogen bonds with the CR nitrogen atoms. The CR molecule has been aligned along the homologous stretch of amino acids in Alzheimer beta peptide (two molecules in antiparallel distorted or pseudo beta-sheet conformation) using the crystal coordinates from the Turnell-Finch paper to arrive at a putative structure for CR binding to Alzheimer's amyloid fibrils.

Prediction of the tertiary structure and substrate binding site of caspase-8

The caspases represent a family of sulfhydryl proteases that play important regulatory roles in the cell. The tertiary structure of the protease domain of caspase-8, also called FLICE, has been predicted by a segment match modeling procedure. First, the atomic coordinates of the catalytic domain of caspase-3, also called CPP32, a member of the family that is closely related to caspase-8, were determined based upon the crystal structure of human caspase-1 (interleukin converting enzyme). Then, the caspase-3 structure was used as a template for modeling the protease domain of caspase-8. The resulting structure shows the expected level of similarity with the conformations of caspases-1 and -3 for which crystal structures have been determined. Moreover, the subsite contacts between caspase-8 and the covalently linked inhibitor, Ac-DEVD-aldehyde, are only slightly different from those seen in the caspase-3 enzyme/inhibitor complex. The model of caspase-8 can serve as a reference for subsite analysis relative to design of enzyme inhibitors that may find therapeutic application.

Prediction and classification of alpha-turn types

Tight turns play an important role in globular proteins from both the structural and functional points of view. Of tight turns, beta-turns and gamma-turns have been extensively studied, but alpha-turns were little investigated. Recently, a systematic search for alpha-turns was conducted by V. Pavone et al. [(1996) Biopolymers, Vol. 38, pp. 705-721] from 190 proteins (221 protein chains). They found 356 alpha-turns that were classified into nine different types according to their backbone trajectory features. In view of this new discovery, a sequence-coupled model based on Markov chain theory is proposed for predicting the alpha-turn types in proteins. The high rates of correct prediction by resubstitution test and jackknife test imply that that the formation of different alpha-turn types is evidently correlated with the sequence of a pentapeptide, and hence can be approximately predicted based on the sequence information of the pentapeptide alone, although the role of its interaction with the other part of a protein cannot be completely ignored. The algorithm presented here can also be used to conduct the prediction in which a distinction between alpha-turns and non-alpha-turns is also required.

Prediction of the tertiary structure of the complement control protein module

Complement control protein (CCP) modules, or short consensus repeats (SCR), exist in a wide variety of complement and adhesion proteins, principally the selectins. We have predicted the three-dimensional structure of a CCP module based upon secondary structural information derived by two-dimensional NMR [Barlow et al. (1991), Biochemistry 30, 997-1004]. Accordingly, the CCP is predicted to contain seven beta-strands with extensive hydrogen-bonding interactions, and shows a compact, globular structure. Comparison of this model to the X-ray structure of a kringle domain suggests that the CCP unit is more compact than a kringle structure, and that despite their similarities in size and disulfide bond format, the two are not homologous. Although the function of CCP domains is unknown, it is hoped that the structural model presented herein will facilitate further inquiry into how they contribute to so many systems of biological importance.

Artificial neural network method for predicting the specificity of GalNAc-transferase

The specificity of GalNAc-transferase is consistent with the existence of an extended site composed of nine subsites, denoted by R4, R3, R2, R1, R0, R1', R2', R3', and R4', where the acceptor at R0 is either Ser or Thr to which the reducing monosaccharide is anchored. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a neural network method--Kohonen's self-organization model is proposed in this paper. Three hundred five oligopeptides are chosen for the training site, with another 30 oligopeptides for the test set. Because of its high correct prediction rate (26/30 = 86.7%) and stronger fault-tolerant ability, it is expected that the neural network method can be used as a technique for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase. It might also be useful for targeting drugs to specific sites in the body and for enzyme replacement therapy for the treatment of genetic disorders.

Classification and prediction of beta-turn types

Although a beta-turn consists of only four amino acids, it assumes many different types in proteins. Is this basically dependent on the tetrapeptide sequence alone or is it due to a variety of interactions with the other part of a protein? To answer this question, a residue-coupled model is proposed that can reflect the sequence-coupling effect for a tetrapeptide in not only a beta-turn or non-beta-turn, but also different types of a beta-turn. The predicted results by the model for 6022 tetrapeptides indicate that the rates of correct prediction for beta-turn types I, I', II, II', VI, and VIII and non-beta-turns are 68.54%, 93.60%, 85.19%, 97.75%, 100%, 88.75%, and 61.02%, respectively. Each of these seven rates is significantly higher than 1/7 = 14.29%, the completely randomized rate, implying that the formation of different beta-turn types or non-beta-turns is considerably correlated with the sequences of a tetrapeptide.

Disposition of amphiphilic helices in heteropolar environments

It is known that alpha helices in globular proteins usually consist of two types of residues, hydrophobic and hydrophilic, with the number of each type being roughly equal. Except for many transmembrane helices, alpha-helices are generally amphiphilic to some degree. This is not entirely surprising because alpha-helices typically reside in heteropolar environments that arise from the polar aqueous solution that surrounds a protein and the apolar "hydrophobic core" located at its center. The packing of alpha-helices in such heteropolar environments is driven by the minimization of free energy brought about by placing hydrophobic sidechains into apolar environments and hydrophilic sidechains into polar environments. The interface between the two environments can be characterized by an interfacial plane, called the demarcation plane, that optimally separates the two classes of residues. The inclination angle omega between the axis of the helix and the demarcation plane provides a measure of the degree of amphiphilicity of an alpha-helix. For highly amphiphilic helices, omega approximately 0. The inclination angle provides a new measure of amphiphilicity that complements the hydrophobic moments of Eisenberg et al. Based on the simple physical model described above, an algorithm is developed for predicting the helix inclination angle. The calculated results show that the inclination angle for most alpha-helices extracted from globular proteins is less than 25 degrees in magnitude. This suggests that helices found in globular proteins tend to be reasonably amphiphilic with half their face dominated by hydrophobic residues and the other half by hydrophilic residues. A new two-dimensional representation that characterizes the disposition of hydrophobic and hydrophilic residues in alpha-helices, called a "wenxiang diagram," is presented. The wenxiang diagram can also be used as an important element to represent a protein molecule.

Prediction of beta-turns

A residue-coupled model is proposed to predict the beta-turns in proteins. The rates of correct prediction for the 455 beta-turn tetrapeptides and 3807 non-beta-turn tetrapeptides in the training database are 94.7 and 81.3%, respectively. The rates of correct prediction for the 110 beta-turn tetrapeptides and 30,229 non-beta-turn tetrapeptides in the testing database are 80.0 and 80.2%, respectively. Compared with the rates of correct prediction based on the residue-independent model reported previously, the quality of prediction is significantly improved by the new model, implying that the residue-coupled effect along a polypeptide chain is important for the formation of reversal turns, such as beta-turns, during the process of protein folding.

The benzylthio-pyrimidine U-31,355, a potent inhibitor of HIV-1 reverse transcriptase

U-31,355, or 4-amino-2-(benzylthio)-6-chloropyrimidine is an inhibitor of human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) and possesses anti-HIV activity in HIV-1-infected lymphocytes grown in tissue culture. The compound acts as a specific inhibitor of the RNA-directed DNA polymerase function of HIV-1RT and does not impair the functions of the DNA-catalyzed DNA polymerase or the Rnase H of the enzyme. Kinetic studies were carried out to elucidate the mechanism of RT inhibition by U-31,355. The data were analyzed using Briggs-Haldane kinetics, assuming that the reaction is ordered in that the template:primer binds to the enzyme first, followed by the addition of dNTP, and that the polymerase is a processive enzyme. Based on these assumptions, a velocity equation was derived that allows the calculation of all the essential forward and backward rate constants for the reactions occurring between the enzyme, its substrates, and the inhibitor. The results obtained indicate that U-31,355 acts as a mixed inhibitor with respect to the template:primer and dNTP binding sites associated with the RNA-directed DNA polymerase domain of the enzyme. The inhibitor possessed a significantly higher binding affinity for the enzyme-substrate complexes, than for the free enzyme and consequently did not directly affect the functions of the substrate binding sites. Therefore, U-31,355 appears to impair an event occurring after the formation of the enzyme-substrate complexes, which involves either inhibition of the phosphoester bond formation or translocation of the enzyme relative to its template:primer following the formation of the ester bond. Moreover, the potency of U-31,355 depends on the base composition of the template:primer in that the inhibitor showed a much higher binding affinity for the enzyme-poly (rC):(dG)10 complexes than for the poly (rA):(dT)10 complexes.

Knowledge-based model building of the tertiary structures for lectin domains of the selectin family

A combination of a knowledge-based approach and energy minimization was used to predict the three-dimensional structures of the lectin domains of P-selectin, E-selectin, and L-selectin, respectively. Each of these domains contains 118 amino acids. The starting points for energy minimization were generated based on a framework that consists of a number of separated segments derived from the structure-known carbohydrate-recognition domain of the mannose-binding protein (MBP), which belongs to the same C-type lectin family as the selectin molecules do. The structures thus found for P-, L-, and E-selectin lectin domains share a common feature, i.e., they all contain two alpha-helices, and two antiparallel beta-sheets of which one is formed by two strands (strands 1 and 5) and the other by three (strands 2, 3, and 4). Besides, they all possess two intact disulfide bonds formed by the pair of Cys-19 and Cys-117, and the pair of Cys-90 and Cys-109. The root-mean-square deviations calculated over the set of backbone atoms between P- and L-selectin lectin domains is 3.10 A, that between P- and E-selectin lectin domains 2.48 A, and that between L- and E-selectin lectin domains 3.07 A. A notable feature is the convergence-divergence duality of the 77-107 polypeptide in the three domains; i.e., part of the peptide is folded into a closely similar conformation, and part of it into a highly different one.

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.