Browsing by Author "Cheng, Cheng-Wei"

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Predicting RNA-binding sites of proteins using support vector machines and evolutionary information
(2008)
Cheng, Cheng-Wei
;
Su, Emily Chia-Yu
;
Hwang, Jenn-Kang
;
Sung, Ting-Yi
;
Hsu, Wen-Lian
Background: RNA-protein interaction plays an essential role in several biological processes, such as protein synthesis, gene expression, posttranscriptional regulation and viral infectivity. Identification of RNA-binding sites in proteins provides valuable insights for biologists. However, experimental determination of RNA-protein interaction remains time-consuming and labor-intensive. Thus, computational approaches for prediction of RNA-binding sites in proteins have become highly desirable. Extensive studies of RNA-binding site prediction have led to the development of several methods. However, they could yield low sensitivities in trade-off for high specificities. Results: We propose a method, RNAProB, which incorporates a new smoothed position-specific scoring matrix (PSSM) encoding scheme with a support vector machine model to predict RNA-binding sites in proteins. Besides the incorporation of evolutionary information from standard PSSM profiles, the proposed smoothed PSSM encoding scheme also considers the correlation and dependency from the neighboring residues for each amino acid in a protein. Experimental results show that smoothed PSSM encoding significantly enhances the prediction performance, especially for sensitivity. Using five-fold cross-validation, our method performs better than the state-of-the-art systems by 4.90%similar to 6.83%, 0.88%similar to 5.33%, and 0.10 similar to 0.23 in terms of overall accuracy, specificity, and Matthew's correlation coefficient, respectively. Most notably, compared to other approaches, RNAProB significantly improves sensitivity by 7.0%similar to 26.9% over the benchmark data sets. To prevent data over fitting, a three-way data split procedure is incorporated to estimate the prediction performance. Moreover, physicochemical properties and amino acid preferences of RNA-binding proteins are examined and analyzed. Conclusion: Our results demonstrate that smoothed PSSM encoding scheme significantly enhances the performance of RNA-binding site prediction in proteins. This also supports our assumption that smoothed PSSM encoding can better resolve the ambiguity of discriminating between interacting and non-interacting residues by modelling the dependency from surrounding residues. The proposed method can be used in other research areas, such as DNA-binding site prediction, protein-protein interaction, and prediction of posttranslational modification sites.
TMPad: an integrated structural database for helix-packing folds in transmembrane proteins
(2011-01-01)
Lo, Allan
;
Cheng, Cheng-Wei
;
Chiu, Yi-Yuan
;
Sung, Ting-Yi
;
Hsu, Wen-Lian
alpha-Helical transmembrane (TM) proteins play an important role in many critical and diverse biological processes, and specific associations between TM helices are important determinants for membrane protein folding, dynamics and function. In order to gain insights into the above phenomena, it is necessary to investigate different types of helix-packing modes and interactions. However, such information is difficult to obtain because of the experimental impediment and a lack of a well-annotated source of helix-packing folds in TM proteins. We have developed the TMPad (TransMembrane Protein Helix-Packing Database) which addresses the above issues by integrating experimentally observed helix-helix interactions and related structural information of membrane proteins. Specifically, the TMPad offers pre-calculated geometric descriptors at the helix-packing interface including residue backbone/side-chain contacts, interhelical distances and crossing angles, helical translational shifts and rotational angles. The TMPad also includes the corresponding sequence, topology, lipid accessibility, ligand-binding information and supports structural classification, schematic diagrams and visualization of the above structural features of TM helix-packing. Through detailed annotations and visualizations of helix-packing, this online resource can serve as an information gateway for deciphering the relationship between helix-helix interactions and higher levels of organization in TM protein structure and function. The website of the TMPad is freely accessible to the public at http://bio-cluster.iis.sinica.edu.tw/TMPad.