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Biomedical subjects

K C Chou

Publications and source records attributed to K C Chou.

At least 37 records · Page 2Linked to original sources
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↗
PubMed1998

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.

Animals↗
PubMed1997

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.

Amino Acid Sequence↗
PubMed1997

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.

Algorithms↗
PubMed1997

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.

Glycosylation↗