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A homology identification method that combines protein sequence and structure information.

ABSTRACT: A new method is presented for identifying distantly related homologous proteins that are unrecognizable by conventional sequence comparison methods. The method combines information about functionally conserved sequence patterns with information about structure context. This information is encoded in stochastic discrete state-space models (DSMs) that comprise a new family of hidden Markov models. The new models are called sequence-pattern-embedded DSMs (pDSMs). This method can identify distantly related protein family members with a high sensitivity and specificity. The method is illustrated with trypsin-like serine proteases and globins. The strategy for building pDSMs is presented. The method has been validated using carefully constructed positive and negative control sets. In addition to the ability to recognize remote homologs, pDSM sequence analysis predicts secondary structures with higher sensitivity, specificity, and Q3 accuracy than DSM analysis, which omits information about conserved sequence patterns. The identification of trypsin-like serine proteases in new genomes is discussed.

SUBMITTER: Yu L

PROVIDER: S-EPMC2143896 | biostudies-other | 1998 Dec

REPOSITORIES: biostudies-other

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A homology identification method that combines protein sequence and structure information.

Yu L L White J V JV Smith T F TF

Protein science : a publication of the Protein Society 19981201 12

A new method is presented for identifying distantly related homologous proteins that are unrecognizable by conventional sequence comparison methods. The method combines information about functionally conserved sequence patterns with information about structure context. This information is encoded in stochastic discrete state-space models (DSMs) that comprise a new family of hidden Markov models. The new models are called sequence-pattern-embedded DSMs (pDSMs). This method can identify distantly ...[more]

PMID: 9865944

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Project description:BACKGROUND: DNA repair is the general term for the collection of critical mechanisms which repair many forms of DNA damage such as methylation or ionizing radiation. DNA repair has mainly been studied in experimental and clinical situations, and relatively few information-based approaches to new extracting DNA repair knowledge exist. As a first step, automatic detection of DNA repair proteins in genomes via informatics techniques is desirable; however, there are many forms of DNA repair and it is not a straightforward process to identify and classify repair proteins with a single optimal method. We perform a study of the ability of homology and machine learning-based methods to identify and classify DNA repair proteins, as well as scan vertebrate genomes for the presence of novel repair proteins. Combinations of primary sequence polypeptide frequency, secondary structure, and homology information are used as feature information for input to a Support Vector Machine (SVM). RESULTS: We identify that SVM techniques are capable of identifying portions of DNA repair protein datasets without admitting false positives; at low levels of false positive tolerance, homology can also identify and classify proteins with good performance. Secondary structure information provides improved performance compared to using primary structure alone. Furthermore, we observe that machine learning methods incorporating homology information perform best when data is filtered by some clustering technique. Analysis by applying these methodologies to the scanning of multiple vertebrate genomes confirms a positive correlation between the size of a genome and the number of DNA repair protein transcripts it is likely to contain, and simultaneously suggests that all organisms have a non-zero minimum number of repair genes. In addition, the scan result clusters several organisms' repair abilities in an evolutionarily consistent fashion. Analysis also identifies several functionally unconfirmed proteins that are highly likely to be involved in the repair process. A new web service, INTREPED, has been made available for the immediate search and annotation of DNA repair proteins in newly sequenced genomes. CONCLUSION: Despite complexity due to a multitude of repair pathways, combinations of sequence, structure, and homology with Support Vector Machines offer good methods in addition to existing homology searches for DNA repair protein identification and functional annotation. Most importantly, this study has uncovered relationships between the size of a genome and a genome's available repair repertoire, and offers a number of new predictions as well as a prediction service, both which reduce the search time and cost for novel repair genes and proteins.

Dataset Information

A homology identification method that combines protein sequence and structure information.

Publications

A homology identification method that combines protein sequence and structure information.

Similar Datasets

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