Local feature frequency profile: A method to measure structural similarity in proteins

Choi, In Geol; Kwon, Jaimyoung; Kim, Sung Hou

doi:10.1073/pnas.0308656100

Cited by 77 publications

(82 citation statements)

References 22 publications

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“…PSI [5] uses SSE-based features, and its accuracy for superfamily is 88%, but its class accuracy is unavailable. LFF profiles [6] only classify 68.5% of the superfamily correctly, but it agrees with SCOP classification at 93% for class level (Note that LFF profiles use a different testing protein dataset than ours). ProGreSS and PSIST could obtain more than 3 proteins within the top 4…”

Section: Classification Testsupporting

confidence: 49%

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PSIST: indexing protein structures using suffix trees

Gao

Zaki

2005

2005 IEEE Computational Systems Bioinformatics Conference (CSB'05)

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Approaches for indexing proteins, and for fast and scalable searching for structures similar to a query structure have important applications such as protein structure and function prediction, protein classification and drug discovery. In this paper, we developed a new method for extracting the local feature vectors of protein structures. Each residue is represented by a triangle, and the correlation between a set of residues is described by the distances between ! C atoms and the angles between the normals of planes in which the triangles lie. The normalized local feature vectors are indexed using a suffix tree. For all query segments, suffix trees can be used effectively to retrieve the maximal matches, which are then chained to obtain alignments with database proteins. Similar proteins are selected by their alignment score against the query. Our results shows classification accuracy up to 97.8% and 99.4% at the superfamily and class level according to the SCOP classification, and shows that on average 7.49 out of 10 proteins from the same superfamily are obtained among the top 10 matches. These results are competitive with the best previous methods.

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Section: Classification Testsupporting

confidence: 49%

“…The LFF profile algorithm [6] first extracts some representative local features from the distance matrix of all the proteins, and then each distance matrix is encoded by the indices of the nearest representative features. Each structure is represented by a vector of the frequency of the representative local features.…”

Section: Prior Researchmentioning

confidence: 99%

PSIST: indexing protein structures using suffix trees

Gao

Zaki

2005

2005 IEEE Computational Systems Bioinformatics Conference (CSB'05)

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“…High-density gene expression arrays have been used to define genes in human airway epithelial cells that are altered by cigarette smoking (31)(32)(33)(34)(35). The data obtained in these studies are expected to provide insights into lung cancer risk in smokers, with or without COPD.…”

Section: The Field Cancerization Effectmentioning

confidence: 99%

Smoking and Lung Cancer: The Role of Inflammation

Walser

Cui²,

Yanagawa³

et al. 2008

Proceedings of the American Thoracic Society

262

158

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Worldwide over 1 million people die due to lung cancer each year. It is estimated that cigarette smoking explains almost 90% of lung cancer risk in men and 70 to 80% in women. Clinically evident lung cancers have multiple genetic and epigenetic abnormalities. These abnormalities may result in activation of oncogenes and inactivation of tumor-suppressor genes. Chronic inflammation, which is known to promote cancer, may result both from smoking and from genetic abnormalities. These mediators in turn may be responsible for increased macrophage recruitment, delayed neutrophil clearance, and increase in reactive oxygen species (ROS). Thus, the pulmonary environment presents a unique milieu in which lung carcinogenesis proceeds in complicity with the host cellular network. The pulmonary diseases that are associated with the greatest risk for lung cancer are characterized by abundant and deregulated inflammation. Pulmonary disorders such as chronic obstructive pulmonary disease (COPD)/emphysema are characterized by profound abnormalities in inflammatory and fibrotic pathways. The cytokines and growth factors aberrantly produced in COPD and the developing tumor microenvironment have been found to have deleterious properties that simultaneously pave the way for both epithelialmesenchymal transition (EMT) and destruction of specific host cellmediated immune responses. Full definition of these pathways will afford the opportunity to intervene in specific inflammatory events mediating lung tumorigenesis and resistance to therapy.

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“…Distributions of atomic distances have been used successfully in structure comparison [26,27]. In protein structure prediction, distributions of distances have been applied as knowledgebased potentials to evaluate the fit of a sequence to a specific structure [28,29].…”

Section: Novel Structural Descriptor and Related Methodsmentioning

confidence: 99%

Structural Descriptors of gp120 V3 Loop for the Prediction of HIV-1 Coreceptor Usage

Sander¹,

Sing²,

Sommer³

et al. 2005

PLoS Comput Biol

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HIV-1 cell entry commonly uses, in addition to CD4, one of the chemokine receptors CCR5 or CXCR4 as coreceptor. Knowledge of coreceptor usage is critical for monitoring disease progression as well as for supporting therapy with the novel drug class of coreceptor antagonists. Predictive methods for inferring coreceptor usage based on the third hypervariable (V3) loop region of the viral gene coding for the envelope protein gp120 can provide us with these monitoring facilities while avoiding expensive phenotypic tests. All simple heuristics (such as the 11/25 rule) as well as statistical learning methods proposed to date predict coreceptor usage based on sequence features of the V3 loop exclusively. Here, we show, based on a recently resolved structure of gp120 with an untruncated V3 loop, that using structural information on the V3 loop in combination with sequence features of V3 variants improves prediction of coreceptor usage. In particular, we propose a distance-based descriptor of the spatial arrangement of physicochemical properties that increases discriminative performance. For a fixed specificity of 0.95, a sensitivity of 0.77 was achieved, improving further to 0.80 when combined with a sequence-based representation using amino acid indicators. This compares favorably with the sensitivities of 0.62 for the traditional 11/25 rule and 0.73 for a prediction based on sequence information as input to a support vector machine and constitutes a statistically significant improvement. A detailed analysis and interpretation of structural features important for classification shows the relevance of several specific hydrogen-bond donor sites and aliphatic side chains to coreceptor specificity towards CCR5 or CXCR4. Furthermore, an analysis of side chain orientation of the specificity-determining residues suggests a major role of one side of the V3 loop in the selection of the coreceptor. The proposed method constitutes the first approach to an improved prediction of coreceptor usage based on an original integration of structural bioinformatics methods with statistical learning.

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Local feature frequency profile: A method to measure structural similarity in proteins

Cited by 77 publications

References 22 publications

PSIST: indexing protein structures using suffix trees

PSIST: indexing protein structures using suffix trees

Smoking and Lung Cancer: The Role of Inflammation

Structural Descriptors of gp120 V3 Loop for the Prediction of HIV-1 Coreceptor Usage

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