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K C Chou

Publications and source records attributed to K C Chou.

At least 19 recordsLinked to original sources
PubMed2000

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.

Amino Acid Chloromethyl Ketones↗
PubMed2000

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.

Adult↗
PubMed1999

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.

Algorithms↗
PubMed1999

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.

Adenosine Triphosphate↗
PubMed1999

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.

Algorithms↗
PubMed1999

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.

Algorithms↗
PubMed1999

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.

Algorithms↗
PubMed1999

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.

Algorithms↗
PubMed1999

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.

Aged↗
PubMed1998

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.

Algorithms↗
PubMed1998

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.

Algorithms↗
PubMed1998

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.

Binding Sites↗
PubMed1998

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.

Amino Acid Sequence↗
PubMed1998

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.

Aged↗
PubMed1998

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.

Amino Acid Sequence↗
PubMed1998

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.

Algorithms↗