Biomedical subjects

Kuo-Chen Chou

Publications and source records attributed to Kuo-Chen Chou.

At least 91 records · Page 5Linked to original sources

PubMed2003

Correlations of amino acids in proteins.

A correlation analysis among 20 amino acids is performed for four protein structural classes (alpha, beta, alpha/beta, and alpha+beta) in a total of 204 proteins. The correlation relationships among amino acids can be classified into the following four types: (1) strong positive correlation, (2) strong negative correlation, (3) weak correlation, and (4) no correlation. The correlation relationships are different for different proteins and are correlated with the features of their structural classes. The amino acids with the weak correlation relationship can be treated as the independent basis functions for the space where proteins are defined. The amino acids with large correlation coefficients are linear correlative with each other and they are not independent. The strong correlation among amino acids reflects their mutual constrained relationship, as exhibited by their relevant structural features. The information obtained through the correlation analysis is used for predicting protein structural classes and a better prediction quality is obtained than that by the simple geometry distance methods without taking into account the correlation effects.

Amino Acids↗

PubMed2003

Support vector machines for predicting membrane protein types by using functional domain composition.

Membrane proteins are generally classified into 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. In this article, based on the concept of using the functional domain composition to define a protein, the Support Vector Machine algorithm is developed for predicting the membrane protein type. High success rates are obtained by both the self-consistency and jackknife tests. The current approach, complemented with the powerful covariant discriminant algorithm based on the pseudo-amino acid composition that has incorporated quasi-sequence-order effect as recently proposed by K. C. Chou (2001), may become a very useful high-throughput tool in the area of bioinformatics and proteomics.

Algorithms↗

PubMed2003

Support vector machines for prediction of protein signal sequences and their cleavage sites.

Given a nascent protein sequence, how can one predict its signal peptide or "Zipcode" sequence? This is an important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy (see, e.g. K.C. Chou, Current Protein and Peptide Science 2002;3:615-22). In this paper, support vector machines (SVMs), a new machine learning method, is applied to approach this problem. The overall rate of correct prediction for 1939 secretary proteins and 1440 nonsecretary proteins was over 91%. It has not escaped our attention that the new method may also serve as a useful tool for further investigating many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells.

Genetic Vectors↗

PubMed2003

Support vector machine for predicting 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 classified alpha-turns into nine different types according to their backbone trajectory features. In this paper, Support Vector Machines (SVMs), a new machine learning method, is proposed for predicting the alpha-turn types in proteins. The high rates of correct prediction 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 incorporation of its interaction with the other part of a protein, the so-called "long distance interaction", will further improve the prediction quality.

Algorithms↗

PubMed2003

Prediction of beta-turns with learning machines.

The support vector machine approach was introduced to predict the beta-turns in proteins. The overall self-consistency rate by the re-substitution test for the training or learning dataset reached 100%. Both the training dataset and independent testing dataset were taken from Chou [J. Pept. Res. 49 (1997) 120]. The success prediction rates by the jackknife test for the beta-turn subset of 455 tetrapeptides and non-beta-turn subset of 3807 tetrapeptides in the training dataset were 58.1 and 98.4%, respectively. The success rates with the independent dataset test for the beta-turn subset of 110 tetrapeptides and non-beta-turn subset of 30,231 tetrapeptides were 69.1 and 97.3%, respectively. The results obtained from this study support the conclusion that the residue-coupled effect along a tetrapeptide is important for the formation of a beta-turn.

Artificial Intelligence↗

PubMed2002

Using functional domain composition and support vector machines for prediction of protein subcellular location.

Proteins are generally classified into the following 12 subcellular locations: 1) chloroplast, 2) cytoplasm, 3) cytoskeleton, 4) endoplasmic reticulum, 5) extracellular, 6) Golgi apparatus, 7) lysosome, 8) mitochondria, 9) nucleus, 10) peroxisome, 11) plasma membrane, and 12) vacuole. Because the function of a protein is closely correlated with its subcellular location, with the rapid increase in new protein sequences entering into databanks, it is vitally important for both basic research and pharmaceutical industry to establish a high throughput tool for predicting protein subcellular location. In this paper, a new concept, the so-called "functional domain composition" is introduced. Based on the novel concept, the representation for a protein can be defined as a vector in a high-dimensional space, where each of the clustered functional domains derived from the protein universe serves as a vector base. With such a novel representation for a protein, the support vector machine (SVM) algorithm is introduced for predicting protein subcellular location. High success rates are obtained by the self-consistency test, jackknife test, and independent dataset test, respectively. The current approach not only can play an important complementary role to the powerful covariant discriminant algorithm based on the pseudo amino acid composition representation (Chou, K. C. (2001) Proteins Struct. Funct. Genet. 43, 246-255; Correction (2001) Proteins Struct. Funct. Genet. 44, 60), but also may greatly stimulate the development of this area.

Proteins↗

PubMed2002

Identification of the N-terminal functional domains of Cdk5 by molecular truncation and computer modeling.

Cyclin dependent kinase (Cdk) 5, an atypical member of the Cdk family, plays a fundamental role in the development of the nervous system, and may also be involved in the pathogenesis of certain neurodegenerative diseases. Further, Cdk5 is activated by the specific regulatory proteins p39, p35, or p25 rather than cyclins, and in contrast to other members of the Cdk family is not involved in the progression of the cell cycle. A three-dimensional computer model of Cdk5-p25-ATP has been generated previously [Chou et al., Biochem Biophys Res Commun 1999;259:420-428], providing a structural basis for the study of the mechanisms of Cdk5 activation. To assess the predicted ATP and p25 binding domains at the N-terminal of Cdk5, two mutants of Cdk5 were prepared in which amino acids 9-15 (Delta9-15) or 9-47 (Delta9-47) were deleted. The results of these studies clearly demonstrate that an N-terminal loop and the PSSALRE helix are indispensable for Cdk5-p25 interactions, and amino acids 9-15 are necessary for ATP binding but are not involved in Cdk5-p25 interactions. Predicted models of Delta9-15 Cdk5 and Delta9-47 Cdk5 were generated, and were used to interpret the experimental data. The experimental and molecular modeling results confirm and extend specific aspects of the original predicted computer model, and may provide useful information for the design of highly selective inhibitors of Cdk5, which could be used in the treatment of certain neurodegenerative conditions.

Adenosine Triphosphate↗

PubMed2002

Prediction of the tertiary structure of the beta-secretase zymogen.

beta-Secretase, also known as BACE, is a transmembrane aspartyl protease, which generates the N terminus of Alzheimer's disease amyloid beta-peptide. The activity of beta-secretase is the rate-limiting step of brain plaques production in vivo, and hence is a potential target for disease modifying drugs for Alzheimer's disease. To better understand the mechanism of action of beta-secretase and help explore novel strategies for drug discovery for Alzheimer's disease, it is important to elucidate the three-dimensional structure of its zymogen. Based on the X-ray structure of the enzyme's protease domain and the X-ray structure of pepsinogen, a model of the three-dimensional structure of the beta-secretase zymogen has been constructed. Comparison of the computed structure of pro-BACE with X-ray structures of pepsinogen and progastricsin (two other pro-aspartyl proteases) reveals a significant difference in the relationship of the pro-segment to the catalytic aspartates. In both pepsinogen and progastricsin a lysine side-chain in the pro-segment forms a salt bridge to the two catalytic aspartates, occupying the position normally occupied by a catalytic water. In the pro-BACE model there is no salt bridge, and the corresponding residue-a proline-does not interact at all with the catalytic residues. These findings can be used to elucidate the recent observations that the pro-domain of beta-secretase does not suppress activity as in a strict zymogen but does appear to facilitate proper folding of an active protease domain. The predicted three-dimensional structure of beta-secretase zymogen and the relevant findings might also provide useful insights for rational design of effective drugs against Alzheimer's disease.

Alzheimer Disease↗

PubMed2002

Support Vector Machines 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 is useful for designing specific and efficient HIV protease inhibitors. The pace in searching for the proper 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 article, a Support Vector Machine 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 the study. Two hundred ninety-nine oligopeptides were chosen for the training set, while the other 63 oligopeptides were taken as a test set. Because of its high rate of self-consistency (299/299 = 100%), a good result in the jackknife test (286/299 = 95%) and correct prediction rate (55/63 = 87%), it is expected that the Support Vector Machine method can be referred to as a useful assistant technique for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs against AIDS. The principle of the Support Vector Machine method can also be applied to analyzing the specificity of other multisubsite enzymes.

Amino Acid Sequence↗

PubMed2002

Support vector machines for prediction of protein subcellular location by incorporating quasi-sequence-order effect.

Support Vector Machine (SVM), which is one class of learning machines, was applied to predict the subcellular location of proteins by incorporating the quasi-sequence-order effect (Chou [2000] Biochem. Biophys. Res. Commun. 278:477-483). In this study, the proteins are classified into the following 12 groups: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracellular, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane, and (12) vacuole, which account for most organelles and subcellular compartments in an animal or plant cell. Examinations for self-consistency and jackknife testing of the SVMs method were conducted for three sets consisting of 1,911, 2,044, and 2,191 proteins. The correct rates for self-consistency and the jackknife test values achieved with these protein sets were 94 and 83% for 1,911 proteins, 92 and 78% for 2,044 proteins, and 89 and 75% for 2,191 proteins, respectively. Furthermore, tests for correct prediction rates were undertaken with three independent testing datasets containing 2,148 proteins, 2,417 proteins, and 2,494 proteins producing values of 84, 77, and 74%, respectively.

Proteins↗

PubMed2002

Support vector machines for the classification and prediction of beta-turn types.

The support vector machines (SVMs) method is proposed because it can reflect the sequence-coupling effect for a tetrapeptide in not only a beta-turn or non-beta-turn, but also in different types of beta-turn. The results of the model for 6022 tetrapeptides indicate that the rates of self-consistency for beta-turn types I, I', II, II', VI and VIII and non-beta-turns are 99.92%, 96.8%, 98.02%, 97.75%, 100%, 97.19% and 100%, respectively. Using these training data, the rate of correct prediction by the SVMs for a given protein: rubredoxin (54 residues. 51 tetrapeptides) which includes 12 beta-turn type I tetrapeptides, 1 beta-turn type II tetrapeptide and 38 non-beta-turns reached 82.4%. The high quality of prediction of the SVMs implies that the formation of different beta-turn types or non-beta-turns is considerably correlated with the sequence of a tetrapeptide. The SVMs can save CPU time and avoid the overfitting problem compared with the neural network method.

Algorithms↗

PubMed2002

Artificial neural network model for predicting protein subcellular location.

The function of a protein is closely correlated to its subcellular location. Is it possible to utilize a bioinformatics method to predict the protein subcellular location? To explore this problem, proteins are classified into 12 groups (Protein Eng. 12 (1999) 107-118) according to their subcellular location: (1) chloroplast, (2) cytoplasm, (3) cytoskeleton, (4) endoplasmic reticulum, (5) extracellular, (6) Golgi apparatus, (7) lysosome, (8) mitochondria, (9) nucleus, (10) peroxisome, (11) plasma membrane and (12) vacuole. In this paper, the neural network method was proposed to predict the subcellular location of a protein according to its amino acid composition. Results obtained through self-consistency, cross-validation and independent dataset tests are quite high. Accordingly, the present method can serve as a complement tool for the existing prediction methods in this area.

Algorithms↗

PubMed2002

Prediction of protein structural classes by support vector machines.

In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a result, high rates of both self-consistency and jackknife test are obtained. This indicates that the structural class of a protein inconsiderably correlated with its amino and composition, and the support vector machine can be referred as a powerful computational tool for predicting the structural classes of proteins.

Artificial Intelligence↗

PubMed2002

Artificial neural network method for predicting protein secondary structure content.

In this paper, the neural network method was applied to predict the content of protein secondary structure elements that was based on 'pair-coupled 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. Both indicated good results obtained when using the neural network method to predict the contents of alpha-helix, beta-sheet, parallel beta-sheet strand, antiparallel beta-sheet strand, beta-bridge, 3(10)-helix, pi-helix, H-bonded turn, bend, and random coil.

Neural Networks, Computer↗

PubMed2002

A study on the correlation of G-protein-coupled receptor types with amino acid composition.

G-protein-coupled receptors have become a target in utilizing bioinformatics and genomics technology to facilitate drug discovery for psychiatric diseases. In this study the covariant-discriminant algorithm [Chou and Elrod (1999) Protein Eng., 12, 107-118] has been used to analyze the correlation between the types of G-protein-coupled receptors and the amino acid composition. It has been found that different types of G-protein-coupled receptors are quite closely correlated with the amino acid composition, implying that the types of G-protein-coupled receptors are predictable to a considerably accurate extent if a good training data set can be established for that purpose. The method derived here can be also used to do preliminary classification of orphan G-protein-coupled receptors. This will significantly expedite the process of identifying proper G-protein-coupled receptors for drug discovery.

Algorithms↗

PubMed2002

Prediction of protein signal sequences.

Newly synthesized proteins have an intrinsic signal sequence, functioning as "address tags" or "zip codes", that is essential for guiding them wherever they are needed. Owing to such a unique function, protein signals have become a crucial tool in finding new drugs or reprogramming cells for gene therapy. However, to effectively use protein signals as a desirable vehicle in the field of proteomics, the first important thing is to find a fast and powerful method to identify the "address tag" or "zip code" entity. Although all signal sequences contain a hydrophobic core region, they show great variation in both overall length and amino acid sequence. It is this variation that makes it possible to deliver thousands of proteins to many different cellular locations by varieties of modes. It is also this variation that makes it very difficult to formulate a general algorithm to predict signal sequences. Nevertheless, various prediction models and algorithms have been developed during the past 17 years. This Review summarizes the development in this area, from the pioneering methods to neural network approaches, and to the sub-site coupling approaches. Meanwhile, the future challenges in this area, as well as some promising avenues for further improving the prediction quality, have been briefly addressed as well.

Algorithms↗

PubMedDate not supplied

Mining biological data using self-organizing map.

This paper presents a novel method of mining biological data using a self-organizing map (SOM). After partitioning a set of protein sequences using SOM, conventional homology alignment is applied to each cluster to determine the conserved local motif (biological pattern) for the cluster. These local motifs are then regarded as rules for prediction and classification. In the application to the prediction of HIV protease cleavage sites in proteins, we found that the rules derived from this method are much more robust than those derived from the decision tree method.

Algorithms↗

PubMedDate not supplied

Virtual screening for SARS-CoV protease based on KZ7088 pharmacophore points.

Pharmacophore modeling can provide valuable insight into ligand-receptor interactions. It can also be used in 3D (dimensional) database searching for potentially finding biologically active compounds and providing new research ideas and directions for drug-discovery projects. To stimulate the structure-based drug design against SARS (severe acute respiratory syndrome), a pharmacophore search was conducted over 3.6 millions of compounds based on the atomic coordinates of the complex obtained by docking KZ7088 (a derivative of AG7088) to SARS CoV M(pro) (coronavirus main proteinase), as reportedly recently (Chou, K. C.; Wei, D. Q.; Zhong, W. Z. Biochem. Biophys. Res. Commun. 2003, 308, 148-151). It has been found that, of the 3.6 millions of compounds screened, 0.07% are with the score satisfying five of the six pharmacophore points. Moreover, each of the hit compounds has been evaluated for druggability according to 13 metrics based on physical, chemical, and structural properties. Of the 0.07% compounds thus retrieved, 17% have a perfect score of 1.0; while 23% with one druggable rule violation, 13% two violations, and 47% more than two violations. If the criterion for druggability is set at a maximum allowance of two rule violations, we obtain that only about 0.03% of the compounds screened are worthy of further tests by experiments. These findings will significantly narrow down the search scope for potential compounds, saving substantial time and money. Finally, the featured templates derived from the current study will also be very useful for guiding the design and synthesis of effective drugs for SARS therapy.

Coronavirus 3C Proteases↗