Dataset Information

CASP5 target classification.

ABSTRACT: This report summarizes the Critical Assessment of Protein Structure Prediction (CASP5) target proteins, which included 67 experimental models submitted from various structural genomics efforts and independent research groups. Throughout this special issue, CASP5 targets are referred to with the identification numbers T0129-T0195. Several of these targets were excluded from the assessment for various reasons: T0164 and T0166 were cancelled by the organizers; T0131, T0144, T0158, T0163, T0171, T0175, and T0180 were not available in time; T0145 was "natively unfolded"; the T0139 structure became available before the target expired; and T0194 was solved for a different sequence than the one submitted. Table I outlines the sequence and structural information available for CASP5 proteins in the context of existing folds and evolutionary relationships. This information provided the basis for a domain-based classification of the target structures into three assessment categories: comparative modeling (CM), fold recognition (FR), and new fold (NF). The FR category was further subdivided into homologues [FR(H)] and analogs [FR(A)] based on evolutionary considerations, and the overlap between assessment categories was classified as CM/FR(H) and FR(A)/NF. CASP5 domains are illustrated in Figure 1. Examples of nontrivial links between CASP5 target domains and existing structures that support our classifications are provided.

SUBMITTER: Kinch LN

PROVIDER: S-EPMC2656935 | biostudies-literature | 2003

REPOSITORIES: biostudies-literature

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CASP5 target classification.

Kinch Lisa N LN Qi Yuan Y Hubbard Tim J P TJ Grishin Nick V NV

Proteins 20030101

This report summarizes the Critical Assessment of Protein Structure Prediction (CASP5) target proteins, which included 67 experimental models submitted from various structural genomics efforts and independent research groups. Throughout this special issue, CASP5 targets are referred to with the identification numbers T0129-T0195. Several of these targets were excluded from the assessment for various reasons: T0164 and T0166 were cancelled by the organizers; T0131, T0144, T0158, T0163, T0171, T01 ...[more]

PMID: 14579323

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Project description:UNLABELLED: BACKGROUND:Cancer therapy is a challenging research area because side effects often occur in chemo and radiation therapy. We intend to study a multi-targets and multi-components design that will provide synergistic results to improve efficiency of cancer therapy. METHODS:We have developed a general methodology, AMFES (Adaptive Multiple FEature Selection), for ranking and selecting important cancer biomarkers based on SVM (Support Vector Machine) classification. In particular, we exemplify this method by three datasets: a prostate cancer (three stages), a breast cancer (four subtypes), and another prostate cancer (normal vs. cancerous). Moreover, we have computed the target networks of these biomarkers as the signatures of the cancers with additional information (mutual information between biomarkers of the network). Then, we proposed a robust framework for synergistic therapy design approach which includes varies existing mechanisms. RESULTS:These methodologies were applied to three GEO datasets: GSE18655 (three prostate stages), GSE19536 (4 subtypes breast cancers) and GSE21036 (prostate cancer cells and normal cells) shown in. We selected 96 biomarkers for first prostate cancer dataset (three prostate stages), 72 for breast cancer (luminal A vs. luminal B), 68 for breast cancer (basal-like vs. normal-like), and 22 for another prostate cancer (cancerous vs. normal. In addition, we obtained statistically significant results of mutual information, which demonstrate that the dependencies among these biomarkers can be positive or negative. CONCLUSIONS:We proposed an efficient feature ranking and selection scheme, AMFES, to select an important subset from a large number of features for any cancer dataset. Thus, we obtained the signatures of these cancers by building their target networks. Finally, we proposed a robust framework of synergistic therapy for cancer patients. Our framework is not only supported by real GEO datasets but also aim to a multi-targets/multi-components drug design tool, which improves the traditional single target/single component analysis methods. This framework builds a computational foundation which can provide a clear classification of cancers and lead to an efficient cancer therapy.

Dataset Information

CASP5 target classification.

Publications

CASP5 target classification.

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