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V V Solovyev

Publications and source records attributed to V V Solovyev.

10 recordsLinked to original sources
PubMed1995

Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments.

Recently Yi & Lander used a neural network and nearest-neighbor method with a scoring system that combined a sequence-similarity matrix with the local structural environment scoring scheme described by Bowie and co-workers for predicting protein secondary structure. We have improved their scoring system by taking into consideration N and C-terminal positions of alpha-helices and beta-strands and also beta-turns as distinctive types of secondary structure. Another improvement, which also decreases the time of computation, is performed by restricting a data base with a smaller subset of proteins that are similar with a query sequence. Using multiple sequence alignments rather than single sequences and a simple jury decision procedure our method reaches a sustained overall three-state accuracy of 72.2%, which is better than that observed for the most accurate multilayered neural-network approach, tested on the same data set of 126 non-homologous protein chains.

Algorithms↗
PubMed1995

Identification of human gene structure using linear discriminant functions and dynamic programming.

Development of advanced technique to identify gene structure is one of the main challenges of the Human Genome Project. Discriminant analysis was applied to the construction of recognition functions for various components of gene structure. Linear discriminant functions for splice sites, 5'-coding, internal exon, and 3'-coding region recognition have been developed. A gene structure prediction system FGENE has been developed based on the exon recognition functions. We compute a graph of mutual compatibility of different exons and present a gene structure models as paths of this directed acyclic graph. For an optimal model selection we apply a variant of dynamic programming algorithm to search for the path in the graph with the maximal value of the corresponding discriminant functions. Prediction by FGENE for 185 complete human gene sequences has 81% exact exon recognition accuracy and 91% accuracy at the level of individual exon nucleotides with the correlation coefficient (C) equals 0.90. Testing FGENE on 35 genes not used in the development of discriminant functions shows 71% accuracy of exact exon prediction and 89% at the nucleotide level (C = 0.86). FGENE compares very favorably with the other programs currently used to predict protein-coding regions. Analysis of uncharacterized human sequences based on our methods for splice site (HSPL, RNASPL), internal exons (HEXON), all type of exons (FEXH) and human (FGENEH) and bacterial (CDSB) gene structure prediction and recognition of human and bacterial sequences (HBR) (to test a library for E. coli contamination) is available through the University of Houston, Weizmann Institute of Science network server and a WWW page of the Human Genome Center at Baylor College of Medicine.

Algorithms↗
PubMed1994

Predicting internal exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames.

A new method which predicts internal exon sequences in human DNA has been developed. The method is based on a splice site prediction algorithm that uses the linear discriminant function to combine information about significant triplet frequencies of various functional parts of splice site regions and preferences of oligonucleotides in protein coding and intron regions. The accuracy of our splice site recognition function is 97% for donor splice sites and 96% for acceptor splice sites. For exon prediction, we combine in a discriminant function the characteristics describing the 5'-intron region, donor splice site, coding region, acceptor splice site and 3'-intron region for each open reading frame flanked by GT and AG base pairs. The accuracy of precise internal exon recognition on a test set of 451 exon and 246693 pseudoexon sequences is 77% with a specificity of 79%. The recognition quality computed at the level of individual nucleotides is 89% for exon sequences and 98% for intron sequences. This corresponds to a correlation coefficient for exon prediction of 0.87. The precision of this approach is better than other methods and has been tested on a larger data set. We have also developed a means for predicting exon-exon junctions in cDNA sequences, which can be useful for selecting optimal PCR primers.

Algorithms↗
PubMed1994

Assignment of position-specific error probability to primary DNA sequence data.

DNA sequence predicted from polyacrylamide gel-based technologies is inaccurate because of variations in the quality of the primary data due to limitations of the technology, and to sequence-specific variations due to nucleotide interactions within the DNA molecule and with the gel. The ability to recognize the probability of error in the primary data will be useful in reconstructing the target sequence of a DNA sequencing project, and in estimating the accuracy of the final sequence. This paper describes the use of linear discriminant analysis to assign position-specific probabilities of incorrect, over- and under-prediction of nucleotides for each predicted nucleotide position in primary sequence data generated by a gel-based DNA sequencing technology. Using this method, most of the error potential in primary sequence data can be assigned to a limited number of discrete positions. The use of probability values in the sequence reconstruction process, and in estimating the accuracy of consensus sequence determination is described.

Base Sequence↗
PubMed1994

Predicting alpha-helix and beta-strand segments of globular proteins.

All current methods of protein secondary structure prediction are based on evaluation of a single residue state. Although the accuracy of the best of them is approximately 60-70%, for reliable prediction of tertiary structure it is more useful to predict an approximate location of alpha-helix and beta-strand segments, especially prolonged ones. We have developed a simple method for protein secondary structure prediction which is oriented on the location of secondary structure segments. The method uses linear discriminant analysis to assign segments of a given amino acid sequence a particular type of secondary structure, by taking into account the amino acid composition of internal parts of segments as well as their terminal and adjacent regions. Four linear discriminant functions were constructed for recognition of short and long alpha-helix and beta-strand segments respectively. These functions combine three characteristics: hydrophobic moment, segment singlet, and pair preferences to an alpha-helix or beta-strand. The last two characteristics are calculated by summing the preference parameters of single residues and pairs of residues located in a segment and its adjacent regions. The final program SSP predicts all possible potential alpha-helices and beta-strands and resolves some possible overlap between them. Overall three-state (alpha, beta, c) prediction gives approximately 65.1% correctly predicted residues on 126 non-homologous proteins using the jackknife test procedure. Analysis of the prediction results shows a high prediction accuracy of long secondary structure segments (approximately 89% of alpha-helices of length > 8 and approximately 71% of beta-strands of length > 6 are correctly located with probability of correct prediction 0.82 and 0.78 respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗
PubMed1994

The prediction of human exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames.

Discriminant analysis is applied to the problem of recognition 5'-, internal and 3'-exons in human DNA sequences. Specific recognition functions were developed for revealing exons of particular types. The method based on a splice site prediction algorithm that uses the linear Fisher discriminant to combine the information about significant triplet frequencies of various functional parts of splice site regions and preferences of oligonucleotides in protein coding and intron regions (Solovyev, Lawrence, 1994). The accuracy of our splice site recognition function is about 97%. A discriminant function for 5'-exon prediction includes hexanucleotide composition of upstream region, triplet composition around the ATG codon, ORF coding potential, donor splice site potential and composition of downstream intron region. For internal exon prediction, we combine in a discriminant function the characteristics describing the 5'-intron region, donor splice site, coding region, acceptor splice site and 3'-intron region for each open reading frame flanked by GT and AG base pairs. The accuracy of precise internal exon recognition on a test set of 451 exon and 246693 pseudoexon sequences is 77% with a specificity of 79% and a level of pseudoexon ORF prediction of 99.96%. The recognition quality computed at the level of individual nucleotides is 89% for exon sequences and 98% for intron sequences. A discriminant function for 3'-exon prediction includes octanucleotide composition of upstream intron region, triplet composition around the stop codon, ORF coding potential, acceptor splice site potential and hexanucleotide composition of downstream region.(ABSTRACT TRUNCATED AT 250 WORDS)

Computer Simulation↗
PubMed1993

A novel method of protein sequence classification based on oligopeptide frequency analysis and its application to search for functional sites and to domain localization.

A new method for distinguishing among protein families based on the analysis of oligopeptide composition of amino acid sequences is presented. It is assumed that any protein family can be characterized by a set of essential oligopeptides (oligopeptide vocabulary). A simple approach to find such a vocabulary is suggested. It is shown that comparison of the vocabularies can distinguish among different families and the latter from random sequences. This comparison can be successfully made with a small set of frequencies of 25 dipeptides (or tripeptides). No preliminary alignment is necessary. It is established that characteristic peptides are located in the regions of functional value, as shown for GTP-binding domains of the translation elongation factors. It is demonstrated that this method is reasonably efficient for localizing functional domains in the amino acid sequences. The average error of prediction does not exceed three or four amino acid residues as shown for several functional domains.

Algorithms↗
PubMed1993

Identification of human gene functional regions based on oligonucleotide composition.

Accurate recognition of coding and intron regions within large regions of uncharacterized genomic DNA is an unsolved problem. A data base of more than 4,240,791 bp coding and 7,790,682 bp noncoding human sequences was extracted from GenBank to develop a function for locating coding regions in anonymous sequences. Several coding measures based on oligonucleotide preferences were tested on a control set that including 1/3 of all extracted sequences. An accuracy of separation of coding/noncoding regions is 87% for 9 bp oligonucleotides on 54 bp windows and 91% on 108 bp windows, respectively. For separation of coding/intron regions the accuracy is 89-90% for 8 bp oligonucleotides on 54 bp windows and up to 95% on 108 bp windows. Using the information about preferences of octanucleotides in protein coding and intron regions and significant triplet frequencies as a function of position near splice junctions, a joint splice site prediction scheme was developed. The accuracy of the joint scheme for predicting splice site positions on the test set was about 96-97%, which exceeds the accuracy of the previously reported splice site selection method based on a more complex artificial neural network approach. A model of splicing using poly-G(C) rich exon flanking sequences is suggested. A remarkable difference of oligonucleotide composition 5'- and 3'- gene regions is displayed and applied in a gene structure predictive system.

Exons↗
PubMed1991

New elements of glucocorticoid-receptor binding sites of hormone-regulated genes.

The structure of the DNA regions recognized by glucocorticoid-receptor complexes (GIRC) was analyzed using frequency matrices and a modified perceptron method. Some complementary conservative elements which may modulate the efficiency of GIRC binding were found at both sides of the previously established conserved nucleotide sequence (core) (Beato, M. et al. (1987) J. Steroid Biochem. 27, 9-14). A criterion based on the concurrent use of several perceptron matrices to search for the potential GIRC binding site sequences has been worked out. By applying this criterion 73 sites were identified in 28 sequences of glucocorticoid regulated genes and 7 sites were identified in 26 sequences independent from glucocorticoid regulation.

Amino Acid Sequence↗