Dataset Information

Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices.

ABSTRACT: The cancer diagnostic is a complex process and, sometimes, the specific markers can interfere or produce negative results. Thus, new simple and fast theoretical models are required. One option is the complex network graphs theory that permits us to describe any real system, from the small molecules to the complex genetic, neural or social networks by transforming real properties in topological indices. This work converts the protein primary structure data in specific Randic's star networks topological indices using the new sequence to star networks (S2SNet) application. A set of 1054 proteins were selected from previous works and contains proteins related or not with two types of cancer, human breast cancer (HBC) and human colon cancer (HCC). The general discriminant analysis method generates an input-coded multi-target classification model with the training/predicting set accuracies of 90.0% for the forward stepwise model type. In addition, a protein subset was modified by single amino acid mutations with higher log-odds PAM250 values and tested with the new classification if can be related with HBC or HCC. In conclusion, we shown that, using simple input data such is the primary protein sequence and the simples linear analysis, it is possible to obtain accurate classification models that can predict if a new protein related with two types of cancer. These results promote the use of the S2SNet in clinical proteomics.

SUBMITTER: Munteanu CR

PROVIDER: S-EPMC7094125 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices.

Munteanu Cristian Robert CR Magalhães Alexandre L AL Uriarte Eugenio E González-Díaz Humberto H

Journal of theoretical biology 20081206 2

The cancer diagnostic is a complex process and, sometimes, the specific markers can interfere or produce negative results. Thus, new simple and fast theoretical models are required. One option is the complex network graphs theory that permits us to describe any real system, from the small molecules to the complex genetic, neural or social networks by transforming real properties in topological indices. This work converts the protein primary structure data in specific Randic's star networks topol ...[more]

PMID: 19111559

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Project description:BACKGROUND:STAR/GSG proteins regulate gene expression in metazoans by binding consensus sites in the 5' or 3' UTRs of target mRNA transcripts. Owing to the high degree of homology across the STAR domain, most STAR proteins recognize similar RNA consensus sequences. Previously, the consensus for a number of well-characterized STAR proteins was defined as a hexameric sequence, referred to as the SBE, for STAR protein binding element. C. elegans GLD-1 and mouse Quaking (Qk-1) are two representative STAR proteins that bind similar consensus hexamers, which differ only in the preferred nucleotide identities at certain positions. Earlier reports also identified partial consensus elements located upstream or downstream of a canonical consensus hexamer in target RNAs, although the relative contribution of these sequences to the overall binding energy remains less well understood. Additionally, a recently identified STAR protein called STAR-2 from C. elegans is thought to bind target RNA consensus sites similar to that of GLD-1 and Qk-1. RESULTS:Here, a combination of fluorescence-polarization and gel mobility shift assays was used to demonstrate that STAR-2 binds to a similar RNA consensus as GLD-1 and Qk-1. These assays were also used to further delineate the contributions of each hexamer consensus nucleotide to high-affinity binding by GLD-1, Qk-1 and STAR-2 in a variety of RNA contexts. In addition, the effects of inserting additional full or partial consensus elements upstream or downstream of a canonical hexamer in target RNAs were also measured to better define the sequence elements and RNA architecture recognized by different STAR proteins. CONCLUSIONS:The results presented here indicate that a single hexameric consensus is sufficient for high-affinity RNA binding by STAR proteins, and that upstream or downstream partial consensus elements may alter binding affinities depending on the sequence and spacing. The general requirements determined for high-affinity RNA binding by STAR proteins will help facilitate the identification of novel regulatory targets in vivo.

Project description:BACKGROUND: Transmembrane ?-barrel proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of transmembrane ?-barrel proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of transmembrane ?-barrel proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis. RESULTS: We present a novel graph-theoretic model for classification and structure prediction of transmembrane ?-barrel proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize ?-barrels by assessing the pseudo free energy. We assess the structure prediction on 41 proteins gathered from existing databases on experimentally validated transmembrane ?-barrel proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject ?-helical bundles with 100% accuracy and ?-barrel lipocalins with 97% accuracy. CONCLUSIONS: We show that it is possible to design models for classification and structure prediction for transmembrane ?-barrel proteins which do not depend essentially on training sets but on combinatorial properties of the structures to be proved. These models are fairly accurate, robust and can be run very efficiently on PC-like computers. Such models are useful for the genome screening.

Dataset Information

Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices.

Publications

Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices.

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