Biomedical subjects

C T Zhang

Publications and source records attributed to C T Zhang.

At least 37 records · Page 2Linked to original sources

PubMed1998

Prediction of the secondary structure contents of globular proteins based on three structural classes.

The prediction of the secondary structural contents (those of alpha-helix and beta-strand) of a globular protein is of great use in the prediction of protein structure. In this paper, a new prediction algorithm has been proposed based on Chou's database [Chou (1995), Proteins 21, 319-344]. The new algorithm is an improved multiple linear regression method, taking into account the nonlinear and coupling terms of the frequencies of different amino acids and the length of the protein. The prediction is also based on the structural classes of proteins, but instead of four classes, only three classes are considered, the alpha class, beta class, and the mixed alpha+beta and alpha/beta class or simply the alphabeta class. Thus the ambiguity that usually occurs between alpha+beta proteins and alpha/beta proteins is eliminated. A resubstitution examination for the algorithm shows that the average absolute errors are 0.040 and 0.035 for the prediction of alpha-helix content and beta-strand content, respectively. An examination of cross-validation, the jackknife analysis, shows that the average absolute errors are 0.051 and 0.045 for the prediction of alpha-helix content and beta-strand content, respectively. Both examinations indicate the self-consistency and the extrapolating effectiveness of the new algorithm. Compared with other methods, ours has the merits of simplicity and convenience for use, as well as high prediction accuracy. By incorporating the prediction of the structural classes, the only input of our method is the amino acid composition and the length of the protein to be predicted.

Algorithms↗

PubMed1998

A new criterion to classify globular proteins based on their secondary structure contents.

MOTIVATION: With the enlargement of protein structure databases, it is hoped that a method to classify proteins automatically will be developed. Although the classification criterion proposed by Nakashima et al. ( J. Biochem., 1986, 99, 153-162) was widely used in the literature, it leads to some inconsistencies with the classification databases currently available in the class assignment of protein structures. To improve their work, a new classification criterion is proposed relying on statistical analysis of the secondary structure contents of more than 200 proteins with well-known structural classes. The Fisher linear discriminant algorithm is used to derive the new classification criterion. RESULTS: Three cross-validation tests are performed to evaluate the new criterion. In the jackknife test, of the 210 proteins used to derive the criterion, 206 are correctly classified with an accuracy of 98.10%. Of the 16 proteins of purely intermediate structure (i.e. structures lying near borderlines between two classes) in the first test set, 15 are correctly classified with an accuracy of 93.75%. For the second test set which consists of 200 proteins selected randomly from SCOP, a testing accuracy of 94.00% is obtained. For comparison, the criterion of Nakashima et al. is also used to classify the 210, 16 and 200 proteins, respectively. Consequently, accuracies of 94.76%, 62.50% and 91.50% are obtained, respectively. On average, the accuracy of the new classification criterion is 4% higher than that of Nakashima et al. AVAILABILITY: The program is available on request from the first author. CONTACT: ctzhang@tju.edu.cn

Algorithms↗

PubMed1998

A new fourier transform approach for protein coding measure based on the format of the Z curve.

MOTIVATION: At the core of most protein gene-finding algorithms are the coding measures used to make a decision on coding/non-coding. Of the protein coding measures, the Fourier measure is one of the most important. However, due to the limited length of the windows usually used, the accuracy of the measure is not satisfactory. This paper is devoted to improving the accuracy by lengthening the sequence to amplify the periodicity of 3 in the coding regions. RESULTS: A new algorithm is presented called the lengthen-shuffle Fourier transform algorithm. For the same window length, the percentage accuracy of the new algorithm is 6-7% higher than that of the ordinary Fourier transform algorithm. The resulting percentage accuracy (average of specificity and sensitivity) of the new measure is 84.9% for the window length 162 bp. AVAILABILITY: The program is available on request fromC.-T. Zhang. CONTACT: ctzhang@tju.edu.cn

Algorithms↗

PubMed1998

Prediction of the helix/strand content of globular proteins based on their primary sequences.

An improved multiple linear regression method has been proposed to predict the content of alpha-helix and beta-strand of a globular protein based on its primary sequence. The amino acid composition and the auto-correlation functions based on the hydrophobicity profile of the primary sequence have been taken into account in the algorithm. The resubstitution test shows that the average absolute errors are 0.077 and 0.073 with the standard deviations 0.059 and 0.057 for the prediction of the content of alpha-helix and beta-strand, respectively. A stringent cross-validation test, i.e., the jackknife test, shows that the average absolute errors are 0.087 and 0.081 with the standard deviations 0.067 and 0.065 for the prediction of the content of alpha-helix and beta-strand, respectively. Both tests indicate the self-consistency and the extrapolating effectiveness of the new algorithm. This greatly improves on previous results (Eisenhaber,F., Imperiale,F., Argos,P. and Frommel,C., 1996, Proteins, 25, 157-168). Compared with other methods currently available, our method has the merits of simplicity and ease-of-use as well as a higher prediction accuracy. The only input of the method is the primary sequence of the query protein to be predicted. The program is available on request via e-mail: ctzhang@tju.edu.cn.

Amino Acid Sequence↗

PubMed1997

A symmetrical theory of DNA sequences and its applications.

A unified symmetrical theory of DNA sequences has been established based on the basic symmetry of the DNA bases. It is shown that the symmetry of DNA sequences is inherently related to that of a cube and its inscribed regular tetrahedron. A DNA group is defined as a particular alternating group of order 4, in which the permuted objects are four bases. The symmetry of DNA sequences is described by the DNA group which is isomorphic to the tetrahedral group. The matrix representation for the DNA group has been obtained, and used to establish the relationships between the transforms of bases and the rotations of the tetrahedron. It is found that any DNA sequence can be uniquely described by three independent distributions, i.e., the distributions of the bases of purine/pyrimidine, of amino group/keto group and of strong/weak hydrogen bonds along the sequence. The three distributions are invariant in some sense under the transforms of the DNA group, indicating that the three distributions are inherent for the sequence. The mathematical format of the theory lays a foundation for further development. The applications of the theory to analyse some DNA sequences are presented.

Animals↗

PubMed1997

Relations of the numbers of protein sequences, families and folds.

The relations among the numbers of protein sequences, families and folds have been studied theoretically. It is found that the number of families is related to the natural logarithm of the number of sequences. The logarithmic relation should not be changed regardless of what value of the homology threshold is applied in the protein sequence comparison routines. To study the relation between the numbers of families and folds, the degenerate degree of a fold has been introduced. The degenerate degree of a fold is the number of protein families which adopt the same fold. The distribution of the degenerate degrees of folds has been found to be very likely exponential. Based on the distribution, the average degenerate degree d is calculated. The number of folds is simply equal to that of families divided by the average degenerate degree of folds. It is shown that d is an increasing function of time. The current value of d is about 2. It will continue to increase and reach the value of at least 3.3 in some years. By using the above result, the numbers of protein folds for four species have been estimated. In particular, the number of folds for human proteins is estimated to be < or =5200.

Animals↗

PubMed1997

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.

Algorithms↗

PubMed1996

Study on the isentropic equations of nucleotide sequences and their application.

The tetrahedral representation of DNA sequences and its applications have been studied by many authors. In this paper we study the isentropic equations of DNA sequences and their application. First, the DNA sequence entropy is introduced, and the entropy current and divergence are defined. Second, the isentropic equations are deduced and the isentropic curves on the three coordinate planes are respectively drawn by a computer. Third, an analysis is given on the entropy distribution on the coordinate planes. Finally, we make use of the results to discuss the relationship of the fastest increasing directions of the entropy for the cytochrome c genes of seven species.

Animals↗

PubMed1996

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.

Base Sequence↗

PubMed1996

A vector projection method for predicting supersecondary motifs.

For the 11 types of most frequently occurring supersecondary motifs, we used a new method--the vector projection method--to predict a protein's supersecondary structure. In a training set of peptides and a test set of peptides we obtained a satisfactory result, with a prediction accuracy of about 90%. The high prediction accuracy indicates that this method is reasonable for predicting the folding motifs of proteins. This work provides insight into the problem of predicting a protein's local structure accurately, and is of particular value in protein modeling, prediction, and molecule design.

Amino Acids↗

PubMed1996

Prediction of the secondary structure content of globular proteins based on structural classes.

The prediction of the secondary structure content (alpha-helix and beta-strand content) of a globular protein may play an important complementary role in the prediction of the protein's structure. We propose a new prediction algorithm based on Chou's database [Chou (1995), Proteins Struct. Funct. Genet. 21, 319]. The new algorithm is an improved multiple linear regression method, taking the nonlinear and coupling terms of the frequencies of different amino acids into account. The prediction is also based on the structural classes of proteins. A resubstitution examination for the algorithm shows that the average errors are 0.040 and 0.033 for the prediction of alpha-helix content and beta-strand content, respectively. The examination of cross-validation, the jackknife analysis, shows that the average errors are 0.051 and 0.044 for the prediction of alpha-helix content and beta-strand content, respectively. Both examinations indicate the self-consistency and the extrapolative effectiveness of the new algorithm. Compared with the other methods available currently, our method has the merits of simplicity and convenience for use, as well as a high prediction accuracy. By incorporating the prediction of the structural classes, the only input of our method is the amino acid composition of the protein to be predicted.

Algorithms↗

PubMed1995

A vector projection method for predicting the specificity of GalNAc-transferase.

The specificity of UDP-GalNAc:polypeptide N-acetylgalactosaminytransferase (GalNAc-transferase) is consistent with the existence of an extended site composed of nine subsites, denoted by P4, P3, P2, P1, P0, P1', P2', P3', P4', where the acceptor at P0 is being either Ser or Thr. To predict whether a peptide will react with the enzyme to form a Ser- or Thr-conjugated glycopeptide, a vector projection method is proposed which uses a training set of amino acid sequences surrounding 90 Ser and 106 Thr O-glycosylation sites extracted from the National Biomedical Research Foundation Protein Database. The model postulates independent interactions of the 9 amino acid moieties with their respective binding sites. The high ratio of correct predictions vs. total predictions for the data in both the training and the testing sets indicates that the method is self-consistent and efficient. It provides a rapid means for predicting O-glycosylation and designing effective inhibitors of GalNAc-transferase.

Amino Acid Sequence↗

PubMed1995

Monte Carlo simulation studies on the prediction of protein folding types from amino acid composition. II. Correlative effect.

A number of methods to predicting the folding type of a protein based on its amino acid composition have been developed during the past few years. In order to perform an objective and fair comparison of different prediction methods, a Monte Carlo simulation method was proposed to calculate the asymptotic limit of the prediction accuracy [Zhang and Chou (1992), Biophys. J. 63, 1523-1529, referred to as simulation method I]. However, simulation method I was based on an oversimplified assumption, i.e., there are no correlations between the compositions of different amino acids. By taking into account such correlations, a new method, referred to as simulation method II, has been proposed to recalculate the objective accuracy of prediction for the least Euclidean distance method [Nakashima et al. (1986), J. Bochem. 99, 152-162] and the least Minkowski distance method [Chou (1989), Prediction in Protein Structure and the Principles of Protein Conformation, Plenum Press, New York, pp. 549-586], respectively. The results show that the prediction accuracy of the former is still better than that of the latter, as found by simulation method I; however, after incorporating the correlative effect, the objective prediction accuracies become lower for both methods. The reason for this phenomenon is discussed in detail. The simulation method and the idea developed in this paper can be applied to examine any other statistical prediction method, including the computer-simulated neural network method.

Amino Acids↗

PubMed1995

An eigenvalue-eigenvector approach to predicting protein folding types.

The accuracy of predicting protein folding types can be significantly enhanced by a recently developed algorithm in which the coupling effect among different amino acid components is taken into account [Chou and Zhang (1994) J. Biol. Chem. 269, 22014-22020]. However, in practical calculations using this powerful algorithm, one may sometimes face ill-conditioned matrices. To overcome such a difficulty, an effective eigenvalue-eigenvector approach is proposed. Furthermore, the new approach has been used to predict a recently constructed set of 76 proteins not included in the training set, and the accuracy of prediction is also much higher than those of other methods.

Algorithms↗

PubMed1995

An analysis of protein folding type prediction by seed-propagated sampling and jackknife test.

In the development of methodology for statistical prediction of protein folding types, how to test the predicted results is a crucial problem. In addition to the resubstitution test in which the folding type of each protein from a training set is predicted based on the rules derived from the same set, cross-validation tests are needed. Among them, the single-test-set method seems to be least reliable due to the arbitrariness in selecting the test set. Although the leaving-one-out (or jackknife) test is more objective and hence more reliable, it may cause a severe information loss by leaving a protein in turn out of the training set when its size is not large enough. In order to overcome the above drawback, a seed-propagated sampling approach is proposed that can be used to generate any number of simulated proteins with a desired type based on a given training set database. There is no need to make any predetermined assumption about the statistical distribution function of the amino acid frequencies. Combined with the existing cross-validation methods, the new technique may provide a more objective estimation for various protein-folding-type prediction methods.

Amino Acids↗

PubMed1995

Predicting protein structural classes from amino acid composition: application of fuzzy clustering.

Most globular proteins can be classified into one of four structural classes--all-alpha, all-beta, alpha + beta and alpha/beta--depending upon the type, amount and arrangement of secondary structures present. In this work a new method, based upon fuzzy clustering, is proposed for predicting the structural class of a protein from its amino acid composition. Here, each of the structural classes is described by a fuzzy cluster and each protein is characterized by its membership degree, a number between zero and one in each of the four clusters, with the constraint that the sum of the membership degrees equals unity. A given protein is then classified as belonging to that structural class corresponding to the fuzzy cluster with maximum membership degree. Calculation of membership degrees is carried out using the fuzzy c-means algorithm on a training set of 64 proteins. Results obtained for the training set show that the fuzzy clustering approach produces results comparable with or better than those obtained by other methods. A test set of 27 proteins also produced comparable results to those obtained with the training set. The success of the present preliminary work on protein structure class prediction suggests that further refinements of method may lead to improved predictions and this is currently being investigated.

Algorithms↗

PubMed1995

Prediction of protein structural classes.

A protein is usually classified into one of the following five structural classes: alpha, beta, alpha + beta, alpha/beta, and zeta (irregular). The structural class of a protein is correlated with its amino acid composition. However, given the amino acid composition of a protein, how may one predict its structural class? Various efforts have been made in addressing this problem. This review addresses the progress in this field, with the focus on the state of the art, which is featured by a novel prediction algorithm and a recently developed database. The novel algorithm is characterized by a covariance matrix that takes into account the coupling effect among different amino acid components of a protein. The new database was established based on the requirement that the classes should have (1) as many nonhomologous structures as possible, (2) good quality structure, and (3) typical or distinguishable features for each of the structural classes concerned. The very high success rate for both the training-set proteins and the testing-set proteins, which has been further validated by a simulated analysis and a jackknife analysis, indicates that it is possible to predict the structural class of a protein according to its amino acid composition if an ideal and complete database can be established. It also suggests that the overall fold of a protein is basically determined by its amino acid composition.

Algorithms↗

PubMed1994

Predicting protein folding types by distance functions that make allowances for amino acid interactions.

Given the amino acid composition of a protein, how may one predict its folding type? Although around this problem a number of methods have been proposed, none of them has taken into account the correlative effect among different amino acids, and hence the accuracy of prediction could not be improved to the extent that it should have. In view of this, a new method has been developed in which the similarity between two protein molecules is based on the scale of Mahalanobis distance rather than on the ordinary intuitive geometric distances, such as Minkowski's distance and Euclidian distance. By introducing the Mahalanobis distance, the correlative effect among different amino acids can be automatically incorporated. Predictions have been performed for 131 real proteins consisting of alpha, beta, alpha+beta, and alpha/beta proteins. The results indicate that the rates of correct prediction for both alpha and beta proteins are 100%, and those for alpha+beta and alpha/beta are 88.9 and 89.7%, respectively, with an average accuracy of 94.7%. Predictions have also been performed for 10,000 simulated proteins generated by Monte Carlo sampling for each of the above four folding types, yielding an average accuracy of 95.9%. The accuracy thus obtained for the simulated proteins can avoid the bias due to the limited number of testing proteins selected arbitrarily by different investigators and hence can be regarded as an objective accuracy. It is anticipated that a method with such a high objective accuracy should become a reliable tool in predicting the protein folding type and a useful tool for improving the prediction of secondary structure as well.

Models, Chemical↗