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Prediction of cystine connectivity using SVM

التفاصيل البيبلوغرافية
العنوان: Prediction of cystine connectivity using SVM
المؤلفون: Rama, JGL, Shilton, AP, Parker, MM, Palaniswami, M
بيانات النشر: BIOMEDICAL INFORMATICS
سنة النشر: 2006
المجموعة: The University of Melbourne: Digital Repository
الوصف: © Rama, G. L. J., Shilton, A., Parker, M. & Palaniswami, M. ; One of the major contributors to protein structures is the formation of disulphide bonds between selected pairs of cysteines at oxidized state. Prediction of such disulphide bridges from sequence is challenging given that the possible combination of cysteine pairs as the number of cysteines increases in a protein. Here, we describe a SVM (support vector machine) model for the prediction of cystine connectivity in a protein sequence with and without a priori knowledge on their bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features (probability of occurrence of each amino acid residue in different secondary structure states along with PSI-blast profiles). We evaluate our method in SPX (an extended dataset of SP39 (swiss-prot 39) and SP41 (swiss-prot 41) with known disulphide information from PDB) dataset and compare our results with the recursive neural network model described for the same dataset.
نوع الوثيقة: article in journal/newspaper
وصف الملف: application/pdf
اللغة: English
تدمد: 0973-8894
0973-2063
العلاقة: https://www.ncbi.nlm.nih.gov/pubmed/17597857Test; Rama, J. G. L., Shilton, A. P., Parker, M. M. & Palaniswami, M. (2005). Prediction of cystine connectivity using SVM. BIOINFORMATION, 1 (2), pp.69-74. https://doi.org/10.6026/97320630001069Test.; http://hdl.handle.net/11343/34067Test
الإتاحة: https://doi.org/10.6026/97320630001069Test
http://hdl.handle.net/11343/34067Test
https://www.ncbi.nlm.nih.gov/pubmed/17597857Test
رقم الانضمام: edsbas.7E575243
قاعدة البيانات: BASE
الوصف
تدمد:09738894
09732063